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S0034425719305449
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Boreal forests are globally extensive and store large amounts of carbon but recent climate change has led to drier conditions and increasing fire activity . The objective of this study is to quantify trends in fire size and frequency using data spanning multiple scales in space and time . We use multi temporal Landsat image compositing on Google Earth Engine and validate results with reference fire maps from the Canadian Park Service . We also interpret general fire trends through the concept of Self Organized Criticality . Our study site is Wood Buffalo National Park which is a fire hot spot in Canada due to frequent lightning ignitions . The relativize differenced normalized burn ratio was the most accurate Landsat based burn severity metric we evaluated . The Landsat based burn severity maps provided a better fit for a linear relationship on the log log scale of fire size and frequency than a manually drawn fire map . Landsat based fire trends since 1990 conformed to a power law distribution with a slope of 1.9 which is related to fractal dimensions of the satellite based fire perimeter shapes . The unburned and low severity patches within the burn severity mosaic influenced the power law slope and associated fractal dimensionality . This study demonstrates a multi scale and multi dataset technique to quantify general fire trends and changing fire cycles in remote locations and establishes a baseline database for assessing future fire activity . Testing criticality by power laws helps to quantify emergent trends of contemporary fire regimes which could inform the strategic application of prescribed fire and other management activities . Natural resource managers can utilize information from this study to understand local ecosystem adaptability to large fire events and ecosystem stability in the context of recent increasing fire activity .
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Image compositing with spectral indices enabled improved mapping of spatiotemporal fire trends. The multi temporal fire map created from satellite images followed self organized criticality. Fire trends were obtained by a slope in log log scale of size and frequency of fire. The slope for fire trends was influenced by fractal dimensions of areas burned by fire.
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S0034425719305528
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A laboratory experiment is set up to study both surface and in depth soil moisture content . For that purpose an aquarium is filled successively with two soils a clay loam and a sand . Reflectance spectra are acquired in the solar domain on the soil surface using an ASD FieldSpec 3 HR spectroradiometer and in depth through the aquarium glass wall using two hyperspectral cameras . Successive amounts of water ranging from low to heavy rainfall in a temperate region are uniformly poured into the aquarium . The MARMITforSMC method based on the MARMIT
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A lab experiment is designed to measure surface and in depth soil reflectance. The MARMIT model is used to associate surface moisture content to reflectance. Vertical maps of soil moisture content are generated on clay loam and sandy soils. The corresponding profiles are calculated for both soils. Their evolution over time is analyzed.
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S003442571930553X
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Retrogressive thaw slumps are among the most dynamic landforms in permafrost areas and their formation can be attributed to the thawing of ice rich permafrost . The spatial distribution and impacts of RTSs on the Tibetan Plateau are poorly understood due to their remote location and the technical challenges of automatic mapping . In this study we innovatively applied DeepLabv3 a cutting edge deep learning algorithm for semantic segmentation to Planet CubeSat images which are satellite images with high spatial and temporal resolution . Our method allows us to automatically delineate 220 RTSs within an area of 5200km
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The first automated mapping of retrogressive thaw slumps RTS on Tibetan Plateau. The method successfully delineates small and obscure RTSs from CubeSat Images. Robustness of the method is proved by approximately 100 independent experiments. Data augmentation improves delineation accuracies but introduces false positives. Analysis reveals that RTSs tend to initiate at locations lower than the surroundings.
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S0034425719305553
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The prediction of grasslands plant diversity using satellite image time series is considered in this article . Fifteen months of freely available Sentinel optical and radar data were used to predict taxonomic and functional diversity at the pixel scale over a large geographical extent 40 000km
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Plant diversity of grasslands is estimated using Sentinel 2 time series. Large intra parcel variability in terms of plant diversity index is observed. Large scale prediction is done using random forest regression. Sentinel 1 time series could complement Sentinel 2 time series.
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S0034425719305565
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Spatio temporal image fusion methods have become a popular means to produce remotely sensed data sets that have both fine spatial and temporal resolution . Accurate prediction of reflectance change is difficult especially when the change is caused by both phenological change and land cover class changes . Although several spatio temporal fusion methods such as the Flexible Spatiotemporal DAta Fusion directly derive land cover phenological change information at different dates the direct derivation of land cover class change information is challenging . In this paper an enhanced FSDAF that incorporates sub pixel class fraction change information is proposed . By directly deriving the sub pixel land cover class fraction change information the proposed method allows accurate prediction even for heterogeneous regions that undergo a land cover class change . In particular SFSDAF directly derives fine spatial resolution endmember change and class fraction change at the date of the observed image pair and the date of prediction which can help identify image reflectance change resulting from different sources . SFSDAF predicts a fine resolution image at the time of acquisition of coarse resolution images using only one prior coarse and fine resolution image pair and accommodates variations in reflectance due to both natural fluctuations in class spectral response and land cover class change . The method is illustrated using degraded and real images and compared against three established spatio temporal methods . The results show that the SFSDAF produced the least blurred images and the most accurate predictions of fine resolution reflectance values especially for regions of heterogeneous landscape and regions that undergo some land cover class change . Consequently the SFSDAF has considerable potential in monitoring Earth surface dynamics .
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SFSDAF is proposed to fuse coarse and fine spatial resolution images. SFSDAF derives endmember change to represent phenological change like FSDAF. SFSDAF derives sub pixel class fraction changes to accommodate land cover change. SFSDAF accommodates the presence of mixed pixels at the fine resolution scale. The combined effect from endmember change and class fraction change is analyzed.
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S0034425719305577
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Accurate estimation of fractional vegetation cover crop residue cover and bare soil in agricultural and vegetation soil ecosystems is critically important . The traditional triangular space method uses a CRC spectral index and vegetation SI to create a two dimensional scatter map in which the three vertices represent pure vegetation crop residue and bare soil . With this method the CRC FVC and BS of each pixel are calculated based on their spatial locations in the triangular space . In practice soil moisture and crop residue moisture affects the values of CRC spectral indices for pure crop residue and soil thereby reducing the accuracy of broadband remote sensing estimates of CRC FVC and BS . In the current work we propose a new method for estimating fractional cover that uses a broadband spectral angle index to estimate CRC . The proposed BAI is the included angle between the line from the reflectance at band a to the reflectance at band b and the line from the reflectance at band b to the reflectance at band c where bands a and b represent the VIS or NIR bands respectively and band c represents the SWIR1 or SWIR2 of the broadband remote sensing band . The proposed BAI method can mitigate the effects of soil and crop residue moisture content on spectral reflectance . This study evaluates proposed BAIs to estimate CRC and BAI NDVI triangular space method to estimate CRC FVC and BS in cropland where water content varies greatly . Several different BAIs were validated using both laboratory based measurements and field based experiments using Sentinel 2 multispectral instrument imaging . We used two laboratory based treatments to analyze the response of BAIs to CRC SM CRM FVC and vegetation water content . Next we evaluated the performance of different BAIs in determining CRC based on the mixed spectral reflectance of crop residue and soil as well as the performance of the BAI NDVI triangular space method and linear spectral unmixing analysis to estimate CRC FVC and BS from mixed spectral reflectance measurements . Our results indicate that the proposed methods reduce the influence of moisture on broadband CRC SIs provide accurate estimates of cropland CRC and fractional estimates of CRC FVC and BS and may be applied in croplands where soil and crop residue moisture content varies greatly .
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Performance of the broadband crop residues indices declines with the increasing moisture. New broadband crop residue angle index BAI is resistant to soil and crop residue moisture effect. Cropland FVC CRC and BS estimation accuracy improved using BAI.
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S0034425719305620
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The extent timing and duration of seasonal freeze thaw state exerts dominant control on boreal forest carbon water and energy cycle processes . Recent and on going L Band spaceborne missions have the potential to provide enhanced information on FT state over large geographic regions with rapid revisit time . However the low spatial resolution of these spaceborne observations makes it difficult to isolate the primary contributions to the FT signal in boreal forest . To better quantify these controls two L Band radiometers were deployed at a black spruce
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Two L Band radiometers were deployed at a boreal forest site. Relationship between tree permittivity and tree temperature under freezing conditions. Vegetation optical depth was correlated to tree permittivity. L Band T. freeze up signal in the fall originated from the ground surface.
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S0034425719305656
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Seasonal freeze thaw affects over half the northern hemisphere and impacts many key processes of the Earth System such as energy exchange hydrology and vegetation . Nearly all past studies using spaceborne FT retrievals have focused on characterizing FT specifically for natural environments . FT in the built environment is also routinely studied and a topic of great interest especially with regards to transportation infrastructure . Whereas natural FT process are frequently investigated using spaceborne observations FT studies of roads are often limited to local scales using in situ or nearby weather station data only . Comparisons between FT retrievals obtained from NASA s Soil Moisture Active Passive satellite and roads in Alaska and the Contiguous United States showed that spaceborne FT retrievals had good agreement with road data . But those results also indicated that NASA FT retrievals in CONUS were relatively too warm compared to road data . If SMAP FT retrievals were to be used for identifying FT transition timing for applications by the transportation community it is also important for frozen conditions to be identified more accurately . This work is primarily concerned with improving frozen retrievals made in CONUS by calculating new Normalized Polarization Ratio thresholds as compared to those currently used in SMAP FT. We found that focusing on a temporal subset of October through May for comparisons greatly improved the correlation between NPR and effective soil temperature T
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AM and PM comparisons of SMAP FT to U.S. road surface temperatures N 1000. Used GEOS 5 effective soil temperature to determine new FT thresholds in U.S. New thresholds greatly improved SMAP correspondence with in situ frozen conditions. Despite physical differences SMAP FT compared well against roads 7080
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S0034425719305668
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Satellite radar altimetry observations of water surface elevation have become an important data source to supplement river gauge records . Sentinel 3 is the first radar altimetry mission operating with a synthetic aperture radar altimeter at global scale and with a new on board tracking system which has great potential in terms of delivering reliable observations of inland water bodies for the next two decades . In this context it is very important to investigate the data quality at an early stage . In this study a comprehensive evaluation of Sentinel 3A is conducted at 50 virtual stations located on a wide range of rivers in China .
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Exhaustive evaluation of Sentinel 3 water surface elevation over 50 virtual stations across diverse river systems in China. The Open Loop Tracking Command version 5 OLTC v5 has significantly improved the data quality over mountain rivers. A new retracker is proposed to retrack multi peak waveforms.
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S0034425719305681
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The Synthetic Aperture Radar mode sea level anomalies of Sentinel 3A altimetry mission around the Australian coastal region were validated using eight in situ tide gauge sea level records and retracked Jason 3 datasets from a modified Brown peaky retracker . The MBP is a modified version of our existing Brown peaky retracker aimed at enhancing BP s performance . We compared the noise of Sentinel 3A SLAs averaged across three posting rates with the 1Hz noise of MBP derived Jason 3 SLAs . At distances 10km from the coast the noise level of 1Hz Sentinel 3A SLAs is lower than that of the MBP retracked Jason 3 SLAs . Moreover the noise level of 2Hz Sentinel 3A SLAs is comparable to that of the 1Hz MBP derived Jason 3 dataset indicating that Sentinel 3A can provide precise SLAs at finer spatial scales . The Root Mean Square Error of differences between the tide gauge SLA time series and the equivalent SLA time series at each along track altimeter point was used to assess pointwise data quality . For both Sentinel 3A and MBP retracked Jason 3 the along track RMSEs of 20Hz SLAs vary between 0.05m and 0.2m . The mean and standard deviation of 1Hz SLA differences at crossover points were computed for each individual altimetry mission to assess overall data quality . When compared with the crossover analysis results the quality of Sentinel 3A SLAs is superior to that of the retracked Jason 3 dataset in terms of smaller STDs at crossover points .
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The 2 Hz Sentinel 3A and 1 Hz retracked Jason 3 sea level data are equally precise. Within 5km to the coast both Sentinel 3A and Jason 3 datasets are problematic. The overall data quality of Sentinel 3A is superior to retracked Jason 3 dataset.
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S0034425719305747
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Lakes and glaciers are widely distributed in the northwestern Tibetan Plateau . A synchronized examination of lake and glacier mass variations there has not previously been conducted which has limited the understanding of the lake water balance and the hydrologic cycle in the region . In this study we comprehensively examine lake area and volume and glacier mass changes using multi sensor satellite data . We find that lake expansion in the northwestern Tibetan Plateau was more robust from 1976 to 2018 when compared to other regions of the plateau especially for the glacier fed and endorheic lakes . Lake volume changes show that most of the lakes had an increase in water volume particularly in 20002018 with a total water storage gain of 28.6Gt . By using high resolution KH 9 and TanDEM X data we observe that the glacier surface elevation of the western Kunlun Mountains had a slight thinning rate of 0.070.16m yr in 19732000 but a positive rate of 0.0020.003m yr in 20002018 . The heterogeneous pattern of glacier elevation changes between the north and south slopes are revealed i.e . 0.020.01m yr against 0.120.03m yr in 19732000 and0.050.02m yr against 0.060.02m yr in 20002018 . Overall the glaciers trend to a stable state in both the south slope of the western Kunlun Mountains and Aru Co regions between 2000 and 2018 . Similar patterns are also found for basin wide examinations of lake storage changes and glacier mass budgets . The seasonal snow cover area changes derived from cloud free MODIS snow cover products present a variable and insignificant trend between 2003 and 2017 . Snow depth derived from passive microwave remote sensing data exhibits a decreasing trend between 1979 and 2015 but the water equivalent could contribute only an insignificant amount to the observed lake changes . The lake water gains and almost positive glacier mass balance imply that the hydrological cycle in the northwestern Tibetan Plateau has become enhanced .
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A synchronized examination of lake glacier and snow variations using multi sensor satellite data. The lake expansions in the northwestern TP are more robust relative to other regions of the TP. Both lake and glacier volume changes show mass gains in 20002018. The enhanced hydrological cycle in the northwestern TP was revealed by lake and glacier changes.
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S0034425719305772
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Models of atmospheric composition rely on fire emissions inventories to reconstruct and project impacts of biomass burning on air quality public health climate ecosystem dynamics and land atmosphere exchanges . Many such global inventories use satellite measurements of active fires and or burned area from the Moderate Resolution Imaging Spectroradiometer . However differences across inventories in the interpretation of satellite imagery the emissions factors assumed for different components of smoke and the adjustments made for small and obscured fires can result in large regional differences in fire emissions estimates across inventories . Using Google Earth Engine we leverage 15years of MODIS observations and 6years of observations from the higher spatial resolution Visible Imaging Infrared Radiometer Suite sensor to develop metrics to quantify five major sources of spatial bias or uncertainty in the inventories primary reliance on active fires versus burned area cloud haze burden on the ability of satellites to see fires fragmentation of burned area roughness in topography and small fires which are challenging to detect . Based on all these uncertainties we devise comprehensive relative fire confidence scores mapped globally at 0.250.25 spatial resolution over 20032017 .
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Large regional discrepancies in fire emissions exist among five global inventories. We develop metrics in Google Earth Engine to assess spatial biases in inventories. Inventory choice can significantly bias modeled smoke PM. for Equatorial Asia. Peat delineation and cloud gap adjustment are crucial for Indonesian fire emissions. We develop an online app FIRECAM to help end users assess fire inventories.
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S0034425719305784
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The use of time series analysis with moderate resolution satellite imagery is increasingly common particularly since the advent of freely available Landsat data . Dense time series analysis is providing new information on the timing of landscape changes as well as improving the quality and accuracy of information being derived from remote sensing . Perhaps most importantly time series analysis is expanding the kinds of land surface change that can be monitored using remote sensing . In particular more subtle changes in ecosystem health and condition and related to land use dynamics are being monitored . The result is a paradigm shift away from change detection typically using two points in time to monitoring or an attempt to track change continuously in time . This trend holds many benefits including the promise of near real time monitoring . Anticipated future trends include more use of multiple sensors in monitoring activities increased focus on the temporal accuracy of results applications over larger areas and operational usage of time series analysis .
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A paradigm shift from change detection to monitoring with remote sensing. Characterization of types of change. Characterization of land surface dynamics and trends
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S0034425719305802
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The three dimensional distribution of water vapour around mountainous terrain can be highly variable . This variability can in turn affect local meteorological processes and geodetic techniques to measure ground surface motion . We demonstrate this general problem with the specific issues of a small tropical island Montserrat . Over a period of 17days in December 2014 we made observations using InSAR and GPS techniques together with concurrent atmospheric models using the WRF code . Comparative studies of water vapour distribution and its effect on refractivity were made at high spatial resolution over short distances . Our results show that model simulations of the observed differences in water vapour distribution using WRF is insufficiently accurate . We suggest that better use could be made of the knowledge and observations of local water vapour conditions at different scales specifically the Inter Tropical Convergence Zone the trade wind fields and the mountain flow perhaps using eddy simulation . The annual perturbations of the ITCZ show that the range of humidity is approximately the same expressed as the differential phase of InSAR imaging . Trade wind direction and speed are particularly important at high wind speeds driving vigorous asymmetrical convection over the island s mountains . We also show that the slant angles of radar can follow distinct separate paths through the water vapour field . Our study is novel in demonstrating how synoptic scale features and climate can advise the modelling of mesoscale systems and sub seasonal InSAR imaging on tropical islands .
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A case study over Montserrat mapping water vapour around mountainous terrain. GPS water vapour and its effect on refractivity for InSAR are determined. Slantwise liquid water water vapour and hydrostatic delay are explored using WRF. The ITCZ and trade wind asymmetry are used to discuss water vapour field variance. We describe a simple toolbox that helps identify good InSAR pairings based on climate.
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S0034425719305814
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Rice growth monitoring using Synthetic Aperture Radar is recognized as a promising approach for tracking the development of this important crop . Accurate spatio temporal information of rice inventories is required for water resource management production risk occurrence and yield forecasting . This research investigates the potential of the proposed Generalized volume scattering model based Radar Vegetation Index for monitoring rice growth at different phenological stages . The GRVI is derived using the concept of a geodesic distance between Kennaugh matrices projected on a unit sphere . We utilized this concept of GD to quantify a similarity measure between the observed Kennaugh matrix and the Kennaugh matrix of a generalized volume scattering model . The similarity measure is then modulated with a factor estimated from the ratio of the minimum to the maximum GD between the observed Kennaugh matrix and the set of elementary targets trihedral cylinder dihedral and narrow dihedral . In this work we utilize a time series of C band quad pol RADARSAT 2 observations over a semi arid region in Vijayawada India . Among the several rice cultivation practices adopted in this region we analyze the growth stages of direct seeded rice and conventional tansplanted rice with the GRVI and crop biophysical parameters viz . Plant Area Index PAI . The GRVI is compared for both rice types against the Radar Vegetation Index proposed by Kim and van Zyl . A temporal analysis of the GRVI with crop biophysical parameters at different phenological stages confirms its trend with the plant growth stages . Also the linear regression analysis confirms that the GRVI outperforms RVI with significant correlations with PAI
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GRVI follows the phenological trend with rice growth. GRVI outperforms RVI with a significant correlation with PAI of rice crop. PAI estimations from GRVI show promising retrieval accuracy. GRVI maps at different growth stages of rice capture the variability in plant growth. GRVI discriminates the direct seeded and transplanted rice.
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S0034425719305826
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Eutrophication and increasing prevalence of potentially toxic cyanobacterial blooms among global inland water bodies is becoming a major concern and requires direct attention . The European Space Agency recently launched the Ocean and Land Color Instrument aboard the Sentinel 3 satellite . The success of the mission will depend on extensive validation efforts for the development of accurate and robust in water algorithms . In this study four full atmospheric correction methods are assessed over four inland water reservoirs in South Africa along with a suite of red NIR based semi analytic and band difference models for chl a estimation which were applied to both full and partial atmospherically corrected data . In addition we tested a novel duplicate pixel correction method to account for duplicate pixels induced by high observation zenith angles . Radiometric errors associated with OLCI Top of Atmosphere radiances over small water targets were also investigated by modeling in situ reflectance measurements to at sensor radiances using MODTRAN . Of the four atmospheric corrections the 6SV1 radiative transfer code showed the most promise for producing reasonable reflectances when compared to in situ measurements . Empirically derived band difference models outperformed all other chl a retrieval methods on both partially and fully corrected reflectances . The Maximum Peak Height algorithm applied to Bottom of Rayleigh Reflectance performed best overall R
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6SV1 provided most accurate atmospheric correction. Empirically derived band difference models most robust for chl a estimation. Novel duplicate pixel correction introduced. OLCI capable of monitoring small water targets
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S0034425719305838
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Land use and land cover maps provide fundamental information that has been used in different kinds of studies ranging from climate change to city planning . However despite substantial efforts in recent decades large scale 30 m land cover maps still suffer from relatively low accuracy in terms of land cover type discrimination due to limits in relation to the data method and design of the workflow . In this work we improved the land cover classification accuracy by integrating free and public high resolution Google Earth images with Landsat Operational Land Imager and Enhanced Thematic Mapper Plus imagery . Our major innovation is a hybrid approach that includes three major components a deep convolutional neural network based classifier that extracts high resolution features from Google Earth imagery traditional machine learning classifiers and Support Vector Machine that are based on spectral features extracted from 30 m Landsat data and an ensemble decision maker that takes all different features into account . Experimental results show that our proposed method achieves a classification accuracy of 84.40 on the entire validation dataset in China improving the previous state of the art accuracies obtained by RF and SVM by 4.50 and 4.20 respectively . Moreover our proposed method reduces misclassifications between certain vegetation types and improves identification of the impervious type . Evaluation applied over an area of around 14 000km
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Combining Google Earth images and Landsat data for land cover mapping. Fusing high resolution spatial features and medium resolution spectral features. Improving the previous highest OA from 80 to 84 on all samples in China. Reducing confusions among different vegetation and impervious types. Validating the method with 5 selected regions with a total area of about 14 000km
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S0034425719306030
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Optical properties of clouds and heavy aerosol retrieved from satellite measurements are the most important elements for calculating surface solar radiation . The Himawari 8 Advanced Himawari Imager satellite measurements receive high spatial temporal and spectral signals which provides an opportunity to estimate cloud aerosol and SSR accurately .
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The AHI official cloud algorithm version 1.0 is developed for the JAXA P Tree system. The Voronoi ice crystal scattering model is used to develop the ice cloud product. High accuracy SSR is estimated using the AHI cloud parameters.
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S0034425719306042
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Aerosols have adverse health effects and play a significant role in the climate as well . The Multiangle Implementation of Atmospheric Correction provides Aerosol Optical Depth at high temporal and spatial resolution making it particularly useful to infer and characterize spatiotemporal variability of aerosols at a fine spatial scale for exposure assessment and health studies . However clouds and conditions of high surface reflectance result in a significant proportion of missing MAIAC AOD . To fill these gaps we present an imputation approach using deep learning with downscaling . Using a baseline autoencoder we leverage residual connections in deep neural networks to boost learning and parameter sharing to reduce overfitting and conduct bagging to reduce error variance in the imputations . Downscaled through a similar auto encoder based deep residual network Modern Era Retrospective analysis for Research and Applications Version 2 GMI Replay Simulation data were introduced to the network as an important gap filling feature that varies in space to be used for missingness imputations . Imputing weekly MAIAC AOD from 2000 to 2016 over California a state with considerable geographic heterogeneity our full residual network achieved mean R
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Massive non random missingness limits satellite AOD applications. Residual learning of deep network boosts training efficiency for AOD imputation. Residual network reliably downscales coarse scale reanalysis data. Adjusted satellite AOD using elevation and coordinates better correlates with AERONET AOD. Residual network generalizable to impute missing satellite or environmental data
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S003442571930608X
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Sea Surface Temperature is an essential variable for understanding key physical and biological processes . Blended and interpolated L4 SST products offer major advantages over alternative SST data sources due to their spatial and temporal completeness yet their ability to discriminate upwelling induced steep temperature transitions in coastal waters remains largely unassessed . Here we analysed the performance of eleven L4 GHRSST compliant products in estimating
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L4 GHRSST products give good estimates of coastal SSTs in areas without upwelling. At the coast the best performing L4 GHRSST products are G1SST and OSTIA. All L4 GHRSST products overestimate coastal temperatures during upwelling. With strong upwelling average bias may exceed 2C. L3 data perform well near the coast but have temporal and spatial gaps.
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S0034425719306091
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This paper applies the Fully Focussed SAR algorithm to CryoSat 2 full bit rate data to measure water levels of lakes and canals in the Netherlands and validates these measurements by comparing them to heights measured by gauges . Over Lake IJssel a medium sized lake the FF SAR height is biased about 6cm below the gauge height and a similar bias is found at six sites where CryoSat 2 crosses rivers and canals . The precision of the FF SAR measurements depends on the extent of multi looking applied . Over Lake IJssel the precision varies from 4 to 11cm decreasing as multi looking increases . The precision of FF SAR with 100m of multi looking is equivalent to that of the standard delay Doppler processing which has an along track resolution of about 300m . The width and orientation of rivers and canals limits the maximum available multi looking . After removing the 6cm bias FF SAR heights of rivers and canals have an accuracy between 2cm and several decimeters primarily depending on the presence of other water bodies lying within the cross track measurement footprint as these contaminate the waveform . We demonstrate that FF SAR processing is able to resolve and measure small ditches only a few meters in width . The visibility of these signals depends on the angle at which CryoSat 2 crosses the ditch and on whether or not the ditch remains straight within CryoSat 2s field of view . In the best case scenario straight ditches at nearly 90 to the CryoSat 2 ground track the ditch signal has high enough signal to noise to allow sub decimeter accuracy of FF SAR height measurement .
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First validation of inland water level estimation using CryoSat 2 fully focussed SAR altimetry data. Complete description of the implementation of the backprojection algorithm for CryoSat 2. Improved precision of FF SAR with respect to delay Doppler demonstrated over a lake. Unprecedented accuracy over rivers and canals smaller than the delay Doppler footprint. Water level estimation of smallest target ever observed with a satellite radar altimeter
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S0034425719306108
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Optimal estimation is a core method in quantitative Earth observation . The optimality of OE depends on the errors in the prior measurements and forward model being zero mean and having well known error covariance . Often these assumptions are not met . We show how to use matches of satellite observations to in situ reference measurements to estimate parameters for use in OE that bring the retrieval framework closer to the theoretical optimality . This is done by retrieving bias correction and error covariance parameters . Bias correction parameters for some components of the retrieved state and for the satellite radiances are anchored by the in situ reference measurements and are obtained by a modification of Kalman filtering . Error covariance matrices for the prior state and for the observation simulation difference are iteratively obtained by applying equations for diagnosing internal retrieval consistency . The theory is applied to the case of OE of sea surface temperature from a sensor on a geostationary platform . Relative to an initial OE implementation all measures of retrieval performance are improved when the optimised OE is tested on independent data mean difference from validation data is reduced from 0.08 K to 0.01 K and the standard deviation from 0.47 to 0.45K retrieval sensitivity to sea surface temperature increases from 71 to 76 and a 20 underestimation of retrieval uncertainty is corrected . Perhaps more significant than the quantitative improvements are the coherent new insights into the forward model simulations and prior assumptions that are also obtained . These include estimates of prior bias in the absence of in situ information an important consideration when in situ information is not globally distributed . Biases and lack of information about error covariances arise in remote sensing very often . While illustrated here for a particular case the principles and methods we present for constraining that lack of knowledge systematically using ground truth will be widely applicable in remote sensing .
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Method to determine bias and covariance parameters for optimal estimation. Ensures assumptions underlying optimal estimation are more closely met. Observation and prior state biases are constrained using matched ground truth. Objective evaluation of prior and observation simulation error covariances. Example application to sea surface temperature but method is widely applicable.
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S0034425719306145
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Evapotranspiration from the land surface is an important component of the terrestrial hydrological cycle . Since the advent of Earth observation by satellites various models have been developed to use thermal and shortwave remote sensing data for ET estimation . In this review we provide a brief account of the key milestones in the history of remote sensing ET model development in two categories temperature based and conductance based models . Temperature based ET models utilize land surface temperature observed through thermal remote sensing to calculate the sensible heat flux from which ET is estimated as a residual of the surface energy balance or to estimate the evaporative fraction from which ET is derived from the available energy . Models of various complexities have been developed to estimate ET from surfaces of different vegetation fractions . One source models combine soil and vegetation into a composite surface for ET estimation while two source models estimate ET of soil and vegetation components separately . Image contexture based triangular and trapezoid models are simple and effective temperature based ET models based on spatial and or temporal variation patterns of LST . Several effective temporal scaling schemes are available for extending instantaneous temperature based ET estimation to daily or longer time periods . Conductance based ET models usually use the Penman Monteith equation to estimate ET with shortwave remote sensing data . A key put to these models is canopy conductance to water vapor which depends on canopy structure and leaf stomatal conductance . Shortwave remote sensing data are used to determine canopy structural parameters and stomatal conductance can be estimated in different ways . Based on the principle of the coupling between carbon and water cycles stomatal conductance can be reliably derived from the plant photosynthesis rate . Three types of photosynthesis models are available for deriving stomatal or canopy conductance big leaf models for the total canopy conductance two big leaf models for canopy conductances for sunlit and shaded leaf groups and two leaf models for stomatal conductances for the average sunlit and shaded leaves separately . Correspondingly there are also big leaf two big leaf and two leaf ET models based on these conductances . The main difference among them is the level of aggregation of conductances before the P M equation is used for ET estimation with big leaf models having the highest aggregation . Since the relationship between ET and conductance is nonlinear this aggregation causes negative bias errors with the big leaf models having the largest bias . It is apparent from the existing literature that two leaf conductance based ET models have the least bias in comparison with flux measurements . Based on this review we make the following recommendations for future work improving key remote sensing products needed for ET mapping purposes including soil moisture foliage clumping index and leaf carboxylation rate combining temperature based and conductance based models for regional ET estimation refining methodologies for tight coupling between carbon and water cycles fully utilizing vegetation structural and biochemical parameters that can now be reliably retrieved from shortwave remote sensing and to improve regional and global ET monitoring capacity .
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Separating ET models into temperature based and conductance based models. Comprehensive accounts for the historical developments of models of these two types. In depth analysis of bias errors by big leaf two big leaf and two leaf ET conductance based ET models. Future directions for ET research are identified.
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S0034425719306170
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Forest aboveground biomass is a key variable in remote sensing based forest monitoring . Active sensor systems such as lidar can generate detailed canopy height products . Relationships between canopy height and biomass are commonly established via regression analysis using information from ground truth plots . In this way many site specific height biomass relationships have been proposed in the literature and applied for mapping in regional contexts . However such relationships are only valid within the specific forest type for which they were calibrated . A generalized relationship would facilitate biomass estimation across forest types and regions . In this study a combination of lidar derived and ancillary structural descriptors is proposed as an approach for generalization between forest types . Each descriptor is supposed to quantify a different aspect of forest structure i.e . mean canopy height maximum canopy height maximum stand density vertical heterogeneity and wood density . Airborne discrete return lidar data covering 194ha of forest inventory plots from five different sites including temperate and tropical forests from Africa Europe North Central and South America was used . Biomass predictions using the best general model nRMSE 12.4 R
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Structural metrics to generalize forest biomass estimation from lidar are proposed. The regression analysis included five megaplots from tropical and temperate sites. The new approach performed almost as well as site specific estimation models. The roles of metrics describing vertical and horizontal structure are analyzed.
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S0034425719306182
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Remote sensing derived cropland products have depicted the location and extent of agricultural lands with an ever increasing accuracy . However limited attention has been devoted to distinguishing between actively cropped fields and fallowed fields within agricultural lands and in particular so in grass fallow systems of semi arid areas . In the Sahel one of the largest dryland regions worldwide crop fallow rotation practices are widely used for soil fertility regeneration . Yet little is known about the extent of fallow fields since fallow is not explicitly differentiated within the cropland class in any existing remote sensing based land use cover maps regardless of the spatial scale . With a 10m spatial resolution and a 5 day revisit frequency Sentinel 2 satellite imagery made it possible to disentangle agricultural land into cropped and fallow fields facilitated by Google Earth Engine for big data handling . Here we produce the first Sahelian fallow field map at a 10m resolution for the baseline year 2017 accomplished by designing a remote sensing driven protocol for generating reference data for mapping over large areas . Based on the 2015 Copernicus Dynamic Land Cover map at 100m resolution the extent of fallow fields in the cropland class is estimated to be 63 403 617km
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Mapping of the land use class fallow field was developed. An RS based approach for creating large scale reference data was designed. A Sahel scale crop fallow field map was produced at a 10m resolution for 2017. Fallow fields remarkably occupy 603 of Sahelian cropland areas. Shares of crop fallow fields were analyzed using rainfall and woody cover.
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S0034425719306194
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Preharvest crop yield prediction is critical for grain policy making and food security . Early estimation of yield at field or plot scale also contributes to high throughput plant phenotyping and precision agriculture . New developments in Unmanned Aerial Vehicle platforms and sensor technology facilitate cost effective data collection through simultaneous multi sensor multimodal data collection at very high spatial and spectral resolutions . The objective of this study is to evaluate the power of UAV based multimodal data fusion using RGB multispectral and thermal sensors to estimate soybean grain yield within the framework of Deep Neural Network . RGB multispectral and thermal images were collected using a low cost multi sensory UAV from a test site in Columbia Missouri USA . Multimodal information such as canopy spectral structure thermal and texture features was extracted and combined to predict crop grain yield using Partial Least Squares Regression Random Forest Regression Support Vector Regression input level feature fusion based DNN and intermediate level feature fusion based DNN . The results can be summarized in three messages multimodal data fusion improves the yield prediction accuracy and is more adaptable to spatial variations DNN based models improve yield prediction model accuracy the highest accuracy was obtained by DNN F2 with an R
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A low cost multi sensor UAV for crop monitoring phenotyping was developed. Canopy structure temperature and texture are important features for yield model. Multimodal data fusion showed effectiveness in yield prediction. DNN provided promising results in yield prediction across genotypes and over space.
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S0034425719306224
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The original data produced by the Shuttle Radar Topography Mission tend to have an abundance of voids in mountainous areas where the elevation measurements are missing . In this paper deep learning models are investigated for restoring SRTM data . To this end we explore generative adversarial nets which represent one state of the art family of deep learning models . A conditional generative adversarial network is introduced as the baseline method for filling voids in incomplete SRTM data . The problem regarding shadow violation that possibly arises from the CGAN restored data is investigated . To address this deficiency shadow geometric constraints based on shadow maps of satellite images are devised . In addition a shadow constrained conditional generative adversarial network which incorporates the shadow geometric constraints into the CGAN is developed . Training the SCGAN model requires both the remote sensing observations and the ground truth data . The integration of the multi source training data enables the SCGAN model to be characterized by comprehensive information including both mountain shape variation and mountain shadow geometry . Experimental results validate the superiority of the SCGAN over the comparison methods i.e . the interpolation the convolutional neural network and the baseline CGAN in SRTM data restoration .
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A shadow constrained conditional generative adversarial net SCGAN is developed. The SCGAN restores SRTM data subject to geometric shadow cues. The SCGAN is endowed with the representational power of deep learning. The SCGAN fuses space and in situ observations.
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S0034425719306236
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A combined snow modelling approach integrating remote sensing data in situ data and an improved hydrological model is presented . Complementary information sources are evaluated in terms of its value for constraining the model parameters and to overcome limitations of individual data such as inadequate scale representation . The study site consists of the Upper Fagge river basin in the Austrian Alps featuring the Weisssee Snow Research Site . The available remote sensing datasets include Terra MODIS based medium resolution and Landsat 7 8 and Sentinel 2A based high resolution fractional snow covered area maps . Recently Sentinel 1 based wet snow covered area maps have become increasingly available . To the knowledge of the authors the first evaluation of their value for snow hydrological modelling is presented . Besides conventional small footprint station data in situ time series of snow water equivalent of a Cosmic Ray Neutron Sensor having a footprint of several hectares is additionally used . For including these data the model now provides respective outputs such as fractional snow cover wet dry snow surface and SWE areal means equivalent to the CRNS sensor footprint . By means of 40 000 model runs the high complementary value of representative SWE data and remote sensing information was assessed with most promising results achieved by combining high resolution fractional snow covered area maps with CRNS SWE data . Regarding mean SWE or mean snow covered area in the catchment the ensemble spreads are reduced by two thirds compared to the results of a benchmark simulation based only on runoff for model calibration . Wet snow covered area maps have a high potential for simulating SWE at Weisssee Snow Research Site but introduce additional uncertainties for runoff simulations likely caused by the uncertain detection of the snow covered area from Sentinel 1 backscatter . The approach has high potential for water resources management in gauged and ungauged mountain basin and gives guidance for efficient data assimilation schemes .
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A framework to analyse the explanatory value of complementary snow data is presented. The applicability of the different data is addressed in terms of scale and accuracy. Cosmic Ray Neutron Sensing increases model realism in gauged and ungauged basins. High resolution optical and SAR based remote sensing data outperform MODIS data.
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S0034425719306261
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The accurate representation of spatio temporal patterns of precipitation is an essential input for numerous environmental applications . However the estimation of precipitation patterns derived solely from rain gauges is subject to large uncertainties . We present the Random Forest based MErging Procedure which combines information from ground based measurements state of the art precipitation products and topography related features to improve the representation of the spatio temporal distribution of precipitation especially in data scarce regions . RF MEP is applied over Chile for 20002016 using daily measurements from 258 rain gauges for model training and 111 stations for validation . Two merged datasets were computed RF MEP
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RF MEP improved P characteristics in a region with diverse topography and climate. RF MEP can be applied at different temporal scales. RF MEP works well even when few rain gauges are available for training. The difference in reporting times between products and stations must be considered. RF MEP performed better than other merging approaches.
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S0034425719306273
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Earth remote sensing using reflected GNSS signals is currently an emerging trend especially in ocean surface wind measurements . Unlike the existing scatterometer missions GNSS R uses L Band navigation signals that can penetrate through clouds and rain . Rain may have a negligible impact on the transmitted signal in terms of path attenuation at this wavelength . However there are other effects due to rain such as changes in surface roughness and rain induced local winds which can significantly alter the measurements . Currently there is no observation based characterization of all possible impacts of rain on radar forward scatter which is the nature of operation of GNSS R missions . In this study we propose a 3 fold rain model which accounts for attenuation surface effects of rain and rain induced local winds . We utilize the large dataset of measurements made by the CYGNSS mission to separate these different effects of rain . The attenuation model suggests that a total of at least 96
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3 fold rain model for attenuation surface effects and downdraft winds due to rain. Statistical analysis of GNSS R measurements under rain using CYGNSS data. Rain reduces. and impact of rain is visible in measurements only up to 15m s. Geometric optics limit of low pass wavenumber is invalid at very low wind speeds. Model suggests a maximum downdraft wind of up to 8m s for very high rain rates.
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S0034425719306285
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Sentinel 1 mission with its wide spatial coverage short revisit time and rapid data dissemination opened new perspectives for large scale interferometric synthetic aperture radar analysis . However the spatiotemporal changes in troposphere limits the accuracy of InSAR measurements for operational deformation monitoring at a wide scale . Due to the coarse node spacing of the tropospheric models like ERA Interim and other external data like Global Navigation Satellite System the interpolation techniques are not able to well replicate the localized and turbulent tropospheric effects . In this study we propose a new technique based on machine learning Gaussian processes regression approach using the combination of small baseline interferograms and GNSS derived zenith total delay values to mitigate phase delay caused by troposphere in interferometric observations . By applying the ML technique over 12 Sentinel 1 images acquired between MayOctober 2016 along a track over Norway the root mean square error reduces on average by 83 compared to 50 reduction obtained by using ERA Interim model .
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Proposing a tropospheric correction method on large scale InSAR using machine learning. Successful implementation of the method for country scale InSAR map of Norway. Performance assessment of the method against external observations
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S0034425719306303
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Geostationary Ocean Color Imager sensor onboard the COMS launched in 2010 was primarily designed to provide high frequency observations in and around the Korean Peninsula to ensure the thorough monitoring of ocean properties . Owing to its pixel resolution of 500m and large set of spectral solar channels GOCI can also be considered for applications related to the characterization of vegetation and the retrieval of aerosol properties over land . However to apply it for the full characterization of land it is mandatory to properly remove clouds from the images . Such a procedure has limitations when there is a lack of thermal bands as is the case with GOCI . However GOCI data are impacted by shadows and radiation scattering effects during the daily course of the sun . Although this yields strong directional effects the bidirectional reflectance distribution function can be determined to a high level of accuracy . This information is used as a reference to detect clouds over land because surface BRDF varies slowly with time compared to that of clouds . The proposed algorithm relies on knowledge of the BRDF field derived from the application of a semi empirical model that simulates the minimum difference between top and bottom of atmosphere reflectance values as the baseline of clear atmosphere . This step also serves to estimate background surface reflectance underneath clouds . Accuracy assessment of the new GOCI cloud mask product is appraised through a comparison with high resolution vertical profiles of lidar data from the polar orbiting Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation . The results for the Probability Of Detection of all cloud types was found to be 0.831 for GOCI this is comparable to that of MODIS . For the case of only thin cirrus GOCI POD value was assessed to be 0.849 similar to that of MODIS underlining the improved efficiency of determining thin cloud pixels .
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We suggest thin cloud detection method for GOCI regarding vegetation or aerosols over land surface. The original GOCI pre processing was improved by applying a semi empirical BRDF model. BRDF was used to determine the minimum reflectance difference between TOA and land. A baseline of clear atmosphere and background surface reflectance were defined. All cloud and thin cloud POD values of GOCI are found to be above MODIS.
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S0034425719306315
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Remote sensing assessments of land use and land cover change are critical to improve understanding of socio economic institutional and ecological processes that lead to and stem from land use change . This is particularly crucial in the emerging frontiers of Southern Africa where there is a paucity of LULCC studies relative to the humid tropics . This study focuses on Guru District 5606km
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Pixel based compositing impacts classification results. Median compositing performed best for disaggregating cropland by field size. Collect Earth data can be used to train and test classification algorithms. Textural features facilitate the separation of small and large scale cropland. Mapping cropland scale dynamics can improve understanding of land and food systems.
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S0034425719306352
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Leaf area index is a key variable for characterizing crop growth conditions and estimating crop productivity . Despite continuing efforts to develop LAI estimation algorithms LAI datasets still need improvement at spatial and temporal resolutions to meet the requirements of agricultural applications . Advancements in data fusion technique and the emergence of new satellite data provide opportunities for LAI data at higher resolutions in both space and time . In this study we derived new LAI estimations by leveraging novel satellite remote sensing datasets STAIR fusion and Planet Labs CubeSat data for a typical agricultural landscape in the U.S. Corn Belt . The STAIR fused data and our reprocessed CubeSat data have both fine spatial resolutions and high frequencies . To reliably estimate LAI from these advanced satellite datasets we used two methods inversion of a radiative transfer model and empirical relationship with vegetation index calibrated from field measured LAI . Compared to the ground truth LAI collected at 36 sites across the study region reliable approximations were achieved by both LAI estimations based on PROSAIL RTM STAIR R
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High spatiotemporal resolution LAI is achieved from MODIS Landsat Fusion and CubeSat. Both empirical and process based LAI estimations achieve high performance. The MODIS and VIIRS LAI products show large bias for cropland.
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S0034425719306364
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The cloud top height product derived from passive satellite instrument measurements is often used to make climate data records . CALIPSO provides CTH parameters with high accuracy but with limited temporal spatial resolution . Recently the Advanced Himawari Imager onboard Japanese Himawari 8 9 provides high temporal and high spatial resolution measurements with 16 spectral bands . This paper reports on a study to derive the CTH from combined AHI and CALIPSO using advanced machine learning algorithms with better accuracy than that from the traditional physical algorithms . We find significant CTH improvements from four different machine learning algorithms particularly in high and optically thin clouds . In addition we also develop a joint algorithm to combine optimal machine learning and traditional physical algorithms of CTH to further reduce MAE to 1.53km and enhance the layered accuracy . While the ML based algorithm improves CTH retrieval over the TRA algorithm the lower or higher clouds still exhibit relatively large uncertainty . Combining both methods provides the better CTH than either alone . The combined approach could be used to process data from advanced geostationary imagers for climate and weather applications .
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A novel machine learning algorithm to retrieve cloud top height using Himawari 8. Significant improvements in cloud top height product from machine learning algorithm. A joint algorithm further reduces uncertainty in cloud top height.
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S0034425719306376
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Antarctic pack ice seals a group of four species of true seals play a pivotal role in the Southern Ocean foodweb as wide ranging predators of Antarctic krill
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First automated system for surveying seals using satellite imagery. Finds 30 of seals while only generating less than 2 false positives per correct detection. Over 10 faster than an experienced human observer using a single GPU
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S0034425719306388
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The seasonal snow cover is one of the most rapidly varying natural surface features on Earth . It strongly modulates the terrestrial water energy and carbon balance . Fractional snow covered area is an essential snow variable that can be retrieved from multispectral satellite imagery . In this study we evaluate fSCA retrievals from multiple sensors that are currently in polar orbit the operational land imager on board Landsat 8 the multispectral instrument on board the Sentinel 2 satellites and the moderate resolution imaging spectroradiometer on board Terra and Aqua . We consider several retrieval algorithms that fall into three classes thresholding of the normalized difference snow index regression on the NDSI and spectral unmixing . We conduct the evaluation at a high Arctic site in Svalbard Norway by comparing satellite retrieved fSCA to coincident high resolution snow cover maps obtained from a terrestrial automatic camera system . For the lower resolution MODIS retrievals the regression based retrievals outperformed the unmixing based retrievals for all metrics but the bias . For the higher resolution sensors retrievals based on NDSI thresholding overestimated the fSCA due to the mixed pixel problem whereas spectral unmixing retrievals provided the most reliable estimates across the board . We therefore encourage the operationalization of spectral unmixing retrievals of fSCA from both OLI and MSI .
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Extensive validation of fSCA retrieved from multiple sensors. High quality reference data set obtained using time lapse terrestrial photography. Aggregating NDSI derived binary snow cover maps leads to biased fSCA. Spectral unmixing can resolve the mixed pixel problem providing near unbiased fSCA.
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S0034425719306406
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The attention towards possible link of earthquakes and ionosphere in the form of seismo ionosphere anomalies has increased exponentially by utilizing new data and more accurate observations . The integrated atmosphere and ionosphere monitoring satellites has played a decisive role in this development and provided detection and analysis of anomalies attributed to seismic processes . In this paper we study EQ anomalies in ionosphere from IGS permanent Global Navigation Satellite Systems based Total Electron Content and
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Earthquakes ionospheric and atmospheric anomalies are investigated. Possible Seismo Ionospheric Anomalies SIA occurred during UT 10 12. The Remote Sensing data supports the evidences of SIA in different RS parameters. The main findings showed the coupling of lithosphere atmosphere ionosphere.
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S0034425719306418
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We present the first flight data using a Computational Reconfigurable Imaging Spectrometer system . CRISP is a novel hyperspectral thermal imaging spectrometer that uses computational imaging to enable high sensitivity measurements from smaller noisier and less expensive components making it useful on small space and air platforms with strict size weight and power requirements . In contrast to other multiplexing hyperspectral solutions it does not require moving parts allowing for a robust system without aggressive engineering solutions . We discuss flight system design and calibration . Spectra from ground targets and gaseous species are compared to performance expectations . We successfully demonstrate the ability to extract airborne longwave infrared imagery and spectra from an uncooled camera based CRISP system .
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CRISP is an ultra compact means of collecting hyperspectral imagery. Airborne collection of longwave infrared spectra using uncooled microbolometers. Airborne detection of gaseous species using longwave spectrometry.
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S0034425719306431
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Climate observations indicate more frequent drought in recent years and model predictions suggest that drought occurrence will continue to rise with global warming . Understanding drought impacts on ecosystem functioning requires accurate quantification of vegetation sensitivity to changes in water supply condition . This is complicated by the seasonal variation in plant structural and physiological response to water stress especially for semi arid grasslands with characteristic strong spatial and temporal variability in carbon uptake . Here we use complementary satellite soil moisture and total water storage observations to delineate plant accessible water supply variations for natural grasslands in the Missouri basin USA . We evaluate how water supply influences the spatiotemporal variations in grassland productivity as a function of seasonal timing and climate condition . We identify a 128 day period from mid June to early October when grassland growth is sensitive to soil moisture changes . We find the strongest SM sensitivity after the peak of the growing season associated with high temperature and VPD . SM limitation can extend to early and late growing season under warm conditions while grassland sensitivity to SM is generally stronger in the late growth stage than in the green up period given similar temperature and soil moisture . We find that complementary to the surface SM observations TWS provides plant available water storage information from the deeper soil and both SM and TWS exert a lagged impact on grassland productivity . We find that the lag between the inter annual variation of SM and associated plant response increases through the season and overall there is a transition from SM limitation to TWS limitation on productivity during the late growing period when the TWS level is near the seasonal low . Future global change projections should account for a seasonally varying vegetation moisture relationship to accurately assess the impact of the water supply constraint on plant productivity in a warming climate .
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Satellite detects seasonally varying vegetation moisture sensitivity. Observed warm temperature strengthens soil moisture constraint on productivity. Late season transition from surface moisture to total water storage constraint. Summer total water storage observations retain early season water storage information.
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S0034425719306443
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Cropping intensity has undergone dramatic changes worldwide due to the effects of climate changes and human management activities . Cropping intensity is an important factor contributing to crop production and food security at local regional and national scales and is a critical input data variable for many global climate land surface and crop models . To generate annual cropping intensity maps at large scales Moderate Resolution Imaging Spectroradiometer images at 500 m or 250 m spatial resolution have problems with mixed land cover types within a pixel and Landsat images at 30 m spatial resolution suffer from low temporal resolution . To overcome these limitations we developed a straightforward and efficient pixel and phenology based algorithm to generate annual cropping intensity maps over large spatial domains at high spatial resolution by integrating Landsat 8 and Sentinel 2 time series image data for 20162018 using the Google Earth Engine platform . In this pilot study we report annual cropping intensity maps for 2017 at 30 m spatial resolution over seven study areas selected according to agro climatic zones in China . Based on field scale sample data the annual cropping intensity maps for the study areas had overall accuracy rates of 8999 with Kappa coefficients of 0.760.91 . The overall accuracy of the annual cropping intensity maps was 93 with a Kappa coefficient of 0.84 . These cropping intensity maps can also be used to enable identification of various crop types from phenological information extracted from the growth cycle of each crop . These algorithms can be readily applied to other regions in China to generate annual cropping intensity maps and quantify inter annual cropping intensity variations at the national scale with a greatly improved accuracy .
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Algorithm for identifying cropping intensity was developed at large scales and high spatial resolution. 30m cropping intensity map in 7 study areas of China by integrating Landsat 8 and Sentinel 2. Maps had accuracies between 89 and 99 all over China.
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S0034425719306455
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Small islands face environmental issues directly or indirectly related to land cover land use changes such as natural hazards climate change loss of biodiversity and proliferation of invasive alien species some of which are caused by direct human exploitation . A Land Cover Land Use Change detection approach based on PCA and vegetation indices derived from low cost high resolution RapidEye multispectral satellite data and available vegetation maps was developed to assess vegetated forested aboveground carbon stocks and their changes in Madeira Island Portugal for the period between December 2009 and August 2011 due to catastrophic events occurred in 2010 .
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RapidEye spectral thematic and vegetation condition based change detection. Methodology for carbon accounting in remote and impervious territories. 20 000Mg C stock depletion in 2010 in Madeira Island Portugal. 2010 wildfires and landslides contributed to C depletion in Madeira. We address Sustainable Development Goals 13 and 15 namely targets 15.1 15.5 and 15.8.
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S0034425719306467
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In the face of the growing challenges brought about by human activities effective planning and decision making in biodiversity and ecosystem conservation restoration and sustainable development are urgently needed . Ecological models can play a key role in supporting this need and helping to safeguard the natural assets that underpin human wellbeing and support life on land and below water . The urgency and complexity of safeguarding forest and mountain ecosystems for example and halting decline in biodiversity in the Anthropocene requires a re envisioning of how ecological models can best support the comprehensive assessments of biodiversity and its change that are required for successful action .
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SDMs inform environmental interventions and policies towards achieving the SDGs. RS helps fill data gaps and improve spatio temporal transferability of projections. Biodiversity monitoring in the Anthropocene needs close integration of RS in SDMs. Joint ventures between the ecological modeling and RS communities are needed.
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S0034425719306480
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The Landsat program since its commencement in 1972 has acquired millions of images of our planet . Those images are one of the most valuable Earth Observation resources for local regional and global land surface monitoring and study due to their moderate spatial resolution and rich spectral information . However their applications are impeded largely by their relatively low revisit frequency and cloud contamination on images . In order to improve their usability a number of studies have been conducted to blend Landsat images with Moderate Resolution Imaging Spectroradiometer images to take merits of the two sensors . All blending models reported that they can predict synthetic Landsat images with various degrees of accuracy . However only a couple of models reported that they can explicitly estimate uncertainty for their blended images .
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An image fusion model based on a Kalman Filter algorithm KFRFM is developed. KFRFM can incorporate data uncertainty for image fusion and uncertainty estimate. KFRFM can smooth its predicted images according to their uncertainty estimate. KFRFM can predict synthetic images with high quality and uncertainty estimate. Time series synthetic images from KFRFM can fill temporal gap of earth observations.
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S0034425719306509
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The kappa coefficient is not an index of accuracy indeed it is not an index of overall agreement but one of agreement beyond chance . Chance agreement is however irrelevant in an accuracy assessment and is anyway inappropriately modelled in the calculation of a kappa coefficient for typical remote sensing applications . The magnitude of a kappa coefficient is also difficult to interpret . Values that span the full range of widely used interpretation scales indicating a level of agreement that equates to that estimated to arise from chance alone all the way through to almost perfect agreement can be obtained from classifications that satisfy demanding accuracy targets . Comparisons of kappa coefficients are particularly challenging if the classes vary in their abundance as the magnitude of a kappa coefficient reflects not only agreement in labelling but also properties of the populations under study . It is shown that all of the arguments put forward for the use of the kappa coefficient in accuracy assessment are flawed and or irrelevant as they apply equally to other sometimes easier to calculate measures of accuracy . Calls for the kappa coefficient to be abandoned from accuracy assessments should finally be heeded and researchers are encouraged to provide a set of simple measures and associated outputs such as estimates of per class accuracy and the confusion matrix when assessing and comparing classification accuracy .
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Kappa is not a measure of accuracy but of agreement beyond chance and chance correction is not needed. All arguments for the use of kappa are flawed or apply equally to other measures. Interpreting a kappa coefficient is difficult due especially to the effects of prevalence and bias. A very accurate classification could be associated with a very wide range of kappa values. Kappa should not be routinely used in accuracy assessment or comparison.
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S0034425719306510
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Coral reefs are the foundation of productive ecosystems in the global tropical oceans and are under threat from a variety of local to global scale stressors . Satellite imagery provides a tool to identify and understand the processes that control coral reef degradation however due to the dynamic nature of seawater constituents current spaceborne multispectral sensors can not reliably discriminate between the many coral reef benthic classes necessary to detect change . Hyperspectral imagers may provide sufficient spectral resolution to estimate water column properties and differentiate benthic classes however the effects of depth seawater constituents and classification algorithm on the accuracy of benthic classifications have not been systematically assessed . Here we simulate the ability of a spaceborne hyperspectral imager to accurately map fractional cover of coral reef benthic classes under a variety of conditions . Benthic reflectance is simulated by combining pure reflectance spectra of coral algae and sand and projecting these mixed spectra through a fully crossed set of water columns . We then use a semi analytical optimization procedure to estimate the water column properties and multiple endmember spectral mixture analysis to estimate the fractional cover of the benthic classes using many independent endmember spectra . We compare our estimated benthic class fractions to the original actual fractions used to produce the mixed coral reef spectra to quantify several measures of error . We found that multiple endmember spectral mixture analysis decreases fractional retrieval error which is also reduced when the first derivative of the mixed and endmember spectra is used prior to unmixing . The estimation of fractional benthic class cover is most accurate for depths 3m for most water conditions . Depths 5m should be classified only if chlorophyll and sediment concentration are 0.1mgm
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Simulated the ability of a hyperspectral imager to map coral reef benthic types. Investigated the effects of multiple water properties on fractional retrieval error. Multiple endmember mixture analysis decreases fractional retrieval error. Retrieval at depths 5m should only be attempted under clear water conditions. Analysis applied to hyperspectral imagery to generate maps of predicted benthic cover fractional retrieval error
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S0034425720300018
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Recently there have been significant efforts in the integration of in situ and satellite observations for effective monitoring of coastal areas
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Integration of in situ and satellite observations for effective water quality monitoring. 15years monitoring of Chla SPM and CDOM from in situ hyperspectral measurements. 15years monitoring of Chla SPM and CDOM from MERIS MSI and OLCI images. Radiative transfer modeling evaluation and validation at the water surface and TOA levels. Generate Chla SPM and CDOM maps over the Wadden Sea
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S0034425720300134
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Several decade long satellite retrievals of solar induced chlorophyll fluorescence have become available during the past few years but understanding the long term dynamics of SIF and elucidating its co variation with historical gross primary production remains a challenge . Part of the challenge is due to the lack of direct comparability among these SIF products as they are derived from various satellite platforms with different retrieval methods instruments characteristics overpass time and viewing illumination geometries . This study presents a framework that circumvents these discrepancies and allows the harmonization of SIF products from multiple instruments to achieve long term coverage . We demonstrate this framework by fusing SIF retrievals from SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY and Global Ozone Monitoring Experiment 2 onboard MetOp A developed at German Research Center for Geosciences . We first downscale both original SIF datasets from their native resolutions to 0.05
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A framework for harmonizing SIF from multiple instruments was developed. A long record of SIF from 2002 to present at 0.05. was generated. agrees well with original retrievals and independent airborne ground SIF. is proved to contain both structural and physiological variations. is capable of detecting large scale plant stress at fine spatial resolution.
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S0034425720300286
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Surface floating macroalgae microalgae and other marine and freshwater organisms have been reported in many specific regions around the globe . However it is technically challenging to identify similar occurrences or other types of floating organisms or materials within the vast global oceans and lakes . In this study we address this challenge through combining global scale 375 m resolution false colored Red Green Blue imagery from the Visible Infrared Imaging Radiometer Suite in NOAA s online Ocean Color Viewer for visual inspection and data from several other satellite sensors for spectral diagnostics . In the FRGB imagery the near infrared band is used as the green channel which is sensitive to floating algae and organisms materials on the water surface . Visual inspection of the daily FRGB VIIRS imagery from January 2018 to October 2019 reveals the appearance of various slicks with different colors in many ocean regions and lakes . Combined with spectral diagnostics of the quasi concurrent Sentinel 3A 3B Ocean and Land Colour Instrument and other higher spatial resolution satellite data as well as knowledge of local oceanography limnology most of these elongated or diffuse image features can be identified as
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An approach is demonstrated to search for floating algae and other organisms. Floating algae and other organisms in global oceans and lakes are identified. Some floating algae e.g. appear to have expanded geographically. Several unknown types of floating organisms are also discovered. Two of these might be inferred to be sea jellies and shrimp eggs respectively.
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S0034425720300316
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A new algorithm called combined constrained multi channel algorithm is presented for simultaneous retrieval of soil moisture and vegetation optical depth in L band with improved resolution . Unlike widely used algorithms the new approach optimally fuses multiple sources of surface temperatures into the inversion process and confines the retrievals to their feasible climatological range rather than the mean and paves the way to account for the slow changes of VOD through a Sobolev norm regularization . Through controlled numerical experiments that assume a random error in the surface temperatures it is shown that the algorithm can decrease the root mean squared error by 78 and 81 when compared with the unconstrained version and 54 and 7 when a single source of surface temperature is used in retrievals of SM and VOD respectively . The use of the Sobolev norm regularization decreases the RMSE by more than 25 at the expense of a negligible bias . Implementation of the algorithm using data from the NASA s Soil Moisture Active Passive satellite in 2016 demonstrates that the monthly RMSE of SM retrievals improves by more than 6 when compared with the SMAP enhanced products considering the ground measurements from the International SM Network as a reference while the monthly RMSE of VOD decreases by more than 62 when compared with the VOD climatology used in the SMAP SCA products . Analysis of the results demonstrates that without increasing the native resolution of radiometric observations the information content of the
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A priori climatological constraints reduce uncertainty of soil moisture retrievals. Assimilating multiple soil temperatures into inversion leads to reduced uncertainty. Sobolev norm regularization improves retrievals of VOD in L band. Retrieval of soil moisture beyond SMAP native resolution is feasible. C CMCA soil moisture retrievals are robust to background water contamination.
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S0034425720300328
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Underground mining activities usually induce large surface displacements thus causing serious safety hazards and potential ecological damage . The capability of conventional Interferometric Synthetic Aperture Radar to monitor tectonic movements volcanic eruptions and city subsidence has been fully demonstrated but its application to mining subsidence is limited because of the failure caused by localized surface displacements with strong spatial gradients . In this paper a new method is presented to utilize SAR pixel Offset Tracking with a single pair of SAR images to resolve three dimensional large surface displacements caused by underground coal mining . Coal mining subsidence theory is utilised to analytically separate the vertical and horizontal components . This method is applied to the Daliuta coal mining area in Shaanxi Province China where a dense GPS network is available . Results show the RMS differences of OT derived displacements against GPS in both horizontal and vertical directions are in the sub centimeter level . In addition a prediction of mining induced ground movements is performed with the Support Vector Regression algorithm and RMS differences of 12.4 13.1 and 14.4cm are observed compared to GPS in the vertical easting and northing directions respectively . The framework demonstrated in this paper is not only able to derive the evolution of the 3D large surface displacements with multi temporal SAR images in a single geometry but also has a potential for short term predication which can provide early warnings and promote strategic decision making for engineering management in the process of coal mining .
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A framework is proposed to map and predict mining induced large ground movements. 3D large surface displacements are retrieved using SAR data in a single geometry. The support vector regression method is used to predict ground movements.
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S0034425720300341
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Insights into the dynamics of coastlines and tidal flats at fine spatial and temporal resolutions are essential for sustainable development . Previous studies were generally conducted at relatively coarse temporal intervals which hardly captured detailed coastal dynamics especially in rapidly developing islands . In this study we developed a new method to map the monthly changes in coastlines and tidal flats in the Zhoushan Archipelago during 19852017 using the full time series of Landsat images based on the Google Earth Engine platform . First we built the full time series of the Modified Normalized Difference Water Index . Second we derived temporal segments of MNDWI using a binary segmentation algorithm . Third we classified the corresponding coastal cover types for each temporal segment based on the features of MNDWI and regional tidal heights . Finally we identified the change information including conversion types and turning years and months . Results indicate that the proposed method can well identify turning years with an overall accuracy of 90 and map coastal cover types with overall accuracies of 8994 in 1985 and 8792 in 2017 . Significant coastline expansions and declines in tidal flats were found in the study area . The areas of water and tidal flats decreased by 6 and 10 during 19852017 respectively while the land area increased by 18 . The land reclamation was accelerated in the recent decade and mainly occurred on the medium large islands their surrounding small islands and the islands close to the mainland . The proposed framework based on the GEE platform is transferable to investigate coastal dynamics in other areas . The derived information of changes in coastlines and tidal flats is of great use for sustainable management and ecological studies in coastal areas .
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We mapped monthly changes in coastlines and tidal flats at pixel level using all Landsat. The derived coastal dynamics is reliable in detecting paces of land reclamation. This study reveals detailed responses of coastlines and tidal flats to human activities. The proposed framework based on the GEE is transferable to other coastal areas.
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S0034425720300353
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Structure from motion and multi view stereo algorithms coupled with the use of unmanned aerial vehicles have become a popular tool in the geosciences for modelling complex landscapes on demand allowing for high resolution topographic change detection studies to be conducted at minimal cost . To identify the effects of UAV image orientation on the accuracy of SfM MVS 3D surface models we tested four different UAV image acquisition schemes that incorporated both nadir and oblique imagery of an agricultural field . The coupling of nadir and oblique imaging angles led to the highest surface model accuracy in the absence of ground control points while with a normative distribution of GCPs the nadir only image sets had similar accuracy metrics to surface models generated with nadir and oblique imaging angles . Homologous keypoint matching between nadir and oblique imagery was poor when the survey conditions were bright and the surface texture of the field was homogeneous leading to broad scale vertical noise in the generated surface models . Results indicate that a nadir only image set accompanied with a dense deployment of GCPs is the most ideal for SfM MVS agricultural 3D surface reconstructions . The diachronic analysis of surface models generated from nadir only image sets were able to detect surface change 0.040m in depth and were comparable to results obtained from a terrestrial laser scanner . Stable GCPs should be used where possible to ensure precise co registration between subsequent UAV surveys .
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Accuracy assessment of UAV SfM MVS 3D agricultural surface models. Comparison of SfM MVS with nadir and oblique UAV imagery. Erosion and deposition 0.040m measured with diachronic UAV surface models. Volumetric quantification of depositional plumes with UAV and TLS surface models
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S0034425720300377
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Knowledge of snow water equivalent magnitude and spatial distribution are keys to improving snowmelt flood predictions . Since the 1980s the operational National Oceanic and Atmospheric Administration s airborne gamma radiation soil moisture and SWE survey has provided over 20 000 SWE observations to regional National Weather Service River Forecast Centers . Because the gamma SWE algorithm is based on the difference in natural gamma emission measurements from the soil between bare and snow covered conditions it requires a baseline fall SM for each flight line . The operational approach assumes the fall SM remains constant throughout that winter s SWE survey . However early winter snowmelt and rainfall events after the fall SM surveys have the potential to introduce large biases into airborne gamma SWE estimates . In this study operational airborne gamma radiation SWE measurements were improved by updating the baseline fall SM with Soil Moisture Active Passive enhanced SM measurements immediately prior to winter onset over the north central and eastern United States and southern Canada from September 2015 to April 2018 . The operational airborne gamma SM had strong agreement with the SMAP SM Pearson s correlation coefficient
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The NOAA airborne gamma radiation surveys provide unique SM and SWE records. Airborne gamma SWE estimate biases result when SM changed after fall SM surveys. SMAP SM has the best agreement with gamma SM as compared to other SM products. Operational gamma SWE improved by updating antecedent SM prior to freeze onset. SMAP updated SWE showed better agreement with two independent SWE observations.
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S0034425720300407
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The natural world has multiple sometimes conflicting sometimes synergistic values to society when viewed through the lens of the Sustainable Development Goals Spatial mapping of nature s contributions to the SDGs has the potential to support the implementation of SDG strategies through sustainable land management and conservation of ecosystem services . Such mapping requires a range of spatial data . This paper examines the use of remote sensing and spatial ecosystem service modelling to examine nature s contribution to targets under SDG 6 also highlighting synergies with other key SDGs and trade offs with agriculture .
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Existing EO datasets can underpin spatial analyses of nature s contributions to SDGs. Such analyses indicate within and between country variability in nature s contribution. A spatial prioritisation suggests the highest priority areas for investment. Ubiquitous EO data enable globally consistent and geographically comprehensive analyses. Challenges remain in using EO data for multi factor models like these.
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S0034425720300419
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Human mediated climate change over the past century has resulted in significant impacts to global ecosystems and biodiversity including accelerating redistribution of the geographic ranges of species . In mountainous regions the transition zone from continuous closed canopy montane forests to treeless alpine tundra areas at higher elevations is commonly referred to as the alpine treeline ecotone . Globally warming climate is expected to drive ATEs upslope which could lead to negative impacts on local biodiversity and changes in ecosystem function . However existing studies rely primarily on field based data which are difficult and time consuming to collect . In this study we define an ATE detection index to automatically identify the ATE positions from 2009 to 2011 in the western United States using geospatial tools and remotely sensed datasets provided by Google Earth Engine . A binomial logistic regression model was fitted between standardized ATEI components and a binary variable of pixel status of 141 sampled Landsat pixels manually classified with high resolution imagery in Google Earth . The average model accuracy was around 0.713 and the average Kappa coefficient was approximately 0.426 based on a 100 time repeated 10 fold cross validation . Furthermore the ATEI estimated elevation is highly correlated Pearson s
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An index ATEI is developed to locate Alpine Treeline Ecotones in the western U.S. The ATEI is developed based on the image gradients of NDVI and elevation. Sampled pixel location can be classified by the ATEI with an accuracy of 0.713. ATEI estimated ATE elevation is highly correlated with a published field dataset.
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S0034425720300444
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Many studies have shown that high elevation environments are among very sensitive to climatic changes and where impacts are exacerbated . Across Central Asia which is especially vulnerable to climate change due to aridity the ability of global climate projections to capture the complex dynamics of mountainous environments is particularly limited . Over montane Central Asia agropastoralism constitutes a major portion of the rural economy . Extensive herbaceous vegetation forms the basis of rural economies in Kyrgyzstan . Here we focus on snow cover seasonality and the effects of terrain on phenology in highland pastures using remote sensing data for 20012017 . First we describe the thermal regime of growing season using MODerate Resolution Imaging Spectrometer land surface temperature data analyzing the modulation by elevation slope and aspect . We then characterized the phenology in highland pastures with metrics derived from modeling the land surface phenology using Landsat normalized difference vegetation index time series together with MODIS LST data . Using rank correlations we then analyzed the influence of four metrics of snow cover seasonality calculated from MODIS snow cover compositesfirst date of snow late date of snow duration of snow season and the number of snow covered dates on two key metrics of land surface phenology in the subsequent growing season specifically peak height and thermal time to peak . We evaluated the role of terrain features in shaping the relationships between snow cover metrics and land surface phenology metrics using exact multinomial tests of equivalence . Key findings include a positive relationship between SCD and PH occurred in over 1664km
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Modeled LSP in pastures at 30m using 17years of Landsat and MODIS data. Prevalent trend of increasing peak NDVI in highland pastures 20012017. Later snowmelt date and more snow covered dates increased peak NDVI. Slope was more important than aspect on linkages between snow cover LSP.
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S0034425720300456
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Sun induced chlorophyll fluorescence has been used to track vegetation photosynthetic activity for improving estimation of gross primary productivity and detecting plant stress . There are both physical and physiological controls of SIF measured at the surface and retrieved from remote sensing including satellite observations . In order to accurately use SIF for monitoring of plant physiology the effects of physically based radiation processes related to leaf and canopy structure notably photosynthetically active radiation absorption and SIF scattering and re absorption must be characterized . In this study we investigate both PAR absorption and SIF scattering processes and find that although it is difficult to quantify their effects individually by using just reflectance the combined effects of the two processes can be well approximated by a reflectance index . This index referred to as FCVI is defined as the difference between near infrared and broad band visible reflectance acquired under identical sun canopy observer geometry of the SIF measurements . The development of the index was based on the physical connection between reflectance and far red SIF which was revealed by using the spectral invariant theory . The utility of FCVI to correct far red SIF for PAR absorption and scattering effects thus improving the link to photosynthesis was tested with data from a field experiment for a growing season and a numerical experiment which included a number of scenarios generated by a radiative transfer model . For both the observations and simulations the FCVI provided a promising estimate of the impact of the physically based radiation processes on far red SIF of moderately dense canopies . Normalizing the TOC far red SIF by both the incident PAR and the FCVI provided a good estimate of the far red fluorescence emission efficiency of the canopies examined . This approach enhances our ability to generalize retrievals for vegetation processes as they change through natural growth phases and seasons . Taken together far red SIF and FCVI may enable the assessment of the light partitioning of vegetation canopies an essential step to facilitate the use of far red SIF for tracking physiological processes .
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We propose an index FCVI for the effects of physical processes on far red SIF. FCVI is the difference between near infrared and broadband visible reflectance. Normalizing SIF by FCVI and PAR is an estimate of fluorescence emission efficiency. FCVI was tested with a field measurement and a numerical experiment.
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S0034425720300468
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Satellite observations for the Arctic and boreal region may contain information of vegetation soil snow snowmelt and or other surface water bodies . We investigated the impacts of vegetation soil snow and surface water on empirical vegetation snow indices on a tundra ecosystem area located around Utqiavik of Alaska with the Moderate Resolution Imaging Spectrometer images in 20012014 . Empirical vegetation indices such as normalized difference vegetation index enhanced vegetation index the index of near infrared of vegetation NIR
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Presence of snow nonlinearly decreases vegetation indices NDVI EVI2 and NIR. NDVI EVI2 and NIR. linearly increase with VGCF. NDSI linearly decreases with VGCF. NDSI non linearly increases with snow cover fraction SNOWCF . NDSI linearly increases with sum of SNOWCF and WaterBodyCF R. 0.976 .
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S0034425720300481
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In this century one of the main objectives of agriculture is sustainability addressed to achieve food security based on the improvement of use efficiency of farm resources the increasing of crop yield and quality under climate change conditions . The optimization of farm resources as well as the control of soil degradation processes can be realized through crop monitoring in the field aiming to manage the local spatial variability with a high resolution . In the case of high profitability crops as the case of vineyards for high quality wines the capability to manage and follow spatial behavior of plants during the season represents an opportunity to improve farmer incomes and preserve the environmental health . However any field monitoring represents an additional cost for the farmer which slows down the objective of a diffuse sustainable agriculture .
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CNN is constructed for pan sharpening of Sentinel 2A images by UAV images. Reconstructed data are validated on MS UAV images and in situ spectral measures. Reconstructed VIs from Sentinel 2A data agreed with dendroecological plant traits.
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S0034425720300493
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The Soil Moisture Experiment in the Luan River was conducted from 2017 to 2018 in the semi arid Luan River watershed located in the North of China . One of the objectives of SMELR is to serve as an assessment tool and demonstration for a new Terrestrial Water Resources Satellite concept with one dimensional synthetic aperture microwave techniques for which soil moisture retrieval under variable satellite observing configurations is the greatest challenge . This proposed mission is targeted to provide continuity for the current satellite L band microwave observations and further improve the accuracy and spatial resolution of soil moisture mapping through the synergistic use of active passive and optical remote sensing data . Multi resolution multi angle and multi spectral airborne data were obtained four times over a 70 km by 12 km area in the Shandian River basin and one time over a 165 km by 5 km area that includes the Xiaoluan River basin . The near surface soil moisture was measured extensively on the ground in fifty 1 km by 2 km quadrats and two hundred and fifty 200 m by 200 m quadrats corresponding to radar observations . Two networks were established for continuous measurement of the soil moisture and temperature profile and precipitation in the Shandian and Xiaoluan River basin respectively . Supporting ground measurements also included ground temperature vegetation water content surface roughness continuous measurement of microwave emission and backscatter at a pasture site reflectance of various land cover types evapotranspiration and aerosol observations . Preliminary results within the experimental area indicate that the near surface soil moisture spatial variability at a 200 m scale was up to 0.1 cm
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First airborne optical and active passive microwave experiment conducted in China. Demonstration of the active passive microwave synergy at varying incidence angles. Addressing prelaunch science questions of the terrestrial water resources mission
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S0034425720300511
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Sediment budgets are a critical metric to assess coastal marsh vulnerability to sea level rise and declining riverine sediment inputs . However calculating accurate sediment budgets is challenging in tidal marsh influenced estuaries where suspended sediment concentrations typically vary on scales of hours and hundreds of meters and where SSC dynamics are driven by a complex and often site specific interplay of hydrodynamic and meteorological conditions . The mapping of SSC using ocean color remote sensing is well established and can help capture the spatio temporal variability of SSC and determine the dominant drivers regulating sediment budgets . However the coarse spatial resolution of traditional ocean color sensors generally precludes their use in coastal marsh estuaries . Here using the Plum Island Estuary as an example we demonstrate that high spatial resolution maps of SSC derived from Landsat 8 Operational Land Imager and Sentinel 2A B Multispectral Instruments can be used to determine the main drivers of SSC dynamics in tidal marsh influenced estuaries despite the long revisit time of these sensors . Local empirical algorithms between SSC and remote sensing reflectance were derived and applied to a total of 46 clear sky scenes collected by the OLI and the MSI between 2013 and 2018 . The analysis revealed that this 5 year record was sufficient to capture a representative range of meteorological and tidal conditions required to determine the main drivers of SSC dynamics in this mid latitude system . The interplay between river and tidal flows dominated SSC dynamics in this estuary whereas wind driven resuspension had a more moderate effect . The SSC was higher during spring because of increased river discharge due to snowmelt . Tidal asymmetry also enhanced sediment resuspension during flood tides possibly favoring deposition on marsh platforms . Together water level water level rate of change river discharge and wind speed were able to explain 60 of the variability in the main channel SSC thereby facilitating future prediction of SSC from these readily available variables . This study demonstrates that the existing multi year records of high resolution remote sensing can provide a representative depiction of SSC dynamics in hydrodynamically complex and small scale estuaries that moderate resolution ocean color remote sensing and in situ measurements are unable to capture .
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Landsat 8 Sentinel 2 record is adequate to study SSC dynamics in tidal estuaries. Record helped reveal tidal flow river discharge are the dominant drivers of SSC. Record able to capture role of tidal flood asymmetry on sediment retention
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S0034425720300535
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Climate change is a threat to many high latitude regions . Changing patterns in precipitation intensity and increasing glacial ablation during spring and summer have major influence on river dynamics and the risk of widespread flooding . To monitor these rapid events more frequent discharge observations are necessary . Having access to near daily satellite based discharge observations is therefore highly beneficial . In this context the recently launched Sentinel 1 and 2 satellites promise unprecedented potential due to their capacity to obtain radar and optical data at high spatial and high temporal resolutions . Here we use both missions to provide a novel approach to estimate the discharge of the jrs river Iceland on a near daily basis . Iceland and many other high latitude regions are affected by frequent cloud cover limiting the availability of cloud free optical Sentinel 2 data . We trained a Random Forest supervised machine learning classifier with a set of Sentinel 1 backscatter metrics to classify water in the individual Sentinel 1 images . A Sentinel 2 based classification mask was created to improve the classification results . Second we derived the river surface area and converted it to the effective width which we used to estimate the discharge using an at a station hydraulic geometry rating curve . We trained the rating curve for a six month training period using in situ discharge observations and assessed the effect of training area selection . We used the trained rating curve to estimate discharge for a one year monitoring period between 2017 10 and 2018 10 . Results showed a Kling Gupta Efficiency of 0.831 indicating the usefulness of dense Sentinel 1 and 2 observations for accurate discharge estimations of a medium sized high latitude river on a near daily basis . We demonstrated that satellite based discharge products can be a valuable addition to in situ discharge observations also during ice jam events .
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A new method for near daily discharge estimations using Sentinel 1 and 2 was proposed. Sentinel 1 backscatter metrics and Random Forest provided water classifications. A Sentinel 2 based classification mask increased the accuracy. The discharge was estimated KGE 0.831 using an effective width based rating curve. The effect of different landscape features on the discharge estimation was assessed.
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S0034425720300547
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Dense time series of Landsat 8 and Sentinel 2 imagery are creating exciting new opportunities to monitor map and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality . By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on board Sentinel 2A and 2B the remote sensing community now has access to moderate spatial resolution imagery with repeat periods of 3days in the mid latitudes . At the same time the large combined data volume from Landsat 8 and Sentinel 2 introduce substantial new challenges for users . Land surface phenology algorithms which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions provide an intuitive way to reduce data volumes and redundancy while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events ecosystem modelling crop type discrimination and land cover land use and land cover change mapping among others . To support the need for operational LSP data sets here we describe a continental scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel 2 imagery . The algorithm creates high quality times series of vegetation indices from HLS imagery which are then used to estimate the timing of vegetation phenophase transitions at 30m spatial resolution . We present results from assessment efforts evaluating LSP retrievals and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed . The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area . Conversely results in evergreen systems are less interpretable and conclusive .
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We present a new algorithm that maps continental scale land surface phenology at 30m resolution. Results capture fine scale variation in phenology related to land use land cover and topography. Comparisons against PhenoCam and MODIS data demonstrate the high quality of estimated metrics.
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S0034425720300559
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Satellite remotely sensed fraction of photosynthetically active radiation products are widely used in land surface monitoring and modeling especially for estimating global terrestrial photosynthetic activity through light use efficiency models . PAR absorbed by active chlorophyll APAR
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We intercompared six currently available satellite FPAR products. Their potential relationships with FPAR. were evaluated with SIF data. OLCI FPAR showed the best relationships with satellite and airborne nSIF. APAR derived from OLCI FPAR exhibited the best relationship with. GPP.
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S0034425720300572
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A wavelet based method for bathymetry retrieval from X band radar images is proposed . The method combines traditional Fast Fourier Transform techniques for retrieving peak frequency maps by evaluating the spectral peaks in the time domain and a localized 2D Continuous Wavelet Transform for retrieving the corresponding peak wavenumbers . The main improvements of the new method compared to conventional FFT based methods are as follows a the wavelet based approach is localized naturally fitting the inhomogeneous conditions typically found in the nearshore environment b it is continuous and uses wave phase information providing smooth bathymetry maps with good accuracy c it requires relatively small number of successive images without the limitation of requiring a uniform time step .
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Wavelet technique allows precise localization of the bathymetry estimation. Wavelet celerity estimation converges fast needing averaging over a few minutes. The new technique shows excellent performance in flat and mildly slopping bottoms. For the surf zone wave setup and storm surge need to be taken into account. Biases of the bathymetry estimation on sandbars need further investigation.
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S0034425720300596
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Many derived Earth Observation products share surface reflectance as a common step in their processing chains . This makes the maintenance and improvement of surface reflectance product quality of fundamental importance to ensure information derived from these downstream products can be trusted . Despite this the literature is relatively light on the implementation of validation methodologies designed for surface reflectance . In response to the need to improve general EO validation methodologies the concept of fiducial reference measurements was created that would produce validation data that is fully characterised and independent with associated uncertainties and traceable to SI .
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Full diffuser angular and spectral characterisation and traceable calibration. Development of simplified atmospheric correction uncertainty using Monte Carlo. In situ uncertainty based on instrument sampling and procedural contributions. Consideration of satellite and in situ measurement quantity condition differences
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S0034425720300602
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The Landsat archive offers great potential for monitoring forest cover change and new approaches moving from categorical towards continuous change products emerge rapidly . Most approaches however require vast amounts of high quality reference data limiting their applicability across space and time . We here propose the use of a generalized regression based unmixing approach to overcome this limitation . The unmixing approach relies on temporally generalized machine learning regression models and support vector regression which are trained on synthetically mixed data from a multi year library of pure and hence easy to identify image spectra . We apply the model to three decades of Landsat data mapping both overall forest cover and broadleaved coniferous forest cover fractions across space and time . The resulting maps well represented the spatial temporal patterns of forest in our study region . The SVR model outperformed the RFR model yielding accuracies of
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A regression based unmixing model for mapping forest cover fractions is presented. Model used for mapping forest cover fractions across three decades of Landsat data. Also broadleaved and coniferous forest cover fractions were mapped. Only pure image spectra required as reference data
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S0034425720300614
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Air temperature is an essential climatological component that controls and influences various earth surface processes . In this study we make the first attempt to employ deep learning for Ta mapping mainly based on space remote sensing and ground station observations . Considering that Ta varies greatly in space and time and is sensitive to many factors assimilation data and socioeconomic data are also included for a multi source data fusion based estimation . Specifically a 5 layers structured deep belief network is employed to better capture the complicated and non linear relationships between Ta and different predictor variables . Layer wise pre training process for essential features extraction and fine tuning process for weight parameters optimization ensure the robust prediction of Ta spatio temporal distribution . The DBN model was implemented for 0.01 daily maximum Ta mapping across China . The ten fold cross validation results indicate that the DBN model achieves promising results with the RMSE of 1.996C MAE of 1.539C and R of 0.986 at the national scale . Compared with multiple linear regression back propagation neural network and random forest method the DBN model reduces the MAE values by 1.340C 0.387C and 0.222C respectively . Further analysis on spatial distribution and temporal tendency of prediction errors both validate the great potentials of DBN in Ta estimation .
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Deep learning is utilized effectively to estimate Ta for the first attempt. Fusing remote sensing station simulation and socioeconomic data do make sense. The 0.01 daily maximum Ta across China has been generated accurately.
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S0034425720300626
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A prompt and overall understanding of building damages following a disaster is critical as they are closely related to casualties . As an important active remote sensing technology synthetic aperture radar can be a valuable tool for assessing building damages in disasters owing to its large coverage quick response non contact and independence of weather and light capabilities . Various approaches have been proposed for SAR based building damage assessment with the development of radar technology and interpretation techniques . While providing numerous choices these multifarious approaches also make it burdensome to ponder the applicable approach in a specific case and to reflect on potential fields for further research . Accordingly this paper hierarchically classified and summarized these approaches to provide a structured understanding of the research status in this field for assisting in approach decisions and promising field considerations . First depending on the pre event data availability the numerous approaches were classified into change detections employing both pre and post event data and assessments applying only post event data . Then determined by the data resolution level and acquisition mode the plentiful change detection approaches were further distinguished into block unit approaches analyzing intensity coherence or polarimetry features and building unit approaches that simply generalize the block unit approaches or that concretely explore the detailed individual building features . The post event data based assessments with relatively fewer approaches were further introduced as methods exploring polarimetry or and texture features . In each classification category the principle was first introduced to explain the basic concept and essence of the approaches . An approach review was then provided by organizing relevant studies in a logical or structured way to facilitate a clear understanding of the overall research status . Favorable parameters in each category were also summarized for easy reference and application in the future .
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Various SAR based building damage assessment methods were hierarchically classified. Principles of each classified category were introduced to explain the method essence. Studies in each category were logically organized to provide a clear understanding. Favorable parameters in each category were summarized to facilitate future reference. Several future efforts for SAR based building damage assessment were noted.
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S003442572030064X
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Landslides and resultant barrier lakes are significant threats to human lives and infrastructures . Three dimensional surface displacements can give vital clues to the exploration of internal structure of landslides but they are difficult to be retrieved from spaceborne Synthetic Aperture Radar observations due to the intrinsic limitation of SAR imaging geometry . Meanwhile studies on predicting slope failure based on SAR measured displacements are rarely seen . Here we used SAR pixel offset tracking to investigate the Baige landslide before the collapse on 10 October 2018 . 3D surface displacements retrieved by combining satellite SAR and optical observations revealed heterogeneous spatial patterns within the landslide complex . We observed linear secondary creep and accelerating tertiary creep prior to the failure from multi sensor SAR data . The possibility of forecasting the failure was demonstrated by applying an inverse velocity method to the time series displacements measured by Sentinel 1 during the tertiary creep which is valuable for risk evaluation and disaster early warning .
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We retrieved 3D displacement field by combining SAR and optical observations. The 3D displacement field revealed the spatial complexity of the Baige landslide. We derived historic displacements of the Baige landslide from three SAR data stacks. Creep evolution from linear to accelerating was shown by time series displacements. We demonstrated the possibility of landslide early warning with SAR measurements.
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S0034425720300651
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The quantification of leaf area index is essential for modeling the interaction between atmosphere and biosphere . The airborne LiDAR has emerged as an effective tool for mapping plant area index in a landscape consisting of both woody and leaf materials . However the discrimination between woody and leaf materials and the estimation of effective LAI have to date rarely been studied at landscape scale . We applied a voxel matching algorithm to estimate eLAI of deciduous forests using simulated and field LiDAR data under leaf on and leaf off conditions . We classified LiDAR points as either a leaf or a woody hit on leaf on LiDAR data by matching the point with leaf off data . We compared the eLAI result of our voxel matching algorithm against the subtraction method where the leaf off effective woody area index is subtracted from the effective leaf on PAI . Our results which were validated against terrestrial LiDAR derived eLAI showed that the voxel matching method with an optimal voxel size of 0.1m produced an unbiased estimation of terrestrial LiDAR derived eLAI with an
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eLAI was estimated using leaf on and leaf off airborne LiDAR. A voxel matching method was developed for the classification between leaf and woody points. Subtracting eWAI from ePAI could cause significant underestimation of eLAI.
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S0034425720300687
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The seasonal snow cover contributes significantly to the water resource in the Indian Himalayas and snow density is one of the vital parameters in the determination of the hydrological potential of snow . The application of conventional methods for snow density retrieval using fully polarimetric SAR data is constrained by the properties of snow primarily due to the melt and frost cycles as compared to fresh dry snow . The surface component of backscatter is significant in case of melt and frost . In the conventional decomposition based methods the surface component is not considered for the inversion of permittivity as proposed in this study . In this paper we also propose a methodology for the estimation of snow density using bi temporal fully polarimetric C band RADARSAT 2 synthetic aperture radar data . We utilize the relation between the differential modified Mueller matrix components the attenuation constants and the Fresnel transmission coefficients . The permittivity of snow is derived using the inversion of the Fresnel transmission coefficients which is used to determine the snow density using a state of the art empirical relation . The snow density estimates from the proposed method are compared with other methods based on coherency matrix decomposition and evaluated against in situ measurements collected during a field campaign carried out in Dhundi in the state of Himachal Pradesh in India . The snow density estimates using the proposed method are observed to correlate with the in situ measurements and were also found to be better than the decomposition based methods .
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C band microwave signal sensitivity to the mountainous snowpack. New method for snow density using bi temporal RADARSAT 2 data. Inversion of snow density with partial information from Quad PolSAR data. Comparison of existing PolSAR decomposition based snow density retrieval methods. Inversion of snow density without a prior knowledge of the study region
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S0034425720300705
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The Landsat 5 orbit changed more than expected over its three decade mission life . Previous modelling research indicated that the resulting Landsat 5 overpass time and so acquisition solar zenith differences will have caused non negligible reflectance and normalized difference vegetation index temporal differences . Rather than use a modelling approach this paper presents the results of a comprehensive examination of the Landsat 5 Thematic Mapper data . Ten years of summer Landsat 5 TM Analysis Ready Data sampled at 19.3 million 30m pixel locations across the conterminous United States are considered . The acquisition solar zenith red near infrared shortwave infrared surface reflectance and NDVI are compared between five consecutive periods that span years with very different Landsat 5 overpass times and are also compared between 1998 and 1999 that have similar overpass times . The different years of Landsat 5 TM data are plotted against each other and reduced major axis regressions passing though the origin are developed and compared with the acquisition solar geometry changes . The red SWIR and NDVI RMA slopes are proportional to the magnitude of solar zenith change between years and are indicative of CONUS wide differences due to orbit drift . The greatest differences occurred between 1995 and 2006 the years with the greatest solar zenith differences with RMA regression slopes quite far from unity . In years with smaller solar zenith differences the RMA slopes are closer to unity . As agricultural crops may be managed inconsistently through time and as drought may have pronounced effects on reflectance the analysis is also undertaken masking out crop pixels and considering locations that have similar precipitation and evapotranspiration conditions between years . These experiments indicate that at CONUS scale the cause of the changing RMA regression slopes between years is not due to agricultural changes or to the influence of above normal drought or wetness . The results confirm previous published modelling based findings but are based on direct examination of CONUS Landsat 5 TM data . They indicate that certain research and applications that require temporally consistent red SWIR and NDVI data may need to take Landsat 5 orbit drift effects into account .
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Landsat 5 orbit and acquisition solar changed over mission life. Reflectance NDVI and acquisition solar zenith compared over 10years. United States inter annual agriculture and drought changes considered. Large volume observation analyses confirm previously published model based findings. Orbit changes caused pronounced changes in reflectance and NDVI among certain years.
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S0034425720300717
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Accurate and consistent broad scale mapping of fire severity is an important resource for fire management as well as fire related ecological and climate change research . Remote sensing and machine learning approaches present an opportunity to enhance accuracy and efficiency of current practices . Quantitative biophysical models of photosynthetic non photosynthetic and bare cover fractions have not been widely applied to fire severity studies but may provide greater consistency in comparisons of different fires across the landscape compared to reflectance based indices . We systematically tested and compared reflectance and fractional cover candidate severity indices derived from Sentinel 2 satellite imagery using a random forest machine learning framework . Assessment of predictive power was undertaken to quantify the accuracy of mapping severity of new fires . The effect of environmental variables on the accuracy of the RF predicted severity classification was examined to assess the stability of the mapping across the landscape . The results indicate that fire severity can be mapped with very high accuracy using Sentinel 2 imagery and RF supervised classification . The mean accuracy was 95 for the unburnt and extreme severity class 85 for high severity class 80 for low severity and 70 for the moderate severity class . Higher canopy cover and higher topographic complexity was associated with a higher rate of under prediction due to the limitations of optical sensors in viewing the burnt understorey of low severity classes under these conditions . Further research is aimed at improving classification accuracy of low and moderate severity classes and applying the RF algorithm to hazard reduction fires .
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Fire severity was mapped with high accuracy with Sentinel 2 and Random Forest. The model with both fractional cover and reflectance indices performed the best. Fractional cover indices especially helped to delineate unburnt from burnt. Under prediction of low severity omission was associated with high canopy density. The Sentinel 2 algorithm has potential application to Landsat for archive mapping.
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S0034425720300754
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Commonly applied water indices such as the normalized difference water index and the modified normalized difference water index were originally conceived for medium spatial resolution remote sensing images . In recent decades high spatial resolution imagery has shown considerable potential for deriving accurate land cover maps of urban environments . Applying traditional water indices directly on this type of data however leads to severe misclassifications as there are many materials in urban areas that are confused with water . Furthermore threshold parameters must generally be fine tuned to obtain optimal results . In this paper we propose a new open surface water detection method for urbanized areas . We suggest using inequality constraints as well as physical magnitude constraints to identify water from urban scenes . Our experimental results on spectral libraries and real high spatial resolution remote sensing images demonstrate that by using a set of suggested fixed threshold values the proposed method outperforms or obtains comparable results with algorithms based on traditional water indices that need to be fine tuned to obtain optimal results . When applied to the ASTER and ECOSTRESS spectral libraries our method identified 3677 out of 3695 non water spectra . By contrast NDWI and MNDWI only identified 2934 and 2918 spectra . Results on three real hyperspectral images demonstrated that the proposed method successfully identified normal water bodies meso eutrophic water bodies and most of the muddy water bodies in the scenes with F measure values of 0.91 0.94 and 0.82 for the three scenes . For surface glint and hyper eutrophic water our method was not as effective as could be expected . We observed that the commonly used threshold value of 0 for NDWI and MNDWI results in greater levels of confusion with F measures of 0.83 0.64 and 0.64 and 0.77 0.63 and 0.59 . The proposed method also achieves higher precision than the untuned NDWI and MNDWI with the same recall values . Next to numerical performance the proposed method is also physically justified easy to implement and computationally efficient which suggests that it has potential to be applied in large scale water detection problem .
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An open water detection method in urban areas using high spatial resolution imagery. Threshold value of 0 is not reliable for NDWI MNDWI with high resolution imagery. The proposed method uses fixed parameter value without fine tuning. Heavy shadows are difficult to distinguish from water in urban environments.
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S0034425720300766
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In the last two decades the advance in nighttime light remote sensing has fueled a surge in extensive research towards mapping human footprints . Nevertheless the full potential of NTL data is largely constrained by the blooming effect . In this study we propose a new concept the Pixel Blooming Effect to delineate the mutual influence of lights from a pixel and its neighbors and an integrated framework to eliminate the PiBE in radiance calibrated DMSP OLS datasets DMSP
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A concept was proposed to characterize the blooming effect at pixel level. An integrated framework was developed to model and correct the PiBE. Low ISF pixels ISF 0.2 were sensitive to the impact of PiBE. The proposed method was effective in eliminating the PiBE. The lost data variation was recovered and pseudo lights in non BU were suppressed.
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S0034425720300778
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Measurements of reflected solar radiation by imaging spectrometers can quantify water in different states thanks to the discriminative absorption shapes . We developed a retrieval method to quantify the amount of water in each of the three states from spaceborne imaging spectroscopy data such as those from the German EnMAP mission . The retrieval couples atmospheric radiative transfer simulations from the MODTRAN5 radiative transfer code to a surface reflectance model based on the Beer Lambert law . The model is inverted on a per pixel basis using a maximum likelihood estimation formalism . Based on a unique coupling of the canopy reflectance model HySimCaR and the EnMAP end to end simulation tool EeteS we performed a sensitivity analysis by comparing the retrieved values with the simulation input leading to an R
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Simultaneously retrieved water vapor and liquid water are quantitatively validated. Imaging spectroscopy tracks changes in canopy water content better than the NDWI. Higher amounts of snow are tracked better than by using the NDSI. More plausible water vapor maps are produced in presence of liquid water absorption. The algorithm leads to accuracy improvements in atmospheric correction procedures.
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S0034425720300857
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Various forms of machine learning methods have historically played a valuable role in environmental remote sensing research . With an increasing amount of big data from earth observation and rapid advances in ML increasing opportunities for novel methods have emerged to aid in earth environmental monitoring . Over the last decade a typical and state of the art ML framework named deep learning which is developed from the traditional neural network has outperformed traditional models with considerable improvement in performance . Substantial progress in developing a DL methodology for a variety of earth science applications has been observed . Therefore this review will concentrate on the use of the traditional NN and DL methods to advance the environmental remote sensing process . First the potential of DL in environmental remote sensing including land cover mapping environmental parameter retrieval data fusion and downscaling and information reconstruction and prediction will be analyzed . A typical network structure will then be introduced . Afterward the applications of DL environmental monitoring in the atmosphere vegetation hydrology air and land surface temperature evapotranspiration solar radiation and ocean color are specifically reviewed . Finally challenges and future perspectives will be comprehensively analyzed and discussed .
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The potential of deep learning DL in environmental remote sensing is analyzed. Typical DL network architectures in remote sensing applications are introduced. Progress on DL in remote sensing of ten more environmental parameters is reviewed. New insights on combining DL and physical geographical laws are discussed.
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S0034425720300869
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Characterisation of the ecosystem functioning of mudflats requires insight on the morphology and facies of these coastal features but also on biological processes that influence mudflat geomorphology such as crab bioturbation and the formation of benthic biofilms as well as their heterogeneity at cm or less scales . Insight into this fine scale of ecosystem functioning is also important as far as minimizing errors in upscaling are concerned . The realisation of high resolution ground surveys of these mudflats without perturbing their surface is a real challenge . Here we address this challenge using UAV supported photogrammetry based on the Structure from Motion workflow . We produced a Digital Surface Model and an orthophotograph at 1cm and 0.5cm pixel resolutions respectively of a mudflat in French Guiana and mapped and classed into different size ranges intricate morphological features including crab burrow apertures tidal drainage creeks and depressions . We also determined subtle facies and elevation changes and slopes and the footprint of different degrees of benthic biofilm development . The results generated at this scale of photogrammetric analysis also enabled us to relate macrofaunal crab burrowing activity to various parameters including mudflat elevation spatial distribution and sizes of creeks and depressions benthic biofilm distribution and flooding duration . SfM photogrammetry offers interesting new perspectives in fine scale characterisation of the geomorphology benthic activity and degree of biofilm development of dynamic muddy intertidal environments that are generally difficult of access . The main shortcomings highlighted in this study are a drift of accuracy of the DSM outside areas of ground control points and the deployment of which perturb the mudflat morphology and biology the water logged or very wet surfaces which generate reconstruction artefacts through the sun glint effect and the time consuming task of manual interpretation of extraction of features such as crab burrow apertures . On going developments in UAV positioning integrating RTK PPK GPS solutions for image georeferencing and precise orientation with high quality inertial measurement units will limit the difficulties inherent to ground control points while conduction of surveys during homogeneous cloudy conditions could reduce the sun glint effect . Manual extraction of image features could be automated in the future through the use of deep learning algorithms .
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Mudflats are highly challenging environments for ground surveys. UAV based SfM photogrammetry gives high resolution maps of mudflat biogeomorphology. Biogeomorphic analysis of mudflat is based on 1cm px DSM and 0.5cm px orthophoto. Mudflat geomorphology mediates pattern of bioturbation by benthic fauna and biofilm formation. Future field software and camera enhancements of UAV photogrammetry of mudflats are evoked.
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S0034425720300870
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Statistical time series models are increasingly being used to fit medium resolution time series provided by satellite sensors such as Landsat for terrestrial monitoring . Cloud and shadows combined with low satellite repeat cycles reduce surface observation availability . In addition only a single year of data can be used where there is high inter annual variation for example over many croplands . These factors reduce the ability to fit time series models and reduce model fitting accuracy . In solution we propose a novel fill and fit
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Accurate prediction of daily gap free Landsat 30m reflectance data. Within year satellite time series. Alternative similar pixel gap filling followed by harmonic model fitting. Evaluated over agricultural Landsat ARD tiles including cloudy regions
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S0034425720300894
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This work presents a new methodology to simultaneously account for both the mean and variance of 17years of Carbon Monoxide measurements from the MOPITT satellite over Southeast and East Asia . We demonstrate that the new technique is stable and produces a set of results which are both consistent with the understood geographical and temporal distribution of CO sources as well as those of some other co emitted species . These regions were chosen because they have high levels of CO loadings and complex factors driving their underlying emissions profiles . We first successfully categorize the region based on the total CO column measurements into those locations impacted by intense urbanization large scale biomass burning those regions undergoing a significant change from one type to another and those regions which are clean . We further reproduce the temporal and spatial distributions of other co emitted species measured by other measurement platforms including aerosols and gasses NO
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MOPITT column CO is used to categorize urban biomass burning and mixed regions. Our Spatial Temporal characterization of CO matches well with FINN and EOF PCA. Significantly more biomass burning emissions of CO is found in Southeast Asia. Eastern China and Southeast Asia are increasing sources of urban CO emissions. Southern China is impacted by biomass burning CO emitted from Southeast Asia.
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S0034425720300900
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Scarcity of water temperature data in rivers may limit a diversity of studies considering this property which regulates many physical chemical and biological processes . We present a robust method to generate a consistent continuous daily river water temperature data series for medium and large rivers using the combined techniques of remote sensing and water temperature modelling . In order to validate our approach we divided this study into two parts
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Scarcity of temperature data constrains the use of most river temperature models. The use of remote sensing in retrieving river temperatures is still underexplored. Assessment of the accuracy of temperature retrievals using Landsat 7 ETM and 8 TIRS. Calibration of river temperature models using only Landsat derived temperature data. Generation of consistent continuous long term daily river temperature data series
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S0034425720300912
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Quantifying global photosynthesis remains a challenge due to a lack of accurate remote sensing proxies . Solar induced chlorophyll fluorescence has been shown to be a good indicator of photosynthetic activity across various spatial scales . However a global and spatially challenging estimate of terrestrial gross primary production based on satellite SIF remains unresolved due to the confounding effects of species specific physical and physiological traits and external factors such as canopy structure or photosynthetic pathway C
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An ensemble of far red SIF from ground and OCO 2 was compared with in situ GPP. BRF data can be used to reduce the effects of canopy structure on SIF. BRF data is used to derive total canopy SIF emission SIF. for OCO 2. SIF. and GPP relationships converge two unique models for C. and C. plants. SIF. based model yields an estimate of GPP of 129.56PgC year for 20152017.
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S0034425720300924
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Multi sensor remote sensing applications consistently gain importance boosted by a growing number of freely available earth observation data increasing computing capacity and increasingly complex algorithms that need as temporally dense data as possible . Using data provided by different sensors can greatly improve the temporal resolution of time series fill data gaps and thus improve the quality of land cover monitoring applications . However multi sensor approaches are often adversely affected by different spectral characteristics of the sensing instruments leading to inconsistencies in downstream products . Spectral harmonization i.e . the transformation of one sensor into the spectral domain of another sensor may reduce these inconsistencies . It simplifies workflows increases the reliability of subsequently derived multi sensor products and may also enable the generation of new products that are not possible with the initial spectral definition . In this paper we compare the effect of multivariate spectral harmonization techniques on the inter sensor reflectance consistency and derived products such as spectral indices or land cover classifications . We simulated surface reflectance data of Landsat 8 and Sentinel 2A from airborne hyperspectral data to eliminate any sources of error originating from unequal acquisition geometries illumination or atmospheric state . We evaluate different methods based on linear quadratic and random forest regression as well as linear interpolation and predict not only matching but also unilaterally missing bands . We additionally consider material dependent spectral characteristics in the harmonization process by using separate transformation functions for spectral clusters of the input dataset . Our results suggest that spectral harmonization is useful to improve multi sensor consistency of remote sensing data and subsequently derived products especially if multiple transformation functions are incorporated . There is a strong dependency between harmonization performance and the similarity of source and target sensor s spectral characteristics . For spectrally transforming Landsat 8 to Sentinel 2A we achieved the lowest radiometric inter sensor deviations with 50 spectral clusters and linear regression . Based on simulated data deviations are below 1.7 reflectance within the red edge spectral region and below 0.3 reflectance for the remaining bands . Regarding spectral indices our results show a reduction of inter sensor deviation to 38 of the initial error for NDVI and to 43 for EVI . Furthermore we computed the REIP with an accuracy of 3.1nm from Sentinel 2 adapted Landsat 8 data . An exemplary multispectral classification use case revealed an increasing inter sensor consistency of classification results from 92.3 to 97.3 mean error . Applied to time series of real Landsat 8 and Sentinel 2 data we observed similar trends albeit intermingled with non sensor induced inconsistencies .
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Spectral harmonization reduces differences between Landsat 8 and Sentinel 2 images. Harmonization technique developed that respects different land cover characteristics. Approach allows to predict unilaterally missing spectral bands e.g. the red edge. Deviations 1.7 reflectance within red edge and 0.3 reflectance for other bands. Software freely available and also applicable to other multispectral sensors.
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S0034425720300936
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The past two decades have been prolific in production of global or near global Digital Elevation Models derived from satellite data . The most recent addition to the family of global DEMs is the TanDEM X DEM with resolution of 0.4arc sec . DEMs are essential for a wide range of environmental applications many of which are related to mountains including studies on natural hazards forestry or glacier mass changes . However synthetic aperture radar interferometry used for acquisition of TanDEM X DEM is especially challenging over steep and irregular mountain surfaces due to shadowing and foreshortening effects . In this study we assessed the absolute vertical accuracy of TanDEM X DEM in European mountains . We compared it with both a Digital Terrain Model and a Digital Surface Model derived from airborne laser scanning data . Our results indicate that the height error of TanDEM X DEM expressed as absolute deviation at the 90 quantile is consistent with the 10m mission specification benchmark . We further concentrated on the absolute height error with respect to environmental characteristics . The comparison of TanDEM X DEM with a reference DTM showed a positive vertical offset however the mean error differed greatly between forested and non forested areas . When compared to reference DSM our results showed a slight underestimation . We observed the highest underestimation in deciduous forests followed by coniferous forests and non forested areas . A significant decrease in accuracy was observed with increasing slope especially for slopes above 10 . In mountains where the imagery was acquired only in one orbit direction the largest TanDEM X DEM error when compared to DSM was recorded for the west facing slopes however the association with terrain orientation diminished in mountains the imagery of which was acquired from both the ascending and descending orbit . Finally we evaluated the effect of data acquisition characteristics provided with TanDEM X DEM as auxiliary data . Our results show that two coverages might not be sufficient in mountain environment . Additional acquisitions especially those with different acquisition geometry improved the absolute vertical accuracy of TanDEM X DEM and eliminated areas of inconsistency . We discourage from using the Height Error Map to estimate the error magnitude . On the other hand auxiliary data provide valuable information that should be always used in pre analyses to identify possible problematic areas .
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Results indicate an outstanding absolute height error of TanDEM X DEM in mountains. Deciduous and coniferous forests have a profound effect on TANDEM X DEM error. Similarly slope and aspect also affect TanDEM X DEM accuracy. Additional acquisitions improved the absolute vertical accuracy of TanDEM X DEM. Auxiliary files COM COV provide valuable data that indicate problematic areas.
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S0034425720300948
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The Geostationary Trace gas and Aerosol Sensor Optimization instrument is an airborne hyperspectral spectrometer measuring backscattered solar radiation in the ultraviolet and visible wavelength regions . This paper presents high resolution sulfur dioxide SO
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SO. columns are retrieved from airborne remote sensing by an algorithm based on PCA. Retrieved SO. columns successfully detect SO. sources in the Korean Peninsula. Distributions of SO. columns are consistent with those of known SO. point sources. Comparisons with spaceborne SO. data highlight the significance of high resolution. Upscaled airborne SO. retrievals allow assessing future geostationary observations.
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S0034425720300997
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Tree mortality from major disturbances can greatly increase dead wood in forested areas affecting fire intensity and behavior wildlife habitat and carbon dynamics . Accurately quantifying regional standing dead tree pools as conducted by the U.S. Forest Service Forest Inventory and Analysis program remains a prominent challenge . Little empirical work has been done accounting for structural changes in SDT volume across decay classes due to measurement and sampling challenges associated with SDT . Light detection and ranging represents a remote sensing technology with the potential to improve sampling efficacy and provide volume estimates of SDT via non destructive sampling . Following this the goal of this study was to explore the feasibility of empirically quantifying and assessing structural volume in southern pine SDT by decay class using terrestrial LiDAR . To meet this goal we addressed three objectives 1 construct empirical volume estimates of SDT by decay class using terrestrial LiDAR and a voxel based volume calculation algorithm capable of accounting for occlusion and point cloud quality 2 develop allometric relationships of aboveground SDT component volumes by decay class and assess error in models and predictions and 3 quantify proportion remaining volume of SDT components from terrestrial LiDAR derived volumes relative to predicted intact tree volumes . This study represents the first to develop empirically based terrestrial LiDAR derived allometric volume relationships and proportion remaining volume of SDT by decay class . Results indicate that terrestrial LiDAR derived volumes of SDT produced robust allometric equations by decay class for total above stump and stem plus bark components adjusted R
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Estimated volume of 49 standing dead trees by decay class via terrestrial LiDAR. Developed robust standing dead tree specific volume allometry by decay class. Height accounted for variability from decay classes for some allometric models. Calculated empirical values of proportion remaining volume of standing dead trees. Lower scan quality impacted variability of tops and branches volume and allometry.
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S0034425720301012
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The lack of aerosol information over the cryosphere introduces large uncertainties to our understanding of phenomenon known as the Arctic Amplification and its feedback mechanisms . The aerosol optical depth describes the optical characteristics of aerosol loading . This paper describes a novel algorithm which retrieves AOD above snow covered regions from the measurements of the up welling radiation at the top of atmosphere observed by the Advanced Along Track Scanning Radiometer and the Sea and Land Surface Temperature Radiometer instruments . The algorithm optimizes the generic eXtensible Bremen Aerosol cloud and surfacE parameters Retrieval approach for longer wavelengths over the cryosphere . The algorithm utilizes the characteristics of solar bidirectional distribution properties of snow and aerosol at wavelength 3.7m to derive above snow AOD . Since the impact of fine mode aerosol on 3.7m is ignorable the retrieved AOD in this manuscript represents mainly coarse mode dominated part . A novel method to extract the solar reflection part at 3.7m is presented and used in the surface parameterization . Two aerosol types are used and the best fit type is derived by an iterative procedure using a Look Up Table approach . Sensitivity studies of the impact on the retrieved AOD using XBAER algorithm which investigate the impacts of aerosol type snow surface emissivity and potential cloud contamination under typical AATSR observation conditions are presented . The sensitivity studies show that the surface parametrization and aerosol typing are suitable for the retrieval of above snow AOD over the Arctic snow covered region . AOD observations retrieved in this study from AATSR observation collocated with those from the Aerosol Robotic Network sites over Greenland show good agreement . 72.1 of the match ups fall into the expected error envelope of . The AATSR derived above snow AOD at 0.55m research product has also been compared with Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations aerosol product the Mineral Aerosols Profiling from Infrared Radiances derived Infrared Atmospheric Sounding Interferometer AOD research product and the Modern Era Retrospective analysis for Research and Applications Version 2 AOD simulations over Greenland on April 2011 . The comparison reveals that all datasets show similar patterns for the AOD above Greenland . The AOD is smaller in central Greenland and larger over the coastline regions . The XBAER derived above snow AOD has improved coverage as compared to that of the existing AATSR aerosol product . The transition between above snow AOD and AOD derived over surrounding ocean surfaces does not indicate any systematic errors . Two aerosol transport events have been well captured by the XBAER derived above snow AOD research product . The new algorithm is also applied to the SLSTR onboard Sentinel 3 demonstrating new SLSTR above snow AOD data products and its value for research in the changing AOD during the period of Arctic Amplification .
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A new aerosol retrieval algorithm to derive AOD over cryosphere was developed. Large AOD coverage extension over cryosphere has been achieved. Validation by AERONET shows 72.1 retrievals fall into 0.15AOD0.025 . The comparison with MERRA CALIOP and IASI products shows promising agreement. Aerosol transport events have been well captured by the new algorithm.
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S0034425720301024
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The Tonle Sap Lake is the largest natural freshwater lake in Southeast Asia and is called the heart of the lower Mekong due to its high aquatic biodiversity and is considered as one of the most productive freshwater ecosystems of the world . Its floodplain eco system which is strongly tied to seasonal flood pulse is extremely important for food security trade and economy of Cambodia supporting the livelihoods of about 1.7million people . On the other hand flood can also be extremely devastating in the region along the TSL . In recent years studies have pointed out that rapid growing number of water infrastructures as well as future climate changes may alter the hydrological cycle of the Mekong River Basin and are expected to influence the flood pulse of the TSL and surrounding TSL floodplain . Therefore it is timely to understand historical inundation extent and predict its likely future state . In this study we proposed a Rotated Empirical Orthogonal Function analysis based daily inundation extent estimation framework integrating multi temporal stack of Sentinel 1A Synthetic Aperture Radar imagery and Jason series satellite altimetry data . The framework can generate daily cloud free and gap free inundation extents for any given time depending on the altimetry data provided . A long term El Nio and Southern Oscillation index based daily TSL level forecasting method with months of lead time was also proposed to fulfill the framework s forecasting capacity . In this study the framework was adopted in the TSL floodplain area for hindcast and forecast of daily inundation extents . Estimated inundation extents were cross compared with MODIS derived and Sentinel 1 derived inundation maps resulting in up to higher than 90 of Critical Success Index . The proposed framework has innovative capacity of estimation of future daily areal inundation extents and is a fully remote sensing based framework which can empower local authorities tasked with water resource management decisions without relying on upstream countries . The framework has potential to be implemented in other major river basins or wetlands . The implementation on SAR imagery from other satellites with different bands of electromagnetic wave is also possible but requires more investigation .
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A daily areal inundation estimating framework for Tonle Sap floodplain was proposed. The framework can forecast areal inundation and is fully remote sensing based. Synthesis of daily SAR intensity images with REOF analysis was proposed. An ENSO based long term Tonle Sap Lake level forecasting system was proposed. Estimated inundation maps well agree with MODIS and Sentinel 1 derived ones.
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S0034425720301036
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Remote sensing of far red sun induced chlorophyll fluorescence has emerged as an important tool for studying gross primary productivity at the global scale . However the relationship between SIF and GPP at the canopy scale lacks a clear mechanistic explanation . This is largely due to the poorly characterized role of the relative contributions from canopy structure and leaf physiology to the variability of the top of canopy observed SIF signal . In particular the effect of the canopy structure beyond light absorption is that only a fraction f
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A mechanistic decomposition of canopy SIF for three in situ crop datasets. The canopy structure and radiation factor outperforms SIF for GPP estimation. Canopy escape fraction of SIF correlates with photosynthetic light use efficiency. Correcting SIF for canopy scattering improves the correlation to APAR but not GPP. Estimates of physiological SIF yield show no clear seasonal patterns.
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S0034425720301085
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A new method combining empirical modeling with time series Interferometric Synthetic Aperture Radar data is proposed to provide an assessment of potential landslide volume and area . The method was developed to evaluate potential landslides in the Heitai river terrace of the Yellow River in central Gansu Province China . The elevated terrace has a substantial loess cover and along the terrace edges many landslides have been triggered by gradually rising groundwater levels following continuous irrigation since 1968 . These landslides can have significant impact on communities affecting lives and livelihoods . Developing effective landslide risk management requires better understanding of potential landslide magnitude . Fifty mapped landslides were used to construct an empirical power law relationship linking landslide area A
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A new approach combining time series InSAR with empirical model is proposed. The volume and area of potential landslides are forecasted. The approach is validated for recent landslides. The approach contributes essential information to landslide risk assessment.
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S0034425720301097
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Historically change detection reviews have examined and categorized algorithms based on their techniques for the remote sensing community . Here we synthesize urban land change algorithms by the types of information they provide to a diverse and growing set of user communities . Two goals of the paper are first to synthesize past and current change detection studies to examine urban land change to help users of remote sensing algorithms understand and navigate the vast variety of available methods and second to identify gaps in knowledge for the urban remote sensing community . We analyzed 644 peer reviewed research papers published in English language journals and conducted a systematic review of
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We conduct a systematic review of the urban land change algorithm literature. Most studies monitor urban change at low temporal frequencies. The majority of urban land change is characterized using only one urban class. Over 75 of studies focus on high or upper middle level income countries. Key knowledge gaps include in geographic coverage city size and land transitions.
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S0034425720301103
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Accurate and detailed soil moisture information is essential for among other things irrigation drought and flood prediction water resources management and field scale decision making . Recent satellite missions measuring soil moisture from space continue to improve the availability of soil moisture information . However the utility of these satellite products is limited by the large footprint of the microwave sensors . This study presents a merging framework that combines a hyper resolution land surface model a radiative transfer model and a Bayesian scheme to merge and downscale coarse resolution remotely sensed hydrological variables to a 30 m spatial resolution . The framework is based on HydroBlocks an LSM that solves the field scale spatial heterogeneity of land surface processes through interacting hydrologic response units . The framework was demonstrated for soil moisture by coupling HydroBlocks with the Tau Omega RTM used in the Soil Moisture Active Passive mission . The brightness temperature from the HydroBlocks RTM and SMAP L3 were merged to obtain updated 30 m soil moisture . We validated the downscaled soil moisture estimates at four experimental watersheds with dense in situ soil moisture networks in the United States and obtained overall high correlations and good mean KGE score . The downscaled product captures the spatial and temporal soil moisture dynamics better than SMAP L3 and L4 product alone at both field and watershed scales . Our results highlight the value of hyper resolution modeling to bridge the gap between coarse scale satellite retrievals and field scale hydrological applications .
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Hyper resolution land surface model improves field scale soil moisture estimates. Hyper resolution heterogeneity leverages the soil moisture spatial variability. HRUs allow for computationally efficient merging of remote sensing observations. The merging skill is sensitive to biases in the model and satellite estimates
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S0034425720301115
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Applications of digital agricultural services often require either farmers or their advisers to provide digital records of their field boundaries . Automatic extraction of field boundaries from satellite imagery would reduce the reliance on manual input of these records which is time consuming and would underpin the provision of remote products and services . The lack of current field boundary data sets seems to indicate low uptake of existing methods presumably because of expensive image preprocessing requirements and local often arbitrary tuning . In this paper we propose a data driven robust and general method to facilitate field boundary extraction from satellite images . We formulated this task as a multi task semantic segmentation problem . We used ResUNet a a deep convolutional neural network with a fully connected UNet backbone that features dilated convolutions and conditioned inference to identify 1 the extent of fields 2 the field boundaries and 3 the distance to the closest boundary . By asking the algorithm to reconstruct three correlated outputs the model s performance and its ability to generalise greatly improve . Segmentation of individual fields was then achieved by post processing the three model outputs
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We extract field boundaries from Sentinel 2 data using a convolutional neural network. High thematic and geometric accuracies were obtained using a composite image. The same model generalised well across sensors resolution space and time. Building consensus by averaging predictions from multiple dates improves accuracy.
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S0034425720301127
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Underground coal mining with high groundwater levels causes many environmental problems one of the main ones being subsidence waterlogging . After the subsidence waterlogging the land faces many problems such as insufficient land development and difficult and costly land restoration . Therefore it is necessary to monitor the spatial range and temporal trajectory pattern of surface subsidence in a timely manner . When information about underground mining is lacking it is difficult by using remote sensing alone to identify and distinguish natural water engineering water and subsidence waterlogging . In this study we used the Google Earth Engine platform to develop a method to detect subsidence waterlogging area and the disturbance year . The method includes pixel based trajectory extraction and object based water type recognition . First LandTrendr is used to extract the change water pixels and its disturbance year . Then the morphological method to further eliminate engineering water patches so as to extract subsidence waterlogging . We selected the Panxie mining area in Huainan China as the study area . Using 33years of Landsat time series data to generate values of the annual water frequency index maps of the year of water accumulation caused by underground coal mining and the year of restoration during 19892016 are drawn with accuracies of 86.5 and 80.7 respectively . The results show that from 1989 to 2016 the accumulated area of subsidence waterlogging was 7715.25ha accounting for 14.5 of the total area of the study area of which 75.8 occurred from 2008 to 2016 . Furthermore the accumulated area of restoration was 207.18ha which occurred after 2007 and accounts for 2.7 of the total area of subsidence waterlogging . Based on the analysis results of the changes of waterlogging types the best time window for restoration of the waterlogging area is 3y after water accumulation . The main innovation of this paper is to make use of the temporal heterogeneity and morphological index of the change water to distinguish engineering water and subsidence waterlogging .
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Map of subsidence waterlogging caused by mining coal using Landsat time series. The annual water frequency index is capable to capture the water variability. A spatial temporal algorithm identifies the timing of subsidence waterlogging. The algorithm identifies the timing of backfill after subsidence waterlogging. The algorithm is applicable to other mining areas too.
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S0034425720301139
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Change detection between images is a procedure used in many applications of remote sensing data . Among these applications the identification of damaged infrastructures in urban areas due to a large scale disaster is a task that is crucial for distributing relief quantifying losses and rescue purposes . A crucial consideration for change detection is that the images must be co registered precisely to avoid errors resulting from misalignments . An essential consideration is that some large magnitude earthquakes produce very complex distortions of the ground surface therefore a pair of images recorded before and after a particular earthquake can not be co registered accurately . In this study we intend to identify changes between images that are not co registered . The proposed procedure is based on the use of phase correlation which shows different patterns in changed and non changed areas . A careful study of the properties of phase correlation suggests that it is robust against misalignments between images . However previous studies showed that in areas with no changes the signal power in the phase correlation is not concentrated in a single component but rather in several components . Thus we study the performance of the
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Phase correlation and sparse model to identify changes in urban areas. The procedure does not require image registration. Designed for areas with very complex ground deformation due to earthquakes. The procedure achieved an averaged overall accuracy of 85 .
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S0034425720301140
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This paper presents the temporal evolution of Global Navigation Satellite System Reflectometry ocean wind speed retrieval performance during three years of the UK TechDemoSat 1 mission . TDS 1 was launched in July 2014 and provides globally distributed spaceborne GNSS R data over a lifespan of over three years including several months of 24 7 operations . TDS 1 wind speeds are computed using the NOC Calibrated Bistatic Radar Equation algorithm version 0.5 and are evaluated against ERA5 high resolution re analysis data over the period 20152018 . Analyses reveal significant temporal variability in TDS 1 monthly wind speed retrieval performance over the three years with the best performance 2ms
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TDS 1 GNSS R wind retrieval performance has large variability over 3years lifetime. Retrieval performance reflects attitude uncertainty and GPS transmit power changes. Spatial and temporal changes in GPS transmit power can bias winds by 3ms
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S0034425720301164
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The modeling of Near Surface Temperature Lapse Rate is of great importance in various environmental applications . This study proposed a new approach for modeling the NSTLR based on the Normalized Land Surface Temperature . A set of remote sensing imagery including Landsat images MODIS products and ASTER Digital Elevation Model land cover maps and climatic data recorded in meteorological stations and self deployed devices located in the three study area were used for modeling and evaluation of NSTLR . First the Split Window and Single Channel algorithms were used to estimate LST and the spectral indices were used to model surface biophysical characteristics . The solar local incident angle was obtained based on topographic and time conditions for different dates . In the second step the NSTLR value was calculated based on the LST DEM feature space at the regional scale . The LST was normalized relative to the surface characteristics based on Random Forest regression and the NSTLR was calculated based on the NLST DEM feature space . Finally the coefficient of determination R
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A new approach to model surface temperature lapse rate STLR is introduced. Remote sensing images biophysical terrain meteorological and climate data were used. STLR was calculated at regional scale using LST DEM feature space. Local STLR was calculated using LST DEM normalized to surface characteristics. Environmental lapse rate prediction significantly increased using NLST over LST.
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S0034425720301176
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Mangrove ecosystems are targeted for many conservation and rehabilitation efforts due to their ability to store large amounts of carbon in their living biomass and soil . Traditional methods to monitor above ground biomass rely on on ground measurements which are expensive labour intensive and cover small spatial scales . Structure from Motion and Multi View Stereo reconstructions from Unmanned Aerial Vehicles imagery have the potential to increase fieldwork efficiency by providing a greater amount of spatial information in less time . However there is still a need to assess the ability of UAV SfM to retrieve structural information of mangrove forests which could pose challenges in areas of high forest complexity and density .
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UAV imagery can be used to assess AGB of natural and rehabilitated mangrove forests. Mangrove tree height and canopy diameter accurately estimated. UAVs are more cost effective than on ground surveys AU 50 000 savings per ha .
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