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S0034425720301188
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Accurate representations of canopy cover are essential for directing natural resource management efforts targeted at issues such as carbon storage habitat modeling fire spread water resources and ecosystem services . A two phase classification approach utilizing an iterative classification of high resolution aerial imagery to develop training data for a regional scale classification of percentage woody canopy cover using Sentinel 2 imagery is presented in this study and is tested for a large portion of South Texas . The modeled PWCC for the study area belonged to the respective classes as follows PWCC
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Automated tool used to sample high resolution aerial imagery to create training data. Iterative classification of high resolution aerial image tiles across study area. Results from previous step used to train Sentinel 2 classification for entire area. Realistic estimates based on LiDAR reference data comparisons. Sentinel 2 classification produced high quality canopy cover estimates.
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S003442572030119X
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Rapid climate change in Arctic regions is resulting in more frequent extreme climatic events . These can cause large scale vegetation damage and are therefore among key drivers of declines in biomass and productivity observed across Arctic regions in recent years .
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New metrics quantified climatic drivers of extreme event driven Arctic browning. These metrics explained up to 63 of variation in greenness at affected sites. Prolonged warmth or vegetation exposure in winter is associated with browning. Event metrics correlated with satellite greenness across Arctic Norway.
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S0034425720301206
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There is limited research focusing on Interferometric Synthetic Aperture Radar applications in the Great Lakes coastal wetlands with large water level fluctuations . In this study we investigated the potential of using C band SAR data to characterize marsh wetland and monitor water level changes along the coast of the Great Lakes . InSAR analysis was conducted using Radarsat 2 and Sentinel 1 data collected at Long Point Ontario Canada over the period of 20162018 . Observations indicated that both backscattering coefficients and coherence from tall plants short plants and water varied with different sensor modes in response to changes in phenology disturbance and water level . InSAR phase changes were closely related to fluctuations in water level and flow direction . We evaluated InSAR time series observations using measurements from water level loggers based on correlation and root mean square error . It was found that correlation between InSAR measurements and water level changes in the field varied depending on the site type of wetland vegetation incidence angle and polarization . Although results from some sensor modes provided good correlation at a few locations the low fringe rate and RMSE between 9 and 28cm indicated that InSAR observations of water level changes were generally underestimated .
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Utility of C band InSAR is confirmed for monitoring water level in the Great Lakes. SAR images are sensitive to marsh vegetation phenology and manmade changes. Correlation between InSAR and field water level measurements varies in marsh. High coherence is consistent in cattail and Phragmites dominated marsh areas. C band InSAR interferogram is saturated in high water level changes.
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S0034425720301218
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Anthropogenic heat flux is a main contributor to the formation of surface urban heat islands . Megacities in particular are facing severe problems due to excessive population growth urban area expansion human activity increased energy consumption and increased anthropogenic heat . In this study a physical modeling approach based on a triple source surface energy balance model was developed to uncover the effect of AHF on land surface temperature and surface anthropogenic heat island intensity . For this purpose satellite imagery along with climatic and meteorological data from 1985 to 2019 were studied for six selected megacities Los Angeles Atlanta Athens Istanbul Tehran and Beijing . First LST and the fraction of different surface covers were calculated by using a single channel algorithm and a normalized spectral mixture analysis model respectively . In the second step impervious surface cover and the urban main boundary area of each city were extracted based on the biophysical composite index and city clustering algorithm respectively . In the third step anthropogenic LST was modeled using a triple SEB model . In the fourth step the ALST and UMBA were used together to model SAHI intensity at different dates . Finally the relationship between the estimated ALST and ISC as well as between SAHI and ISC was examined . Results show that the average value of estimated ALST for the megacities increased from 2.02 0.55 0.61 0.64 0.58 and 0.72 to 2.99 1.73 1.66 1.19 2.32 and 2.76C respectively . The coefficient of determination between the mean value of ISC and the estimated ALST for all megacities yielded 0.8 which was higher than that between ISC and satellite derived LST . Moreover the SAHI intensity for these megacities was found to have increased to 0.73 0.92 0.95 0.98 0.95 and 1.32C respectively which can be predicted by ISC with a coefficient of determination of 0.78 0.79 0.79 0.73 0.71 and 0.52 respectively . This suggests that the triple SEB model proposed by this study allowed for independent modeling of AHF s influence on SUHI and a better determination of the effect of ISC on LST and SUHI intensity . This approach facilitated comparative analysis of LST and SAHI for a city at different times as well as SAHIs in different cities with different geographic and climate settings .
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A novel method based on triple source energy balance model developed for LST. LST due to anthropogenic heat flux used to model surface anthropogenic heat island. SAHI and the effect of ISC on SUHI in five global megacities were investigated. Better determination and modeling of the effect of ISC on LST and SUHI intensity. New method for comparative analysis of LST and SAHI at different times and cities
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S003442572030122X
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Crop emergence date is a critical input to models of crop development and biomass accumulation . The ability to robustly detect and map emergence date using remote sensing would greatly benefit operational yield estimation and crop monitoring efforts however this has proven to be challenging . Previous remote sensing phenology algorithms showed that crop stages can typically be detected starting only around the V3 V4 vegetative stage . Furthermore traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes . Most approaches were not designed for within season operational mapping particularly in the early growing season . In the current paper we describe a new within season emergence approach to mapping crop green up date using satellite observations available during early growth stages . The approach was first optimized using high spatiotemporal resolution imagery from the Vegetation and Environment monitoring New MicroSatellite research mission and assessed using ground observations of early crop growth stages collected over the Beltsville Agricultural Research Center experimental fields in Beltsville MD during the 2019 growing season . Results show that early crop growth stages can be reliably detected at sub field scale about two weeks after crop emergence . The remote sensing green up dates were about 45days after crop emergence on average . Coefficients of determination R
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A robust approach detected crop emergence in sub fields during early growing season. VENS time series revealed green up events within two weeks after crop emergence. Sentinel 2 produced similar results to VENS when observations are frequent. HLS could be used for mapping crop emergence operationally.
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S0034425720301255
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Interannual variability in ecosystem productivity may reveal vulnerabilities of vegetation to climate stressors . We analyzed IAV of northern hemisphere ecosystems using several remote sensing datasets including longstanding observations of the normalized difference vegetation index and more novel metrics for productivity including solar induced chlorophyll fluorescence and the near infrared reflectance of vegetation NIR
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Satellite vegetation data show limited interannual agreement with tower fluxes. Interannual agreement between satellite vegetation data is strongest during spring. Satellite vegetation data exhibit similar interannual patterns in productivity. High spring temperatures lead to high spring and low fall productivity. Summer moisture is tied to higher summer productivity.
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S0034425720301267
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Climatology errors often cause large differences between soil moisture products . However relatively little work has been done to objectively evaluate soil moisture mean seasonal cycle information acquired from different sources . This study evaluates surface SMCs obtained from four land surface models two C X band and two L band remote sensing products using 5 dense networks and 75 sparse in situ soil moisture measurement sites located within the contiguous United States . Results show that relative to older C X band products derived from the Advanced Microwave Scanning Radiometer for EOS newer L band products derived from the Soil Moisture Ocean and Salinity mission provide more accurate SMC estimates . In fact the latest SMOS INRA CESBIO product provides SMC intra seasonal variability and dynamic range information that is 334 and 237 more accurate respectively than all four LSM based SMCs examined here . Hence SMC validation against SMOS IC SMC results may improve LSMs ability to accurately capture SMC characteristics and the common strategy of scaling remote sensing SMC information to match LSM SMC estimates is likely sub optimal for assimilating L band soil moisture retrievals . Although the SMOS IC product has made significant progresses towards retrieving absolute soil moisture values a temporally constant dry bias is found in SMOS IC surface SMCs over all land cover types . Addressing this bias should be a priority for future generations of SMOS retrieval algorithms .
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Soil moisture climatological SMC error of multiple sources are investigated. Sparse network based SMC evaluation is unbiased when averaged regionally. L band remotely sensed RS SMC significantly outperforms C X band retrievals. Relative to modeled SMC L band RS can better describe SMC temporal variability. L band RS provides a new data source for large scale model parameterization.
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S0034425720301279
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The area distribution and temporal dynamics of anthropogenic impervious surface at large scale are significant for environmental ecological and socio economic studies . Remote sensing has become an important tool for monitoring large scale AIS while it remains challenging for accurate extraction of AIS using optical datasets alone due to the high diversity of land covers over large scale . Previous studies indicated the complementary use of synthetic aperture radar to improve the AIS estimation while most of them were limited to local and small scales . The potential of SAR for large scale AIS mapping is still uncertain and underexplored . In this study first a machine learning framework incorporating both optical and SAR data based on Google Earth Engine platform was developed for mapping and analyzing the annual dynamics of AIS in China . Feature level fusion for SAR and optical data across large scale was tested applicable considering the backscattering coefficients texture measures and spectral characteristics . Improved accuracy and better delineation between the bright impervious surface and bare land was observed comparing with using optical data alone . Second comprehensive assessment was conducted using high resolution samples from Google Earth census data from China Statistic Yearbook and benchmark datasets from the GlobeLand30 and GHSL demonstrating the feasibility and reliability of the proposed method and results . Last but not the least we analyzed the spatial and temporal patterns of AIS in China from national regional and provincial levels .
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We incorporated SAR and optical images for large scale impervious surfaces mapping. Results were validated with high resolution samples census and benchmark data. SAR generally improved the overall accuracy by 2 . SAR better delineated the impervious surface from bare land.
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S0034425720301280
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Nitrogen is considered as one of the most important plant macronutrients and proper management of N therefore is a pre requisite for modern agriculture . Continuous satellite based monitoring of this key plant trait would help to understand individual crop N use efficiency and thus would enable site specific N management . Since hyperspectral imaging sensors could provide detailed measurements of spectral signatures corresponding to the optical activity of chemical constituents they have a theoretical advantage over multi spectral sensing for the detection of crop N. The current study aims to provide a state of the art overview of crop N retrieval methods from hyperspectral data in the agricultural sector and in the context of future satellite imaging spectroscopy missions . Over 400 studies were reviewed for this purpose identifying those estimating mass based N and area based N N content N
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Most plant nitrogen is bound in proteins and only a small part in chlorophylls. Parametric regressions and chemometrics were the most popular methods. Machine learning and radiative transfer modelling are increasingly used. Leaf RTMs with spectral contributions of proteins need to be further developed.
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S0034425720301292
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Quantifying the spectral variation of column aerosol absorption in the ultraviolet and visible wavelengths is required for accurate satellite based aerosol and trace gas retrievals . Retrievals of the column averaged imaginary part of refractive index and single scattering albedo in the UVVis range have been performed at Yonsei University Seoul Korea since 2016 by combining co located measurements from the NASA Aerosol Robotic Network Cimel sun sky photometer the Ultraviolet Multifilter Rotating Shadowband Radiometer the SKYNET Prede sky radiometer and the NASA Pandora sun spectrometer . We investigated the spectral variation of column averaged imaginary part of refractive index for UVVis wavelengths to refine models used in our aerosol retrieval algorithm to process measurements from the upcoming Geostationary Environment Monitoring Satellite . The retrieved imaginary part of refractive index for highly absorbing fine pollution particles dust and non absorbing particles in the selected UVVis range showed 020 30 and 040 of spectral dependence respectively . Retrievals of Ozone Monitoring Instrument measurement data using the improved aerosol model showed improved correlation with AERONET data compared to the old algorithm that did not properly account for aerosol absorption effects . These results corroborate the advantage of using local climatology derived from ground based UVVis spectral aerosol absorption measurements for satellite GEMS aerosol retrievals over East Asia . Moreover this study reveals that spectral variations in the UV column aerosol absorption in East Asia differ from those in other regions .
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Column average aerosol single scattering albedo of UVVis range was observed. Spectral dependence of column aerosol absorption improved GEMS aerosol algorithm. GEMS the first GEO instrument to monitor atmospheric aerosol and precursors
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S0034425720301309
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The angle dependent scattering effect of aerosols in the atmosphere not only influences climate through radiative forcing effects but also impacts trace gas remote sensing by modifying the path of radiation through the atmosphere . The aerosol phase function which characterizes the angular signature of scattering has been continuously monitored from ground based and space borne observations . However the range of scattering angles these instruments can sample is very limited . Here we report multi year measurements from a mountain top remote sensing instrument the California Laboratory for Atmospheric Remote Sensing Fourier Transform Spectrometer which overlooks the Los Angeles megacity . The observational geometries of CLARS FTS provide a wide range of scattering angles from about 20 to about 140 which is larger than the range provided by any existing aerosol remote sensing instrument . We then quantify the aerosol angular scattering effect using the O
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A mountain top observatory for monitoring aerosols in megacities is introduced. The observatory makes measurements at a wide range of scattering angles. Aerosol scattering is quantified based on retrieved oxygen slant column. The aerosol scattering pattern can be explained by variations in scattering angle. No significant change in aerosol phase function in LA from 2011 to 2018.
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S0034425720301383
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One of the challenging tasks in modern aquatic remote sensing is the retrieval of near surface concentrations of Total Suspended Solids . This study aims to present a
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Model SOLID is developed for estimating TSS in coastal inland waters. Validated with a wide range of trophic turbidity conditions. Performance is thoroughly gauged against five other models. Model produces stable performance in optically complex aquatic ecosystems. Performance is assessed for several satellite missions.
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S0034425720301395
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Operated since the end of 2009 the European Space Agency Soil Moisture and Ocean Salinity satellite mission is the first orbiting radiometer that collects regular and global observations from space of two Essential Climate Variables of the Global Climate Observing System Sea Surface Salinity and Soil Moisture . The National Aeronautics and Space Administration Aquarius mission with the primary objective to provide global SSS measurements from space operated from mid 2011 to mid 2015 . NASA s Soil Moisture Active Passive mission primarily dedicated to soil moisture measurements but also monitoring SSS has been operating since early 2015 . The primary sensors onboard these three missions are passive microwave radiometers operating at 1.4GHz . SSS is retrieved from radiometer measurements of the sea surface brightness temperature T
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Historical review of sea surface salinity estimates with passive L band radiometry. SMOS Aquarius and SMAP sensor characteristics and algorithms are presented. Quality assessment of latest satellite SSS products is provided. The major scientific achievements of the first decade of satellite SSS are reviewed.
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S0034425720301401
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Knowledge on forest structure is vital for sustainable forest management decisions . Currently Airborne Laser Scanning has been well established as an effective tool to delineate and characterize the structure of canopies across a range of forested biomes . However the use of ALS to provide information on sub canopy structure is less well developed . Sub canopy structure consists of suppressed mature trees regenerating tree saplings shrubs herbs snags and coarse woody debris . With the increasing density of ALS point clouds new opportunities exist to describe these sub canopy structural components in forests that were previously difficult to detect using passive remote sensing technologies . In this research we use discrete return ALS data acquired at a density of 23 points m
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Introduced methods for isolating and removing forest canopy from ALS point clouds. Sub canopy point cloud metrics generated strong models for sub canopy attributes. Prediction of key sub canopy attributes improved with canopy removal method. Predictive map produced for sub canopy tree volume.
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S0034425720301474
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Region wide investigation of glacier change in High Mountain Asia marked the strongest recession in southeastern Tibetan Plateau in recent decades . However evident differences of quantitative glacier mass balance estimations on fine scales exist in the prior reports . The large uncertainties in current geodetic observations over this region highlight the need for more independent validations and investigations on the spatial variability of glacier change . This study enriched glacier MB estimates over the SETP by using the newly released global Digital Elevation Model the TanDEM X and analyzed the spatial pattern of glacier change on multi spatial scales by comparing available satellite based geodetic observations . Results reveal that the TanDEM X SRTM elevation differences which show no obvious horizontal shifts and no significant elevation dependent bias provide reliable elevation change information over this challenging area . On the regional scale the geodetic datasets achieved a highly consistent estimate of glacier MB at 4.111.52 Gt a
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Multi scale spatial pattern of glacier change was analyzed from independent data. Glacier change showed high heterogeneity related to climatic and topographic forcing. Glaciers connected with lakes experienced markedly faster mass loss than other types. Region wide glacier mass change can be updated from the new global TanDEM X DEM.
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S0034425720301486
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Water in oil and oil in water emulsions from marine oil spills have different physical properties volume concentrations and spectral characteristics . Identification and quantification of these different types of oil emulsions are important for oil spill response and post spill assessment . While the spectral characteristics of WO and OW emulsions have been presented in previous studies including Part I of this series their application to airborne and satellite imagery is further demonstrated here . Using AVIRIS and Landsat observations we firstly show that false color Red Green Blue composite images from Landsat like sensors are effective in differentiating WO and OW emulsions as they show reddish and greenish colors respectively in such composite images . This is a consequence of the relative difference in the reflectance of WO and OW emulsions at 1677 and 839nm which is not impacted by the presence of medium strength sunglint or the surface heterogeneity within medium resolution pixels . Based on image statistics a decision tree method is proposed to classify oil type and oil quantification is further attempted with results partially validated through spectral analysis and spatial coherence test . The numerical mixing experiments using AVIRIS pixels further indicate that the SWIR bands might be used to develop linear unmixing models in the future once the coarse resolution oiled pixels are first classified to WO and OW types and 1295nm is the optimal wavelength to perform spectral unmixing of mixed coarse resolution pixels .
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Principles to differentiate WO and OW emulsions applied to multi band imagery. False color RGB composite images from multi band sensors effective for this purpose. A decision tree method proposed to classify emulsion types and estimate thickness. Linear mixing effects found from multi band coarse resolution pixels. SWIR band at 1295nm being optimal for unmixing coarse resolution pixels
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S0034425720301504
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Habitat mapping is an essential descriptor to monitor and manage natural or semi natural ecosystems . Habitats integrate both the environmental conditions and the related biodiversity . However it remains challenging to map certain habitats such as inland wetlands due to spectral spatial and temporal variability in the vegetation cover . Currently no satellite constellations optimize the spectral spatial and temporal resolutions required to map wetlands according to the habitats discriminated from in situ surveys . Our approach aims to combine satellite and unmanned aerial vehicle data to exceed their respective limitations . Both data sources were combined through a spectral unmixing algorithm with the hypothesis that endmembers from UAV data are pure enough to enhance plant community abundances estimated from satellite data . The experiment was conducted on the regional preserve of the Sougal marsh a wet grassland of 174ha located upstream of the Mont Saint Michel Bay . Two satellite data sources Sentinel 2 and Pleiades and three acquisition periods November 2017 April 2018 and May 2018 were considered . A reference map of plant community distribution was produced from UAV multitemporal data and floristic surveys to validate the unmixing of satellite data . This study shows innovative results and perspectives while UAV can improve habitat discrimination results vary among acquisition periods and habitats . Results illustrate well the great potential of combined UAV and satellite data but also demonstrate the influence of endmembers on the unmixing process and technical limitations which can be overcome using domain adaptation .
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Flexible implementation of UAV facilitate synergy with satellite vectors. The unmixing approach allows good estimation of plant communities distribution. Most suitable endmembers UAV vs. Satellite vary among acquisition periods. UAV endmembers allow a more accurate discrimination of meso hygrophilic habitats. Sensors intercalibration issues can be addressed by domain adaptation methods.
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S0034425720301516
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Quantification of snow cover changes and related phenology in global mountain areas has not been consistently addressed despite the well known importance of the snow in this environment . By using MODIS products from 2000 to 2018 this study reveals that around 78 of the global mountain areas are undergoing a snow decline characterized by snow cover duration decrease up to 43days and a snow cover area decrease up to 13 . Few areas show positive changes with snow cover duration increase up to 32days and snow cover area increase up to 11 mainly during wintertime in Northern Hemisphere . Significant snow cover duration changes are related in 58 of the areas to both delayed snow onset and earlier melt moreover the rate of earlier snowmelt is greater than the rate of later snow onset in the analyzed time period . Snow cover and phenology changes are highly variable at mid elevations while from 4000m upward only negative changes are detected . Air temperature is the main driver for snow onset and melt while a combined effect of air temperature and precipitation dominates the winter season . These changes have multiple implications on water resources ecosystem services tourism and energy production . The results are provided with the caveats that the short time period of the analysis limit the attributions of long term trends and snow cover estimates are affected by uncertainties which may be stronger in complex terrain .
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Around 78 of observed areas are affected by snow decline. In 54 of the cases snow cover duration changes are due to snow melt variability. More than 50 of the areas show a rate of earlier melt greater than later onset. From 4000m upward most parameters show only significant negative changes. In spring period the snow cover changes are mainly controlled by temperature.
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S0034425720301528
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Landfast sea ice is an important feature prevalent around the Antarctic coast which is affected by climate change and energy exchanges with the atmosphere and ocean . This study proposed a method for detection of the West Antarctic fast ice using the Advanced Land Observing Satellite Phased Array L band SAR images . The algorithm has combined image segmentation image correlation analysis and machine learning techniques extremely randomized trees and logistic regression . We used SAR images with a baseline of 5days that are not in the same orbit but overlap each other as overlaps between swaths in adjacent orbits are often available in the polar regions . The underlying assumption for the proposed fast ice detection algorithm is that fast ice regions in SAR images with a time interval of 5days are highly correlated . The object based approach proposed in this study was well suited to high resolution SAR images in deriving spatially homogeneous fast ice regions . The image segmentation results using the optimized parameters showed a distinct difference in the backscatter temporal evolution between fast ice and pack ice regions . Correlation and STD of backscattering coefficients were found to be the most significant variables for the object based fast ice detection from two temporally separated images . In overall the quantitative and qualitative evaluation demonstrated that the algorithm was an effective approach to detect fast ice with high accuracies . The models well detected various fast ice regions in the West Antarctica but misclassified some objects . The misclassifications occurred toward the edge of fast ice regions with relatively rapid changes in backscattering between both data acquisitions . On the other hand few fast ice objects were misclassified as uniform backscattering over time occurred by chance on very small objects far from the coast . Very old multi year fast ice regions with high backscattered signals were also a source for some misclassifications . This may be due to the sensitivity of L band to snow structure to some extent and a thinner ice over the region with either ice growth or closing between both images . Heavy snow load on the ice could be another error source for some misclassification as well . The approach allowed for the reliable detection of fast ice regions by using L band SAR images with a small local incidence angle difference .
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A novel landfast sea ice detection approach was proposed over West Antarctica. Landfast sea ice was detected using L band SAR image pairs with a 5 day interval. The approach combines image segmentation object correlation and machine learning. The proposed approach was evaluated using time series ALOS PALSAR data.
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S003442572030153X
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In this study the mechanism of channel floodplain seasonal connectivity along the Amazon River is analyzed over a full hydrological year through the use of satellite radar altimetry data . This is done via a novel observation based approach which employs the concurrent measurement of water levels
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Channel floodplain connectivity process is characterized using satellite altimetry. Decoupled channelized and overbank flows during the rising phase. Roles of the two processes on floodplain hydrogeomorphology are highlighted.
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S0034425720301619
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The impacts of climate change such as extreme heat waves are exacerbated in cities where most of the world s population live . Quantifying urbanization impacts on ambient air temperatures has relevance for human health risk building energy use efficiency vector borne disease control and urban biodiversity . Remote sensing of urban climate has been focused on land surface temperature due to a scarcity of data on Tair which is usually interpolated at 1km resolution . We assessed the efficacy of mapping hyperlocal Tair over Oslo Norway by integrating Sentinel Landsat and LiDAR data with crowd sourced Tair measurements from 1310 private weather stations during 2018 . Using Random Forest regression modelling we found that annual mean daily maximum and minimum Tair can be mapped with an average
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Hyperlocal air temperatures mapped at 10 30m resolution with RMSE of 0.52C. Little difference between maps with open vs closed source data inputs. Mapping accuracy decreases with 1 station km. Accuracies are highest when taking a 100 500m neighbourhood into account
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S0034425720301620
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Global surface water extent is changing due to natural processes as well as anthropogenic drivers such as reservoir construction and conversion of wetlands to agriculture . However the extent and change of global inland surface water are not well quantified . To address this we classified land and water in all 3.4 million Landsat 5 7 and 8 scenes from 1999 to 2018 and performed a time series analysis to produce maps that characterize inter annual and intra annual open surface water dynamics . We also used a probability sample and reference time series classification of land and water for 19992018 to provide unbiased estimators of area of stable and dynamic surface water extent and to assess the accuracy of the surface water maps . From the reference sample data we estimate that permanent surface water covers 2.93 million km
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First sample based global estimates of open surface water extent and change. Area of multiple water land transitions far exceeds area of unidirectional change. 40 of area with surface water detected between 1999 and 2018 was not permanent. The produced maps highlight 7 types of 19992018 global surface water dynamics. 10.9 of global inland surface water is within mixed pixels at Landsat resolution.
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S0034425720301632
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There is a growing realization amongst policy makers that reliable and accurate soil monitoring information is required at scales ranging from regional to global to support ecosystem functions and services in a sustainable manner under the amplifying climate change enabling countries in target setting of the Sustainable Development Goals . In this line the need of access to and integration of existing regional in situ Earth Observation data and different sources such as contemporary and forthcoming satellite imagery is highlighted . The current study puts major emphasis on leveraging existing open soil spectral libraries and EO systems and bridging them with memory based learning algorithms that create more cost efficient and targeted large scale mapping of soil properties . Relying mostly on contemporary capacities and open resources it can be readily applied to countries with differing capacities and levels of development . To test our methodology the GEOCRADLE SSL developed in the Balkans Middle East and North Africa region and a hyperspectral airborne image were utilized to provide Soil Organic Carbon maps of cropland fields over an agricultural region near the city of Netanya Israel . Furthermore simulated data of forthcoming space borne satellite and current super spectral mission were explored . The SOC content of the collected in situ soil samples was predicted using a novel local regression approach that combines spatial proximity and spectral similarities . These predictions were subsequently used to develop models using the airborne and simulated satellite spectra achieving a fair prediction accuracy of R
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A spiked bottom up approach for soil mapping was developed and evaluated. Potential of integrating existing SSLs to predict soil properties at field level. This cost effective solution assists the baseline determination of soil related SDG. Forthcoming hyperspectral missions increase the opportunities for soil assessment.
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S0034425720301644
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Due to global climate change very large areas of reef are susceptible to warming induced coral bleaching leaving coral reef stakeholders reliant upon remote sensing forecasts of coral bleaching for estimates of when and where bleaching will occur . Coral bleaching prediction methods to date based on satellite sensed sea surface temperature are being developed further to improve the accuracy of predictions . This review examines the coral physiological and bleaching forecasting literature to identify biological and geophysical parameters that explain variance in coral bleaching and knowledge gaps related to the application of this knowledge to bleaching prediction . Identified areas for the advancement of prediction methods include improvements in sea surface temperature product resolution and past datasets incorporating the influence of UV irradiance on coral bleaching and locally varying thermal bleaching thresholds . More empirical data is necessary for some aspects of bleaching prediction development though the potential exists for gains in predictive skill to be achieved through the implementation of current physiological and remote sensing knowledge .
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Widely used satellite based bleaching prediction tools use thermal dose and duration. Bleaching thresholds also depend on other factors such as local acclimation. Abiotic parameters that cause bleaching are measurable by satellite methods. A photophysiological bleaching prediction tool includes irradiance as a predictor. Light at benthos remote sensing products may advance coral bleaching prediction.
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S0034425720301656
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Mapping and assessment of water related ecosystems is a challenging task that requires advanced processing techniques with clear rules and standards in terminology and definition of class features . These ecosystems are hydrologically and ecologically connected at catchment level and co exist within a human context . The SWOS national service case for mapping and assessing the 10 Greek Ramsar wetland sites and their catchment areas is built on the requirements of the Ramsar Convention on Wetlands and of the Aichi Biodiversity Targets of the Strategic Plan for Biodiversity 20112020 . It contributes directly to Sustainable Development Goal Global Indicator 6.6.1 Sub Indicator 1 spatial extent of water related ecosystems . An Object Based Image Analysis approach was adopted using Sentinel 2 satellite images for the year 2017 to discriminate 31 classes over an area of 2 015 591ha . The classification model was further adjusted to Landsat 5 TM imagery of previous years in order to extract possible changes in the spatial extent of water related ecosystems . The Mapping and Assessment of Ecosystems and their Services ecosystem typology as this was enhanced within SWOS was applied . Results demonstrate the effectiveness of the employed classification model techniques and rules in obtaining highly accurate mapping results on the spatial extent of water related ecosystems . Also they highlight the contribution of Earth Observation and geospatial analysis in assessments of area based changes and their causes as well as in identification of conservation and management priorities . In addition to address the need to strengthen national capacities the established SWOS service lines have been used as a contribution to the user community .
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Hierarchical OBIA effectiveness over 20.150Km. in mapping 24 wetland classes. SWOS modified MAES typology applicability in wetland ecosystems. Applicability in reporting the SDG 6.6.1 and updating the Ramsar Information Sheets. Changes estimation in spatial extent of Greek Ramsar sites along with their causes. All mapping products are accessible via SWOS portal.
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S0034425720301668
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Decision making that impacts sustainability occurs at national and subnational levels highlighting the need for multi scale Earth observations and geospatial data for assessing the United Nations Sustainable Development Goals . EnviroAtlas developed by the United States Environmental Protection Agency and partners provides a collection of web based interactive maps of environmental and socio economic data relevant to the SDGs . EnviroAtlas maps ecosystem services indicators at national regional and local extents that can contribute to targets set forth in numerous goals such as SDG 6 for clean water SDG 11 for sustainable cities and communities and SDG 15 for life on land . Examples of EnviroAtlas indicators that provide a way to view spatial inequalities help fill gaps in environmental indicators and integrate socio economic and environmental data for the SDGs are explored herein . Remotely sensed EO data are essential for producing these indicators and informing planning and decision making for the SDGs at subnational scales . The National Land Cover Dataset is the basis for many EnviroAtlas maps at the national extent while National Agriculture Imagery Program and Light Detection and Ranging data are used to classify Meter scale Urban Land Cover in select US metro areas . These 30m and 1m land cover products are combined with demographic and other geospatial data to produce integrated indicators that can aid in target setting of the SDGs . Though EnviroAtlas was created for the conterminous US the methods for indicator creation are transferable and the open source code for the EnviroAtlas resource may serve as an example for other nations . Achieving the SDGs means assessing targets and decision making outcomes at local regional and national levels using consistent and accurate data . Geospatial resources like EnviroAtlas that provide open access to indicators based on EO data and allow for assessment at multiple extents and resolutions are critical to broadly addressing national to subnational SDG goals and targets .
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EnviroAtlas provides multi resolution spatial indicators related to the SDGs. Existing EO based data on geospatial platforms can be leveraged for SDG monitoring. EO based indicators from EnviroAtlas fill gaps in environmental indicators. Integrated indicators are created combining land cover and demographic data. Addressing spatial inequalities at local levels contributes to national SDGs.
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S0034425720301681
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Sustainable Development Goal no . 15 addresses the protection of terrestrial ecosystems and sustainable forest management and Target 15.2 encourages countries to sustainably manage forests and halt deforestation by 2020 . SDG indicator 15.1.1 proposes tracking forest area as an indicator for achieving that SDG . Though mangrove forests represent only about 5 of Belize s overall forest cover the critical ecosystem services they provide are recognized in the country s Forests Act which regulates the modification of mangrove ecosystems . Preceding the SDGs from 2008 to 2009 the Government of Belize piloted a complete moratorium on mangrove removal building on the Forests Act . As Earth Observation systems provide a means to track effectiveness of Belize s management of its mangrove forests this paper examines historic and recent changes in mangrove cover across all of Belize applying statistical adjustments to rates of change derived from Landsat satellite data . Particular attention was paid to the country s only World Heritage Site the Belize Barrier Reef Reserve System where mangrove clearing was prohibited since the site s designation in December 1996 . The data indicate that within the BBRRS approximately 89ha of mangroves were lost from 1996 to 2017 compared to the estimated loss of 2703ha outside the BBRRS during the same period and nationwide loss of almost 4100ha from 1980 to 2017 . Thus compared to the mangroves outside of the BBRRS the annual rate of mangrove loss within the BBRRS over the period 19962017 was merely 4.24ha per year versus 129.11ha per year outside the BBRRS . Furthermore almost 75 of the 19962017 mangrove loss outside the BBRRS were concentrated in three particular geographic zones associated with tourism infrastructure . It was also estimated that Belize s overall mangrove cover declined 5.4 over 36years from 76 250ha in 1980 to 72 169ha in 2017 . In terms of its implications in addition to contributing to SDG 15 this work also addresses SDG Target 14.2 regarding sustainable management of marine and coastal ecosystems . This study serves as a use case of how EO data can contribute to monitoring changes in baseline data and thus tracking of progress toward SDG Targets .
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Mangrove loss rates across Belize were examined for 19802017. Remote sensing data were statistically adjusted. The Belize Barrier Reef World Heritage Site lost 89ha. of mangroves over 21years. Outside the World Heritage Site 2703ha of mangrove were lost. Belize s overall mangrove area declined by 5.4 from 1980 to 2017.
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S0034425720301693
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Synthetic Aperture Radar amplitude measurements from spaceborne sensors are sensitive to surface roughness conditions near their radar wavelength . These backscatter signals are often exploited to assess the roughness of plowed agricultural fields and water surfaces and less so to complex heterogeneous geological surfaces . The bedload of mixed sand and gravel bed rivers can be considered a mixture of smooth and rough surfaces . Here we assess backscatter gradients over a large high mountain alluvial river in the eastern Central Andes with aerially exposed sand and gravel bedload using X band TerraSAR X TanDEM X C band Sentinel 1 and L band ALOS 2 PALSAR 2 radar scenes . In a first step we present theory and hypotheses regarding radar response to an alluvial channel bed . We test our hypotheses by comparing backscatter responses over vegetation free endmember surfaces from inside and outside of the active channel bed area . We then develop methods to extract smoothed backscatter gradients downstream along the channel using kernel density estimates . In a final step the local variability of sand dominated patches is analyzed using Fourier frequency analysis by fitting stretched exponential and power law regression models to the 2 D power spectrum of backscatter amplitude . We find a large range in backscatter depending on the heterogeneity of contiguous smooth and rough patches of bedload material . The SAR amplitude signal responds primarily to the fraction of smooth sand bedload but is further modified by gravel elements . The sensitivity to gravel is more apparent in longer wavelength L band radar whereas C and X band is sensitive only to sand variability . Because the spatial extent of smooth sand patches in our study area is typically 50m only higher resolution sensors are useful for power spectrum analysis . Our results show the potential for mapping sand gravel transitions and local geomorphic complexity in alluvial rivers with aerially exposed bedload using SAR amplitude .
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SAR amplitude data are used to measure alluvial channel bed roughness. Backscatter gradients are mostly due to changes in smooth sand surface contribution. L band SAR has a more dynamic backscatter signal modified by gravel size. 5 m resolution SAR can assess localized spatial extents of sand gravel patchiness.
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S003442572030170X
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The eutrophication of lake and reservoir has attracted concerns from the public and government in China . Water clarity is a reliable indicator for quantifying eutrophic status because of its strong association with chlorophyll a total suspended matter and nutrients . Traditionally water clarity is measured using Secchi disk depth . By linking the spectral signal from water surface with in situ measured SD remote sensing provides a useful tool for SD estimation at a large scale in a repetitive manner . Remote sensing derived water clarity has been reported in many regions with specific models established for different satellite overpasses concurrent with in situ measured SD but national water clarity remained unknown in China . In this study 2152 samples were collected from 34 field campaigns in 20132018 of which 1792 samples were gathered within 7days of Landsat OLI overpasses . We used Landsat 8 OLI bands 14 to develop regression models
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The rhos method was the best of four atmospheric correction methods tested. Model based on red and blue band performed stable for national clarity estimate. Model based on the rhos method demonstrated spatiotemporal transferability. China s water clarity of lake and reservoir area 8ha was mapped nationally. Lake SDD in the TQR and NLR demonstrated the max and min clarity in China.
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S0034425720301711
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Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires to quantify their drivers and to assess the impacts on human and natural systems . In this study we developed the Landsat Burned Area algorithm an update from the Landsat Burned Area Essential Climate Variable algorithm . Here we present the BA algorithm and products changes relative to the BAECV algorithm and products and updated validation metrics . We also present spatial and temporal patterns of burned area across the conterminous U.S. how burned area varies in relation to the number of operational Landsat sensors and a comparison with other burned area datasets including the BAECV Monitoring Trends in Burn Severity GeoMAC and Moderate Resolution Imaging Spectroradiometer MCD64A1.006 data . The BA algorithm identifies burned areas in analysis ready data time series of Landsat imagery from 1984 through 2018 using machine learning thresholding and image segmentation . Validation with reference data from high resolution commercial satellite imagery resulted in omission and commission error rates averaging 19 and 41 respectively . In comparison validation with Landsat reference data had omission and commission error rates averaging 40 and 28 respectively when burned areas in cultivated crops and pasture hay land cover types were excluded . Both validation tests documented lower commission error rates relative to the BAECV products . The amount of burned area detected varies not only in response to climate but also with the number of operational sensors and scenes collected . The combined amount of burned area detected by multiple sensors was larger than from any individual sensor but there was no significant difference between individual sensors . Therefore we used BA products from individual sensors to assess trends over time and all available sensors to compare with other existing BA products . From 1984 through 2018 annual burned area averaged 30 000km
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We describe the Landsat Burned Area BA algorithm and products for CONUS. The algorithm operationalizes Landsat TM ETM and OLI burned area products. Commission error for wildland fires improved over the Landsat BAECV products. Omission and commission error rates were lower than coarse resolution BA products. Burned area products can be consistently generated from the Landsat archive.
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S0034425720301723
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Automatically monitoring newly constructed building areas is essential for efficient land resource management and sustainable urban development particularly in the rapidly urbanizing country of China . In this regard time series multi view high resolution optical satellite images can provide fine spatial details for clearly characterizing NCBAs but this leads to great heterogeneity and complexity owing to the high spectral variation complicated imaging conditions and different viewing angles . Moreover to date the vertical features and time series information from these images have not been fully exploited for urban change detection . In this paper our primary objective is to automatically detect the presence of NCBAs and meanwhile to investigate the feasibility of identifying their change timing using time series multi view ZY 3 high resolution satellite images . To this aim we propose an automatic change detection method consisting of three components 1 firstly we jointly use planar vertical features to delineate the NCBAs 2 object based temporal correction is subsequently applied to improve the spatiotemporal consistency of the features and 3 finally a multi temporal change detection model is used to simultaneously capture the NCBAs and the change timing . We applied the method on two urban fringe areas of Beijing and Shanghai respectively which are cities that have been experiencing rapid urbanization . The experimental results confirmed the effectiveness of the proposed method . For both study areas the F score values reached nearly 90 in terms of NCBA detection and with respect to the change timing the overall accuracies with a one year tolerance strategy reached around 92 . The joint use of the planar vertical features and the inclusion of multi temporal images make the proposed method a promising approach for automatically providing the spatiotemporal information of NCBAs in practical applications .
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An automatic method for monitoring newly constructed building areas NCBAs. Time series multi view high resolution optical satellite images were adopted. Joint use of planar vertical features for delineating NCBAs. Object based temporal correction for improving spatiotemporal consistency. Multi temporal change detection for deriving change location and timing
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S0034425720301735
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The first national product of Surface Water Dynamics in France is generated on a monthly temporal scale and 10 m spatial scale using an automatic rule based superpixel approach . The current surface water dynamic products from high resolution multispectral satellite imagery are typically analyzed to determine the annual trend and related seasonal variability . Annual and seasonal time series analyses may fail to detect the intra annual variations of water bodies . Sentinel 2 allows us to investigate water resources based on both spatial and temporal high resolution analyses . We propose a new automatic RBSP approach on the Google Earth Engine platform . The RBSP method employs combined spectral indices and superpixel techniques to delineate the surface water extent this approach avoids the need for training data and benefits large scale dynamic and automatic monitoring . We used the proposed RBSP method to process Sentinel 2 monthly composite images covering a two year period and generate the monthly surface water extent at the national scale i.e . over France . Annual occurrence maps were further obtained based on the pixel frequency in monthly water maps . The monthly dynamics provided in SWDF products are evaluated by HR satellite derived water masks at the national scale and at local scales . The monthly trends between SWDF and GSW were similar with a coefficient of 0.94 . The confusion matrix based metrics based on the sample points were 0.885 0.963 0.932 and 0.865 . The annual surface water extents are validated by two HR satellite image based water maps and an official database at the national scale and small water bodies at the local scale at Loir et Cher . The results show that the SWDF results are closely correlated to the previous annual water extents with a coefficient 0.950 . The SWDF results are further validated for large rivers and lakes with extraction rates of 0.929 and 0.802 respectively . Also SWDF exhibits superiority to GSW in small water body extraction with an extraction rate improved by approximately 20 . Thus the SWDF method can be used to study interannual seasonal and monthly variations in surface water systems . The monthly dynamic maps of SWDF improved the degree of land surface coverage by 25 of France on average compared with GSW which is the only product that provides monthly dynamics . Further harmonization of Sentinel 2 and Landsat 8 and the introduction of enhanced cloud detection algorithm can fill some gaps of no data regions .
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Propose a rule based superpixel approach to automatically extract surface water extent. Detect water bodies in various scenes e.g. urban agricultural and mountainous areas. Generate a product of surface water dynamics in metropolitan France. Release first national water dynamics at monthly temporal and 10 m spatial scales
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S0034425720301747
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Crop lodging assessment is essential for evaluating yield damage and informing crop management decisions for sustainable agricultural production . While a few studies have demonstrated the potential of optical and SAR data for crop lodging assessment large scale crop lodging assessment has been hampered by the unavailability of dense satellite time series data . The unprecedented availability of free Sentinel 1 and Sentinel 2 data may provide a basis for operational detection and monitoring of crop lodging . In this context this study aims to understand the effect of lodging on backscatter coherence and spectral reflectance derived from Sentinel 1 and Sentinel 2 data and to detect lodging incidence in wheat using time series analysis . Crop biophysical parameters were measured in the field for both healthy and lodged plots from March to June 2018 in a study site in Ferrara Italy and the corresponding Sentinel images were downloaded and processed . The lodged plots were further categorised into different lodging severity classes . Temporal profiles of backscatter coherence reflectance and continuum removed spectra were studied for healthy and lodging severity classes throughout the stem elongation to ripening growth stages . The Kruskal Wallis and posthoc Tukey tests were used to test for significant differences between different classes . Our results for Sentinel 2 showed that red edge and NIR bands could best distinguish healthy from lodged wheat . For Sentinel 1 the analysis revealed the potential of VH backscatter and the complementarity of VV and VH VV backscatter in distinguishing a maximum number of classes . Our findings demonstrate the potential of Sentinel data for near real time detection of the incidence and severity of lodging in wheat . To the best of our knowledge there is no study that has contributed to this application .
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Sentinel 1 and Sentinel 2 time series data are analysed for healthy and lodged wheat. VH backscatter red edge and NIR reflectance were highly sensitive to wheat lodging. VV and VH VV backscatter could be used complementarily for lodging detection.
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S0034425720301759
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There is an urgent need to include northern peatland hydrology in global Earth system models to better understand land atmosphere interactions and sensitivities of peatland functions to climate change and ultimately to improve climate change predictions . In this study we introduced for the first time peatland specific model physics into an assimilation scheme for L band brightness temperature data from the Soil Moisture Ocean Salinity mission to improve groundwater table estimates . We conducted two sets of model only and data assimilation experiments using the Catchment Land Surface Model applying in one of them a peatland specific adaptation . The evaluation against in situ measurements of peatland groundwater table depth indicates the superiority of PEATCLSM model physics and additionally improved performance after assimilating SMOS Tb observations . The better performance of PEATCLSM over nearly all Northern Hemisphere peatlands is further supported by the better agreement between SMOS Tb observations and Tb estimates from the model only and data assimilation runs . Within the data assimilation scheme PEATCLSM reduces Tb observation minus forecast residuals and leads to reduced data assimilation updates of water storage components and thus reduced water budget imbalances in the assimilation system .
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Peatland specific model physics in an assimilation scheme for L band brightness temperature Tb. Improved L band brightness temperature Tb estimates over nearly all Northern Hemisphere peatlands. Groundwater estimates from Tb data assimilation correlate better with in situ measurements. Reduced water budget imbalances in assimilation system due to smaller required assimilation updates
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S0034425720301760
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This paper presents a community effort to develop good practice guidelines for the validation of global coarse scale satellite soil moisture products . We provide theoretical background a review of state of the art methodologies for estimating errors in soil moisture data sets practical recommendations on data pre processing and presentation of statistical results and a recommended validation protocol that is supplemented with an example validation exercise focused on microwave based surface soil moisture products . We conclude by identifying research gaps that should be addressed in the near future .
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Satellite soil moisture validation methods are reviewed. Community agreed validation good practice guidelines are presented. A standardized satellite soil moisture validation protocol is provided
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S0034425720301772
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Historical wetland hydrology data are instrumental to support the design of wetland management and restoration strategies but are rarely available . In this study we tested the capabilities and limitations of a simple methodological framework based on publicly available MODIS Land Reflectance Products to estimate wetland soil surface saturation and inundation spatiotemporal dynamics . Using supervised learning and high resolution groundwater and surface water elevation data the framework searches for spectral algorithms referred to as the wet dry wetland status classifier and the continuous wetland dynamics identifier that best predict upper soil layer wetness status in the study wetland . We used Google Earth Engine for fast access and processing of the full range of MODIS data . The capabilities of GEE also enabled us to readily conduct a comparative assessment of the MODIS 8 day composite and daily collections and test various pixel level quality filters to select reliable data at the highest possible temporal resolution . We tested the framework on the internationally recognized Ramsar site Palo Verde National Park wetland in Costa Rica and we obtained good results overall prediction accuracy of 86.6 and kappa coefficient of 0.7 for the WSC r
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A systematic framework for wetland soil saturation and flooding detection is proposed. The framework is tested with high temporal resolution water elevation data. Hydric status diagnostic mismatch when working with multiple sensors is explained. Opportunities and limitations of using the full range of MODIS data is presented. New insights into Palo Verde wetland hydroperiod history and drivers is gained.
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S0034425720301802
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The increasing impact of humans on land and ongoing global population growth requires an improved understanding of land cover and land use processes related to settlements . The heterogeneity of built up areas and infrastructures as well as the importance of not only mapping but also characterizing anthropogenic structures suggests using a sub pixel mapping approach for analysing related LC from space . We implement a regression based unmixing approach for mapping
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Sub pixel built up woody non woody vegetation fractions mapped at national scale. Wide range of rural and urban settings investigated for Germany and Austria. Synthetic training data from spectral temporal metrics leads to robust results. A single model achieved MAE of 1318 RMSE 1822 from coastal to alpine regions. Sub pixel fractions structurally characterize settlements in Germany and Austria.
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S0034425720301814
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With the promise of transformative changes for the management of rural and urban forests the discrimination of tree species from satellite imagery has been a long standing goal of remote sensing . For the species rich urban setting of Washington D.C. USA we evaluate current prospects toward this goal by combining a Random Forest object based tree species classification method with two large datasets 1 A suite of 12 very high resolution WorldView 3 images whose image acquisition date cover each pheno phase of the growing season from April to November and 2 the 16 496 street trees from Washington D.C. Department of Transportation s field inventory . We classify the 19 most abundant tree species with an overall accuracy of 61.3 and classify the ten most abundant genera with an overall accuracy of 73.7 . We observe that there are larger declines in accuracy when attempting to classify species in the same genus and the most valuable phenological period is fall senescence for classification at different taxonomic levels . Especially if satellite data can be matched to the key pheno phases our study highlights that current VHR satellite sensors now have the radiometric spectral and spatial resolution to potentially help manage species rich urban forests .
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High spatial resolution images to classify trees at two different taxonomic levels. Overall accuracy is 61.3 for 19 species 73.7 for 10 genera. Most valuable phenological period is fall senescence at two taxonomic levels.
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S0034425720301826
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This study assesses the impact of an improved soil moisture initialization using direct insertion methodology for convection resolving modelling of heavy precipitation events . State of the art 1km SM data from the Soil Moisture and Ocean Salinity mission SMOS BEC L4 version 3 are used for this purpose . A strategy is developed to prepare the SMOS L4 surface soil moisture product for the COnsortium for Small scale MOdelling model initialization by applying a cumulative density function matching bias correction and the exponential filter method to calculate corresponding SM profiles . The processed satellite derived product is validated with 38 observing sites from three in situ SM networks REMEDHUS SMOSMANIA and VAS . All networks measure at a soil depth of 5cm only at the SMOSMANIA network additional measurements at 10 20 and 30cm are available . Four HPEs are selected to evaluate the impact of the high resolution realistic initialization . The results show a high agreement index and a low root mean square deviation 0.03m
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Novel methodology for model initialization with state of the art soil moisture data. Improvement of heavy precipitation forecasts with 1km SMOS L4 soil initialization. Optimal time for SMOS L4 initialization is 3days before precipitation initiation. Improved understanding of soil atmosphere feedbacks and related processes
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S0034425720301838
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Droughts can cause tremendous losses to agricultural and economic development humans have explored many anti drought measures to mitigate the influence of drought and accordingly altering the characteristics of actual agricultural drought . In this study a method is proposed to detect the spatiotemporal changes in drought characteristics which show the effects of anti drought measures on drought mitigation . Two agricultural drought mitigation evaluation indices are proposed the agricultural drought frequency change and agricultural drought area change which are calculated by combining the Palmer drought severity index and vegetation health index two widely used drought monitoring indices . The PDSI and VHI represent the natural and actual agricultural drought severity under natural and actual conditions respectively and their differences in drought frequency and affected area reflect the level of anti drought measures in mitigating agricultural drought . The feasibility of using ADFC and ADAC to quantify the effects of anti drought measures for agriculture is explored using data from six typical agricultural provinces in the North China Plain and Northeast China . The results show that ADFC and ADAC could reflect both the spatiotemporal changes in agricultural drought characteristics and the influence of anti drought measures on agricultural drought . The trend of the drought mitigation index is consistent with agricultural activity statistics . These two indices could be further used to evaluate the effects of different anti drought methods and aid in defeating agricultural drought across many countries .
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Assessing drought mitigation performance with changes in drought characteristics. Two drought mitigation indices ADFC and ADAC are proposed and tested. ADAC has the ability to assess mitigation performances to extreme drought event. PDSI and VHI detect natural and actual agricultural drought separately.
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S0034425720301851
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Successful post fire management depends on accurate burn severity maps that are increasingly derived from satellite data replacing field based estimates . Post fire vegetation and soil changes besides modifying the reflected and emitted radiation recorded by sensors onboard satellites strongly alters water balance in the fire affected area . While fire induced spectral changes can be well represented by fraction images from Multiple Endmember Spectral Mixture Analysis changes in water balance are mainly registered by evapotranspiration . As both types of variables have a clear physical meaning they can be easily understood in terms of burn severity providing a clear advantage compared to widely used spectral indices . In this research work we evaluate the potential of Landsat derived ET to estimate burn severity together with MESMA derived Sentinel 2 fraction images and important environment variables . In this study we use the random forest classifier which provides information on variable importance allowing us to identify the combination of input variables that provided the most accurate estimate . Our study area is located in Central Portugal where a mega fire burned 450km
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Post fire fine resolution evapotranspiration is useful as indicator of burn severity. Post fire ET reflected fire induced changes in water balance. MESMA fractions showed post fire spectral changes. Burn severity could be accurately estimated from post fire ET and MESMA fractions. Method applied to Central Portugal 2017 with potential for extrapolation.
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S0034425720301875
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Remote sensing images contain abundant land cover information . Due to the complex nature of land cover however mixed pixels exist widely in remote sensing images . Sub pixel mapping is a technique for predicting the spatial distribution of land cover classes within mixed pixels . As an ill posed inverse problem the uncertainty of prediction can not be eliminated and hinders the production of accurate sub pixel maps . In contrast to conventional methods that use continuous geospatial information to enhance SPM in this paper a SPM method with point constraints into SPM is proposed . The method of fusing point constraints is implemented based on the pixel swapping algorithm and utilizes the auxiliary point information to reduce the uncertainty in the SPM process and increase map accuracy . The point data are incorporated into both the initialization and optimization processes of PSA . Experiments were performed on three images to validate the proposed method . The influences of the performances were also investigated under different numbers of point data different spatial characters of land cover and different zoom factors . The results show that by using the point data the proposed SPM method can separate more small sized targets from aggregated artifacts and the accuracies are increased obviously . The proposed method is also more accurate than the advanced radial basis function interpolation based method . The advantage of using point data is more evident when the point data size and scale factor are large and the spatial autocorrelation of the land cover is small . As the amount of point data increases however the increase in accuracy becomes less noticeable . Furthermore the SPM accuracy can still be increased even if the point data and coarse proportions contain errors .
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Sub pixel mapping is an ill posed problem with inevitable uncertainty. The point data are proposed to enhance sub pixel mapping. A new sub pixel mapping method with point constraint is proposed. More accurate sub pixel maps can be produced by exerting point constraints.
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S0034425720301954
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Measurements made by spaceborne Global Navigation Satellite System Reflectometry instruments have shown strong reflected power over inland waters that has been attributed to coherent scattering coming from the first Fresnel zone . Previous work in the field has shown the ability of GNSS R to observe the global surface water distribution by generating dynamic maps of wetlands and other inundated areas . These maps can be generated by leveraging the large difference in received power of the GNSS signals as they reflect from water surfaces compared to land . In this paper we utilize a full forward scattering model approach to evaluate the accuracy of these maps . The CYGNSS End to End Simulator was extended to include the contributions from coherent surface scattering in heterogeneous regions where the area around the specular point contains both water and land in complex geometries . The simulator is then used to analyze the accuracy of a current approach to estimate the surface water content in the first Fresnel zone from a single measurement called a fractional water in footprint approach . We find that contributions to the total received power by the scattering from outside the first Fresnel zone as well as CYGNSS instrument effects impact the accuracy of this approach . Furthermore the measured signal from calibration scattering targets is compared to the results of simulation to validate the scattering model . The work shows that the variability of the peak reflected power over inland bodies of water due to the scene heterogeneities should be accounted for in designing retrieval algorithms to map wetland extent .
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A scattering model for GNSS R coherent scattering from inland waters was developed. Areas outside the first Fresnel zone contribute to the total received power. The peak power of a given scene varies depending on the observation geometry. Surface geometry vegetation and surface roughness cause variance in received power. Proposed scattering model was validated using measured CYGNSS raw IF data.
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S0034425720301978
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Polar ocean ecosystems are experiencing rapid environmental change but measuring the associated phytoplankton responses is challenging using traditional satellite passive ocean color measurements due to signal contamination from clouds and sea ices and to low solar elevation angles . Active satellite lidar measurements allow retrieval of ocean phytoplankton properties under conditions prohibitive to passive ocean color sensors . The ICESat 2 satellite lidar measurements provide two dimensional distributions of upper ocean phytoplankton properties . The spring phytoplankton blooms extending about 230km horizontally from dense packs ice near Antarctic marginal ice zones and 15m vertically below ocean surface are observed from space for the first time . Our findings highlight the advantages of satellite lidar technology for understanding high latitude plankton ecology and biogeochemistry .
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The vertical structures of the ice edge blooms are first uncovered from space. The ice edge blooms extend as far as 230km horizontally and 15m vertically. ICESat 2 lidar measurements can be used for ocean biology studies. High latitude plankton in both day and night can be observed from ICESat 2.
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S003442572030198X
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Generalized assessments of the accuracy of spectroscopic estimates of ecologically important leaf traits such as leaf mass per area and leaf dry matter content are still lacking for most ecosystems and particularly for non forested and or seasonally dry tropical vegetation . Here we tested the ability of using leaf reflectance spectra to estimate LMA and LDMC and classify plant growth forms within the
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We measured spectra and leaf traits for an understudied dry tropical grassland. We could not accurately predict LMA values 300g m. from spectral data. We could infer plant lifeform from spectra despite absence of leaf trait differences. These key findings have seldom been studied due to biases towards humid forests
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S0034425720301991
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Aquatic vegetation is an important component in the ecosystems of wetlands and coastal waters for example it can take up harmful substances and pollutants from the water . Constructing a remote sensing model of aquatic vegetation can deepen our understanding of the spectral characteristics and directional reflection characteristics of the aquatic vegetation canopy under various environmental conditions . However the existing aquatic vegetation models are limited to continuously distributed canopies . This paper proposes an aquatic vegetation geometric optical model at canopy scale for discrete aquatic vegetation . After dividing the reflectance of water background into specular components and diffuse components the reflectances of all reflecting surfaces are classified into different reflectance components based on the differences in their materials and on whether the reflectors are illuminated by direct sunlight . Then the scene bidirectional reflectance factor is regarded as an area weighted sum of different reflectance components . Detailed verifications were conducted to ensure that the simulation model operates as intended . The performance of the AVGO model was qualitatively validated with the WCRM model and showed the physical plausibility of the model prediction . Quantitative validations against an artificially designed experiment were consistent with the bidirectional reflectance and the nadir viewed spectral reflectance . This model can be used for canopy modeling of discrete emergent or submerged aquatic vegetation . Potential applications include retrieval of biophysical or biochemical parameters and aquatic vegetation abundance based on hyperspectral data .
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A new geometric optical spectral reflectance model of aquatic vegetation. Can handle both discrete emergent and submerged aquatic vegetation. Can be applied to spectra analysis and parameter retrieval of aquatic vegetation. Rough water surface turbid water and water bottom are considered.
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S0034425720302005
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Drones offer entirely new prospects for precision agriculture . This study investigates the utilisation of drone remote sensing for managing and monitoring silage grass swards . In northern countries grass swards are fertilised and harvested three times per season when aiming to maximise the yield . Information about the grass quantity and quality is necessary to optimise these operations . Our objectives were to investigate and develop machine learning techniques for estimating these parameters using drone photogrammetry and spectral imaging . Trial sites were established in southern Finland for the primary growth and regrowth of grass in the summer of 2017 . Remote sensing datasets were captured four times during the primary growth season and three times during the regrowth period . Reference measurements included fresh and dry biomass and several quality parameters such as the digestibility of organic matter in dry matter neutral detergent fibre indigestible neutral detergent fibre water soluble carbohydrates the nitrogen concentration in dry matter and nitrogen uptake . Machine learning estimators based on random forest and multiple linear regression methods were trained using the reference measurements and tested using independent test datasets . The best results for the biomass estimation nitrogen amount and digestibility were obtained when using hyperspectral and 3D data followed by the combination of multispectral and 3D data . During the training process the best normalised root mean square errors were 14.66 for the dry biomass and 12 for fresh biomass the best RMSE values for NU the D value and NDF were 13.6 1.98 and 3 respectively . For the primary growth the accuracies of all quality parameters were better than 20 with the independent test datasets for the regrowth the estimation accuracies of the D value iNDF NDF Ncont and NU were better than 20 . The results showed that drone remote sensing was an excellent tool for the efficient and accurate management of silage production .
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Drone remote sensing procedures for estimating grass swards quantity and quality. Quantitative processing and calibration of data captured in challenging conditions. Biomass estimation accuracy better than 16 for primary growth and regrowth. Accuracy better than 2 for digestibility and 11 for nitrogen concentration. Machine learning with spectral images and 3D data is viable for such estimation.
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S0034425720302029
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Contemporary estimates of glacier changes are necessary to assess the impact of climate change associated hazards and water resources management . Glaciers in High Mountain Asia are mostly retreating except the Karakoram and western Kunlun Shan confirmed by remote sensing measurements . However ground validation with precise measurements of these mass balance estimates are scarce . This study selected Guliya ice cap in the western Kunlun Shan to observe its recent changes regarding surface dynamics and mass balance using ASTER DEM of 2005 and 2015 . Our findings indicate that one of the north facing glaciers surged during July and early November 2015 advancing at about 8m per day on average . The mass balance shows a balance condition 0.010.02m w.e . a
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Mass balance of Guliya ice cap is stable between 2005 and 2015. ICESat and dGPS results between 2004 and 2015 confirm these results. A north facing glacier in Guliya ice cap surged during July and November 2015. The surge was striking considering the nearby Aru glacier collapse in 2016.
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S0034425720302030
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Satellite interferometric synthetic aperture radar is emerging as a viable low cost alternative method to airborne laser scanning for forest inventory though little research has examined its efficacy for plantation forests located in temperate regions on steep terrain . InSAR and ALS data were collected from Geraldine Forest which is located on rolling to very steep topography in the southeast of New Zealand . These data were combined with an extensive set of plot measurements from which mean top height
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Models using InSAR data provided a reasonable level of precision for H and TSV. CHM derived from InSAR DSM and ALS DTM significantly improved the predictions. InSAR DSM and DTM errors were within the WorldDEM target but increased with slope. CHM errors increased with slope but the final InSAR models were invariant to slope.
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S0034425720302042
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We studied the influence of changing carotenoid pigments on the sensitivity of the photochemical reflectance index to photosynthesis dynamics . The goal of the measurements was to examine how the introduction of PRI into the working dataset can improve the estimation of photosynthesis . Spectral and photosynthetic characteristics of European beech and Norway spruce saplings were periodically measured in growth chambers with an adjustable irradiance and temperature . Patterns of environmental changes inside the growth chambers were created by periodic changes in irradiance and temperature . Four general irradiance periods lasting 1012days each were established . Introduced irradiance regimes varied in the sum of daily irradiance and amplitude of irradiance changes . Temperature was changed with more complex patterns to induce changes in xanthophyll cycle pigments at various time scales within these regimes . Our measurements confirmed the PRI linkage to photosynthetic light use efficiency . However the strength of this connection was found to be dependent on changing pigment concentrations specifically on the change in the ratio of chlorophylls to carotenoids . Furthermore a negative interference in photosynthesis estimation from PRI was recorded if the temperature was lowered overnight to 12C . The differential PRI calculated as the simple difference between the PRI value measured during the daytime period and in early morning PRI
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We studied PRI dynamics in changing irradiance and temperature setup. Sensitivity of PRI to LUE was dependent on the changing ratio of Chl. Car. Interference from Chl. Car. in LUE estimation was lowered when using PRI. Effect of low temperature on LUE estimation from PRI was reduced with PRI. Leaf area index of trees appeared to be an important driver in observed relationships.
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S0034425720302054
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Surface snowmelt affects the energy balance through melt albedo feedback and may endanger the ice shelves in the Antarctic Peninsula through hydrofracture . Here we introduce an automatic snowmelt identification algorithm based on Quick Scatterometer and Advanced Scatterometer . The proposed method can provide self adaptive thresholds for snowmelt detection based on Rosin thresholding and is able to detect weak melt signals with a wavelet denoising procedure . Results suggest the AP surface snowmelt has slightly declined during 19992018 in the context of recent cooling . However winter melt index has significantly increased with a rate of 83 decade
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An automatic snowmelt detection algorithm is proposed to detect snowmelt. Antarctic Peninsula winter snowmelt has significantly increased over 19992018. Intense winter snowmelt in 2016 was driven by a deepening of Amundsen Sea Low.
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S0034425720302066
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Terrestrial Laser Scanning has been used during the past decade to capture the complexity of 3D forest canopy structures especially Leaf or Plant Area Density . TLS data i.e . point cloud can be divided into voxels to estimate the three dimensional distribution of LAD PAD . However the combination effects of vegetation occlusion and shooting pattern of TLS scanners lead to a highly heterogeneous sampling which limits the reliability of some local estimates since several voxels are either not explored or poorly explored by laser beams . In practice recommendations vary regarding the minimum number of beams crossing voxels or the minimum path lengths required to provide reliable predictions . In addition assigning a value to non explored and poorly explored voxels is still an open question .
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A new method for LAD estimation from T LiDAR in occluded areas is developed. This method use kriging on LAD estimator LAD Kriging and relies on TLS data only. LAD Kriging can be applied to all voxels whatever their reliability. LAD Kriging retrieves reliable estimates in not explored and poorly sampled voxels. This method can be fitted to other unbiased estimators extracting metrics in point clouds.
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S0034425720302091
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The ability to map fire severity is a requirement for fire management agencies worldwide . The development of repeatable methods to produce accurate and consistent fire severity maps from satellite imagery is necessary to document fire regimes to set priorities for post fire management responses and for research applications . Machine learning techniques such as random forest have shown great promise for mapping of wildfire severity in woodland and forest ecosystems using satellite imagery . However an assessment of the properties of training data required for automated mapping with random forest is currently lacking . This study examined how training data properties affect fire severity classification across forest woodland and shrubland communities of southern Australia . The aims of this study were to examine how sample size and sample imbalance affect classification accuracy to determine whether models were transferrable across geographic regions and to assess the need for classifiers for prescribed burns and wildfires . We sampled 33 wildfires and 57 prescribed burns occurring across southern Australia between 2006 and 2019 to derive an extensive dataset
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Fire severity mapping using Random Forest is sensitive to training data properties. Highest accuracy was achieved using large datasets with balanced class proportions. Model accuracy was robust to extrapolation when data was sampled over a broad area. Fire type wildfire vs burn used for training had little effect on model accuracy. Overall accuracy was very high for wildfires 88 and satisfactory for burns 68
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S0034425720302108
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Altimeters can provide global long duration observations of oceanic wind speed and wave height . However altimeters face an undersampling problem in estimating wind and wave climate because of their sparse sampling pattern and the changing number of in orbit satellites . In this study the undersampling error of altimeters was studied by sampling the oceanic wind speed and wave height data from the ERA5 and the Integrated Ocean Waves for Geophysical and other Applications datasets using the track information of multiplatform altimeters . Comparisons were made between the statistics of the original reanalysis hindcast data and the gridded along track sampling of the reanalysis hindcast data . The results show a large discrepancy with respect to the extreme values . The undersampling of altimeters can lead to significant underestimations of monthly extreme values of oceanic wind speed and wave height . Meanwhile this underestimation is alleviated with the increase of the number of in orbit altimeters leading to very large overestimations of long term trends of these extreme values over the period 19852018 . In contrast the annual extreme values of oceanic wind speed and wave height and their long term trends are more reliable although slight aforementioned biases of extreme values still exist and the data from GEOSAT are not suitable for computing annual statistics . For altimeter data the annual values are a better option to compute long term trends than the monthly data . This study also presents a correction scheme of using model data to compensate for the wind and wave events missed by altimeter tracks . After the correction the global trends in oceanic wind speed and wave height over 19922017 are recomputed using annual statistics . The results show a clear discrepancy between the trends of wind speed and wave height during this period the wind speed increased while the wave height decreased . However uncertainty still exists in the results and the reason for this discrepancy is unknown at this stage .
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ERA5 wind wave data are observed using altimeter tracks to study undersampling. Monthly extreme wind wave from altimeters is underestimated due to undersampling. Long term trends of monthly extreme wind wave from altimeters are overestimated. Long term trends of annual wind wave statistics from altimeters are more reliable. Altimeters show global wind speed and wave height trends 19922017 to be opposite.
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S0034425720302224
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The influence of adjacency effect on high spatial resolution satellite imagery in the visible and near infrared region has been studies since the 1980s . However the adjacency effect is usually neglected in the thermal infrared region . The conventional TIR radiative transfer equation only takes into account radiance from target pixel and neglects radiance contribution from surrounding background pixels . In this study two TIR RTEs taking into account the adjacency effect for uniform and non uniform Lambertian surfaces were deduced based on the radiative transfer theory . In terms of the TIR RTEs we analyze the dominating influence factors of the adjacency effect and quantify the magnitude of the adjacency effect in the TIR region using simulation and actual satellite data . Two different definitions of the adjacency effect are presented in this study the effect due to radiance from background pixels scattered into sensor s instantaneous field of view and the effect due to apparent thermal contrast between target and background pixels . The results show that atmospheric visibility water vapor content sensor spectral band and background pixel land surface temperature have significant influence on the adjacency effect . For a specific simulation scene the magnitude of the first definition of the adjacency effect is larger than 0.5K when atmospheric visibility is lower than 23km . This magnitude is as much as 2K under haze weather conditions with atmospheric visibility of 5km . The results indicate that the adjacency effect should be taken into account in the radiative transfer simulation . The magnitude of the second definition of the adjacency effect is less than 0.3K even at atmospheric visibility of 5km . The results reveal that the accuracy of the TIR RTE for a uniform Lambertian surface is enough for the development of LST retrieval algorithm under relatively haze weather conditions . In situ LST measurements collected at the Hailar Urad Front Banner and Wuhai sites in China were used to validate the accuracies of the LST retrieved by the RTE based single channel algorithm with without adjacency effect correction . There are nearly no discrepancies between the LST retrieved with without adjacency effect correction when aerosol optical depth is low than 0.3 . However the RMSE of the differences between the retrieved LST and the in situ LST decreases from approximately 1.4K for the LST retrieved without adjacency effect correction to approximately 0.6K for the LST retrieved with adjacency effect correction when AOD is high than 0.3 .
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Two TIR RTEs taking into account adjacency effect were deduced. Dominating influence factor of adjacency effect in the TIR region was analyzed. The magnitude of adjacency effect in the TIR region was quantified. RMSE of LST derived with adjacency effect correction reduces 0.8K for AOD 0.3.
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S0034425720302273
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Urban composition can be analyzed through spectral unmixing of images from airborne imaging spectrometers . Unmixing given a spectral library can be accomplished by set based methods or distribution based methods . For computational efficiency and optimal accuracy set based methods employ a library reduction procedure when applied to large spectral libraries . On the other hand distribution based methods model the library by only a few parameters hence innately accept large libraries . A natural question arises that can distribution based methods with the original large spectral library achieve comparable performance to set based methods in urban imagery .
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Distribution based unmixing methods can directly utilize a large spectral library. We apply distribution based methods GMM NCM BCM on urban imagery. The unmixing accuracy from GMM is on par with MESMA in 16m imagery. GMM has potential in applications with a universal spectral library.
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S0034425720302297
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Urban land use is often characterized based on the presence of built up land while the land use intensity of different locations is ignored . This narrow focus is at least partially due to a lack of data on the vertical dimension of urban land . The potential of Earth observation data to fill this gap has already been shown but this has not yet been applied at large spatial scales . This study aims to map urban 3D building structure i.e . building footprint height and volume for Europe the US and China using random forest models . Our models perform well as indicated by R
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We present the first 3D building structure for Europe the US and China. Building footprint height and volume are mapped at a 1km. resolution. Our random forest models are very robust with R. values higher than 0.81. The spatial pattern of 3D building structure differs across case study regions.
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S0034425720302303
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The measurement of chlorophyll fluorescence in remote way represents a tool that is becoming increasingly important in relation to the diagnosis of plant health and carbon budget on the planet . However the detection of this emission is severely affected by distortions due to processes of light re absorption both in the leaf and in the canopy . Even though some advances have been made to correct the signal in the far red the whole spectral range needs to be addressed in order to accurately assess plant physiological state . In 2018 we introduced a model to obtain fluorescence spectra at leaf level from what was observed at canopy level . In this present work we publish a revision of that physical model with a more rigorous and exact mathematical treatment . In addition multiple scattering between the soil and the canopy and the fraction of land covered by vegetation have also been taken into consideration . We validate this model upon experimental measures in three types of crops of agronomic interest with different architecture . Our model accurately predicts both the shape of fluorescence spectra at leaf level from that measured at canopy level and the fluorescence ratio . Furthermore not only do we eliminate artifacts affecting the spectral shape but we are also able to calculate the quantum yield of fluorescence corrected for re absorption from the experimental quantum yield at canopy level . This represents an advance in the study of these systems because it offers the opportunity to make corrections for both the fluorescence ratio and the intensity of the observed fluorescence .
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Light re absorption in canopies thoroughly revisited. Accurate photophysical model validated for active measurements. Fraction of land covered by vegetation considered in the new equations. Chlorophyll fluorescence spectrum at leaf level obtained from that at TOC. Observed and corrected quantum yield of fluorescence for the canopy calculated.
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S0034425720302315
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The characteristics of urban land surfaces contribute to the urban heat island and in turn can exacerbate the severity of heat wave impacts . However the mechanisms and complex interactions in urban areas underlying land surface temperature are still being understood . Understanding these mechanisms is necessary to design strategies that mitigate land temperatures in our cities . Using the recently available night time moderate resolution thermal satellite imagery and employing advanced nonlinear statistical models we seek to answer the question
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The first study for LST and urban factors using Landsat nighttime temperatures. A comparative analysis of surface temperature for four USA cities. Advanced statistical techniques for geospatial data capture nonlinear behavior. Increased perviousness and vegetation lower nighttime urban surface temperature. Data model and spatial resolution uncertainty quantified
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S0034425720302327
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Forest top of canopy reflectance observed by remote sensing instruments is driven by a wide range of parameters including optical properties of leaves being the major driver understory and bark together with structural properties of the forest spatial distribution of leaves and branches in the canopy contributing also to variable extent . In addition the instantaneous observation geometry largely influences the observed TOC reflectance due to the changes in shadow fraction in the canopies . As a result forest TOC reflectance is highly dynamic following the seasonal patterns of optical and structural properties of vegetation .
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Trends in dorsiventral leaf reflectance are driven by leaf structure. Biggest differences in studied leaf traits were observed in early spring and autumn. Seasonal leaf reflectance asymmetry upscaled in complex radiative transfer model. Neglecting lower side reflectance may underestimate relative reflectance by 20 .
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S0034425720302339
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Land surface temperature is among the most important variables in monitoring land surface processes . LST is often retrieved from thermal infrared remote sensing data which have a tradeoff between the spatial and temporal resolutions and are spatially incomplete due to cloud contamination . Land surface model output can reflect LST under all weather conditions but the spatial resolution is relatively coarse . In this study a two step LST data fusion framework was proposed for generating MODIS like LST at a 1km spatial resolution under all weather conditions . First MODIS LST on clear days for a given study region and China Land Data Assimilation System LST at a spatial resolution of 7km7km were fused using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model . Second systematic biases of the fused LST estimates were corrected by MODIS LST for clear pixels on cloudy days . Results indicate that the fused LST after bias correction fused LST
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A new data fusion scheme is developed to generate LST by blending multisource data. LST from land surface models on all days and MODIS LST on clear sky days are used. The fused LST estimates feature spatial completeness under all weather conditions. The fused LST estimates are validated with in situ measurements at multiple sites. The accuracy of the LST estimates is generally better than that of published studies.
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S0034425720302364
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Land surface temperature as an effective indicator measuring urban thermal environment is significantly influenced by a range of human and natural factors at different scales . However the scale dependence of LST influencing factors has not been fully explored due to relatively discrete scales or single factor used in previous studies . It is a great challenge to explore the approach to prioritizing research scales in view of the influencing factors of LST . Taking the urban group of Xi an City and Xianyang City in Western China as a case study area this study proposed a wavelet coherence approach to identifying the prioritizing LST influencing factors and research scales . Based on the sample transects the results showed that around 1km could be used as the prioritizing research scale for simultaneously exploring multiple LST influencing factors . And the normalized difference build up index was the dominant influencing factor with the strongest multi scale stability . The coherence relationships with LST of the area percentage of blue land and the mean patch area of blue land represented high spatial heterogeneity with multi scale stability in the area of widespread water body . The normalized difference vegetation index should also be highlighted due to the multi scale stability and stable medium coherence with LST . This study proposed a wavelet coherence approach to exploring spatial heterogeneity and scale dependence of the relationship between LST and multiple influencing factors .
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Spatial continuous wavelet transform was used to obtain long scale information. LST influencing factors across long scales were measured with coherence analysis. The priority research scales and LST influencing factors were identified.
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S0034425720302376
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Knowledge of the status of flying aircraft is vital for efficient and safe air space management . However this requirement is often compromised due to the complexity of the aviation environment . Satellite remote sensing provides a complementary means for tracing aircraft but is often limited to finding motionless aircraft under specific scenarios . Here based on the inter band offsets due to hardware parallax in push broom sensors we develop a method for detecting flying aircraft in an automated fashion . Supported by a hybrid computation framework specifically designed to address the challenge of processing large volume of moderate resolution RS data at a global scale the method is applied to more than 2.31 million MSI images to establish a map of the global distribution of flying aircraft . The detected flying aircraft coincide well with those determined using traditional techniques when both datasets co exist . With the existing and future moderate resolution data captured by push broom satellite sensors the method is believed to provide a robust and cost effective means of detecting aircraft status at a global scale thus supplementing the traditional methods for tracking flying aircraft . The same method is also used to estimate the inter band and inter granule time offsets in multi band MSI and Landsat 8 Operational Land Imager images which may provide critical information needed to correct artifacts in aquatic applications .
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A method for detecting high speed objects from Sentinel 2 MSI images is proposed. The method employs the inter band measurement parallax caused by hardware design. The method is executed using a combined local and cloud computation environment. A global map of flying aircraft distribution is derived from 2.31 million MSI images. Inter band granule time offsets of Sentinel 2 MSI Landsat OLI are estimated.
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S003442572030239X
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Mapping inundation dynamics and flooding extent is important for a wide variety of applications from providing disaster relief and predicting infectious disease transmission to quantifying the effects of climate change on Earth s hydrologic cycle . Due to the rapid and highly spatially heterogeneous nature of flooding events acquiring data with both high spatial and temporal resolutions is paramount yet doing so has remained a challenge in satellite remote sensing . The potential for Global Navigation Satellite System Reflectometry to help address this challenge has been explored in several studies the bulk of which use data from the Cyclone GNSS constellation of GNSS R satellites . This work presents a simple forward model that describes how surface reflectivity measured by CYGNSS should change due to flooding for different land surface types . We corroborate our model findings with observations from the Amazon Basin and Lake Eyre Australia . Both the model and observations indicate that the relationship between surface reflectivity and surface water extent strongly depends on the micro scale surface roughness of the land and water . We show that the increase in surface reflectivity due to flooding or inundation is greatest in areas where the surrounding land has dense vegetation . In areas where the land surface surrounding inundated areas is perfectly smooth the increase in surface reflectivity due to flooding is not as strong and confounding effects of soil moisture and water roughness could lead to large uncertainties in resulting surface water retrievals . However even a few centimeters of surface roughness will result in several dB sensitivity to surface water provided that the water is smoother than the land surface itself .
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CYGNSS observations of surface reflectivity are sensitive to inundation extent. A forward model describes how inundation extent affects surface reflectivity. The sensitivity of reflectivity to inundation is greatest for rough surfaces. Soil moisture confounds the inundation signal for smooth surfaces.
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S003442572030242X
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A systematic review and inventory of recent research relating to optical remote sensing of Arctic vegetation was conducted and thematic and geographical trends were summarized . Research was broadly categorized into four major themes of time series including NDVI trends and shrub expansion disturbance and recovery including tundra fires winter warming herbivory permafrost disturbance and anthropogenic change vegetation properties including biomass primary productivity seasonality phenology and pigments and classification and mapping . Remaining challenges associated with remote sensing of Arctic vegetation were divided into three categories and discussed . The first are issues related to environmental controls including disturbance hydrology plant functional types phenology and the tundra taiga ecotone and understanding their influence on interpretation and validation of derived remote sensing trends . The second are issues of upscaling and extrapolation related to sensor physics and the comparability of data from multiple spatial spectral and temporal resolutions . The final category identifies more philosophical challenges surrounding the future of data accessibility big data analysis sharing and funding policies among major data providers such as national space agencies and private companies as well as user groups in the public and private sectors . The review concludes that the best practices for the advancement of optical remote sensing of Arctic vegetation include a continued effort to share and improve
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Optical RS of Arctic vegetation benefits from legacy and emerging technologies. NDVI time series dominate recent optical remote sensing in the Arctic. Derivation of Arctic tundra vegetation properties lacks in situ validation. Better characterization of environmental controls on Arctic RS trends is needed. Sensor continuity and comparability are necessary for temporally dense datasets.
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S0034425720302431
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Cropland abandonment is a widespread land use change but it is difficult to monitor with remote sensing because it is often spatially dispersed easily confused with spectrally similar land use classes such as grasslands and fallow fields and because post agricultural succession can take different forms in different biomes . Due to these difficulties prior assessments of cropland abandonment have largely been limited in resolution extent or both . However cropland abandonment has wide reaching consequences for the environment food production and rural livelihoods which is why new approaches to monitor long term cropland abandonment in different biomes accurately are needed . Our goals were to 1 develop a new approach to map the extent and the timing of abandoned cropland using the entire Landsat time series and 2 test this approach in 14 study regions across the globe that capture a wide range of environmental conditions as well as the three major causes of abandonment i.e . social economic and environmental factors . Our approach was based on annual maps of active cropland and non cropland areas using Landsat summary metrics for each year from 1987 to 2017 . We streamlined per pixel classifications by generating multi year training data that can be used for annual classification . Based on the annual classifications we analyzed land use trajectories of each pixel in order to distinguish abandoned cropland stable cropland non cropland and fallow fields . In most study regions our new approach separated abandoned cropland accurately from stable cropland and other classes . The classification accuracy for abandonment was highest in regions with industrialized agriculture and drylands where fields were large or spectrally distinct from non cropland . Abandonment of subsistence agriculture with small field sizes or highly variable climate was not accurately mapped . Cropland abandonment occurred in all study regions but was especially prominent in developing countries and formerly socialist states . In summary we present here an approach for monitoring cropland abandonment with Landsat imagery which can be applied across diverse biomes and may thereby improve the understanding of the drivers and consequences of this important land use change process .
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We propose an approach to map cropland abandonment using all available Landsat images. A novel method is developed to generate training dataset semi automatically. Annual cropland maps are generated using Landsat spectral temporal metrics. Our approach is successful in most of 14 study regions across the globe. Strong spatial and temporal variations exist in cropland abandonment.
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S0034425720302546
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We developed an algorithm called Cmask for cirrus cloud detection in Landsat 8 imagery using time series of Cirrus Band observations . For each pixel a harmonic model which includes a water vapor regressor based on all available Cirrus Band observations is estimated using the Robust Iteratively Reweighted Least Squares regression approach and pixels affected by cirrus cloud are identified by comparing model predictions and actual satellite observations of the Cirrus Band Top Of Atmosphere reflectance . Furthermore we analyzed the effect of increasing Cirrus Band TOA reflectance on the surface reflectance of the Blue Green Red Near Infrared and two Shortwave Infrared Bands based on a set of globally distributed random samples . The goal of this study is to answer the question of what are cirrus clouds in the context of a Landsat observation or more specifically when should we identify a pixel as cirrus cloud such that we know the reflectance in the other spectral bands has been seriously affected by cirrus clouds . The challenge is to then develop a simple and operational algorithm for accurate detection of cirrus clouds in Landsat 8 images . The Cmask algorithm reduced almost by half the errors found in the U.S. Geological Survey Quality Assessment Band for distinguishing cirrus cloud and clear observations .
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We analyzed cirrus cloud impacts on the surface reflectance of Landsat 8 data. We developed a cirrus cloud detection algorithm called Cmask. TOA reflectance from the Cirrus Band and water vapor data were used. The combined use of Cmask and Fmask is recommended.
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S0034425720302571
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Ground based evaporative fraction observations have been used widely for validation purposes in previous remote sensing based EF models . Few studies have investigated whether such measurements can be utilized for calibration use . In this paper an observation driven optimization method is proposed to quantify EF over a large heterogeneous area within the surface temperature vegetation index framework . It is designed at both daily scale and seasonal scale with MODIS products and
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An optimization method is proposed to calibrate RS based EF models. Accuracy produced using one site for calibration has reached an acceptable level. The method has the capacity to provide a quick and continuous monitoring of EF. The method is also characterized by its simplicity stability and extensibility.
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S0034425720302595
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Assessing the oceanic surface layer s optical properties through CALIOP has been one of the reasons of the extension of the CALIOP mission for 3 more years . This is the first work evaluating the potential use of CALIOP for ocean applications at regional scale in mid latitude regions and investigating the added information on ocean particles given by the column integrated depolarization ratio
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The potential use of CALIOP for ocean applications at basin scale was evaluated. CALIOP column integrated depolarization ratio. distributions were computed. None. vs b. and. vs Chl a agreement depends on water trophic regimes. None. parameter could add valuable information about ocean particulate optical properties.
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S0034425720302601
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The eutrophication problems in lakes on the Yangtze Plain of China have attracted global concern . However a comprehensive assessment of the eutrophication status and its evolution is still lacking for these regional lakes mostly because of technical difficulties and or insufficient data to cover the large region . Our study attempts to fill this knowledge gap by using the entire archive of remote sensing images from two satellite ocean color missions and Ocean and Land Color Instrument or OLCI together with in situ data on remote sensing reflectance and chlorophyll a concentrations across various lakes on the Yangtze Plain . A machine learning based piecewise Chla algorithm was developed in this study with special considerations to improve algorithm performance under lower Chla conditions . Remotely sensed Chla and algal bloom areas were then used to classify the eutrophication status of 50 large lakes on the Yangtze Plain and the frequent satellite observations enabled us to estimate the probability of eutrophication occurrence for each examined lake . The long term mean Chla ranged from 17.58mgm
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We developed a machine learning based Chla algorithm for Yangtze Plain lakes. Chla and algal bloom areas were used to classify eutrophication in 50 lakes. Our results show a high probability of eutrophication in Yangtze Plain lakes. We found an overall improvement in water quality of these lakes.
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S0034425720302625
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Wetlands are the most fragile and threatened ecosystems worldwide and also one of the most rapidly declining . At the same time wetlands are typically biodiversity hotspots and provide a range of valuable ecosystem services such as water supply and purification disaster risk reduction climate change adaptation and carbon sequestration .
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Tools for EO based SDG indicator 6.6.1 reporting are demonstrated. Standardized nomenclature and GEOclassifier tools for indicator development. GEO Wetlands Community Portal to demonstrate policy related wetland reporting tools. Albania example to show case nationwide policy related reporting opportunities.
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S0034425720302637
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Providing accurate information on fire effects is critical to understanding post fire ecological processes and to design appropriate land management strategies . Multispectral imagery from optical passive sensors is commonly used to estimate fire damage yet this type of data is only sensitive to the effects in the upper canopy . This paper evaluates the sensitivity of full waveform LiDAR data to estimate the severity of wildfires using a 3D radiative transfer model approach . The approach represents the first attempt to evaluate the effect of different fire impacts i.e . changes in vegetation structure as well as soil and leaf color on the LiDAR signal . The FLIGHT 3D radiative transfer model was employed to simulate full waveform data for 10 plots representative of Mediterranean ecosystems along with a wide range of post fire scenarios characterized by different severity levels as defined by the composite burn index . A new metric is proposed the waveform area relative change which provides a comprehensive severity assessment considering all strata and accounting for changes in structure and leaf and soil color . It showed a strong correlation with CBI values outperforming the relative change of LiDAR metrics commonly applied for vegetation modeling such as the relative height of energy quantiles . Logarithmic models fitted for each plot based on the WARC yielded very good performance with R
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The potential of LiDAR to estimate fire damage is assessed using a 3D RTM approach. The new metric WARC provides a comprehensive evaluation of severity. The WARC outperformed common LiDAR metrics used for vegetation modeling. The robustness and generalization power of the method was shown over the King Fire.
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S0034425720302649
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The fine mode aerosols generally generated from anthropogenic sources play an essential role in global radiation balance climate change atmospheric environment and human health . However the FMA retrieval remains a challenge . An improved high resolution FMA retrieval algorithm based on the Spectral Neutrality of Surface Polarized Reflectance was developed using polarized satellite data . SNOSPR utilizes a lookup table approach to simultaneously retrieve high spatial resolution FMA parameters such as FMA optical depth AOD
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A new algorithm retrieving FMA parameters and SPR simultaneously based on satellite polarized data was developed. Spectrally neutrality and spatial invariance of SPR were first incorporated to choose the best retrievals. The derived fine mode AOD has a high spatial resolution and wide coverage. Comparison and validation with POLDER and AERONET products show a good agreement and a high accuracy. Fine mode AOD retrieved from DPC proves the applicability of this algorithm.
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S0034425720302650
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Synchronous observations with similar illumination observation and meteorological conditions are critical components of cross calibration analysis . This study outlines data quality control criteria for obtaining the stable synchronous data needed for developing and evaluating a cross calibration algorithm . With image data from the Visible Infrared Imaging Radiometer and Medium Resolution Spectral Imager II we developed a cross calibration algorithm using 35 image pairs of four ocean gyres and we evaluated the data using 11 image pairs of the global ocean . We found that our new algorithm provided well calibrated MERSI II reflectance at the top of atmosphere . The coefficients of determination
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A new data quality controlling criterion is used to exclude unstable pixels in cross calibration. A cross calibration method has been constructed for improving the imperfect MERSI II radiance. The MERSI II instrument is able to provide the good ocean color products after cross calibration.
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S0034425720302662
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Atmospheric water vapor plays a key role in the global water and energy cycles . Accurate estimation of water vapor and consistent representation of its spatial temporal variation are critical to climate analysis and model validation . This study used ground observational data from global radiosonde and sunphotometer networks to evaluate MODIS precipitable water vapor products for 20002017 . The products included the thermal infrared and its updated version and near infrared products . Our results demonstrated that compared to its earlier version subject to sensor crosstalk problem the C061 TIR data showed improved accuracy in terms of bias standard deviation mean absolute error root mean square error and coefficient of determination regression slope and intercept . Among the PWV products C061 NIR data achieved the best overall performance in accuracy evaluation . The C061 NIR revealed the PWV had a multi year average of 2.500.08cm for the globe 2.030.06cm for continents and 2.700.09cm for oceans in 20002017 . The PWV values yielded an increasing rate of 0.015cm year for the globe 0.010cm year for continents and 0.017cm year for oceans . Nearly 98.95 of the globe showed an increasing trend 80.74 of statistical significance mainly distributed within and around the tropical zones . The findings should be valuable for understanding of global water and energy cycles .
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MODIS near infrared water vapor products had the highest overall accuracy. Downward trend is an artifact in previous version of thermal infrared products. Water vapor had a multi year average of 2.500.08cm for 20002017. Water vapor increased significantly at a rate of 0.015cm year for globe. Oceanic regions contributed more than continental to global PWV change.
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S0034425720302674
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Fire disturbance is a significant component of the climate system . Analysis of satellite derived burned areas has allowed the identification of fire patches and their morphology as a new resource for tracking fire spread to improve fire models used to assess the impact of fires on climate and the carbon cycle . A critical parameter of the flood fill algorithm used to create fire patches is the cut off below which it aggregates two contiguous burned pixels to the same fire patch . However the current level of validation is insufficient to understand the effect of the cut off values and sensor resolutions on the subsequent fire patch morphology . The FRY v1.0 database of functional fire patch traits emanates from the analyses of two global burned area products derived from MODIS and MERIS sensors with different spatial and temporal resolutions and with cut off values of 3 5 9 and 14days . To evaluate whether the FRY products are accurately identifying the spatial features of fire patches and what are the most realistic cut off values to use in different sub regions of North America we propose a new functional diversity trait based approach which compares the satellite derived fire patches to forest service perimeters as reference data . This paper shows the accuracy of the FRY fire patches 300ha in North America during 20052011 . Our analysis demonstrates that fire patches with a high cut off of 14days and those derived from the MODIS sensor with their high temporal resolution better identify the fire diversity in North America . In conclusion our statistical framework can be used for assessing satellite derived fire patches . Furthermore the temporal resolution of satellite sensors is the most important factor in identifying fire patches thus space agencies should consider it when planning the future development of cost effective climate observation systems .
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The trait based method can be used for assessing satellite derived fire patches. Functionalstructure relationships are complemented by functional dissimilarities. High temporal resolution is most important for fire patch identification. Fire patches with a cut off of 14days are better identified in North America.
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S0034425720302686
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Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science . To make these measurements investigators calibrate instrument data to absolute units invert physical models to estimate atmospheric effects and then determine surface properties from the spectral reflectance . This study quantifies the uncertainty in this process . Global missions demand predictive uncertainty models that can estimate future errors for varied environments and observing conditions . Here we validate uncertainty predictions with remote surface composition retrievals and
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New remote imaging spectroscopy missions will measure global surface composition. Such missions require comprehensive uncertainty models to predict inversion errors. We track uncertainty for calibration atmospheric inversion and mineral retrieval. We evaluate model performance in a field experiment demonstrating mineral detection. Predicted uncertainties agree stataistically with remote. discrepancies
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S0034425720302698
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Synthetic aperture radar remote sensing is a potential technique for long term monitoring of landslide prone areas . Pixel offset tracking methods work well for fast moving landslides . However existing methods may present some limitations such as a high dependence of estimation window design on experience a tradeoff between the accuracy of single points and the overall efficiency and a low confidence in the results caused by heterogeneous in window samples . In this paper an improved offset tracking method is proposed to address these problems . First the workflow is optimized by a preseparation step added before offset estimation to distinguish between feature matching and speckle pattern matching . The optimized workflow is more efficient for natural scenes containing both feature and non saliency regions . Second an improved algorithm called adaptive incoherence speckle offset tracking based on homogeneous samples is proposed for non saliency regions . Its two key points are adaptive design of the optimal estimation windows by introducing a coherence map as a guide and offset estimation without heterogeneous samples . We apply the proposed method to study the evolution of the 2018 Jinsha River landslide using SAR data from the Gaofen 3 satellite and the Phased Array type L band Synthetic Aperture Radar 2 system onboard the Advanced Land Observing Satellite satellite . Compared with the traditional method the proposed method improves the efficiency and reduces the uncertainty . We also analyze the spatiotemporal displacement pattern of this landslide which shows that the Jinsha River landslide was most likely a thrust load caused landslide . This study demonstrates the role of SAR remote sensing in global landslide monitoring especially where ground truth data are scarce .
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Improved offset tracking with optimization flow and AISOT HS algorithm is proposed. AISOT HS designs adaptive windows and estimates offsets based on homogeneous patches. Apply it to study the 2018 Jinsha River landslide with GF 3 data and ALOS 2 data. Compared with Trad OT the proposed method improves the efficiency and confidence. Displacement pattern analysis indicates this case is a thrust load caused landslide.
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S0034425720302716
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Remote sensing optical sensors onboard operational satellites can not have high spectral spatial and temporal resolutions simultaneously . In addition clouds and aerosols can adversely affect the signal contaminating the land surface observations . We present a HIghly Scalable Temporal Adaptive Reflectance Fusion Model algorithm to combine multispectral images of different sensors to reduce noise and produce monthly gap free high resolution observations over land . Our approach uses images from the Landsat and the MODIS missions both from Terra and Aqua platforms . We implement a bias aware Kalman filter method in the Google Earth Engine platform to obtain fused images at the Landsat spatial resolution . The added bias correction in the Kalman filter estimates accounts for the fact that both model and observation errors are temporally auto correlated and may have a non zero mean . This approach also enables reliable estimation of the uncertainty associated with the final reflectance estimates allowing for error propagation analyses in higher level remote sensing products . Quantitative and qualitative evaluations of the generated products through comparison with other state of the art methods confirm the validity of the approach and open the door to operational applications at enhanced spatio temporal resolutions at broad continental scales .
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Presented a new fusion algorithm to produce gap free Landsat reflectance datasets. The algorithm is highly scalable and runs optimally in cloud computing environments. The algorithm also provides the uncertainty associated with the final estimates. Quantitative and qualitative evaluation of the algorithm obtained good results.
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S0034425720302728
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Topography is one of the key factors that impact remotely sensed data and their interpretation . Indeed combined with the viewing geometry and neighbour effects it strongly affects the direct diffuse and multi scattered scene irradiance which in turn impacts the radiative budget and remote sensing signals of the landscapes . The increased availability of digital elevation models and the advancement of 3D radiative transfer models allow us to better address these topographic effects . DART is one of the most accurate and comprehensive 3D RT models that simulate remote sensing observations of natural and urban landscapes with topography and atmosphere . It simulates environmental effects using a so called infinite slope mode that infinitely duplicates the observed landscape while ensuring the continuity of slope and altitude at the DEM edges . Up to DART version 5.7.4 this mode was slightly inaccurate and computer intensive depending on the topography . This paper presents an innovative modelling strategy that greatly improves it in terms of accuracy image quality and computer efficiency . For that a fictive auxiliary oblique plane adapted to the landscape topography is introduced for managing the scene illumination the Earth Atmosphere coupling and the storage of the radiation that exits the scene before being projected onto the sensor plane . Improvements and validations are illustrated both visually and quantitatively by DART images radiometric products and radiative budget . For example the observed reflectance of a Lambertian slope is equal to the expected analytical value . In addition the solar plane reflectance of a forest on a mountain slope has an average error of about 0.01 relative to the reflectance of the same forest stand in the reference scene . This new modelling is already integrated in the official DART version
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Improved DART accuracy and efficiency for sloping landscapes. Accurate and uniform scene lighting for any topography. Energy conservation in Earth and atmosphere RT modelling. Generation of accurate and high quality remote sensing images
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S003442572030287X
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Indicators of landscape condition should be sensitive and specific to environmental change and provide early warning detection of ncipient changes . We assessed a suite of five spectral metrics derived from Landsat 5 TM imagery spanning a decade to quantify ecosystem condition in the ridge slough mosaic of the Everglades . These included the normalized difference vegetation index the same index using green instead of red band as the visible reference the normalized difference water index the simple ratio of NIR and red bands and the moisture stress index . Mean and variance from pure ridge or slough pixels were quantified for twentyfour 25km blocks across a gradient of hydrologic and ecological condition . Metrics were compared with field measures of landscape condition from block scale soil elevation surveys which capture dramatic spatial gradients between conserved and degraded locations . Elevationbased measures of landscape condition validated as diagnostic in previous work included soil elevation bi modality BI
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Spectral metrics can diagnose the spatial extent and severity of ecological change. Most metrics are lagging indicators indicating poor early warning detection. Multivariate models outperformed univariate models for predicting degradation.
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S0034425720302881
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Rivers are essential to the Earth s ecosystem but the current understanding of river width variability is limited owing to the sparse distribution of gauging stations . Remote sensing data enable the surveying and analysis of river geomorphology by providing multi temporal Earth observation data from satellites at fine spatial and temporal resolutions . We proposed an optimized RivWidth method to automatically calculate width for all channels in a water map and parallelized it to produce the Multi temporal China River Width dataset which is the first 30 m multi temporal river width dataset for China during 19902015 including estimates under both seasonal fluctuations and dynamic inundation frequencies . The MCRW dataset is made up of 1.310
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A Landsat based dynamic river width dataset has been generated. A locally adaptive search method was proposed to quantify river width variation. River width of major rivers in China have generally increased during 19902015. Optimized strategies were proposed to improve the RivWidth algorithm. Larger width values were observed in winter for some rivers due to the ice jams.
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S0034425720302972
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The changing climate is affecting the frozen soil at an unprecedented rate across the Northern Hemisphere . However due to sparse ground measurements the changes of frozen soil and the environmental controls over the vast cryosphere are still unclear such as in the Tibetan Plateau . In this study a process based model solely driven by satellite remote sensing data is employed to investigate the spatiotemporal changes of seasonally frozen ground and permafrost over the entire TP 3 million km
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Satellite based model well reproduced frozen soil changes FSC across the TP. Permafrost show consistent degradation over 20022016. Seasonally frozen ground show roughly competent increasing and decreasing trends. Both in season and off season LST are found to impact on FSC. Precipitation affects FSC in arid regions and regions near permafrost lower limit
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S003442572030300X
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With less than a decade left to attain the Sustainable Development Goals this communication aims to improve understanding of the
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Progress and challenges motivating the special issue are discussed. 17 manuscripts highlight the Earth science contributions to SDG targets indicators. Assessment of existing Earth observation systems for SDG indicators is outlined. Highlights from country use cases global and regional initiatives and future outlook. Understanding the. is pivotal for EO to fully support the SDGs.
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S0034425720303011
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There is considerable demand for satellite observations that can support spatiotemporally continuous mapping of land surface temperature because of its strong relationships with many surface processes . However the frequent occurrence of cloud cover induces a large blank area in current thermal infrared based LST products . To effectively fill this blank area a new method for reconstructing the cloud covered LSTs of Terra Moderate Resolution Imaging Spectroradiometer daytime observations is described using random forest regression approach . The high temporal resolution of the Meteosat Second Generation LST product assisted in identifying the temporal variations in cloud cover . The cumulative downward shortwave radiation flux was estimated as the solar radiation factor for each MODIS pixel based on the MSG DSSF product to represent the impact from cloud cover on incident solar radiation . The RF approach was used to fit an LST linking model based on the datasets collected from clear sky pixels that depicted the complicated relationship between LST and the predictor variables including the surface vegetation index normalized difference water index solar radiation factor surface albedo surface elevation surface slope and latitude . The fitted model was then used to reconstruct the LSTs of cloud covered pixels . The proposed method was applied to the Terra MODIS daytime LST product for four days in 2015 spanning different seasons in southwestern Europe . A visual inspection indicated that the reconstructed LSTs thoroughly captured the distribution of surface temperature associated with surface vegetation cover solar radiation and topography . The reconstructed LSTs showed similar spatial pattern according to the comparison with clear sky LSTs from temporally adjacent days . In addition evaluations against Global Land Data Assimilation System NOAH 0.25 3 h LST data and reference LST data derived based on
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A new LST reconstruction method for cloud covered pixels is proposed. The reconstructed LSTs well captured the distribution of surface temperature. The evaluations with GLDAS and air temperature data suggest its good performance. The proposed method shows good potential for reconstructing cloud covered LSTs.
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S0034425720303059
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With a growing number of Earth observation products available through operational programmes such as the European Unions Copernicus there is increasing emphasis on product accuracy and uncertainty necessitating evaluation against in situ reference measurements . Whilst existing reference datasets have proven a valuable resource they incorporate little data with which products from recent EO instruments can be assessed . A reliance on individual field campaigns has also led to several inconsistencies whilst limiting the extent to which temporal variations in EO product performance can be captured . Recently established environmental monitoring networks such as the National Ecological Observatory Network which collect routine in situ measurements using standardised instruments and protocols provide a promising opportunity in this respect . The Copernicus Ground Based Observations for Validation service was initiated in recognition of this fact . In the first component of the project raw observations from existing networks have been collected and processed to provide reference data for a range of EO land products . In this study we focus on leaf area index and the fraction of absorbed photosynthetically active radiation . Raw digital hemispherical photography from twenty NEON sites was processed to derive in situ reference measurements which were then upscaled to provide high spatial resolution reference maps . Using these data we assess the recently released Copernicus Global Land Service 300 m Version 1 products derived from PROBA V in addition to existing products derived from the Moderate Resolution Imaging Spectroradiometer and Visible Infrared Radiometer Suite . When evaluated against reference data the CGLS 300 m V1 product demonstrated the best agreement followed by the Collection 6 VNP15A2H and MOD15A2H products . Differing assumptions of the products and in situ reference measurements which cause them to be sensitive to slightly different quantities are thought to explain apparent biases over sparse vegetation and forest environments . To ensure their continued utility future work should focus on updating the GBOV in situ reference measurements implementing additional corrections and improving their geographical representativeness .
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LAI FAPAR products evaluated over twenty NEON sites between 2015 and 2018. In situ measurements upscaled to moderate spatial resolution using Sentinel 2. CGLS 300 m V1 demonstrates best agreement RMSD 0. 57 for LAI and 0.08 for FAPAR. MOD15A2H VNP15A2H perform similarly RMSD 0.81 to 0.89 for LAI 0.12 for FAPAR
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S0034425720303060
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The NASA L Band Soil Moisture Active Passive satellite mission launched in 2015 has produced soil moisture and freeze thaw products at a global scale . While the use of L band passive microwave radiometry has proven useful in detecting changes in the surface FT state these classifications have not been comprehensively assessed against similar existing FT products such as the global FT record from the Special Sensor Microwave Imager as part of the FT Earth System Data Record . In order to fill in this gap this study investigates regions in which FT classifications diverge and identifies potential sources of classification variability . The SMAP and SSM I FT records are compared over an extended period covering multiple seasonal cycles from April 2015 through December 2017 . The spatially and temporally varying relationship between these products is examined in relation to climate MODIS land cover and topography . SMAP and SSM I FT product agreement proportion was corrected for seasonality and then separated by land cover classes and compared to the global Ap mean . The agreement between these products vary most notably during freeze and thaw onset and in areas near abundant surface water snow and ice and wetlands . Relative to other vegetation types reduced agreement between FT products is also observed over grasslands sparsely vegetated lands as well as mixed and evergreen forests . Distinct seasonal differences in FT classification agreement were also detected between products over cold arid regions and between continental and temperate classes . Similarly as topographic complexity increases a decreasing trend in agreement between L and Ka band FT products is observed . While reiterating challenges in FT classifications identified by prior studies this work also contributes new insights by providing detailed geospatial and seasonal analyses into the factors contributing to FT product divergence .
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We compare 3years of global microwave derived freeze thaw FT records. Variations in defined frozen extent are observed between L and Ka bands. FT records from FT ESDR and SMAP had classification agreement of 83.5 . Product agreement is substantially diminished in shoulder seasons. Distinct variability identified across climate topography and landcover.
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S0034425720303096
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Reliable information on salt marsh extent and condition is crucial to promote effective management strategies towards their maintenance and recovery . Most of previous studies on salt marsh extent assessment used Landsat derived Normalized Difference Vegetation Index being limited the current knowledge about the performance of other Vegetation Indices . Based on Landsat imagery this study proposes a new methodology to map salt marsh extent in estuarine systems by combining Normalized Difference Water Index and VI exploring their performance when using different VI . Moreover it aims to assess the extent and condition changes between 1984 and 2018 in two salt marshes located within Tagus Estuary . The VI best performing salt marsh extent was determined and the methodology applied to assess salt marsh extent changes . Condition change was investigated by statistically analyzing spatially averaged VI over salt marsh extent change regions . Results demonstrated that NDWI and VI combined can be used to efficiently map the marsh extent and NDVI was the VI with the best performance . Corroios revealed mostly stable without noticeable changes in its extent and condition . Oppositely Pancas registered a continuous seaward progression at a mean rate of 3ha year since 1984 while restricted upper regions dieback after 20042005 likely due to high soil salinity conditions . In general NDVI performs better salt marsh extent but others VI corrected to minimize soil effects perform better when tidal flats are entirely or almost entirely exposed opening perspectives to the development of new methods combining the use of different VI to optimize salt marsh extent detection .
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NDWI and VI combined can efficiently map salt marsh extent in estuaries. NDVI was found the VI best performing salt marsh extent. VI corrected to minimize soil effects perform better when tidal flats are exposed. Mature marsh revealed stable without marked changes in its extent and condition. Young marsh front can expand seaward while inner regions experience dieback.
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S0034425720303114
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The interferometric synthetic aperture radar small baseline subset technique can be applied to land with varying deformation magnitudes ranging from mm yr to tens of cm yr. SBAS defines a network of interferograms that is limited by temporal and spatial baseline thresholds that are often applied arbitrarily or in apparently subjective ways in the literature . We use simulated SAR data to assess the influence of residual noise and SBAS network configuration on InSAR derived deformation rates and how the number of interferograms and data gaps in the time series may further impact the estimated rates . This leads us to an approach for defining a SBAS network based on geodetic reliability theory represented by the redundancy number
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Small magnitude deformation with high noise can lead to contradictory SBAS trends. A Mean linear fit rate can be biased in the presence of annual periodic signals. Extended data gaps cause larger rate errors and time series RMSs than random gaps. number between 0.8 and 0.9 appears most suitable for SBAS network design. High. numbers are needed to resolve small magnitude trends in noisy SAR data.
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S0034425720303126
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Satellite observed night time light in urban areas has been widely used as an indicator for socioeconomic development and light pollution . Up to present the diurnal dynamics of city light during the night which are important to understand the nature of human activity and the underlying variables explaining night time brightness have hardly been investigated by remote sensing techniques due to limitation of the revisit time and spatial resolution of available satellites . In this study we employed a consumer grade unmanned aerial vehicle to monitor city light in a study area located in Wuhan City China from 8 08PM April 15 2019 to 5 08AM April 16 2019 with an hourly temporal resolution . By using three ground based Sky Quality Meters we found that the UAV recorded light brightness was consistent with the ground luminous intensity measured by the SQMs in both the spatial R
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A consumer grade UAV was used to investigate hourly dynamic of city light. The UAV recorded light is highly correlated to SQM measured luminous intensity. UAV analysis implies DMSP OLS and VIIRS images may be hard to inter calibrate. Considering urban function the city light shows highly dynamic in space and time. The city light colour shows heterogeneous in space and dynamic in time.
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S0034425720303138
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Winter cover crops such as barley rye and wheat help to improve soil structure by increasing porosity aggregate stability and organic matter while reducing the loss of agricultural nutrients and sediments into waterways . The environmental performance of cover crops is affected by choice of species planting date planting method nutrient inputs temperature and precipitation . The Maryland Department of Agriculture oversees an agricultural cost share program that provides farmers with funding to cover costs associated with planting winter cover crops and the U.S. Geological Survey and the U.S. Department of Agriculture Agricultural Research Service have partnered with the MDA to develop satellite remote sensing techniques for measuring cover crop performance . The MDA has developed the capacity to digitize field boundaries for all fields enrolled in their cover crop programs to support a remote sensing performance analysis at a statewide scal e and has requested assistance with the associated imagery processing from the National Aeronautics and Space Administration . Using the Google Earth Engine cloud computing platform scripts were developed to process Landsat 5 7 8 and Harmonized Sentinel 2 imagery to measure winter cover crop performance . We calibrated cover crop performance models using linear regression between satellite vegetation indices and USGS USDA ARS field sampling data collected on Maryland farms between 2006 and 2012 . Satellite derived Normalized Difference Vegetation Index values showed significant correlation with the natural logarithm of cover crop biomass the USDA Lower Chesapeake Bay Long Term Agricultural Research Project the Maryland Department of Agriculture and the NASA DEVELOP National Program .
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Google Earth Engine script facilitates winter cover crop remote sensing analysis. Cost share program data combined with harmonized Landsat and Sentinel imagery. Seasonal analysis of cover crop performance biomass vegetative cover . Results showed different performance for various agronomic management strategies. Informs adaptive management of cover crop cost share incentive programs.
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S0034425720303151
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Shadows are prevalent in urban environments introducing high uncertainties to fine scale urban land cover mapping . In this study we developed a Recurrent Shadow Attention Model capitalizing on state of the art deep learning architectures to retrieve fine scale land cover classes within cast and self shadows along the urban rural gradient . The RSAM differs from the other existing shadow removal models by progressively refining the shadow detection result with two attention based interacting modules Shadow Detection Module and Shadow Classification Module . To facilitate model training and validation we also created a Shadow Semantic Annotation Database using the 1m resolution NAIP aerial imagery . The SSAD comprises 103 image patches containing various types of shadows and six major land cover classes building tree grass shrub road water and farmland . Our results show an overall accuracy of 90.6 and Kappa of 0.82 for RSAM to extract the six land cover classes within shadows . The model performance was stable along the urban rural gradient although it was slightly better in rural areas than in urban centers or suburban neighborhoods . Findings suggest that RSAM is a robust solution to eliminate the effects in high resolution mapping both from cast and self shadows that have not received equal attention in previous studies .
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We propose a model to retrieve fine scale land cover classes within shadows. We developed a shadow semantic annotation database comprising 103 image patches. The model is a robust solution to eliminate the effects from cast and self shadows. The model performance is stable along the urban rural gradient.
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S0034425720303187
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A method to differentiate open water from sea ice using quad polarization C band synthetic aperture radar imagery is presented based on the roughness properties of the sea surface scattering facets and the polarization ratio between VV and HH polarization backscatter from the SAR images . In quad polarization SAR images the normalized radar cross section which is linked to sea surface roughness can be used to derive the orientation angle shift and its standard deviation as well as to compute the measured polarization ratio PR . An X Bragg backscatter model is explored to calculate a theoretical look up table for the polarization ratio as a function of incidence angle dielectric constant orientation angle shift and its standard deviation . Thus ocean surface backscatter can be identified as open water or sea ice by comparing the measured PR with a theoretical model for PR . The proposed method is validated by comparison to collocated optical video images and manual visual interpretations . Estimates of the size shape and locations of open water from our method are shown to be in good agreement with observed data . The overall accuracy of our algorithm is about 96 . The classification accuracy for sea ice and open water is about 96 and 94 respectively .
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An algorithm for identification of sea ice and open water is proposed. The SAR polarization ratio is affected by roughness of the sea surface. Changes in open water and sea ice areas are observed by SAR and helicopter video images.
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S0034425720303205
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The Sustainable Development Goal 6.3.2 of the United Nations focuses on ambient water quality while water clarity simplistically and visually reflect water quality and can potentially support SDG 6.3.2 reporting . In this study based on extensive field data and Sentinel 3 Ocean and Land Color Instrument imagery a random forest regression Secchi depth
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A random forest regression water clarity algorithm was developed for turbid lakes. Performances of six atmospheric correction processors were examined on OLCI data. OLCI showed most lakes were turbid with low. in Eastern China. A novel. based scheme was developed for evaluating SDG 6.3.2.
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S0034425720303217
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Sugarcane is a major crop for sugar and ethanol production and its area has increased substantially in tropical and subtropical regions in recent decades . Updated and accurate sugarcane maps are critical for monitoring sugarcane area and production and assessing its impacts on the society economy and the environment . To date no sugarcane mapping tools are available to generate annual maps of sugarcane at the field scale over large regions . In this study we developed a pixel and phenology based mapping tool to produce an annual map of sugarcane at 10 m spatial resolution by analyzing time series Landsat 7 8 Sentinel 2 and Sentinel 1 images during August 31 2017 July 1 2019 in Guangxi province China which accounts for 65 of sugarcane production of China . First we generated annual maps of croplands and other land cover types in 2018 . Second we delineated the cropping intensity for all cropland pixels in 2018 . Third we identified sugarcane fields in 2018 based on its phenological characteristics . The resultant 2018 sugarcane map has producer user and overall accuracies of 88 96 and 96 respectively . According to the annual sugarcane map in 2018 there was a total of 8940km
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A sugarcane mapping tool was developed using time series Landsat and Sentinel 1 2. Annual maps of sugarcane fields and phenology metrics at 10 m were developed. Green up dates of sugarcane fields were monitored in time for planting management.
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S0034425720303230
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The Greater Maasai Mara Ecosystem in Kenya is an iconic savanna ecosystem of high importance as natural and cultural heritage notably by including the largest remaining seasonal migration of African ungulates and the semi nomadic pastoralist Maasai culture . Comprehensive mapping of vegetation distribution and dynamics in GMME is important for understanding ecosystem changes across time and space since recent reports suggest dramatic declines in wildlife populations alongside troubling reports of grassland conversion to cropland and habitat fragmentation due to increasing small holder fencing . Here we present the first comprehensive vegetation map of GMME at high spatial resolution . The map consists of nine key vegetation cover types which were derived in a two step process integrating data from high resolution WorldView 3 images and Sentinel 2 images using a deep learning workflow . We evaluate the role of anthropogenic topographic and climatic factors in affecting the fractional cover of the identified VCTs in 2017 and their MODIS derived browning greening rates in the preceding 17years at 250 m resolution . Results show that most VCTs showed a preceding greening trend in the protected land . In contrast the semi and unprotected land showed a general preceding greening trend in the woody dominated cover types while they exhibited browning trends in grass dominated cover types . These results suggest that woody vegetation densification may be happening across much of the GMME alongside vegetation declines within the non woody covers in the semi and unprotected lands . Greening and potential woody densification in GMME is positively correlated with mean annual precipitation and negatively correlated with anthropogenic pressure . Increasing woody densification across the entire GMME in the future would replace high quality grass cover and pose a risk to the maintenance of the region s rich savanna megafauna thus pointing to a need for further investigation using alternative data sources . The increasing availability of high resolution remote sensing and efficient approaches for vegetation mapping will play a crucial role in monitoring conservation effectiveness as well as ecosystem dynamics due to pressures such as climate change .
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A deep learning based high resolution mapping workflow was proposed. Vegetation cover types were mapped in 10 m in Greater Maasai Mara Ecosystem GMME . Woody vegetation densification is happening across much of the GMME. A density decline in grass cover was observed in semi and unprotected lands in GMME. The woody vegetation densification is correlated with rainfall and human pressure.
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S0034425720303254
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Lichens dominate a significant part of the Earth s land surface and are valuable bioindicators of various environmental changes . In the northern hemisphere the largest lichen biomass is in the woodlands and heathlands of the boreal zone and in tundra . Despite the global coverage of lichens there has been only limited research on their spectral properties in the context of remote sensing of the environment . In this paper we report spectral properties of 12 common boreal lichen species . Measurements of reflectance spectra were made in laboratory conditions with a standard spectrometer and a novel mobile hyperspectral camera which was used in a multiangular setting . Our results show that interspecific differences in reflectance spectra were the most pronounced in the ultraviolet and visible spectral range and that dry samples always had higher reflectance than fresh samples in the shortwave infrared region . All study species had higher reflectance in the backward scattering direction compared to nadir or forward scattering directions . Our results also reveal for the first time that there is large intraspecific variation in reflectance of lichen species . This emphasizes the importance of measuring several replicates of each species when analyzing lichen spectra . In addition we used the data in a spectral clustering analysis to study the spectral similarity between samples and species and how these similarities could be linked to different physical traits or phylogenetic closeness of the species . Overall our results suggest that spectra of some lichen species with large ground coverage can be used for species identification from high spatial resolution remote sensing imagery . On the other hand for lichen species growing as small assemblages mobile hyperspectral cameras may offer a solution for in situ species identification . The spectral library collected in this study is available in the SPECCHIO Spectral Information System .
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Reflectance spectra of 12 boreal ground lichen species were measured. Intraspecific variation in reflectance was large. Interspecific differences in spectra were largest in ultraviolet and visible regions. All species had highest reflectance in backscattering direction.
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S0034425720303266
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The impacts of climate change and extreme weather events on the continuous phenological development over the entire seasonal cycle remained poorly understood . Previous studies mainly focused on modeling key phenological transition dates based on aggregated climate variables . We developed and evaluated a Bayesian Hierarchical Space Time model for Land Surface Phenology to synthesize remotely sensed vegetation greenness with climate covariates at a daily temporal scale from 1981 to 2014 across the entire conterminous United States . The BHST LSP model incorporated both temporal and spatial information and exhibited high predictive power in simulating daily phenological development with an overall out of sample R
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A Bayesian model was developed to simulate continuous phenological development. Rate of daily temperature changes impact speed of both spring and fall phenology. Increased precipitation benefit phenological development over an entire season. Frost events slow spring leaf expansion and accelerate fall leaf senescence. Cropland and evergreen forest show resistance to drought events.
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S0034425720303278
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Soil Moisture is a direct indicator of dryness of the land surface and the amount of precipitation vegetation status and Land Surface Temperature are directly related to SM thus these factors indirectly characterize the dryness of the land surface . However there are limitations and shortcomings of using a single factor to assess dryness because of the interactions among factors . A method that can combine the advantages of the three factors is needed to better monitor dryness . In this study a new Remote Sensing dryness index called the Temperature Vegetation Precipitation Dryness Index was defined and developed using the Euclidean distance method and three dimensional P Normalized Difference Vegetation Index LST.The reasonableness of this index was tested and verified using SM data three variables other recognized dryness indices crop yield per unit area and Net Primary Productivity . In addition the reliability of the TVPDI results was analyzed at different spatial scales and using different data sources . The results demonstrated that the TVPDI was highly correlated with SM
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Proposed a dryness index TVPDI according to 3D space and Euclidean Distance method. TVPDI is compared with four kinds of dryness index to test the applicability. TVPDI. is compared with TVPDI. in sample regions to test the stability. TVPDI is applied to analyze the spatial temporal pattern of dryness wetness status.
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S003442572030328X
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Surface soil moisture is a vital variable in the process of energy exchange between the land and atmosphere . Monitoring the surface soil moisture at the local and global scales has become feasible due to the development of microwave remote sensing . With the new development of potential satellite missions it is important to evaluate existing soil moisture retrieval algorithms which will greatly contribute to the improvement of current soil moisture products and the development of new methodologies . This paper compared the performance of four well known soil moisture retrieval algorithms with L band radiometry at fixed incidence angles including the single channel algorithm at horizontal polarization the single channel algorithm at vertical polarization the dual channel algorithm and the land parameter retrieval model . The experimental data used for evaluation was from the Soil Moisture Experiment in the Luan River which consists of variable angular ground based and airborne observations covering a wide range of incidence angles at L band . The microwave radiative transfer models are set to be consistent to guarantee that the four different algorithms are comparable . The results showed that the retrieval accuracy of the SCA H and SCA V is significantly affected by the input vegetation optical depth and the calibration of vegetation parameters should be considered in the implementation of the SCA H and SCA V. The DCA does not rely on the auxiliary vegetation information and can also achieve good performance for both the soil moisture and vegetation optical depth . However its retrieval requires a penalty on parameter constraints since the input brightness temperatures at horizontal and vertical polarization are correlated . The LPRM has a poor performance at incidence angles less than 30 as it analytically utilizes the polarization difference in the brightness temperature which is quite small at lower incidence angles . The accuracy of four soil moisture algorithms achieve their best performances at intermediate incidence angles of 40 to 45 and is slightly degraded when the incident angles increased to larger than 50 which is contributed to the increasing vegetation effects and depolarization that leads to an information loss . These findings provide quantitative evidence to help understand the differences in various current soil moisture algorithms and further promote the development of new methodologies for future soil moisture missions .
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Evaluation of soil moisture retrieval algorithms for various vegetation types. Vegetation parameters show angular dependence especially at vertical polarization. Intermediate angles from 40 to 45 degree are optimal for soil moisture retrieval.
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