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S0034425720303308
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The phenological dynamics of crops reflect the response and feedback of agricultural systems to climate and environmental constraints and have significant controls on carbon and nutrient cycling across the globe . Remote monitoring of crop phenological dynamics in a consistent and systematic manner is vitally crucial for optimizing the farm management activities and evaluating the agricultural resilience to extreme weather conditions and future climate change . Yet our ability to retrieve crop growing stages with satellite time series is limited . The remotely sensed phenological transition dates may not be characteristic of crop physiological growing stages . The objective of this study is to develop a remote sensing phenological monitoring framework that can reconcile satellite based phenological measures with ground based crop growing observations with corn and soybean in Illinois as a case study . The framework comprises three key components time series phenological pre processing time series phenological modeling and time series phenological characterization . As an exploratory prototype the framework retrieved a total of 56 phenological transition dates that were subsequently evaluated with the district level ground phenological observations . The results indicated that the devised framework can adequately retrieve a wide range of physiological growing stages for corn and soybean in Illinois with R square greater than 0.6 and RMSE less than 1 week for most stages . The devised framework largely extends the limited satellite phenological measures to a range of phenological transition dates that are characteristic of essential crop growing stages . It paves the way for formulating standard crop phenological monitoring protocols via remote sensing . The wealth of retrieved phenological characteristics open up unique opportunities to enhance our understanding of the complex mechanisms underlying the crop growth in response to varying environmental stresses and to make more adaptive farm management strategies towards sustained agricultural development .
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Prototype phenological monitoring framework to characterize crop growing stages. The framework retrieves a wide range of crop growing stages beyond current studies. The framework reconciles satellite retrieved with ground crop phenological measures. The framework helps make standard crop phenological monitoring protocols. The framework shows good performance with most stages of 1 week retrieval accuracy.
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S0034425720303345
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Monsoon rain and rivers bring large freshwater input to the Northern Bay of Bengal yielding low Sea Surface Salinity after the monsoon . The resulting sharp upper ocean salinity stratification is thought to influence tropical cyclones intensity and biological productivity by inhibiting vertical mixing . Despite recent progresses the density of
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The new debiased CATDS SMOS SSS product resolves major issues in the Bay of Bengal. New SMOS has a comparable quality with SMAP and Aquarius but over a full decade. Confirms the post monsoon southward transport of low saline water by the EICC. Confirms that this transport is interannually modulated by the Indian Ocean Dipole.
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S0034425720303357
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Differential synthetic aperture radar interferometry has been applied in permafrost environments to detect surface deformation caused by freeze thaw processes in the active layer and underlying permafrost . The effectiveness of Sentinel 1 InSAR in monitoring ground surface deformation over continuous permafrost terrain above the treeline has been proven . The heterogeneous landscape and developed vegetation cover increase the difficulty of applying D InSAR in sub Arctic discontinuous permafrost terrain . The potential of Sentinel 1 InSAR in such an environment has not been fully explored . In this study we explore the capabilities and limitations of applying Sentinel 1 time series data for monitoring surface deformation over discontinuous permafrost terrain . The interferometric coherence time series from September 2016 to April 2018 were analyzed over typical landscapes in discontinuous permafrost environments and their thaw subsidence curves are revealed by the small baseline subset InSAR technique . The seasonal thaw subsidence in the summer of 2017 was in the range of 1580mm in the study area . The land cover types with thaw subsidence magnitudes from low to high are exposed land peatland lichenlow shrub lichen dominated and wetland low vegetation . The difference in displacement pattern between lichen dominated and wetland low vegetation dominated permafrost terrains is especially clear at the end of the thawing stage in September and October . The differences in thaw subsidence magnitude and pattern reveal the influence of the soil water content in the active layer and permafrost properties on the thaw subsidence patterns . We also compared the Sentinel 1 retrieved cumulative displacement with the X band TerraSAR X and L band ALOS PALSAR results . The difference of retrieved deformation magnitude using the three sensors is amplified when shrubs are more developed . The findings indicate that Sentinel 1 time series with a 6 day or 12 day span work well over discontinuous permafrost terrain above the tree line during the thawed season but the results and accuracy are not promising over developed shrub tundra and especially forest tundra environments .
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Determination of the applicability of Sentinel 1 in monitoring surface deformation over discontinuous permafrost terrain. Description of C band coherence time series over various landscape types in discontinuous permafrost terrain. Retrieval of thaw subsidence curves over six landcover types in temporal detail. Comparison of displacements derived from C band Sentinel 1 data X band TerraSAR X data and L band ALOS PALSAR data.
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S0034425720303382
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The history of Earth observation from space is well reflected through the Landsat program . With data collection beginning with Landsat 1 in 1972 the program has evolved technical capabilities while maintaining continuity of land observations . In so doing Landsat has provided a critical reference for assessing long term changes to Earth s land environment due to both natural and human forcing . Poised for launch in mid 2021 the joint NASA USGS Landsat 9 mission will continue this important data record . In many respects Landsat 9 is a clone of Landsat 8 . The Operational Land Imager 2 is largely identical to Landsat 8 OLI providing calibrated imagery covering the solar reflected wavelengths . The Thermal Infrared Sensor 2 improves upon Landsat 8 TIRS addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror . In addition Landsat 9 adds redundancy to TIRS 2 thus upgrading the instrument to a 5 year design life commensurate with other elements of the mission . Initial performance testing of OLI 2 and TIRS 2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality . Landsat 9 will maintain the current data acquisition rate of up to 740 scenes per day with these scenes available from the Landsat archive at no cost to users . In this communication we provide background and rationale for the Landsat 9 mission describe the instrument payloads and ground system and discuss data products available from the Landsat 9 mission through USGS .
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The Landsat 9 mission will continue and improve on the capabilities of Landsat 8. Performance of OLI 2 and TIRS 2 instruments appears to be excellent. USGS will provide standard surface reflectance and temperature products. Surface temperature will initially be derived via single channel algorithm.
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S0034425720303412
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We provide major updates to the top down Fire Radiative Energy Emissions approach to biomass burning emissions calculations bypassing the estimation of fuel consumption that is a major source of uncertainty in widely used bottom up approaches . The FREM approach links satellite observations of fire radiative power to emission rates of total particulate matter via spatially varying smoke emissions coefficients g.MJ
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New top down smoke emission coefficients using high resolution MAIAC AOD. Improved biome stratification using CCI Landcover and 30m Landsat Tree Cover data. Presents a 6 year fire inventory of TPM CO. CO and CH. emissions for Africa. Highest spatio temporal resolution emissions estimates yet available over Africa. We find similar emissions to FEER but much higher emissions than GFED and GFAS.
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S0034425720303424
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The cloud base temperature is one of the parameters that dominates the cloudy sky surface downward longwave radiation . However CBT is rarely available at regional and global scales and its application in estimating cloud sky SDLR is limited . In this study a framework to globally estimate cloud sky SDLR during both daytime and nighttime is proposed . This framework is composed of three parts . First a global cloudy property database was constructed by combing the extracted cloud vertical structure parameters from the active CloudSat data and cloud properties from passive MODIS data . Second the empirical methods for estimating cloud thickness under ISCCP cloud classification system and MODIS cloud classification system were developed . Additionally the coefficients of CERES CT estimate models were refitted using the constructed cloud property database . With the estimated CT and reanalysis data calculating the CBT is straightforward . The accuracy of the estimated CT for ISCCP cloud type is compared with the existing studies that were conducted at local scales . Our CT estimate accuracy is comparable to that of the existing studies . According to the validation results at ARM NSA and SGP stations the CT estimated by the developed CT model for MODIS cloud type is better than that estimated by the original CERES CT model . Finally the cloudy sky SDLR values were derived by feeding the estimated CBT and other parameters to the single layer cloud model . When validated by the ground measured SDLR collected from the SURFRAD network the bias and RMSE are 5.42Wm
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We propose a framework to globally estimate cloud sky SDLR during both daytime and nighttime. The CT estimation models are developed for ISCCP cloud type and MODIS cloud type. The cloudy sky SDLR is retrieved by the SLCM using the CBT derived from CT. The bias and RMSE of the derived cloudy sky SDLR over SURFRAD are 5.42Wm. and 30.3Wm
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S0034425720303436
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Spatiotemporal fusion is a feasible solution to resolve the tradeoff between the temporal and spatial resolutions of remote sensing images . However the development of spatiotemporal fusion algorithms has not yet reached maturity and existing methods still face many challenges e.g . accurately retrieving land cover changes and improving the robustness of fusion algorithms . The Flexible Spatiotemporal DAta Fusion method proposed by Zhu et al . in 2016 solved the abovementioned problems to some extent . However FSDAF has two shortcomings that can be further improved FSDAF is prone to losing spatial details and predicting a blurrier image due to the input of coarse pixels containing type change information and a large amount of boundary information for unmixing calculation and FSDAF does not optimize the areas of land cover change . In this paper an improved FSDAF method incorporating change detection technology and an optimized model for changed type areas was proposed to improve the aforementioned problems . Based on the existing FSDAF algorithm FSDAF 2.0 excludes changed pixels and boundary pixels for unmixing calculation and establishes a model to optimize the changed pixels . Its performance was compared with that of the Spatial and Temporal Adaptive Reflectance Fusion Model the original FSDAF and the enhanced FSDAF that incorporates sub pixel class fraction change information . Two sites consisting of landscapes with heterogeneous and large scale abrupt land cover changes were employed for testing . The results of the experiments demonstrate that FSDAF 2.0 effectively improves the shortcomings of FSDAF blends synthetic fine resolution images with higher accuracy than that of the other three methods at two different sites and strengthens the robustness of the fusion algorithm . More importantly FSDAF 2.0 has a powerful ability to retrieve land cover changes and provides a feasible way to improve the performance of retrieving land cover changes . Consequently FSDAF 2.0 has great potential for monitoring complex dynamic changes in the Earth s surface .
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FSDAF 2.0 is developed to fuse coarse and fine spatial resolution images. FSDAF 2.0 integrates the change detection technology to find the areas of land cover change. FSDAF 2.0 optimizes the unmixing process to better preserve spatial details. FSDAF 2.0 establishes a model to optimize the values of changed type pixels. FSDAF 2.0 can better predict both phenological change and land cover change than original FSDAF.
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S003442572030345X
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Accurate and high resolution sea ice surface temperature data is of great importance for Arctic climate studies . However the validation of high resolution IST data using in situ measurements in polar sea ice regions is lacking . This study assesses the accuracy of three split window and two single channel methods based on Landsat 8 thermal infrared imagery at 100m resolution over Arctic sea ice regions . The SW methods are proposed by Jin et al . Jimnez Muoz et al . and Du et al . . The SC methods are proposed by Jimnez Muoz et al . and Barsi et al . . IST data derived from 58 scenes of the Landsat 8 images were compared with coincident in situ ice skin temperatures and near surface air temperatures as measured by a combination of Ice Mass Balance buoys Snow and Ice Mass Balance Array buoys and automatic weather stations . SW Du offers the best accuracy when compared with the skin temperature 2.08K and near surface air temperature . SC Barsi ranks second with a bias of 1.55K and RMSE of 2.40K for the skin temperature . As for precision IST from the Moderate Resolution Imaging Spectrometer has best performance 1.69K followed by SW Du SW JM and SC Barsi . The Landsat IST outperforms the MODIS IST in narrow lead areas owing to its better spatial resolution and SW JM and SC Barsi methods agree best with the MODIS IST in leads and marginal ice zone scenes respectively . As all three SW methods are constrained by banding effects with different degrees in a lead scene they are not recommended to be applied on an image scene with severe banding artifacts . The small bias and high correlation between skin temperature and near surface air temperature prove the capability of using near surface air temperature as a substitute for validating a satellite IST data if skin temperature data are not available .
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IST from Landsat 8 TIRS images is accurate enough for sea ice related use. Split window algorithm has better accuracy than single channel method. Fine resolution IST images can capture small details on sea ice regions. Banding artifacts in TIRS images still contaminate IST maps.
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S0034425720303461
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The Hong Kong International Airport was constructed on a platform mainly reclaimed from the sea . The platform has experienced some significant settlement since its operation in 1998 due mainly to consolidation of the materials used in the land reclamation . Although some studies have been carried out to measure the settlement with techniques such as Interferometric Synthetic Aperture Radar the past studies have each only covered some limited time periods . Therefore a complete history of settlement since the operation of the Airport has never been available to aid the understanding of the spatiotemporal behavior of the land settlement . We attempt for the first time to make full use of most of the available SAR data from multiple SAR sensors and Sentinel 1A to generate a settlement time series of the HKIA over 19982018 with an improved multi temporal InSAR technique . In order to fill the time gaps between the different SAR datasets a settlement model is developed based on the InSAR measurements . The results reveal both the spatial and temporal variations of the land settlement . They also show for the first time that the accumulative settlement reaches up to 40cm over the past two decades . The settlement is largely associated with the material types used in the landfill works and the underlying alluvial sediments as some earlier research has indicated and the stages of the reclamation works . The results are validated through cross validation between the datasets and with leveling and GPS measurements on the Airport platform .
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Two decades of land settlement history of HKIA is retrieved for the first time. Land settlement correlates strongly to landfill materials and reclamation methods. Research results inform future land reclamation practice.
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S0034425720303485
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The interest in tree phenology monitoring is increasing because this trait is a robust indicator of the impacts of climate change on natural and managed ecosystems . Different approaches to monitor phenology at different spatial scales from
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Sentinel 1 proved to be an operative monitoring tool for beech leaf phenology. Beech leaf emerging and unfolding were the best detected phenophases. Phenophases detecting errors ranged between 3 and 5days. A human assisted was the best performing approach. Lower altitudinal belts were synchronised due to similar ecological condition.
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S0034425720303497
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This paper proposed an improved method for separating soil and vegetation component temperatures from one pixel land surface temperature using multi pixel and multi temporal data . The two main features of the method are the use of a diurnal temperature cycle model to describe component temperatures and the application of a spatial weighting matrix to consider the spatial correlation of component temperatures . The proposed method was evaluated using an extensive simulated dataset with five component temperature types three LST errors and 69 fractional vegetation cover types and field measurements with a high temporal frequency . Due to the time extendibility of DTC model the possibility for retrieving component temperatures at any time was analyzed . Correspondingly the schemes for selecting the best observations for four representative periods i.e . 10 0012 00 09 0018 00 18 0003 00 and 09 0003 00 were determined . The validations showed satisfactory accuracies and it was found that the errors were significantly influenced by the original LST retrieval error . In addition the difference between the ideal temperature pattern from the DTC model and the actual temperature variation also affected the accuracy of the temporally extended component temperatures . Furthermore sensitivity analyses indicated that the separation accuracy was independent of the uncertainty of the component emissivity but was influenced by FVC . Specifically the retrieval accuracy was sensitive to the size and variation of FVC and the latter had a more significant influence but the result was less sensitive to the retrieval error and angular effect of FVC . Considering its accuracy operability and robustness the proposed method is effective for separating soil and vegetation component temperatures .
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An improved method for separating soil and vegetation component temperatures was proposed. A diurnal temperature cycle model was used to describe component temperatures. The best schemes to estimate component temperatures at any time were determined. The influences of LST retrieval error emissivity uncertainty and FVC were analyzed in detail.
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S0034425720303503
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The long term urban dynamics at regional and global scales is essential to understanding the urbanization processes and environmental consequences for providing better scientific insights and effective decision making . The time series of consistent nighttime light data generated by integrating the Defense Meteorological Satellite Program Operational Linescane System and the Suomi National Polar Orbiting Partnership Visible Infrared Imaging Radiometer Suite provide a longer consistent record of the nightscape beyond a single dataset for monitoring urban dynamics . In this study we developed a new framework based on the spatial variation of NTL gradient to map urban dynamics in Southeast Asia using the consistent NTL data . First we identified the potential urban clusters in the region using the cluster based segmentation approach in 2018 . Second we applied the SVNG framework in each potential urban cluster to extract the initial annual urban extent from corresponding time series NTL images . Finally we performed a temporal consistency check on the initial urban extent to obtain the final urban sequence in Southeast Asia . The evaluation on the spatiotemporal patterns and consistency of urban dynamics using other urban products indicates that the SVNG framework can effectively capture the urban dynamics in areas with different development levels and patterns . Moreover we investigated urban dynamics in Southeast Asia at the local national and regional scales . This study opens new research avenues for monitoring and understanding the long term urban dynamics and the pathways of urban growth from local to global scales .
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A new mapping framework is proposed to monitor annual urban dynamics. This is the first work for mapping urban dynamics using fused nighttime lights. A view of annual urban dynamics in Southeast Asia 19922018 is presented. Our framework is transferable for monitoring urban dynamics over large areas.
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S0034425720303527
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Sentinel 1 is a synthetic aperture radar platform with an operational mode called extra wide that offers large regions of ocean areas to be observed . A major issue with EW images is that the cross polarized HV and VH channels have prominent additive noise patterns relative to low backscatter intensity which disrupts tasks that require manual or automated interpretation . The European Space Agency provides a method for removing the additive noise pattern by means of lookup tables but applying them directly produces unsatisfactory results because characteristics of the noise still remain . Furthermore evidence suggests that the magnitude of the additive noise dynamically depends on factors that are not considered by the ESA estimated noise field .
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New objective function measures level of additive noise in Sentinel 1 image. Model implemented linearly rescales estimated noise in subswaths. Evaluation on five oceans shows significant denoising ability
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S0034425720303539
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Mapping and monitoring landslides in remote areas with steep and mountainous terrain is logistically challenging expensive and time consuming . Yet in order to mitigate hazards and prevent loss of life in these areas and to better understand landslide processes high resolution measurements of landslide activity are necessary . Satellite based synthetic aperture radar interferometry provides millimeter scale measurements of ground surface deformation that can be used to identify and monitor landslides in remote areas where ground based monitoring techniques are not feasible . Here we present a novel InSAR deformation detection approach which uses double difference time series with local and regional spatial filters and pixel clustering methods to identify and monitor slow moving landslides without making a priori assumptions of the location of landslides . We apply our analysis to freely available Copernicus Sentinel 1 satellite data acquired between 2014 and 2017 centered on the Trishuli River drainage basin in Nepal . We found a minimum of 6 slow moving landslides that all occur within the Ranimatta lithologic formation . These landslides have areas ranging from 0.39 to 1.66km
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A novel method is developed to detect landslides in mountainous terrain. InSAR time series is used to identify and monitor slow moving landslides. 6 slow moving landslides in Trishuli Nepal unaffected by the Gorkha earthquake. Landslides have rates between 2 and 9cm yr and likely driven by monsoonal rainfall.
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S0034425720303552
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Leaf chlorophyll content as an important indicator of photosynthetic capacity and nitrogen status has been non destructively estimated from canopy reflectance spectra in recent studies . They have also found significant canopy structure and solar angle effects on canopy reflectance spectra especially for row structured open crop canopies with varying sunlit and shaded soil backgrounds over seasonal and diurnal cycles . Since the canopy reflectance signature of crops is negligibly contaminated by shaded soil it can be inferred that LCC should be better estimated when the soil background is under shaded conditions than under sunlit conditions . However the effects of solar angle or spectral observation time and canopy structure on LCC estimation for row crops have been poorly understood . This study aimed to determine the optimal observation time and to reduce the canopy structural effect for LCC estimation over row structured crops . To achieve these goals we simulated canopy reflectance spectra covering four typical crop orientations over seasonal and diurnal cycles based on the leaf optical models coupled with canopy radiative transfer models . Moreover this study proposed the leaf area index insensitive chlorophyll index to mitigate the canopy structural effect on the LCC LICI relationship . LICI and 11 traditional vegetation indices were investigated to calibrate semi empirical LCC VI models with canopy spectral simulations . The performance of those models in LCC estimation was evaluated with experimental datasets measured from both ground and unmanned aerial vehicle platforms .
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We model diurnal and seasonal effects of solar angle on crop canopy reflectance. We develop a LAI insensitive chlorophyll index LICI for estimating canopy LCC. A semi empirical and linear LCC LICI model is calibrated with synthetic data. LCC is best estimated from the 15 00h spectra for north south row orientation.
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S0034425720303734
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The prediction of carbon uptake by forests across fertility gradients requires accurate characterisation of how biochemical limitations to photosynthesis respond to variation in key elements such as nitrogen and phosphorus . Over the last decade proxies for chlorophyll and photosynthetic activity have been extracted from hyperspectral imagery and used to predict important photosynthetic variables such as the maximal rate of carboxylation
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Limitations to photosynthesis. were predicted in one year radiata pine. Significant relationships were found between N Chlorophyll and both Vcmax Jmax. However these relationships did not hold over both the N and P limiting ranges. Relationships between SIF PRI and both Vcmax Jmax were generalisable for all data. SIF and PRI may have greater generality than proxies for Chlorophyll and N.
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S0034425720303758
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A primary challenge in cloud detection is associated with highly mixed scenes that are filled with broken and thin clouds over inhomogeneous land . To tackle this challenge we developed a new algorithm called the Random Forest based cloud mask which can improve the accuracy of cloud identification from Landsat Thematic Mapper Enhanced Thematic Mapper Plus and Operational Land Imager and Thermal Infrared Sensor images . For the development and validation of the algorithm we first chose the stratified sampling method to pre select cloudy and clear sky pixels to form a prior pixel database according to the land use cover around the world . Next we select typical spectral channels and calculate spectral indices based on the spectral reflection characteristics of different land cover types using the top of atmosphere reflectance and brightness temperature . These are then used as inputs to the RF model for training and establishing a preliminary cloud detection model . Finally the Super pixels Extracted via Energy Driven Sampling segmentation approach is applied to re process the preliminary classification results in order to obtain the final cloud detection results . The RFmask detection results are evaluated against the globally distributed United States Geological Survey cloud cover assessment validation products . The average overall accuracy for RFmask cloud detection reaches 93.8
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A cloud detection algorithm combining the Random Forest and SEEDS segmentation is proposed for Landsat imagery. The overall accuracy of the RFmask algorithm reaches 93.8 Kappa coefficient 0.77 . The RFmask algorithm works well in detecting broken and thin clouds over both dark and bright surfaces. The RFmask algorithm is accurate computationally efficient and useful for various remote sensing applications.
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S003442572030376X
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The Fine Mode Fraction of atmospheric aerosol is very important for environment and climate studies . Attempts have been made to retrieve the FMF from satellite data with varying success . In this work the development of an artificial Neural Network for AEROsol retrieval is presented . NNAero uses data from the NASA MODerate resolution Imaging Spectroradiometer flying on the NASA Terra and Aqua satellites . The MODIS derived spectral reflectances of solar radiation at the top of the atmosphere and at the surface were used together with ground based Aerosol Robotic Network measurements of Aerosol Optical Depth and FMF to train a Convolutional Neural Network for the joint retrieval of FMF and AOD . The NNAero results over northern and eastern China were validated against an independent reference AERONET dataset . The results show that 68 of the NNAero AOD values are within the MODIS expected error envelope over land of which is similar to the results from the MODIS Deep Blue algorithm and both are better than the Dark Target algorithm . The validation of the NNAero FMF vs AERONET data shows a significant improvement with respect to the DT FMF with Root Mean Squared Prediction Errors of 0.1567 and 0.34 . The NNAero method shows the potential of improved retrieval of the FMF .
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Development of an improved FMF and AOD retrieval method with high accuracy. Terra and Aqua MODIS data and ground based AERONET data are jointly used. Highest spatial resolution of neural network retrieved FMF AOD could be 0.5km. The MODIS FMF and AOD retrieved with no restriction to dark targets. Impact of aerosol absorption in the FMF bias was discussed
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S0034425720303771
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In cold regions where soils freeze and thaw annually the ground surface deforms due to the density difference between groundwater and ground ice . Here we mapped thaw subsidence and frost heave signals over the Toolik Lake area on the North Slope of Alaska using 12 ALOS PALSAR Interferometric Synthetic Aperture Radar scenes . For the first time we jointly analyzed InSAR observations with a large number of soil measurements collected within 100km of the Toolik Field Station . We found that the InSAR observed deformation patterns are mainly related to soil water content and the seasonal active layer freeze thaw cycle . We did not observe any substantial long term subsidence trend outside the 2007 Anaktuvuk River Fire scar . This suggests that the magnitude of the maximum annual thaw subsidence did not change much outside the fire zone during the study period . The joint analysis of InSAR and field observations allows us to show that the amplitude of the seasonal thaw subsidence is proportional to the total amount of ice that has melted into liquid water at any given time . We note that topography influences the spatial distribution of soil water content and the availability of soil water influences the type of vegetation that can grow . As a result we found that the average seasonal thaw subsidence increases along a geomorphic ecohydrologic transect with heath vegetation on the drier ridge tops tussock tundra on hillslopes and sedge tundra at the wet lowland riparian zones . In addition we detected a net uplift between late July and early September mostly in the wetter riparian zone that experienced a larger seasonal thaw subsidence . Toolik Field Station in situ records suggest that the air temperature fluctuated around or below freezing in early September during the ALOS PALSAR data acquisition times . In this scenario ice can be formed at the top of the soil which leads to frost heave in saturated soils . Our results highlight how InSAR can improve our understanding of active layer freeze thaw and water storage dynamics in permafrost environments .
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InSAR data were analyzed with 220 in situ Arctic Foothills soil samples. Maximum seasonal thaw subsidence is proportional to the active layer soil water content. Topography and vegetation covers influence the magnitude of thaw subsidence. No long term subsidence was detected outside the 2007 Anaktuvuk River fire zone. InSAR and temperature data suggest that frost heave occurred in early September.
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S0034425720303783
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The ability to accurately detect and quantify the presence of invasive plants is integral in their management treatment and removal . Remotely piloted aircraft systems are becoming an important remote sensing tool for mapping invasive plants . Spotted knapweed
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Metapixel based method identified. in multispectral imagery. Suitable for relative abundance estimates in a diverse grassland ecosystem. GLCM based metrics improved overall performance. Feature optimization of critical importance when training a classifier. Rotation and grey tone transform invariant GLCM based metrics recommended.
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S0034425720303795
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Spatio temporal fusion is a technique used to produce images with both fine spatial and temporal resolution . Generally the principle of existing spatio temporal fusion methods can be characterized by a unified framework of prediction based on two parts the known fine spatial resolution images and the fine spatial resolution increment predicted from the available coarse spatial resolution increment that is the difference between the coarse spatial resolution images acquired at the known and prediction times . Owing to seasonal changes and land cover changes there always exist large differences between images acquired at different times resulting in a large increment and further great uncertainty in downscaling . In this paper a virtual image pair based spatio temporal fusion approach was proposed to deal with this problem . VIPSTF is based on the concept of a virtual image pair which is produced based on the available known MODIS Landsat image pairs . We demonstrate theoretically that compared to the known image pairs the VIP is closer to the data at the prediction time . The VIP can capture more fine spatial resolution information directly from known images and reduce the challenge in downscaling . VIPSTF is a flexible framework suitable for existing spatial weighting and spatial unmixing based methods and two versions VIPSTF SW and VIPSTF SU are thus developed . Experimental results on a heterogeneous site and a site experiencing land cover type changes show that both spatial weighting and spatial unmixing based methods can be enhanced by VIPSTF and the advantage is particularly noticeable when the observed image pairs are temporally far from the prediction time . Moreover VIPSTF is free of the need for image pair selection and robust to the use of multiple image pairs . VIPSTF is also computationally faster than the original methods when using multiple image pairs . The concept of VIP provides a new insight to enhance spatio temporal fusion by making fuller use of the observed image pairs and reducing the uncertainty of estimating the fine spatial resolution increment .
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VIPSTF is proposed for spatio temporal fusion based on the new concept of VIP. The VIP is closer to the data at the prediction time in the feature space. VIPSTF is suitable for spatial weighting and spatial unmixing based methods. VIPSTF is especially advantageous when the observed image pairs are temporally far. VIPSTF is robust to the use of multiple image pairs and computationally faster.
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S0034425720303801
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After 1991 major events such as the collapse of socialism and the transition to market economies caused land use change across the former USSR and affected forests in particular . However major land use changes may have occurred already during Soviet rule but those are largely unknown and difficult to map for large areas because 30 m Landsat data is not available prior to the 1980s . Our goal was to analyze the rates and determinants of forest cover change from 1967 to 2015 along the Latvian Russian border and to develop an object based image analysis approach to compare forest cover based on declassified Corona spy satellite images from 1967 with that derived from Landsat 5 TM and Landsat 8 OLI images from 1989 1990 and 2014 2015 . We applied Structure from Motion photogrammetry to orthorectify and mosaic the scanned Corona images and extracted forest cover from Corona and Landsat mosaics using object based image analysis in eCognition and expert classification . In a sensitivity analysis we tested how the scale parameters for the segmentation affected the accuracy of the change maps . We analyzed forest cover and forest patterns for our full study area of 22 209km
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Corona spy satellite plus Landsat data captured half a century of forest change. Object based image analysis was robust and mapped forest change well. Widespread agricultural abandonment and forest regrowth along the Latvia Russia border. Abandonment rates higher during Soviet rule than after the collapse of the USSR. Forest became substantially less fragmented over time.
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S0034425720303825
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Unmanned aerial vehicle borne hyperspectral systems can acquire hyperspectral imagery with a high spatial resolution which we refer to here as H
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AV borne datasets with high spectral and spatial resolution H. is acquired. A deep convolutional neural network with CRF classifier CNNCRF is proposed. UAV H. datasets is classified by CNNCRF for precise crop identification. Wuhan UAV borne hyperspectral image WHU Hi has been built as the benchmark. Effective results were achieved from the accuracy and visualization performance.
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S0034425720303837
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Satellite derived phenology metrics are valuable tools for understanding broad scale patterns and changes in vegetated landscapes over time . However the extraction and interpretation of phenology in ecosystems with subtle growth dynamics can be challenging . US National Park Service monitoring of evergreen pinyon juniper ecosystems in the western US revealed an unexpected winter peaking phenological pattern in normalized difference vegetation index time series derived from Moderate Resolution Imaging Spectroradiometer imagery . In this paper we assess the validity of the winter peaks through ground based observation of phenology and examination of solar and satellite geometry effects . To test the premise of a true vegetation response we analyzed NDVI values extracted from a time series of ground based digital camera images collected September 2017 to December 2018 in a pinyon juniper woodland in Arizona US . Results show pinyon and juniper growth peaked in the warm season as did the other species in the phenocam field of view . NDVI time series from four other sensors confirmed that winter peaks in this ecosystem are not limited to MODIS products . Examination of NDVI time series derived from daily 250 m MODIS data in the broader pinyon juniper ecosystem revealed that solar to sensor angle sensor zenith angle and forward back scatter reflectance explained 80 of intra annual variability . Solar to sensor angle exerted the greatest control and the direction of its correlation was the opposite of that which would be expected if it were driven by vegetation greenness . Solar to sensor angle is controlled seasonally by solar zenith angle and daily by variations in satellite overpass geometry . We mapped winter peaks across the western US in Google Earth Engine using 500 m MODIS MCD43A4 data which correct for reflectance differences caused by view angle . In areas where winter vegetation peaks are ecologically improbable consistent winter peaks are widespread in both pinyon juniper and non pinyon juniper conifer ecosystems winter peaks are common across areas of shrubland . We attribute winter peaks to the positive correlation of NDVI with solar to sensor angle and solar zenith angle in combination with sparse vertically oriented evergreen vegetation canopies . Increasing shadow visibility has been shown to increase overall NDVI and the prevalence of the winter peaking in evergreen western sparse canopy ecosystems is consistent with this hypothesis . The extent of winter peaking patterns may have been previously overlooked due to temporal compositing curve fitting and incomplete snow screening .
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Satellite data time series show winter peaking NDVI in pinyon juniper ecosystems. Phenocam data confirm warm season peaks in a pinyon juniper system in Arizona USA. Solar sensor geometry explains 80 of variability in pinyon juniper satellite NDVI. Shadowing is the likely cause of false winter increases in NDVI. NDVI is an inappropriate phenological tool across widespread western ecosystems.
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S0034425720303849
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Imbalance of residential environments in cities causes serious social inequality negatively impacting livable city development and attracting worldwide attentions . Previous studies on residential environmental quality mainly focused on general evaluations at city level while ignored the spatial heterogeneity of REQ inside cities and failed to survey REQ at local scales . This study recognizes the heterogeneity of REQ strongly related to land use patterns and aims to explore how local land use patterns influence REQ . Firstly a multimodal semantic segmentation method is presented to classify land uses by using satellite images building data and points of interests . Secondly a feature system is defined to characterize land use patterns which can be extracted at multiple scales based on land use classification results . Thirdly these features are fitted with REQ survey data by random forest regressions which can predict REQ scores across Beijing and give deep insights into how land use patterns influence REQ . Experimental results indicate that 1 REQ of Beijing is strongly heterogeneous and our method can generate a REQ map revealing REQ s imbalance across the city 2 land use patterns within 700m have significant impacts on the local REQ 3 spatial allocations of land uses are more important than proportions for influencing REQ and 4 our method visualizes the rules that land use patterns influence REQ thus can assist urban land use planning to balance and improve REQ .
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The work presents a multimodal semantic segmentation to map urban land uses. It generates a residential environment quality REQ map of Beijing city. It finds land use patterns within 700. that have significant impacts on REQ. It concludes spatial allocations of land uses measure REQ proportions refine it. It visualizes how land use patterns influence REQ assisting land use planning.
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S0034425720303850
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Deriving inherent optical properties from multispectral imagery of shallow water environments using physics based inversion models require prior knowledge of the spectral reflectance of the bottom substrate . The use of an incorrect bottom reflectance adversely affects the IOPs and in part the depth derived from inversion models . To date an operational approach that determines the bottom reflectance from multispectral imagery is lacking development in this area is especially paramount for locations that exhibit temporal variability in the spatial distributions of submerged aquatic vegetation and benthic microalgae . In this work we develop a multispectral implementation of the HOPE LUT algorithm and apply the approach to MERIS imagery of the Great Bahama Bank . Overall benthic classification accuracy of this approach was 80.0 revealing the areal coverage of benthic flora can range from 1052.3km
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Deriving shallow water IOPs using MERIS over a temporally variable bottom substrate. MERIS derived benthic habitat maps highlight temporal variability in benthic flora. Benthic flora was shown to range from 1052.3 to 6169.3 km. in the Exumas. GBB. Development of a portable and automated benthic classification approach for MERIS. Performance of common atmospheric corrections on depth and benthic classification.
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S0034425720303874
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Forest canopies act as intermediaries in radiation energy exchange between the atmosphere and the snow surface . The size location and distribution of forest discontinuities are important controls on forest shortwave radiation transmission and subsequent snow surface shading and radiation energy exchange between the atmosphere and the canopy but challenges arise when accounting for these vegetation characteristics at large spatial scales . Airborne LiDAR datasets contain detailed information about canopy structure across large spatial scales which can be exploited within 2D transmission models . However airborne LiDAR data typically does not resolve lower canopy elements leading to unrealistic depictions of individual trees . We present a methodology to enhance airborne LiDAR data by calculating additional trunk and branch points based on segmentation of a canopy height model allowing more accurate estimates of canopy shortwave transmissivity . To demonstrate this we deployed a computationally efficient 2D radiation transfer modelling framework that calculates direct and diffuse radiation from a set of distributed synthetic hemispherical images . The model can predict incoming direct and diffuse solar radiation at the snow surface at high spatial and temporal resolutions . Comparison between synthetic and real hemispherical photographs showed that synthetic images if based on enhanced LiDAR data featured canopy and individual tree crowns that were much denser than the original LiDAR portrays improving the representation of vegetation structure especially within dense environments and along canopy edges . Corresponding modelled total shortwave radiation matched well with spatially gridded measurements from a moving pyranometer at two sites where model RMSE was reduced to 59 and 29Wm
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LiDAR enhanced with additional trunk and branch points using CHM segmentation. Synthetic hemispheric images from enhanced LiDAR resolve lower canopy structures. Time varying canopy transmissivity and sky view fraction calculated from images. Simulations of sub canopy shortwave radiation accurately reproduce measured patterns. Analysis over 0.3km. at meter and minute scale show realistic shadows and sunflecks.
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S0034425720303886
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In open forest canopies such as in boreal forests forest floor can contribute significantly to the observed top of canopy reflectance . In order to retrieve the biophysical properties of the tree layer correcting for forest floor is essential . Traditionally the algorithms for retrieval of forest floor reflectance depend on tree layer information such as leaf area index canopy cover and site fertility . To overcome these circular dependencies we propose an algorithm that can be applied only using airborne remote sensing data . We acquired airborne hyperspectral imagery over the Hyytil forest research station in central Finland on July 3rd in 2015 using a hyperspectral pushbroom line scanner . The image data had a spectral resolution of 4.6nm and the spatial resolution was 0.6m . We developed a linear spectral unmixing algorithm which is based on the definition of the reflectance factor taking into account the variation of incident irradiance inside the canopy . The weights of the mixture can be computed from tree canopy gap fractions a tree species insensitive leaf albedo and average tree stand reflectance . Canopy gap fractions were retrieved with empirical methods available in scientific literature . The forest floor reflectance in the near infrared increased with site fertility in agreement with the forest floor field measurements . Moreover we found that in near infrared the reflectance of moderately rich and moist upland forests was significantly different from all other fertility classes . Finally we tested the reflectance decomposition on the photochemical reflectance index known to be heavily affected by understory reflectance and canopy structure and the forest PRI to be decoupled from the PRI of the over and understory .
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Airborne hyperspectral imagery used to estimate boreal forest floor reflectance. Separation of forest floor and overstory reflectance without field stand variables. Boreal forest floor reflectance in near infrared increases with site fertility. Calculated forest floor reflectance matches moist upland spectra. Forest floor reflectance in NIR significantly different among fertility classes.
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S0034425720303898
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Monitoring of surface soil moisture through microwave radiometry typically relies on the inversion of a radiative transfer model . Conventional inversion algorithms require proper calibration of surface roughness and radiometric parameters of the overlying canopy including the scattering albedo and optical depth . However uncertainty in global characterization of these parameters is one of the main sources of error in satellite SM retrievals . To cope with this uncertainty this paper presents a new algorithm called temporal polarization ratio algorithm that enables retrieval of SM independent of surface roughness and vegetation parameters . This approach uses the temporal differences of polarized emissivity observations assuming that the surface roughness and vegetation parameters are invariant over a window of time . Unlike the classical dual channel algorithms TPRA is not only free of surface calibration but also robust to systematical errors arising from surface soil temperature errors . One caveat is that the algorithm is unable to retrieve SM when surface emissivity does not change appreciably in time . The performance of TPRA is evaluated through several controlled numerical experiments and validated using the Soil Moisture Active Passive satellite retrievals as well as
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A new algorithm is designed for the retrieval of soil moisture at L band. Soil moisture retrievals are independent of surface roughness. Soil moisture retrievals are independent of vegetation parameters. Soil moisture retrievals are robust to systematical errors in soil temperature.
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S0034425720303904
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Accurate habitat mapping methods are urgently required for the monitoring conservation and management of blue carbon ecosystems and their associated services . This study focuses on exposed intertidal seagrass meadows which play a major role in the functioning of nearshore ecosystems . Using Sentinel 2 data we demonstrate that satellite remote sensing can be used to map seagrass percent cover and leaf biomass and to characterize its seasonal dynamics .
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A method is proposed to map seagrass cover in exposed intertidal meadows. The method can be applied to seagrass meadows along the northwest Atlantic coast. Sentinel 2 derived seagrass percent cover was mapped with an uncertainty of 14 . Due to Sentinel 2 revisit time it was possible to describe the seasonal cycle.
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S0034425720303916
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Advances in differential interferometric synthetic aperture radar processing algorithms such as the Intermittent Small Baseline Subset and increased data availability from SAR systems such as Sentinel 1 provide the opportunity to increase the spatial and temporal density of ground deformation measurements . Such measurements when combined with modelling have the potential to make a significant cost effective contribution to the progressive abandonment strategy of recently closed coalfields . Applications of DInSAR over coalfields have observed heave in coal measures rocks and temporal correlations between the rise of mine water and deformation time series . The cessation of systematic dewatering can have a variety of detrimental impacts and knowledge of the time scales and structure of the mine system are crucial to the remediation strategy . Although mine plans and borehole measurements provide vital information in this regard mine plans are often incomplete or inaccurate whereas monitoring boreholes are spatially sparse . Consequently groundwater can flow in unanticipated directions via goaf mine shafts and roadways making it difficult to determine where the impacts of rebound are likely to occur . In this study ground deformation data obtained using ISBAS DInSAR on ENVISAT and Sentinel 1 data are used to develop a simple method to model groundwater rebound in abandoned coalfields . A forward analytical model based upon the principle of effective stress and mine water ponds is first implemented to estimate surface heave in response to changes in groundwater levels measured in monitoring boreholes . The forward model is then calibrated and validated using the ground deformation data . The DInSAR data were subsequently inverted to map the change in groundwater levels in greater detail across the coalfield and forecast surface discharges in order to support mitigation strategies .
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Groundwater rebound in the Nottinghamshire coalfield UK is investigated. A forward analytical model relates changes in groundwater to surface deformation. The model is calibrated and validated using ISBAS DInSAR on ENVISAT and Sentinel 1 data. The DInSAR data is inverted to map changes in groundwater levels in greater detail. The inverse map is used to forecast surface discharges to aid management strategies.
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S003442572030393X
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We analysed 7188 Sentinel 1A Synthetic Aperture Radar images acquired in a 3 year period between October 2014 and September 2017 in the Western Mediterranean Sea and found imprints of about 14 300 oceanic eddies with diameters ranging from 0.4km to 160.1km . Those eddies manifest on the SAR imagery either through the accumulation of surfactants or through wave current interaction and considering the favourable wind speed ranges for both mechanisms we performed statistical analyses to gain insight into the eddies spatio temporal distribution their typical sizes and shapes and into the predominant mechanisms that are responsible for their formation . The vast majority of the detected eddies is elliptical with a tendency for only large mesoscale eddies towards a circular shape . We found no general influence of the wind on the eddies orientation although mesoscale eddies tend to orient into the main current direction in each subregion . Submesoscale eddies do not show such tendency . Submesoscale eddies in the Western Mediterranean Sea are predominantly cyclonic and with increasing eddy diameter there is a transition from cyclonic to anticyclonic sense of rotation . In general we found highest densities of submesoscale eddies in autumn when the biological activity is still high and when mean regional winds are already strong but variable . In addition instabilities of thermal fronts during summer and autumn and instabilities of the main and secondary currents influence the formation of submesoscale eddies . Some of our results confirm those previously obtained for other regions .
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Imprints of 14 272 oceanic eddies found on more than 7188 Sentinel 1A SAR C images. Improved statistical results through the inclusion of local wind speeds. Statistics of eddy geometries in relation to regional wind and current conditions. Time series of eddy densities compared to mean wind speeds and mean SST gradients. History of eddy density allows conclusions on the formation mechanisms.
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S0034425720303953
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Fire severity assessment is crucial for predicting ecosystem response and prioritizing post fire forest management strategies . Although a variety of remote sensing approaches have been developed more research is still needed to improve the accuracy and effectiveness of fire severity mapping . This study proposes a unitemporal simulation approach based on the generation of synthetic spectral databases from linear spectral mixing . To fully exploit the potential of these training databases the Random Forest machine learning algorithm was applied to build a classifier and regression model . The predictive models parameterized with the synthetic datasets were applied in a case study the Sierra de Luna wildfire in Spain . Single date Landsat 8 and Sentinel 2A imagery of the immediate post fire environment were used to develop the validation spectral datasets and a Pliades orthoimage providing the ground truth data . The four defined severity categories unburned partial canopy unburned canopy scorched and canopy consumed demonstrated high accuracy in the bootstrapped and real validation sets with a slightly better performance observed when the Sentinel 2A dataset was used . Abundance of four ground covers was also quantified with moderate or high accuracy . No specific pattern in the comparison of sensors was observed . Variable importance analysis highlighted the complementary behavior of the spectral bands although the contrast between the near and shortwave infrared regions stood out above the rest . Comparison of procedures reinforced the usefulness of the approach as RF image derived models and the multiple endmember spectral unmixing technique showed lower accuracy . The capabilities for detailed mapping are reflected in the development of different types of cartography . The approach holds great potential for fire severity assessment and future research needs to extend the predictive modeling to other burned areas also in different ecosystems and analyze its competence and the possible adaptations needed .
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Approach to fire severity mapping from unitemporal Landsat 8 and Sentinel 2 data. Accurate estimation of fire severity from synthetic spectral data and Random Forest. Successful application to a Mediterranean burned area with high adaptability. Towards improvement of fire severity mapping for forest management practices
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S0034425720303965
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Numerous algorithms are used in remote sensing to detect changes in vegetation . Majority of them require several tunable parameters or can only detect abrupt forest disturbances . The aim of this study was to develop a new threshold and trend based vegetation change monitoring algorithm that can detect abrupt and gradual changes in vegetation within forested and non forested areas . To test the algorithm the Polish and Slovak Tatra Mountains were used as the study area . Strong winds and bark beetle outbreaks are the primary causes of vegetation disturbances in this region . An annual time series of vegetation indices from 1984 to 2016 was used as the input . The long time span necessitated the use of scenes from the Landsat Thematic Mapper Enhanced Thematic Mapper Plus and Operational Land Imager . Fifty one images were atmospherically and topographically corrected . The collected in situ data included the chlorophyll content leaf area index absorbed photosynthetically active radiation and spectral signatures of non forest vegetation dwarf pine and forest stands in 190 sample plots . To select the vegetation indices most suitable for disturbance detection ten satellite based VIs were correlated with the acquired field data . The normalized difference moisture index was found to be more sensitive to vegetation disturbances and more resistant to data noise than any other tested index . The TVCMA uses two separate approaches namely thresholding which indicates where and when the disturbances occurred and a regression analysis which presents the general trend in the time series for each pixel . The number of detected disturbances the Spearman s correlation coefficient between the modeled trend line and satellite observations and
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We propose a new threshold. and trend. based vegetation change monitoring algorithm. The algorithm enables detection of abrupt and gradual changes in annual time series. The approach is suitable for forest and non forest vegetation. Using in situ and Landsat data we found the NDMI as the best disturbance indicator. Results of the vegetation disturbance detection had a high overall accuracy 98
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S0034425720303977
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Since 2010 the Soil Moisture and Ocean Salinity satellite mission monitors the earth emission at L Band . It provides the longest time series of Sea Surface Salinity from space over the global ocean . However the SSS retrieval at high latitudes is a challenge because of the low sensitivity L Band radiometric measurements to SSS in cold waters and to the contamination of SMOS measurements by the vicinity of continents of sea ice and of Radio Frequency Interferences . In this paper we assess the quality of weekly SSS fields derived from swath ordered instantaneous SMOS SSS distributed by the European Space Agency . These products are filtered according to new criteria . We use the pseudo dielectric constant retrieved from SMOS brightness temperatures to filter SSS pixels polluted by sea ice . We identify that the dielectric constant model and the sea surface temperature auxiliary parameter used as prior information in the SMOS SSS retrieval induce significant systematic errors at low temperatures . We propose a novel empirical correction to mitigate those sources of errors at high latitudes .
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Large signal to noise ratio of SMOS salinity in fresh and variable Arctic Seas. Near surface vertical stratification critical for SMOS salinity validation. Improved sea ice filtering using SMOS pseudo dielectric constant. Improved SMOS salinity considering SST and dielectric constant uncertainties.
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S0034425720304016
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The ability of high resolution synthetic aperture radar to detect marine atmospheric boundary layer roll induced roughness modulation of the sea surface wave field is well known . This study presents SAR measurements of MABL rolls using global coverage data collected by the European Space Agency s C band Sentinel 1A satellite in 20162017 . An automated classifier is used to identify likely roll events from more than 1.3 million images that were acquired at two incidence angles of 23 and 36.5 in either VV or HH polarization . Characteristics of the detected rolls are examined for different wind speeds polarizations incidence and relative azimuth angles . Roll detection counts are much higher at the higher incidence angle and nearly equivalent for VV and HH polarizations . Detection depends strongly on the relative azimuth with roll detection rates at crosswind being 310 times lower than for up or downwind . All data show a low wind speed threshold near 2ms
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First global statistics of SAR response to MABL rolls using new S 1 WV data. Data show 50 greater sensitivity to rolls for 36 vs. 23 SAR incidence angle. Crosswind relative to SAR look direction shows weakest sensitivity to roll imprints. Rolls observed at 23 and crosswind are more stationary in more unstable conditions. SAR estimated surface wind perturbations due to roll impacts are 83.5 .
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S0034425720304028
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NASA s Orbiting Carbon Observatory 3 was installed on the International Space Station on 10 May 2019 . OCO 3 combines the flight spare spectrometer from the Orbiting Carbon Observatory 2 mission which has been in operation since 2014 with a new Pointing Mirror Assembly that facilitates observations of non nadir targets from the nadir oriented ISS platform . The PMA is a new feature of OCO 3 which is being used to collect data in all science modes including nadir sun glint target and the new snapshot area mapping mode .
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Orbiting Carbon Observatory 3 has been installed on the International Space Station. OCO 3 passed in orbit checkout and began collecting science data August 2019. The planned mission lifetime of OCO 3 is 3years August 2019 to August 2022. OCO measures total column carbon dioxide and solar induced chlorophyll fluorescence. The first data product release vEarly is publicly available on the NASA DISC.
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S003442572030403X
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Rock glaciers are an important component of the cryosphere and are one of the most visible manifestations of permafrost . While the significance of rock glacier contribution to streamflow remains uncertain the contribution is likely to be important for certain parts of the world . High resolution remote sensing data has permitted the creation of rock glacier inventories for large regions . However due to the spectral similarity between rock glaciers and the surrounding material the creation of such inventories is typically conducted based on manual interpretation which is both time consuming and subjective . Here we present a novel method that combines deep learning and object based image analysis into one workflow based on freely available Sentinel 2 optical imagery Sentinel 1 interferometric coherence data and a digital elevation model . CNNs identify recurring patterns and textures and produce a prediction raster or heatmap where each pixel indicates the probability that it belongs to a certain class or not . By using OBIA we can segment the datasets and classify objects based on their heatmap value as well as morphological and spatial characteristics . We analysed two distinct catchments the La Laguna catchment in the Chilean semi arid Andes and the Poiqu catchment in the central Himalaya . In total our method mapped 108 of the 120 rock glaciers across both catchments with a mean overestimation of 28 . Individual rock glacier polygons howevercontained false positives that are texturally similar such as debris flows avalanche deposits or fluvial material causing the user s accuracy to be moderate even if the producer s accuracy was higher . We repeated our method on very high resolution Pliades satellite imagery and a corresponding DEM for a subset of the Poiqu catchment to ascertain what difference image resolution makes . We found that working at a higher spatial resolution has little influence on the producer s accuracy however the rock glaciers delineated were mapped with a greater user s accuracy . By running all the processing within an object based environment it was possible to both generate the deep learning heatmap and perform post processing through image segmentation and object reshaping . Given the difficulties in differentiating rock glaciers using image spectra deep learning combined with OBIA offers a promising method for automating the process of mapping rock glaciers over regional scales and lead to a reduction in the workload required in creating inventories .
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We present a novel method for identifying rock glaciers from satellite data. Our method combines deep learning and object based image analysis. We trial our method in two periglacial catchments. We mapped 108 of 120 rock glaciers with a mean overestimation of area of 28 . Potential for automating the production of future rock glacier inventories.
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S0034425720304041
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Aquatic land cover represents the land cover type that is significantly influenced by the presence of water over an extensive part of a year . Monitoring global aquatic land cover types plays an essential role in preserving aquatic ecosystems and maintaining the ecosystem service they provide for humans while at the same time their accurate and consistent monitoring for multiple purposes remains challenging . Although a number of global aquatic land cover datasets are available for use to monitor aquatic ecosystems there are prominent variabilities among these datasets which is primarily caused by the inconsistency between different land versus water related monitoring approaches and characterization schemes . As aquatic land cover exists in many different forms on Earth and can be mapped by different approaches it is necessary to consider a much more consistent and comprehensive characterization framework that not only ensures the consistency in the monitoring of aquatic land cover but also serves the needs of multiple users interested in different aspects of aquatic lands . In this study we addressed this issue by 1 reviewing 33 GALC datasets and user needs identified from the citing papers of current datasets and international conventions policies and agreements in relation to aquatic ecosystems 2 proposing a global characterization framework for aquatic land cover based on the Land Cover Classification System classifier principles and the identified user needs and 3 highlighting the opportunities and challenges provided by remote sensing techniques for the implementation of the proposed framework . Results show that users require or prefer various kinds of information on aquatic types including vegetation type water persistence the artificiality of cover water salinity and the accessibility to the sea . Datasets with medium to high spatial resolution intra annual dynamics and inter annual changes are needed by many users . However none of the existing datasets can meet all these requirements and a rigorous quantitative accuracy assessment is lacking to evaluate its quality for most of the GALC datasets . The proposed framework has three levels and users are allowed to derive their aquatic land cover types of interest by combining different levels and classifiers of information . This comprehensive mapping framework can help to bridge the gap between user needs and current GALC datasets as well as the gap between generic and aquatic land cover monitoring . The implementation of the framework can benefit from evolving satellite data availability improved computation capability and open source machine learning algorithms although at the same time it faces challenges mainly coming from the complexity of aquatic ecosystems . The framework proposed in this study provides insights for future operational aquatic land cover monitoring initiatives and will support better understanding and monitoring of complex aquatic ecosystems .
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A review of user needs towards global aquatic land cover GALC dataset was done. Despite increasing availability existing GALC datasets cannot satisfy user needs. A comprehensive GALC mapping framework was proposed to serve various users. This framework can grow from evolving satellite data and technological advancements. Operational GALC mapping should be bridged with generic land cover monitoring.
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S0034425720304053
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Polar orbiting ocean color satellites such as Landsat 8 Suomi National Polar orbiting Partnership and Sentinel 3 offer valuable image data for the derivation of water bathymetry in optically shallow environments . Because of the multi spectral limitation however it is challenging to derive bathymetry over global shallow waters without reliable mechanistic algorithms . In this contribution we present and test a physics based algorithm for improved retrieval of bathymetry with multi spectral sensors . The algorithm leverages the temporal variation of water column optical properties in two satellite measurements . By incorporating two remote sensing reflectance spectra in an optimization procedure it enhances the spectral constraining condition for the optimization thus leading to improved retrieval accuracy . This scheme is evaluated using synthetic multi spectral data . It is shown that the new approach can provide accurate estimation of water depths over 030m range with three types of benthic substrates and for a wide range of water column optical properties . Based on the degree of improvement Landsat 8 appears to be benefited the most followed by SNPP and then Sentinel 3 . The application of the new approach is demonstrated with satellite images over shallow waters dominated with coral reefs seagrass and sand respectively . This proof of concept study confirms the promise of multi spectral satellite sensors for accurate water depth retrieval by accounting for the temporal characteristics in multiple measurements suggesting a path forward for the derivation of bathymetry from the existing satellites over global shallow waters .
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A physics based approach is introduced for depth retrieval from multi spectral data. It leverages the temporal variation in satellite image data. It can improve depth retrievals greatly with multi spectral ocean color spectra.
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S0034425720304065
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Satellite remote sensing offers an effective remedy to challenges in ground based and aerial mapping that have previously impeded quantitative assessments of global seagrass extent . Commercial satellite platforms offer fine spatial resolution an important consideration in patchy seagrass ecosystems . Currently no consistent protocol exists for image processing of commercial data limiting reproducibility and comparison across space and time . Additionally the radiometric performance of commercial satellite sensors has not been assessed against the dark and variable targets characteristic of coastal waters . This study compared data products derived from two commercial satellites DigitalGlobe s WorldView 2 and Planet s RapidEye . A single scene from each platform was obtained at St. Joseph Bay in Florida USA corresponding to a November 2010 field campaign . A reproducible processing regime was developed to transform imagery from basic products as delivered from each company into analysis ready data usable for various scientific applications . Satellite derived surface reflectances were compared against field measurements . WorldView 2 imagery exhibited high disagreement in the coastal blue and blue spectral bands chronically overpredicting . RapidEye exhibited better agreement than WorldView 2 but overpredicted slightly across all spectral bands . A deep convolutional neural network was used to classify imagery into deep water land submerged sand seagrass and intertidal classes . Classification results were compared to seagrass maps derived from photointerpreted aerial imagery . This study offers the first radiometric assessment of WorldView 2 and RapidEye over a coastal system revealing inherent calibration issues in shorter wavelengths of WorldView 2 . Both platforms demonstrated as much as 97 agreement with aerial estimates despite differing resolutions . Thus calibration issues in WorldView 2 did not appear to interfere with classification accuracy but could be problematic if estimating biomass . The image processing routine developed here offers a reproducible workflow for WorldView 2 and RapidEye imagery which was tested in two additional coastal systems . This approach may become platform independent as more sensors become available .
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A reproducible WorldView and RapidEye processing regime was developed for seagrass. WorldView 2 had disagreement positive bias in shorter wavelengths against field data. RapidEye had lower overall disagreement slight positive bias compared to field data. WorldView 2 RapidEye classification had 97 agreement with aerial photointerpretation.
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S0034425720304089
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Airborne and orbital imaging spectroscopy can facilitate the quantification of chemical and physical attributes of surface materials through analysis of spectral signatures . Prior to analysis estimates of surface reflectance must be inferred from radiance measurements in a process known as atmospheric correction which compensates for the distortion of the electromagnetic signal by the atmosphere . Inaccuracies in the correction process can alter characteristic spectral signatures leading to subsequent mischaracterization of surface properties . Global observations pose new challenges for mapping surface composition as varied atmospheric conditions and surface biomes challenge traditional atmospheric correction methods . Recent work adopted an optimal estimation approach for retrieving surface reflectance from observed radiance measurements providing the reflectance estimates with a posterior probability . This work incorporates these input probabilities to improve the accuracy of surface feature measurements . We demonstrate this using a generic feature fitting method that is applicable to a wide range of Earth surface studies including geology ecosystem studies hydrology and urban studies . Specifically we use a probabilistic framework based on generalized Tikhonov regularized least squares a rigorous formulation for appropriate weighting of features by their observation uncertainty and leveraging of prior knowledge of material abundance for improving estimation accuracy . We demonstrate the validity of this procedure and quantify the increase in model performance by simulating expected accuracies in the reflectance estimation . To evaluate global uncertainties in mineral estimation we simulate observations representative of the expected global range of atmospheric water vapor and aerosol levels and characterize the sensitivity of our procedure to those quantities .
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Optimal estimation is a principled framework to quantify and propagate uncertainties. Propagating observation uncertainties in to surface products increases performance. High aerosol and water vapor loads reduce the accuracy in retrieving VNIR features. Mineral estimation with error propagation is superior in every noise level. Quantifying band depth with Continuum Removal is sensitive to feature edges noise.
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S0034425720304090
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The urban heat island is a major topic in the study of urban climates . However comprehensive research on the atmospheric UHI UHI
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Vertical profiles of UHI. UHI. and UHI. were characterized. Relationships of UHI. and UHI. relative to UHI. were explored. UHI. intensity shows distinct diurnal and seasonal variations with altitude. UHI. intensity shows opposite variations with depth during the day and night.
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S0034425720304107
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Within crown heterogeneity exists in multiple vegetation scenes including forest stands with heterogeneous distribution of damaged foliage . The existing radiative transfer approaches implement the within crown heterogeneity either using complicated three dimensional scenes with excessive amounts of parameters or simplifying the scenes to homogeneous cases by averaging canopy optical properties . In order to ascertain the optical response to within crown heterogeneity both efficiently and accurately we proposed a method for simulating bi directional reflectance factor of forests infected by pests based on the stochastic radiative transfer theory . Each damaged tree crown was classified into one of the three types top only damage bottom only damage and random damage . The statistical properties of such canopy structures were described with extended definitions of the stochastic moments of the SRT model including the probabilities of finding different foliage classes and the conditional pair correlation functions between healthy and damaged foliage . Field measurements and remote sensing data from unmanned aerial vehicle over infected Yunnan Pine
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The radiation regime of heterogeneous forest canopies is modeled in a 1D form. Considering vertical distribution of damaged foliage increases modeling accuracy. Optical remote sensing is easier to detect spectral changes of upper parts of canopy. Knowledge of canopy cover and understory condition helps to invert forest damage.
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S0034425720304119
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Biochemical traits in forest vegetation are key indicators of leaf physiological processes specifically photosynthetic and other photochemical light pathways and are critical to the quantification of the terrestrial carbon cycle . Advances in remote sensing sensors and platforms are allowing multi dimensional and continuous spatial information to be acquired in a fast and non destructive way to quantify forest biochemical traits at multiple spatial scales . Here we demonstrate the use of high spectral resolution hyperspectral data combined with high density three dimensional information from Light Detection and Ranging both acquired from an unmanned aerial system platform to quantify and assess the three dimensional distribution of biochemical pigments on individual tree canopy surfaces . To do so a DSM based fusion method was developed to integrate the 3D LiDAR point cloud with hyperspectral reflectance data . Regression based models were then developed to predict a number of biochemical traits
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Quantifying 3D distribution of biochemical traits using UAS LiDAR and HS data. A DSM based fusion approach was developed to link structure and spectrum. Vegetation indices are strongly correlated with a number of biochemical traits. Biochemical pigments distribution has significant vertical effects. Vertical profiles of biochemical traits change by tree species and age.
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S0034425720304120
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Scatterometer observations over land are sensitive to the water content in soil and vegetation but have been rarely used to study seasonal changes in the plant water status and seasonal development of deciduous trees . Here we use Advanced Scatterometer observations to investigate the sensitivity of C band backscatter to spring phenology of temperate deciduous broadleaf forests in Austria . ASCAT s multi angle looking capability enables the observation of backscatter over a large range of incidence angles . The vegetation status affects the slope of the backscatter incidence angle relationship . We discovered a maximum in the slope around the month April hereafter referred to as spring peak predominantly in regions covered by deciduous broadleaf forest . We hypothesized that the spring peak indicates the average timing of leaf emergence in the deciduous trees in the sensor footprint . The hypothesis was tested by comparing the timing of the spring peak to leaf unfolding observations from the PEP725 phenology database to the increase of leaf area index during spring and to temperature . Our results demonstrate a good agreement between the ASCAT spring peaks phenology observations and temperature conditions . The steepest increase in LAI however lags behind the ASCAT peak by several days to a few weeks suggesting that the spring peak in fact marks the timing of maximum woody water content which occurs right before leaf emergence . Based on these observations we conclude that the ASCAT signal has a high sensitivity to spring reactivation and in particular water uptake of bare deciduous broadleaf trees . Our findings might provide the basis for novel developments to estimate eco physiological changes of forests during spring at large scales .
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Metop ASCAT backscatter and slope are sensitive to vegetation phenology. In spring a peak in the slope is observed over deciduous broadleaf forest DBF . This spring peak is hypothesized to be caused by leaf emergence over DBF. Comparison to ground phenology leaf area index and temperature data. Time delay to LAI suggests that the real cause is spring water uptake in DBF.
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S0034425720304132
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Imaging spectroscopy is a valuable tool for mapping canopy foliar traits in forested ecosystems at landscape and larger scales . Most efforts to date have involved two dimensional mapping of traits typically representing top of canopy conditions . However traits and their associated biological functions vary through the canopy vertical profile such that incorporating information about vertical patterns may improve modeling of ecosystem processes like primary productivity . In 2016 and 2017 we collected extensive field data in forests in Domain 5 of the National Ecological Observatory Network to characterize the vertical variation in leaf mass per area an important foliar trait related to plant growth and defense . Fieldwork was coincident with NEON Airborne Observation Platform overflights which collected imaging spectroscopy and lidar data . Using imaging spectroscopy to map top of canopy LMA and lidar to model vertical gradients of transmittance we developed a method to map three dimensional patterns in LMA in temperate broadleaf forests . Partial least squares regression was used to estimate top of canopy LMA R
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3D patterns in leaf mass per area are mapped with lidar and imaging spectroscopy. Lidar transmittance was strongly correlated with within canopy leaf mass per area. Within canopy gradients can be mapped without consideration of species composition.
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S0034425720304144
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Passive microwave datasets have been used to quantify the extent and duration of surface melt in Greenland and Antarctica from 1978 on with daily and near daily intervals . These results have important implications for climate analysis and may help evaluate ice shelf stability . However the accuracy of passive microwave methods used to detect melt is difficult to quantify especially on the Antarctic Peninsula . Here four different melt detection methods are employed including a new formulation of a statistical analysis of brightness temperature time series using a K means clustering algorithm . Strikingly two of the most widely used passive microwave melt detection methods are found to vary by 48 mean days of melt per year across six different locations on the Larsen C Wilkins and George VI Ice Shelves . In the absence of ground truth observations time series of Sentinel 1 SAR observations from 2016 on provide a comparison dataset . In topographically flat regions where surface melt is spatially uniform the passive microwave melt detection method based on a K means analysis and the cross polarization gradient ratio method demonstrate the highest agreement and correlation with active radar melt detection methods . One issue which has plagued passive microwave analysis is its coarse spatial resolution . High resolution SAR images are able to demonstrate and quantify the spatial variability of melt within individual passive microwave pixels . Melt is shown to be suitably uniform in space for passive microwave applications at the study sites on Antarctic Peninsula ice shelves but not so in other regions of the Antarctic Peninsula . Spatial heterogeneity of surface melt on the sub pixel scale is often related to varying surface topography .
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Evaluation of several traditional passive microwave melt detection methods. Description of revised statistic melt detection method using K means clustering. Sentinel 1 SAR time series identifies melt with 13day revisit times for 20162019. SAR images highlight where melt is spatially uniform or too mixed for radiometer analysis.
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S0034425720304156
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Cloud cover is a common and inevitable phenomenon that often hinders the usability of optical remote sensing image data and further interferes with continuous cartography based on RS image interpretation . In the literature the off the shelf cloud detection methods either require various hand crafted features or utilize data driven features using deep networks . Overall deep networks achieve much better performance than traditional methods using hand crafted features . However the current deep networks used for cloud detection depend on massive pixel level annotation labels which require a great deal of manual annotation labor . To reduce the labor needed for annotating the pixel level labels this paper proposes a weakly supervised deep learning based cloud detection method using block level labels indicating only the presence or the absence of cloud in one RS image block . In the training phase a new global convolutional pooling operation is proposed to enhance the ability of the feature map to represent useful information . In the testing phase the trained deep networks are modified to generate the cloud activation map via the local pooling pruning strategy which prunes the local pooling layers of the deep networks that are trained in the training phase to improve the quality of CAM . One large RS image is cropped into multiple overlapping blocks by a sliding window and then the CAM of each block is generated by the modified deep networks . Based on the correspondence between the image blocks and CAMs multiple corresponding CAMs are collected to mosaic the CAM of the large image . By segmenting the CAM using a statistical threshold against a clear sky surface the pixel level cloud mask of the testing image can be obtained . To verify the effectiveness of our proposed WDCD method we collected a new global dataset for which the training dataset contains over 200 000 RS image blocks with block level labels from 622 large GaoFen 1 images from all over the world the validation dataset contains 5 large GaoFen 1 images with pixel level annotation labels and the testing dataset contains 25 large GaoFen 1 and ZiYuan 3 images with pixel level annotation labels . Even under the extremely weak supervision our proposed WDCD method could achieve excellent cloud detection performance with an overall accuracy as high as 96.66 . Extensive experiments demonstrated that our proposed WDCD method obviously outperforms the state of the art methods . The collected datasets have been made publicly available online
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A weakly supervised deep learning framework is proposed to address cloud detection. A novel global convolutional pooling GCP operation is proposed. This paper proposes a new local pooling pruning LPP strategy. A large scale remote sensing image dataset for cloud detection is released.
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S0034425720304168
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The Mississippi Delta in coastal Louisiana has some of the highest rates of land loss in the world . This land loss crisis might become a global problem because most major deltas are expected to be vulnerable to land loss during the remainder of the 21st century and beyond . Despite this predicament we do not understand how land loss in a deltaic environment proceeds in time and space . Here we evaluate the spatial and temporal trends of land loss in the Lower Mississippi River Delta region using spatial statistics and landscape metrics . We used nearly 4800 Landsat images to construct a series of three year cloud free composites from 1983 to 2016 . From these data we created a stability index which is a dimensionless measure of the number of land to water transitions that a land pixel makes before being considered lost . Our results indicate that on the LMRD 75 of land loss is a single transition from land to water while about 25 of land pixels have two or more transitions before being considered lost . Using a local indicator of spatial association we show that pixels with similar SI tend to cluster together . Single transition clusters form elongated shapes they are densely packed and they are predominantly but not always found on marsh edges . On the other hand multi transition clusters form square like shapes they are more fragmented and they are usually found in marsh interiors . Our analysis further shows that the land loss area within the spatial patches with both high and low stability is strongly related to the density of land patches and their shape R
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We define two types of landloss in the Mississippi Delta. Single transition loss occurs when land converts to water. Multiple transition loss occurs when land converts to water multiple times. 75 of land loss is single transition the rest undergo multiple transition. These transition types have different shapes and occur in different locations
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S003442572030417X
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Accurate bathymetric data is essential for marine coastal ecosystems and related studies . In the past decades a lot of studies were investigated to obtain bathymetric data in shallow waters using satellite remotely sensed data . Satellite multispectral imagery has been widely used to estimate shallow water depths based on empirical models and physics based models . However the
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Estimating bathymetric topography with only satellite remotely sensed data. Using new ICESat 2 lidar points and Sentinel 2 multispectral imageries. Proposing signal detection and bathymetric error correction method for ICESat 2. Training empirical models by ICESat 2 bathymetric points to estimate water depths. Drawing and validating bathymetry in two study areas with multi date datasets.
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S0034425720304211
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Wetland is a very fragile ecosystem that provides important services to a large variety of flora and fauna species as well as for humans . As wetland depends on water availability protecting this important ecosystem requires careful hydrological monitoring . The Sentinel 1 mission featuring a wide swath coverage high temporal observations and open data policy provides unprecedented opportunity for high spatio temporal resolution water level change mapping over regional wide wetland areas . In this study we assess Sentinel 1 InSAR observations for routine water level change measurements over the entire south Florida Everglades wetlands . The study utilizes 91 Sentinel 1 images acquired over a three year period and generates routine 12 days Interferograms and correspondingly 30m spatial resolution water level change maps over the entire Everglades . The high spatial resolution interferograms detect hydrological signals induced by both natural and human induced flow including tides gate operations and canal overflow all these can not be detected by terrestrial measurements . The large number of both InSAR and ground based gauge observations allow us to quantify the overall accuracy of the Sentinel 1 InSAR measurements which is 3.9cm for the entire wetland area but better for smaller hydrological units within the Everglades . Our study reveals that the tropospheric delay for individual interferograms can be very large as much as 30cm . When applying tropospheric corrections to all three years of Sentinel 1 InSAR observations the overall accuracy level improved by 13 to 3.4cm . Although our study is focused on the Everglades its implications in term of the suitability of Sentinel 1 observations for space based hydrological monitoring of wetlands and the derived accuracy level are applicable to other wetlands with similar vegetation types located all over the world .
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Sentinel 1 InSAR enables mapping of water level change over the entire Everglades. Interferograms detected both natural and human induced hydrological signals. Accuracy of water level observations with no tropospheric correction is 3.9cm. Accuracy with tropospheric correction increases by 13 to 3.4cm.
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S0034425720304223
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Over the past four decades satellite systems and land surface models have been used to estimate global scale surface soil moisture . However in areas such as densely vegetated and irrigated regions obtaining accurate SSM remains challenging . Before using satellite and model based SSM estimates over these areas we should understand the accuracy and error characteristics of various SSM products . Thus this study aimed to compare the error characteristics of global scale SSM over vegetated and irrigated areas as obtained from active and passive satellites and model based data Advanced Scatterometer Soil Moisture and Ocean Salinity Advanced Microwave Scanning Radiometer 2 Soil Moisture Active Passive European Centre for Medium Range Weather Forecasts Reanalysis 5 and Global Land Data Assimilation System . We employed triple collocation analysis and caluclated conventional error metrics from in situ SSM measurements . We also considered all possible triplets from 6 different products and showed the viability of considering the standard deviation of TCA based numbers in producing robust results .
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Satellite based SSM can provide better quality than model based SSM over densely vegetated areas. The results from TCA based and conventional metrics are consistent. Use of a single triplet for TCA can create biased results in SSM error estimations. Synergetic use of satellite and model SSM ensures the strongest signal to noise ratio.
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S0034425720304259
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Land surface temperature is a key diagnostic indicator of agricultural water use and crop stress . LST data retrieved from thermal infrared band imagery however tend to have a coarser spatial resolution than surface reflectance data collected from shortwave bands on the same instrument . Spatial sharpening of LST data using the higher resolution multi band SR data provides an important path for improved agricultural monitoring at sub field scales . A previously developed Data Mining Sharpener approach has shown great potential in the sharpening of Landsat LST using Landsat SR data co collected over various landscapes . This work evaluates DMS performance for sharpening ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station LST and Visible Infrared Imaging Radiometer Suite LST data using Harmonized Landsat and Sentinel 2 SR data providing the basis for generating 30 m LST data at a higher temporal frequency than afforded by Landsat alone . To account for the misalignment between ECOSTRESS VIIRS and Landsat HLS caused by errors in registration and orthorectification we propose a modified version of the DMS approach that employs a relaxed box size for energy conservation . Sharpening experiments were conducted over three study sites in California and results were evaluated visually and quantitatively against LST data from unmanned aerial vehicles flights and from Landsat 8 . Over the three sites the modified DMS technique showed improved sharpening accuracy over the standard DMS for both ECOSTRESS and VIIRS suggesting the effectiveness of relaxing EC box in relieving misalignment induced errors . To achieve reasonable accuracy while minimizing loss of spatial detail due to the EC box size increase an optimal EC box size of 180270m was identified for ECOSTRESS and about 780m for VIIRS data based on experiments from the three sites . Results from this work will facilitate the development of a prototype system that generates high spatiotemporal resolution LST products for improved agricultural water use monitoring by synthesizing multi source remote sensing data .
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A modified thermal sharpening method for multi platform data is proposed. The method is tested using Harmonized Landsat Sentinel ECOSTRESS and VIIRS data. Relaxing the box size for energy conservation leads to improved performance. We suggest a box size of 180270m and 780m for ECOSTRESS and VIIRS sharpening.
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S0034425720304260
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Tropical forests are characterized by large carbon stocks and high biodiversity but they are increasingly threatened by human activities . Since structure strongly influences the functioning and resilience of forest communities and ecosystems it is important to quantify it at fine spatial scales .
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Tree by tree reconstructions of tropical forests in 3D. Inference of allometric equations from field plots and airborne lidar scanning. Virtual inventories across thousands of hectares of forest area. Fine scale mapping of aboveground biomass 1ha and 0.25ha scale . Error propagation through Approximate Bayesian Computation.
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S0034425720304284
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This study provides the first report of reflectance spectra of coral spawn slicks and their detection using optical satellite data . The reflectance spectra show that coral spawn slicks can be discriminated from other sea surface features such as wave foam and floating
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First report of reflectance spectra of coral spawn slicks and their detection. The reflectance spectra can be discriminated from other sea surface features. Satellite constellations allowed observation after a coral mass spawning event. The red green band ratio detected coral spawn slicks successfully. Detected slicks were small possibly because of the 2016 coral bleaching event.
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S0034425720304296
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Soil salinity is one of the crucial factors which undermines agricultural production in the semi arid region . The study was attempted for the Vellore district one of the worst hit semi regions of Tamil Nadu by salinization . The Sentinel 1 data product of C band frequency was instrumental in the development of the model . In the present research a semi empirical dielectric model was proposed and its potential in demonstrating the rate of soil salinity was validated with in situ measurements under semi saturated conditions . The dielectric behavior of saline and non saline soil was simulated by investigating the paradigm of the parameters namely the backscattering coefficient of VV polarization soil texture and in situ dielectric constant in the three dimensional density space . The imaginary part of the dielectric constant was retrieved by simulating the dielectric loss from the partition observed between the dielectric constant of saline and non saline soils in the third dimension . The resultant product of the proposed model has achieved the best fit R
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The dielectric behavior of soil was simulated in three dimensional density space. The imaginary part of the dielectric constant was simulated from dielectric loss. The imaginary part to quantify soil salinity over vegetated and bare surfaces. The simulation is dependent on soil texture under a semi saturated state.
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S0034425720304314
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Airborne laser scanning is a remote sensing technology known for its applicability in natural resources management . By quantifying the three dimensional structure of vegetation and underlying terrain using laser technology ALS has been used extensively for enhancing geospatial knowledge in the fields of forestry and ecology . Structural descriptions of vegetation provide a means of estimating a range of ecologically pertinent attributes such as height volume and above ground biomass . The efficient processing of large often technically complex datasets requires dedicated algorithms and software . The continued promise of ALS as a tool for improving ecological understanding is often dependent on user created tools methods and approaches . Due to the proliferation of ALS among academic governmental and private sector communities paired with requirements to address a growing demand for open and accessible data the ALS community is recognising the importance of free and open source software and the importance of user defined workflows . Herein we describe the philosophy behind the development of the lidR package . Implemented in the R environment with a C C backend lidR is free open source and cross platform software created to enable simple and creative processing workflows for forestry and ecology communities using ALS data . We review current algorithms used by the research community and in doing so raise awareness of current successes and challenges associated with parameterisation and common implementation approaches . Through a detailed description of the package we address the key considerations and the design philosophy that enables users to implement user defined tools . We also discuss algorithm choices that make the package representative of the state of the art and we highlight some internal limitations through examples of processing time discrepancies . We conclude that the development of applications like lidR are of fundamental importance for developing transparent flexible and open ALS tools to ensure not only reproducible workflows but also to offer researchers the creative space required for the progress and development of the discipline .
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We present the lidR package for ALS processing. We document the design and aims of the package with an emphasis on its flexibility. lidR assembles state of the art algorithms from the literature. lidR was conceived for users to implement transparent and reproducible workflows. We provide evidence that liDR is increasingly being used for ALS focused research.
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S0034425720304326
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Soil moisture and gross primary productivity estimates from the Soil Moisture Active Passive and solar induced chlorophyll fluorescence from the Orbiting Carbon Observatory 2 provide new opportunities for understanding the relationship between soil moisture and terrestrial photosynthesis over large regions . Here we explored the potential of the synergistic use of SMAP and OCO 2 based data for monitoring the responses of ecosystem productivity to drought . We used complementary observational information on root zone soil moisture and GPP from SMAP and fine resolution SIF 0.05
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We combine SMAP and OCO 2 derived products to examine impacts of the 2018US drought. SMAP GPP and GOSIF data can capture ecosystem responses to soil moisture anomalies. SMAP GPP and GOSIF can well characterize drought impacts on tower GPP and crop yield. GOSIF has a higher performance than SMAP GPP and other products MODIS GPP and EVI . Synergistic use of SMAP and OCO 2 data reveals how drought impacts plant productivity.
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S0034425720304338
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The Central Valley in California is characterized by a semi arid climate prone to droughts a variable surface water supply and immense agricultural areas dependent on groundwater irrigation . The groundwater is stored in a complex aquifer system composed of alternating layers of coarse sediments and fine grained sediments acting as confining materials . Groundwater fluctuations are coupled with both the elastic and inelastic land surface deformation historically observed in the Central Valley . Surface deformation poses a hazard to the California Aqueduct which supports Central Valley agriculture and large urban populations in Southern California . The risk of reduced aqueduct efficacy and expensive repairs compels water resource managers to carefully monitor land deformation in the Valley .
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UAVSAR detects rapid subsidence adjacent to 10.5 km of California Aqueduct. Exceptional drought conditions from 2012 to 2016 coincide with subsidence acceleration. Permanent aquifer volume storage loss is greater than 7122 m. day for 2.7years.
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S0034425720304363
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Terrain elevation is essential for land management navigation and earth science applications . Remote sensing advancements have led to an increase in the availability of a range of digital elevation models with global to quasi global land coverage . However the generation of these models in water bodies requires specialized approaches such as the delimitation of the shorelines of lakes over time . Therefore the processing costs are high in complex areas with many lakes . Currently there is no systematic topographic mapping of lakes and channels in large and complex floodplains using remote sensing data . We present here the first high resolution topographic mapping of the non forested portion of the middle lower Amazon floodplain using a new method based on in situ Amazon river water levels and a flood frequency map derived from the Landsat Global Surface Water Dataset . Validation using locally derived bathymetry showed a root mean square error of 0.89m for floodplain elevation and a good representation of spatial patterns with Pearson s correlation coefficient of 0.77 . Our approach for improving topographic representation in open water areas is an alternative to SRTM3 DEM or MERIT DEM which represents these areas as a flat surface . We also generated the Amazon River bathymetry using nautical charts from the Brazilian Navy and floodplain depths maps corresponding to the high and low water periods of the river flood wave . The results show that the storage volume in the open water floodplain varies 104.3km
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Landsat flood frequency based method applied for floodplain topography estimation. 0.9m floodplain elevation RMSE after vegetation bias removal with ICESat 2. River bathymetry 1100km extension obtained from nautical charts RMSE of 7.5m. High resolution 30m water depth and topography maps for middle lower Amazon. Storage volume on floodplain open water areas varies 104.3km. on average each year.
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S0034425720304545
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This paper assessed the precision of 20 Hz sea level anomaly estimates with different sea state bias corrections from reprocessed Jason 1 2 3 and Sentinel 3A data over the period of 20022019 within 100km from the entire Australian coast . The altimeter waveforms were retracked by the modified Brown peaky retracker for Jason missions and by the SAMOSA retracker for Sentinel 3A . We recalculated the 20 Hz and composite SSB corrections using a regional parametric model from 20 Hz retracked estimates of the SLA significant wave height and wind speed . The composite SSB correction which was recomputed after removing the retracker dependant correlated error in 20 Hz SLA is found to achieve better performance than other SSB corrections in the study area . Applying the 20 Hz and composite SSB corrections has reduced 10 and 13 of noise respectively in the MBP retracked 20 Hz Jason SLA estimates while only 2 of noise reduction is shown by applying the 1 Hz standard SSB correction . It is also found that the improvement of retracked Sentinel 3A SLA estimates by SSB corrections is very low indicating a dedicated SSB correction model should be developed for the SAR mode altimeter .
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Recomputation of regional SSB corrections for 20 Hz retracked SLA data. The first time analysis of different SSB corrections around Australia. Quantified precision of Jason and Sentinel 3A retracked SLAs around Australia.
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S0034425720304569
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Many studies have shown that solar induced fluorescence has a good potential to predict gross primary production of vegetation . What we measured by remote sensing or near surface platforms is top of canopy SIF SIF
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Total emitted SIF SIF. can be estimated from top of canopy SIF SIF. . The sensor only received on average 22.9 of total emitted SIF. SIF. improves the diurnal estimate of canopy GPP. SIF. produced a stronger correlation with GPP for plants with complex structure. The SIF. GPP relationship shows a stronger resilience to environmental stresses.
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S0034425720304648
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Spectrally based retrieval of bathymetry is challenging in inland coastal waters due to variations in factors other than water depth across a water body including bottom type optically significant constituents water surface roughness and others . Optimal band ratio analysis is the most widely used method to deal with these confounding effects to retrieve water depth . OBRA identifies the pair of bands that provides the highest accuracy among all possible pairs when using a log transformed band ratio model . However this approach fits a single ratio model to all training samples without fully accounting for the heterogeneity of the aforementioned complicating factors . To deal with this problem we introduce a novel method called Sample specific Multiple bAnd Ratio Technique for Satellite Derived Bathymetry . The proposed SMART SDB technique partitions the feature space of the spectral data and creates for every subspace a different band ratio model that performs better than the other models within that subspace . Bathymetry is then predicted based on the closest model s in the feature space by performing a sample specific k nearest neighbor
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Partition specific band ratios for improving bathymetry modeling. Use of sample specific multiple ratio models for depth estimation. Proposed SMART SDB outperformed the standard ratio model.
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S003442572030465X
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The recent increase in population growth and urbanisation demands for real time riverine water management to fulfil the daily scale domestic and ecological water needs necessitating for high frequent streamflow estimation at any river section . Although there are several conventional methods available in the literature using these methods to obtain streamflow information at finer spatiotemporal resolutions is not feasible . Moreover streamflow estimation using single or multi satellite remote sensing approaches is still in the experimental stage . As advancement in the existing approaches this study advocates two novel models namely CMOD and CFUS which use the Frank copula based single MODIS satellite data and copula based multi satellite MODIS Landsat fusion data respectively . These developed models fit a box centre matrix to the pixel ratios of water and land in the near infrared spectrum . Two other approaches using the stand alone MODIS data and enhanced spatiotemporal fusion of MODIS Landsat datasets are also developed to inter compare with the CMOD and CFUS models for reproducing the daily time series of streamflow at three gauging stations on the Brahmani River in eastern India . The calibration and validation results reveal that the 30m resolution CFUS model is the best approach for daily scale streamflow estimation with sufficient accuracy with an average Nash Sutcliffe Coefficient of 0.92 followed by the CMOD FUS and MOD models . Hence the CFUS model can be used as a potential tool for the next generation hydrometry in semi gauged river basins for riverine water resources assessment and inter state water sharing conflict resolution .
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Advocated four hierarchical spectral model frameworks for daily streamflow estimation using Frank copula and ESTARFM. These models use fusion of land water pixel ratios of MODIS and Landsat with near infrared bands. Evaluated these methods using in situ streamflow data along a scantily gauged river. Copula based spatiotemporal fusion approach performed the best with 96 efficiency.
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S0034425720304661
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For the past two decades quantitative retrievals of aerosol optical depth have been made from both geostationary and polar orbiting satellites and the results have been widely used in numerous studies . Despite the progress made in improving the accuracy of AOD retrievals there are still major challenges especially over land . A notable one for the so called Dark Target algorithms is building the surface reflectance relationships to derive SR in the visible channels from SR in the short wave infrared channel mainly because these relationships are strongly subjected to entangled factors . In this study we examine the benefits of a new method for deriving the SRR using deep learning techniques . The SRR constructed by the deep neural network considers multiple related inputs such as the SWIR normalized difference vegetation index
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The surface reflectance relationship is built based on deep neural network. The AOD algorithm combines dark target method and deep learning techniques. The biases of AOD are largely reduced under various environmental conditions. Independent tests indicate the algorithm can be applied for untrained regions.
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S0034425720304673
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Climate change is amplified in the Arctic region relative to elsewhere . By analyzing climate model simulations it has been found that the largest factor in Arctic Amplification comes from enhanced temperature feedbacks caused by the different vertical atmospheric warming profiles and a larger temperature change for longwave emission per unit of warming at high compared to low latitudes . Radiometers on polar orbiting satellites offer the best mechanism to derive sea surface temperature in the Arctic but given that the retrieval algorithms in the infrared are designed to compensate for the effects of the atmosphere mainly water vapor IR satellite derived SSTs have larger inaccuracies at high latitudes because the atmosphere can be very dry and cold . So the motivation of this study is to improve the algorithms to obtain more accurate SSTs which can be used to research the feedback mechanisms . To undertake the study the 5 year matchup database for MODIS on Aqua has been analyzed to characterize the differences between collocated and simultaneous satellite retrieved skin SSTs SST
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Understand the characteristics of satellite retrieved SST errors at high latitudes. Improving Aqua MODIS SSTs in the Arctic regions north of 60N . Emissivity effect is proven to be significant in MODIS SST retrievals in Arctic. MODIS SST accuracies are improved after the corrections.
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S0034425720304685
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Accurate estimation of cropping intensity an indicator of food production is well aligned with the ongoing efforts to achieve sustainable development goals under diminishing natural resources . The advancement in satellite remote sensing provides unprecedented opportunities for capturing CI information in a spatially continuous manner . However challenges remain due to the lack of generalizable algorithms for accurately and efficiently mapping global CI with a fine spatial resolution . In this study we developed a 30 m planetary scale CI mapping framework with the reconstructed time series of Normalized Difference Vegetation Index from multiple satellite images . Using a binary crop phenophase profile indicating growing and non growing periods we estimated pixel by pixel CI by enumerating the total number of valid cropping cycles during the study years . Based on the Google Earth Engine cloud computing platform we implemented the framework to estimate CI during 20162018 in eight geographic regions across continents that are representative of global cropping system diversity . Comparison with PhenoCam network data in four cropland sites suggests that the proposed framework is capable of capturing the seasonal dynamics of cropping practices . Spatially overall accuracies based on validation samples range from 80.0 to 98.9 across different regions worldwide . Regarding the CI classes single cropping systems are associated with more robust and less biased estimations than multiple cropping systems . Finally our CI estimates reveal high agreement with two widely used land surface phenology products including Vegetation Index and Phenology V004 and Moderate Resolution Imaging Spectroradiometer Land Cover Dynamics meanwhile providing much more spatial details . Due to its robustness the developed CI framework can be potentially generalized to produce global fine resolution CI products for food security and other applications .
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A new framework is proposed for cropping intensity mapping at fine resolution. Cropping intensity is determined by a binary phenophase profile for each cropland pixel. The framework can be applied to various agro ecological conditions. Overall accuracies range 80.0 98.9 based on results from eight regions across the globe.
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S0034425720304697
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Landsat imagery is an unparalleled freely available data source that allows reconstructing land cover and land use change including urban form . This paper addresses the challenge of using Landsat data particularly its 30m spatial resolution for monitoring three dimensional urban densification . Unlike conventional convolutional neural networks for scene recognition resulting in resolution loss the proposed semantic segmentation framework provides a pixel wise classification and improves the accuracy of urban form mapping . We compare temporal and spatial transferability of an adapted DeepLab model with a simple fully convolutional network and a texture based random forest model to map urban density in the two morphological dimensions horizontal
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A workflow capturing 30m urban dynamics based on Landsat imagery and deep learning. CNN based semantic segmentation models outperform random forest approaches. Spatial and temporal transferability of 3D urban form mapping is feasible. Urban density growth in Denmark varies between cities and through time 19852018.
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S0034425720304703
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The 2016 Mw 7.8 Kaikura earthquake represents an extremely complex event involving over ten major crustal faults altering conventional understanding of multi fault ruptures . Although evidence for coseismic slip on the Hikurangi subduction interface is controversial we present afterslip on the subduction zone beneath Marlborough using 13months of Interferometric Synthetic Aperture Radar and Global Positioning System observations . The spatially and temporally correlated atmospheric errors in SAR interferograms are problematic and hence a new InSAR time series approach combining the Generic Atmospheric Correction Online Service with a spatial temporal Atmospheric Phase Screen filter to facilitate the InSAR time series analysis is developed . For interferograms with over 250km spatial extent we achieve a 0.77cm displacement RMS difference against GPS improving 61 from the conventional InSAR time series method . Comparisons between the overlapping region of two independent tracks show an RMS difference of 1.1cm for the TS GACOS APS combined method improving 54 from the TS method and 27 from using TS with an APS filter only . The APS filter reduces the short wavelength residuals substantially but fails to remove the long wavelength error even after the ramp removal revealing that the GACOS correction has played a key role in mitigating long wavelength atmospheric effects . The resultant InSAR displacements together with the GPS displacements are used to recover the time dependent afterslip distribution on the Hikurangi subduction interface which provides insights for reviewing the co seismic slip sources the present status of the subduction plate boundary and future seismic hazards .
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An atmospheric correction method for InSAR time series over a large spatial extent. Evidence of triggered afterslips on the southwestern Hikurangi subduction interface. Afterslip implies a low co seismic moment release on the subduction interface.
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S0034425720304715
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Over the past decade sequential laser scanning has become increasingly routine for geomorphic monitoring in steep mountainous environments . Laser scans represent a permanent record of the terrain topology at a given point in time . Comparison of multitemporal datasets facilitates the identification of changes occurring in the terrain over time . Vegetation points in laser scans can inhibit the alignment of sequential scans .
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Raster based tree extraction is hindered when applied in mountainous terrain. Multi scale geometric operators are used to classify vegetation in 3D point clouds. Voxel based region growing is used to delineate individual trees. Delineated trees are directly incorporated into rockfall runout modelling. Overclassification of trees overestimates the protective capacity of the forest
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S0034425720304727
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The recent and projected investments across the world on radar satellite missions provide a great opportunity for operational radar soil moisture mapping with high spatial and temporal resolution . However there is no retrieval algorithm that can make complementary use of the multi frequency data from those missions due to the large uncertainties in observations collected by the different sensors different validity regions of the forward models and the fact that inversion algorithms have been designed for specific data sources . In this study the principle of ensemble learning was introduced to provide two general soil moisture retrieval frameworks accounting for these issues . Instead of trying to find an optimal global solution multiple soil moisture retrievals with moderate performance were first obtained using different channels and or time instances randomly selected from the available data with the retrieved ensemble of results being the final output . The ensemble retrievals taking one existing snapshot method and two multi temporal methods as the base retrieval algorithms were evaluated using a synthetic data set with the effectiveness confirmed under various uncertainty sources . An evaluation using the Fifth Soil Moisture Active Passive Experiment data set showed that the ensemble retrieval outperformed the non ensemble retrieval in most cases with a decrease of 0.004 to 0.014m
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Ensemble learning was introduced in multi SAR soil moisture retrieval. Ensemble retrieval outperformed single retrieval using existing algorithms. Robust and diverse sub retrievals is the condition of effective ensemble retrieval
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S0034425720304752
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Terrestrial laser scanning was introduced for basic forest measurements such as tree height and diameter in the early 2000s . Recent advances in sensor and algorithm development have allowed us to assess in situ 3D forest structure explicitly and revolutionised the way we monitor and quantify ecosystem structure and function . Here we provide an interdisciplinary focus to explore current developments in TLS to measure and monitor forest structure . We argue that TLS data will play a critical role in understanding fundamental ecological questions about tree size and shape allometric scaling metabolic function and plasticity of form . Furthermore these new developments enable new applications such as radiative transfer modelling with realistic virtual forests monitoring of urban forests and larger scale ecosystem monitoring through long range scanning . Finally we discuss upscaling of TLS data through data fusion with unmanned aerial vehicles airborne and spaceborne data as well as the essential role of TLS in validation of spaceborne missions that monitor ecosystem structure .
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Terrestrial laser scanning TLS provides explicit in situ 3D forest structure. We provide a review on current developments in TLS to monitor forest structure. TLS data opens a realm of untapped ecological questions.
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S0034425720304764
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Information about forest stand species distribution is essential for biodiversity modelling forest disturbances fire hazard and drought monitoring biomass and carbon estimation detection of non native and invasive species as well as for planning forest management strategies . High temporal and spectral resolution remote sensing data from the Sentinel 2 mission enables the derivation of accurate and timely maps of tree species in forests in a cost efficient way . However there is still a lack of studies regarding forest stand species mapping for large areas like the Polish Carpathian Mountains approx . 20 000km
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Forest stand species mapping in a large mountainous area using Sentinel 2. FORCE software was used to produce seamless BAP and STM composites. SVM provided highest classification accuracy outperforming RF and XGB models. Ensemble approach provided valuable information on classification precision.
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S0034425720304776
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Global lake evaporation is a critical component of the terrestrial water cycle . Accurate quantification of lake evaporation dynamics is of high importance for understanding lake energy budgets land atmosphere interactions as well as regional water availability . However the accurate quantification of lake evaporation has been hindered by the complexity involved with addressing the heat storage of water bodies . In this study a new modelthe Lake Temperature and Evaporation Model was developed to simulate lake water temperature profiles which were then used to calculate heat storage changes and evaporation rates . Inputs for the LTEM include the meteorological and bathymetric data as well as the Moderate Resolution Imaging Spectroradiometer water surface temperature which is the land surface temperature over water . The MODIS WST was leveraged to constrain the hydrodynamic simulations . Model results over 11 lakes around the world show robust performance of LTEM . The long term average temperature biases range from 0.5 C to 0.5 C and the evaporation rate biases range from 0.19 mm day to 0.28 mm day . In particular it is found that LTEM significantly improves the simulation of the seasonality of lake evaporation rates . The validation results suggest that the averaged coefficient of determination R
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A framework was developed to quantify evaporation losses for lakes using MODIS. MODIS water surface temperature was used to constrain the heat storage simulation. Both lake temperature profiles and evaporation rates were accurately simulated. The framework can potentially benefit a broad spectrum of applications across scales.
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S0034425720304788
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Land cover mapping in complex environments can be challenging due to their landscape heterogeneity . With the increasing availability of various open access remotely sensed datasets more images acquired by different sensors and on different dates tend to be used to improve land cover classification accuracy . Selecting an appropriate feature domain with the best landscape separability is therefore crucial in meeting the requirement of computational efficiency and model interpretability . Variable selection is widely used in pattern recognition to enhance model parsimony . This study focused on the variable selection process and proposed a series of methods to select the optimal feature domain to improve land cover classification in a complex urbanized coastal area . Two decision tree models and five variable importance measures based on random forests were considered . Variable importance measures were applied to a set of spectral spatial and temporal features derived from medium resolution satellite images . Backward elimination methods were used to select the optimal feature subset . It is found that compared to the traditional band only model the variable selection process can significantly improve the model parsimony and computational efficiency . The CPVIM based on CIT decision tree model was more reliable in selecting relevant features regardless their correlations but CART tended to generate higher classification accuracy . Therefore the combination of the CART model and the ranking from the CPVIM variable measure is recommended to achieve higher classification accuracy and better data interpretability . The novelty of our work is with the insight into the merits of integrating variable selection in the land cover classification process over complex environments .
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Variable selection can significantly improve coastal land cover classification. The selection of variable importance measures may vary by data types. Conditional permutated variable importance measure was reliable for correlated data. Conditional Inference Tree took more time but did not necessarily improve accuracy.
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S0034425720304806
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Rivers are among the world s most threatened ecosystems . Enabled by the rapid development of drone technology hyperspatial resolution images of fluvial environments are now a common data source used to better understand these sensitive habitats . However the task of image classification remains challenging for this type of imagery and the application of traditional classification algorithms such as maximum likelihood still in common use among the river remote sensing community yields unsatisfactory results . We explore the possibility that a classifier of river imagery based on deep learning methods can provide a significant improvement in our ability to classify fluvial scenes . We assemble a dataset composed of RGB images from 11 rivers in Canada Italy Japan the United Kingdom and Costa Rica . The images were labelled into 5 land cover classes water dry exposed sediment green vegetation senescent vegetation and roads . In total 5 billion pixels were labelled and partitioned for the tasks of training and validation . We develop a novel supervised learning workflow based on the NASNet convolutional neural network called CNN Supervised Classification . First we compare the classification performance of maximum likelihood a multilayer perceptron a random forest and CSC . Results show median F1 scores of 71 78 72 and 95 respectively . Second we train our classifier using data for 5 of 11 rivers . We then predict the validation data for all 11 rivers . For the 5 rivers that were used in model training median F1 scores reach 98 . For the 6 rivers not used in model training median F1 scores are 90 . We reach two conclusions . First in the traditional workflow where images are classified one at a time CSC delivers an unprecedented mix of labour savings and classification F1 scores above 95 . Second deep learning can predict land cover classifications for rivers not used in training . This demonstrates the potential to train a generalised open source deep learning model for airborne river surveys suitable for most rivers out of the box . Research efforts should now focus on further development of a new generation of deep learning classification tools that will encode human image interpretation abilities and allow for fully automated potentially real time interpretation of riverine landscape images .
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Deep Learning can classify RGB river imagery to 90 99 F1. This result exceeds the state of the art in fluvial scene classification. Deep Learning models can encode river features that transfer to new rivers. Hyper and multispectral data are not required. We provide open source GIS integration via PyQGIS.
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S0034425720304879
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Characterizing pre and post fire fuels remains a key challenge for estimating biomass consumption and carbon emissions from wildfires . Airborne laser scanning data have demonstrated effectiveness for estimating canopy and to a lesser degree surface fuel components at fine scale across landscapes . Using pre and post fire ALS data and corresponding field data this study estimated consumption of canopy fuel understory fuel total fuel and canopy bulk density for the 2012 Pole Creek fire in Oregon USA and portions of the 2011 Las Conchas fire in New Mexico USA . Additionally the feasibility of predicting fuel consumption was tested using separate pre and post fire models models combining all pre and post fire data and models using all data from both fires . Estimates of TF were then compared to fire radiative energy derived from Fire Radiative Power observations from the Moderate Resolution Imaging Spectroradiometer sensor onboard NASA Terra and Aqua satellites to mechanistically derive a biomass combustion coefficient BCC units kg MJ
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Multitemporal ALS quantifies consumption of canopy and understory fuel. Capturing full range of forest fuels is important for accurate modelling. ALS fuel models exhibit temporal and spatial transferability. Link between biomass consumption and FRE is consistent with prior studies.
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S0034528817306422
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Idiopathic dilated cardiomyopathy is an important etiology of mortality and morbidity in dogs and its diagnosis relies on systolic dysfunction chambers dilation electrical instability and congestion . During the last decades veterinary cardiologists have been joining efforts to obtain diagnostic resources to correctly identify canine DCM in the preclinical stage . Unfortunately most diagnostic resources have been used with the support of research with weak evidence without high quality methodologies such as systematic reviews or meta analysis . Therefore the support of evidence based medicine is tailored by empiricism and diagnostic criteria lose out the ability to properly classify dogs suffering DCM . The presentation of the evidence in medicine is established by multiple sources and the most reliable source has been the presentation of evidence based medicine from systematic reviews and meta analysis . Rapid reviews can be interpreted as a pragmatic approach to systematic reviews and although a rapid review follows most of the critical steps of a systematic review to provide timely evidence some components of a systematic review process are either simplified or omitted . The objective of this narrative evidence based rapid review is twofold . First To recognize and to stratify the level of evidence offered by rigorous selected papers about the diagnosis of DCM . Second To classify the degree of clinical recommendation of the diagnostic resources available .
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There is a lack of studies for diagnosis of DCM offering the highest level of evidence. cTnI measurement to rule out preclinical stage of DCM must be not recommended. The assessment of NT proBNP is not recommended to detect DCM in preclinical stage. biplanar Simpson s method of discs must be considered as gold standard tests. narrative evidence base rapid review is not an attempt to create guidelines.
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S0034528818315327
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To examine the adaptive physiological responses to increasing salinity of drinking water in a choice situation twelve female non lactating Boer goats were used . After a control period with fresh water in phase 2 the choice between different salt concentrations and tap water was offered for two weeks . Subsequently goats were stepwise habituated to saline water by only offering the choice between salted water with different increasing concentrations for four weeks . In phase 4 the procedure of phase 2 was repeated . BW was not affected by saline water intake whereas BCS decreased . Total water intakes differed between ages
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Stepwise adaptation to saline water concentrations up to 1.5 is a suitable method. High saline water intake over a short duration had no detrimental effect on health. Goats adapted to salt water without damage to liver and kidney functions. Adult goats tolerate higher salt water concentrations compared to younger ones.
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S0034528818315844
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Chondrocyte dedifferentiation is a key limitation in therapies based on autologous chondrocyte implantation for cartilage repair . Articular chondrocytes obtained from joints of different aged horses were cultured in monolayer for several passages . Cumulative Populations Doublings Levels and gene expression of relevant chondrocyte phenotypic markers were analysed during culturing . Overall data confirmed that during proliferation
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Proliferative phase after P3 seems to be critical for maintenance loss of differentiation potential. Chondrocytes from elderly horses go through an early dedifferentiation and a reduction of their proliferative capacity. Related trend of Col6 fibromodulin Sox6 and TGF 1 to changes of Runx2 for monitoring of the dedifferentiation process. Chondrocytes from adult donors showed a more stable expression of mature cartilage markers.
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S0034528818316424
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The dog has been used extensively as an experimental model to study meniscal treatments such as meniscectomy meniscal repair and regeneration . Accurate quantification of meniscal size and morphology are a crucial step for developing models of the meniscus . 3.0T magnetic resonance imaging has been found to be highly accurate in analyzing the meniscus in both clinical and research fields . However 3.0T MRI systems are still uncommonly used in veterinary medicine . The goal of the study was to compare meniscal volume measurements from 1.5T MRI system with 3.0T MRI system using proton density sequence a clinically relevant protocol . The MR images were segmented to reconstruct 3D surface representations of both medial and lateral menisci to compare the meniscal volumes measurements . Average volume differences were 8.8
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MR meniscal volume measurements from 1.5T and 3.0T MRI systems using a clinically relevant protocol were compared. Meniscal volumes showed no significant differences between MRI magnet strength image views and different stifle positions. 1.5T MRI is as accurate as a 3T MRI for meniscal volume measurements. Meniscal volume measurements from both MRI systems showed high inter and intraobserver reproducibility.
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S0034528818353979
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The aim of this study is to evaluate the safety and efficacy of gamithromycin for the treatment of naturally occurring bacterial swine respiratory disease administered IM . A total of 240 pigs were selected from two sites in Heilongjiang Province of China . The pigs showed severe signs of respiratory disease . Among them 120 pigs were randomly divided into 4 groups of low dose middle dose high dose GAM IM injection and 2.5mg kg tulathromycin IM injection for phase II clinical trial to screen effective therapeutic dose . The other 120 pigs were randomly divided into 2 groups of 6mg kg GAM IM injection and 2.5mg kg TUL IM injection for phase III clinical trial to further confirm the efficacy . Animals were clinically observed daily for 14days after treatment initiation . The predominant pathogens present in pretreatment respiratory tract samples were
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We suggest that gamithromycin injection given as a single dose of 6mg kg is an effective treatment for bacterial SRD. We found two strains of. insensitive to gamithromycin although gamithromycin has not been used in China. Gamithromycin can be used to treat infections caused by
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S0034528818354468
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It was the aim of the study to assess the impact of a minor surgical intervention under general anaesthesia on results of a low dose dexamethasone suppression test in dogs . Five clinically healthy dogs underwent a LDDST prior and 1 4 7 14 and 28days after a dental restoration under general anaesthesia . All LDDSTs revealed negative results . On all test days after intervention some dogs had basal cortisol concentrations below the reference range . Accordingly plasma cortisol concentrations 4 and 8h after dexamethasone injection were noticeably lower than before surgery and often even below the lower detection limit of 2.0ng ml . The study results may indicate a suppressive effect of a minor surgery under general anaesthesia on cortisol measurements during LDDSTs . It may be speculated that this could possibly lead to false negative test results in the postsurgical period although transfer of these results to clinical cases is subject to limitations .
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Low dose dexamethasone suppression test is negative post surgery in dogs without HAC. Hypothalamus pituitary adrenal axis maybe surprisingly long suppressed in dogs after minor surgery under anaesthesia. Low dose dexamethasone suppression test results must be carefully interpreted in the postsurgical period
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S0034528819300979
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As most pathogens invade the bodies through the mucosa it is crucial to develop vaccines that induce mucosal immunity . To this end we generated a safe and effective vaccine candidate that displayed fimbrial protein 987P of enterotoxigenic
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We displayed fimbrial protein 987P of ETEC by using PgsA as an anchoring matrix on the surface of. CICC 6105. The recombinant. expressing ETEC 987P fimbrial protein elicited an effective protective immune response against ETEC 987P infection. The recombinant. could be a safe and effective mucosal vaccine.
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S0034528819301304
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Pediculosis is a parasitic disease that is considered a serious global public health problem . It is caused by the ectoparasite that is popularly known as lice mainly affecting children in early childhood . The most commonly used treatment to combat this parasitosis is the macrocyclic lactone ivermectin . However the use of IVM is contraindicated in children who are younger than 5years old or who weigh 15kg because some types of drugs that are used during certain periods of brain maturation can lead to behavioral disorders . The present study evaluated the effects of IVM treatment during the prepubertal and pubertal period on sexual behavior in adulthood in male rats . Genital grooming preputial separation sexual behavior sexual motivation relative organ weight the gonadosomatic index and histopathology were evaluated . Oral dose of 0.2mg kg of a commercial IVM formulation was administered . IVM affected genital grooming but did not influence preputial separation in prepubertal rats . Prepubertal IVM administration did not impair sexual behavior in adult rats with the exception of the time of residence with female rats in the sexual motivation test . It did not affect relative organ weights with the exception of the relative weight of the full seminal vesicle . It did not alter the gonadosomatic index and no histopathological alterations were observed in different organs . These results indicate that administration of a therapeutic dose of IVM during the prepubertal and pubertal period does not alter parameters of sexual development or sexual behavior in adult male rats .
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Ivermectin altered the frequency of genital grooming on postnatal day 40 in male pubertal rats. Ivermectin did not influence preputial separation in male pubertal rats. Ivermectin altered sexual motivation in adult male rats. Ivermectin did not impair sexual behavior in adult male rats.
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S0034528819301444
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In the context of significant public health benefits of brucellosis control and shrinking public resources for livestock vaccination this paper considers the willingness of small ruminant livestock owners to pay for vaccination of their animals against brucellosis . The willingness to pay is estimated through a binary choice contingent valuation approach using data from a rural household survey specially designed for this purpose . The survey was conducted in southern Tajikistan one of its poorest regions in March 2009 . The study used a non parametric method for estimating the willingness to pay and a parametric model for identifying determinants . The results show that households including poor households were willing to pay for continuing vaccination of their sheep and goats against brucellosis . Controlling for other attributes of willingness to pay there was practically no correlation between willingness to pay and household asset level . This means both poor and rich alike are willing to pay for the service . On the other hand the results also show that the willingness to pay was comparatively higher in households with relatively higher levels of education of adult females . This suggests that an awareness campaign targeted at female members of households would enhance the ownership and coverage of cost recovery programs and should form an integral part of any efforts towards introducing financial participation from sheep and goat owners for brucellosis vaccination .
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Smallholder households are willing to share the cost of controlling brucellosis in small ruminants. Prior demonstration that bi annual animal vaccinations were convenient and effective facilitated willingness to pay. Willingness to pay is not correlated with household asset level or small ruminant flock size. Vaccinated animals must be identified in order to monitor vaccination coverage. Comparatively higher education of adult females in households is correlated with higher willingness to pay.
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S0034528819302358
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Flavonoids have shown beneficial effects in various disease conditions as reported by various previous studies . Biochanin A is a flavonoid present in various plants in nature . Present investigation was done to assess the vasorelaxant potential of biochanin A on isolated coronary artery of goat and its possible mechanism of action . Vascular reactivity experiments were done on circumflex coronary artery of goats using the tension experiments . Goat coronary arterial rings were relaxed with biochanin A in concentration dependent manner . Endothelium had no effect on biochanin A induced relaxation . Maximum relaxation induced by biochanin A was 116.5412.21 in endothelium intact artery and it was not significantly different with maximal relaxation of endothelium denuded vessel . L NAME did not show any effect on biochanin A induced relaxation . TEA BK
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Biochanin A showed concentration dependent relaxation in the isolated coronary artery of goat. Relaxation response induced by the biochanin A was mediated through the voltage gated potassium channels. Ca. 1.2 channels were also involved in the biochanin A induced relaxation in the goat coronary artery.
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S0034528819302474
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Mammary tumors represent the second most common neoplasia in the canine species where more than 50 of the cases are classified as malignant . The histological classification is used as a prognostic tool . Cadherins and catenins are responsible for cell adhesion and are intrinsic connected with the process of metastasis . E cadherin expression in canine mammary tissues have been extensively studied . However the studies with catenins are still scarce in the canine species . This study evaluated 74 canine mammary tissues by assessing the expression of E cadherin and and P 120 catenin molecules using the immunohistochemistry technique and their relationship with clinicopathological parameters . Three patterns of expression were identified in this study membranous cytoplasmic and both . In benign tumors more than 80 of the cases had preserved expression and in malignant tumors 20 of the cases had reduced expression . A correlation between E cadherin and P 120 catenin expression was found as well as a significant relationship between the histological type and the expression of catenin in malignant tumors .
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The Nottingham System was statistically significant when compared histological type. 0 020 and metastasis. 0 027. The relationship between E cadherin expression and p 120 catenin was statistically significant p 0 024. There was a significant relationship between catenin expression and the histological type in malignant tumors p 0 050 .
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S0034528819303108
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is a parasitic filarial nematode responsible for heartworm disease in domestic as well as wild canines and felines and pulmonary or cutaneous infections in humans . This systematic review and meta analysis aimed to evaluate the status of
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The prevalence of. infection in dogs worldwide was 10.91 . Significant differences detected between the prevalence of. in dogs and country.
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S0034528819303285
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Presentation of viral epitopes by swine MHC I to cytotoxic T lymphocytes is crucial for swine immunity . The SLA 2 structure however remains largely unknown . To illustrate the structural basis of swine CTL epitope presentation the crystal structure of SLA 2 04 02 02 complexed with one peptide derived from foot and mouth disease virus was analyzed in this study . SLA 2 04 02 02 and swine 2 microglobulin were refolded
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Structure of swine leukocyte antigen 2 SLA 2 was analyzed. A potential FMDV epitope was identified with SLA 2. Results are relevant to development of an FMDV vaccine.
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S0034528819304138
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People s concern with the health of their animals has grown as the concern with their own health . The phenomenon known as humanization of animals has contributed to the emergence of this awareness about the care that animal owners need to have in relation with food thus creating a new market nourishment of natural and healthy foods . With revenues of US 91 billion in 2018 the pet food market is seen as a valuable market with great growth prospects for years to come . Nutrition is one of the most important parameters for the maintenance of animal health . Paying close attention to this new trend the pet industry has been launching and betting on new products that work for the improvement of the quality of life of domestic animals . The present work carries out a national and international technological review on natural food interesting components and their benefits in pet food definitions food market and their trends .
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Nutrition is one of the main factors related to the maintenance of the health of dogs and cats. The new natural commercial food options are niches of great potential market. With revenues of US 91 billion in 2018 the pet food market is seen as a valuable market.
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S0034528819304254
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In dogs decreasing telomere length is a biomarker for cellular aging . On a systemic level aging affects the locomotor system in particular leading to restricted joint mobility . As aging is thought to be related to oxidative stress it may be counteracted by a diet enriched with antioxidants mitochondrial cofactors and omega 3 fatty acids . This randomized blinded and placebo controlled study examined the influence of an accordingly enriched diet compared to a control diet on 36 young and 38 old shepherd dogs . At the outset after 3 and after 6 months mean and minimum telomere lengths were measured . Furthermore minimum and maximum joint angles and range of motion of the shoulder elbow carpal hip stifle and tarsal joints were measured by computer assisted gait analysis . A positive influence of the enriched diet on old dogs could be verified for minimum telomere length and all three parameters of the shoulder joint on the side with the higher vertical ground reaction force after 6 months . In the other joints there were less significant differences in some cases they indicated a contrary influence of the enriched diet on young dogs probably due to its reduced protein content . The greater effect of the enriched diet on minimum than on mean telomere length may be due to the higher preference of telomerase for short telomeres . The greater effect on shoulder joint mobility is explained by the greater influence of musculature and connective tissue in this joint . For elderly dogs it is advisable to feed these nutritional supplements .
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For elderly dogs it is useful to feed a diet enriched with antioxidants mitochondrial cofactors and omega 3 fatty acids. This was confirmed for minimum telomere length and shoulder joint mobility on the side with the higher vertical impulse. Greater effect on minimum than on mean telomere length may be due to telomerases higher preference for short telomeres. Greater effect on shoulder joint mobility may be due to a greater influence of muscles and connective tissue in this joint. In younger dogs the results were inconclusive and need further investigation.
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S0034528819304321
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Feral pigeons have increased in urban settings worldwide becoming a potential health risk for humans and other animals . Control and surveillance programs are essential to prevent the possible transmission of zoonotic pathogens carried by pigeons . A surveillance program was carried out in Madrid City during 20052014 to determine the role of urban pigeons as carriers of zoonotic agents comparing these results with studies performed elsewhere in the last fifteen years .
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1372 pigeons as zoonotic carriers were assessed in Madrid Spain during 20052014. spp. and or. spp. were investigated. A relative low prevalence was detected suggesting a limited health risk in Madrid. Management programs of peridomestic animals should be implemented in urban settings.
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S0034528819304618
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Disease surveillance systems effectiveness relies on participants following prescribed practices . We developed a general method to improve a previous cost effectiveness evaluation of three French screening program protocols for bovine tuberculosis to account for the practices of participants by scenario tree modelling . This method relies on 1 semi directive interviews of participants to identify the variability of practices and potentially influential factors and to understand the sociological context 2 a quantitative survey based on multiple choice questions to quantify various practices and identify significantly influential factors by multivariable regression analyses 3 addition of the scenario tree nodes corresponding to the practices and their influential factors and configuration of the new limbs according to the data of the quantitative survey .
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A method for integrating data concerning the practices and perception of veterinarians in scenario tree modelling. ICCT practices in the field decrease the effectiveness of bTB surveillance on French farms. Notification habits decrease the effectiveness of bTB surveillance on French farms.
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S0034528819304667
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Zinc plays an important role in the regulation of insulin like growth factor I . IGF system in turn has a key role in the development and functions of the reproductive organs . This research was performed to investigate the effects of different sources of zinc on IGF I gene expression and testicular development in pre pubertal male Japanese quail . A total of 512 unsexed day old Japanese quail chicks were randomly divided into 16 groups and kept for 35days . The control group diet was not supplemented with zinc whereas the diets of three groups were supplemented with 25mgkg
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Zn Met or ZnON may accelerate the quail sexual development and the onset of puberty. Zn Met or ZnON can improve the IGF family gene expression in the testis of male quail. Maturation of male quail was more efficiently accelerated by Zn Met supplementation.
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S0034528819304886
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The concern over increasing resistance to tetracyclines such as tetracycline and chlortetracycline necessitates exploration of new approaches to combating infection in antimicrobial therapy . Given that bacteria use the chemical language of autoinducer 2 signaling molecules in order to communicate and regulate group behaviors we asked whether the AI 2 signaling influence the tetracyclines antibiotics susceptibility in
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Bacteria use autoinducer 2 AI 2 signaling molecules regulate bacteria behaviors. AI 2 signaling molecules influences tetracyclines resistance in. This regulation mechanism achieved by regulating. M gene via Tn916 transposon.
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S0034528819305090
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Hen eggs provide valuable nutrients for humans including proteins carbohydrates lipids and vitamins . Recent studies revealed a number of novel egg derived proteins peptides and EDPs may play a crucial role in food industry and medical therapy . First these EDPs were purified from the enzyme catalyzed hydrolysates of egg proteins and were characterized by biochemical assays such as gel electrophoresis HPLC mass spectrometry proteomic and peptideomic analysis
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Recent novel biological functions of egg derived peptides EDPs were summarized. Functional EDPs were produced and identified by multiple biological methodologies. The first potential of EDPs is to facilitate modern food processing as additives. The promising medical values of EDPs were demonstrated comprehensively. EDPs may play more crucial roles in food industry as well as medicine development.
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S003452881930565X
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A biomarker is any measurement reflecting an interaction between a biological system and a potential hazard which may be chemical physical or biological . The One World One Health concept established that human and animal health and the environmental state are highly interconnected sharing common aspects that can be applied globally in these three components .
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Information from biomarkers used in one species could be applied to others in the One Health concept. A correct selection of the biomarker to be studied is of high importance since it could be species or specimen dependant. Validation should be made prior to the use of any new assay in the sample type or species were it is planning to be applied.
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S0034528819305958
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On a spring calving pastoral dairy farm the first 40 heifer calves born after calving mid point were blood sampled within 24h . Thirty were selected using stratified randomisation to form two equal groups with the same distribution of serum total protein copper selenium zinc and manganese concentrations age and breed . From the remaining 10 calves five were randomly selected into a sentinel group to assess field exposure to
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Infectious diseases are a major threat to new born calves with 5 11 of dairy calves dying before weaning. Previous studies show lower perinatal sickness and death in calves injected at birth with a trace mineral supplement. We show TMS at birth increased the percentage of white blood cells phagocytosing and bacteria ingested per cell.
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