Reviewing the Potential of Sentinel-2 in Assessing the Drought

This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.

[1]  Andreas Walli,et al.  A highly automated algorithm for wetland detection using multi-temporal optical satellite data , 2019, Remote Sensing of Environment.

[2]  C. Justice,et al.  The Harmonized Landsat and Sentinel-2 surface reflectance data set , 2018, Remote Sensing of Environment.

[3]  Hector Nieto,et al.  Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion , 2020, Remote. Sens..

[4]  Xiaodong Li,et al.  Water Bodies' Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band , 2016, Remote. Sens..

[5]  Bryan C. Weare.  Monitoring and Predicting Agricultural Drought: A Global Study , 2006 .

[6]  S. Jones,et al.  The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations , 2017 .

[7]  Shuanghe Shen,et al.  The application of normalized multi-band drought index (NMDI) method in cropland drought monitoring , 2009, Remote Sensing.

[8]  J. Estornell,et al.  Sentinel-2 Application to the Surface Characterization of Small Water Bodies in Wetlands , 2020, Water.

[9]  George Tsakiris,et al.  Assessment of Hydrological Drought Revisited , 2009 .

[10]  Yafit Cohen,et al.  Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery , 2019, Remote. Sens..

[11]  N. Kussul,et al.  Local-scale agricultural drought monitoring with satellite-based multi-sensor time-series , 2020, GIScience & Remote Sensing.

[12]  K. Tian,et al.  Wetland changes and droughts in southwestern China , 2012 .

[13]  Marion Pause,et al.  Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices - Prospects and Case Study , 2020, Remote. Sens..

[14]  K. Cheng,et al.  Spatiotemporal analysis of the agricultural drought risk in Heilongjiang Province, China , 2018, Theoretical and Applied Climatology.

[15]  R. Scott Murray,et al.  An Empirical Algorithm for Estimating Agricultural and Riparian Evapotranspiration Using MODIS Enhanced Vegetation Index and Ground Measurements of ET. II. Application to the Lower Colorado River, U.S , 2009, Remote. Sens..

[16]  F. Gao,et al.  Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data , 2014 .

[17]  Jianchu Xu,et al.  Using leaf area index (LAI) to assess vegetation response to drought in Yunnan province of China , 2017, Journal of Mountain Science.

[18]  Marco Heurich,et al.  Comparison of Landsat-8 and Sentinel-2 Data for Estimation of Leaf Area Index in Temperate Forests , 2019, Remote. Sens..

[19]  Xin Li,et al.  Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data , 2020, Remote. Sens..

[20]  P. Thenkabail,et al.  Semi‐automated methods for mapping wetlands using Landsat ETM+ and SRTM data , 2008 .

[21]  G. Fitzgerald,et al.  WHEAT IRRIGATION MANAGEMENT USING MULTISPECTRAL CROP COEFFICIENTS: I. CROP EVAPOTRANSPIRATION PREDICTION , 2007 .

[22]  Claudia Notarnicola,et al.  Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale , 2018, Remote. Sens..

[23]  Simona Consoli,et al.  Combining Electrical Resistivity Tomography and Satellite Images for Improving Evapotranspiration Estimates of Citrus Orchards , 2019, Remote. Sens..

[24]  Anthi-Eirini K. Vozinaki,et al.  Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach , 2017, Sensors.

[25]  R. P. Pandey,et al.  Integrating Hydro-Meteorological and Physiographic Factors for Assessment of Vulnerability to Drought , 2010 .

[26]  G. Jia,et al.  Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data , 2013 .

[27]  Saeid Homayouni,et al.  The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform , 2018, Remote. Sens..

[28]  K. Cheng,et al.  A Novel Method for Agricultural Drought Risk Assessment , 2019, Water Resources Management.

[29]  Michael. Horswell,et al.  Assessing Vegetation Response to Soil Moisture Fluctuation under Extreme Drought Using Sentinel-2 , 2018, Water.

[30]  A. Dai.  Drought under global warming: a review , 2011 .

[31]  A. Gupta,et al.  Drought disaster challenges and mitigation in India: strategic appraisal , 2011 .

[32]  Heather McNairn,et al.  Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data , 2020 .

[33]  Mannava V. K. Sivakumar,et al.  Information systems in a changing climate: Early warnings and drought risk management , 2014 .

[34]  M. Svoboda,et al.  Propagation of Drought: From Meteorological Drought to Agricultural and Hydrological Drought , 2016 .

[35]  G. Clarke Topp,et al.  State of the art of measuring soil water content , 2003 .

[36]  Zhuguo Ma,et al.  Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China , 2014 .

[37]  Xiaojing Bai,et al.  A Synergistic Methodology for Soil Moisture Estimation in an Alpine Prairie Using Radar and Optical Satellite Data , 2014, Remote. Sens..

[38]  Qinhuo Liu,et al.  Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity , 2019, Remote. Sens..

[39]  Luis S. Pereira,et al.  Estimating reference evapotranspiration with the FAO Penman-Monteith equation using daily weather forecast messages , 2007 .

[40]  M. S. Moran,et al.  Appropriate scale of soil moisture retrieval from high resolution radar imagery for bare and minimally vegetated soils , 2008 .

[41]  P. Nagler,et al.  Vegetation index‐based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems , 2011 .

[42]  Mehrez Zribi,et al.  Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas , 2017, Remote. Sens..

[43]  P. Bogawski,et al.  Comparison and Validation of Selected Evapotranspiration Models for Conditions in Poland (Central Europe) , 2014, Water Resources Management.

[44]  D. Intrigliolo,et al.  Integrating forecast meteorological data into the ArcDualKc model for estimating spatially distributed evapotranspiration rates of a citrus orchard , 2020 .

[45]  Ugur Avdan,et al.  Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands , 2018 .

[46]  William P. Kustas,et al.  Use of remote sensing for evapotranspiration monitoring over land surfaces , 1996 .

[47]  Holly Croft,et al.  Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework , 2015 .

[48]  Aleixandre Verger,et al.  Empirical and Physical Estimation of Canopy Water Content from CHRIS/PROBA Data , 2013, Remote. Sens..

[49]  E. Vaudour,et al.  Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems , 2019, Remote Sensing of Environment.

[50]  Jan G. P. W. Clevers,et al.  Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop , 2017, Remote. Sens..

[51]  Peter M. Atkinson,et al.  Spatio-temporal fusion for daily Sentinel-2 images , 2018 .

[52]  Rodrigo Lilla Manzione,et al.  agriwater: An R package for spatial modelling of energy balance and actual evapotranspiration using satellite images and agrometeorological data , 2019, Environ. Model. Softw..

[53]  Brian O'Connor,et al.  Analysis of Sentinel-2 and RapidEye for Retrieval of Leaf Area Index in a Saltmarsh Using a Radiative Transfer Model , 2019, Remote. Sens..

[54]  Tao Liu,et al.  Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product , 2019 .

[55]  J. Moreno,et al.  Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach , 2019, Agronomy.

[56]  J. Bai,et al.  A combined drought monitoring index based on multi-sensor remote sensing data and machine learning , 2019, Geocarto International.

[57]  Lars Ribbe,et al.  Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon) , 2020, Remote. Sens..

[58]  L. S. Pereira,et al.  Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data , 2015, Remote. Sens..

[59]  Rui Jin,et al.  Estimation of surface soil moisture and roughness from multi-angular ASAR imagery in the Watershed Allied Telemetry Experimental Research (WATER) , 2011 .

[60]  J. F. Maestre-Valero,et al.  Irrigation-Advisor—A Decision Support System for Irrigation of Vegetable Crops , 2019, Water.

[61]  Henry Lin,et al.  Earth's Critical Zone and hydropedology: concepts, characteristics, and advances , 2009 .

[62]  Lei Yan,et al.  Agricultural drought monitoring using European Space Agency Sentinel 3A land surface temperature and normalized difference vegetation index imageries , 2019 .

[63]  Nicolas Barbier,et al.  Remote sensing detection of droughts in Amazonian forest canopies. , 2010, The New phytologist.

[64]  Zifeng Wang,et al.  Multi-Spectral Water Index (MuWI): A Native 10-m Multi-Spectral Water Index for Accurate Water Mapping on Sentinel-2 , 2018, Remote. Sens..

[65]  Mehrez Zribi,et al.  Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data , 2018, Remote. Sens..

[66]  Christian Berger,et al.  Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8 , 2018, Remote. Sens..

[67]  Hamideh Noory,et al.  Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2 , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[68]  Prasad S. Thenkabail,et al.  Evaluation of the Wetland Mapping Methods using Landsat ETM+ and SRTM Data , 2008 .

[69]  G. Senay,et al.  A novel approach for next generation water-use mapping using Landsat and Sentinel-2 satellite data , 2020 .

[70]  David P. Roy,et al.  Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach , 2019, Remote Sensing of Environment.

[71]  Lixin Wang,et al.  A new multi-sensor integrated index for drought monitoring , 2017, Agricultural and Forest Meteorology.

[72]  Guoqing Zhou,et al.  Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review , 2016, Sensors.

[73]  Radoslaw Guzinski,et al.  Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations , 2019, Remote Sensing of Environment.

[74]  Christian Mielke,et al.  Assessment of Red-Edge Position Extraction Techniques: A Case Study for Norway Spruce Forests Using HyMap and Simulated Sentinel-2 Data , 2016 .

[75]  G. D’Urso,et al.  Integrating Sentinel-2 Imagery with AquaCrop for Dynamic Assessment of Tomato Water Requirements in Southern Italy , 2019, Agronomy.

[76]  B. Laxman,et al.  Geospatial analysis of agricultural drought vulnerability using a composite index based on exposure, sensitivity and adaptive capacity , 2015 .

[77]  Luigi Boschetti,et al.  Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion , 2020, Remote. Sens..

[78]  L. Armstrong,et al.  A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in Northeast Thailand , 2015, Natural Hazards.

[79]  M. Hill.  Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect , 2013 .

[80]  Abduwasit Ghulam,et al.  Drought monitoring in Iran using the perpendicular drought indices , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[81]  N. U. Ahmed,et al.  Relations between evaporation coefficients and vegetation indices studied by model simulations , 1994 .

[82]  Tae-Woong Kim,et al.  Assessment of drought hazard, vulnerability, and risk: a case study for administrative districts in South Korea. , 2015 .

[83]  Peter A. Troch,et al.  Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought , 2009, Proceedings of the National Academy of Sciences.

[84]  Qiming Qin,et al.  A re‐examination of perpendicular drought indices , 2008 .

[85]  Wenzhong Shi,et al.  Fusion of Sentinel-2 images , 2016 .

[86]  M. Dumitrașcu,et al.  The assessment of socio-economic vulnerability to drought in Southern Romania (Oltenia Plain) , 2018 .

[87]  F. Rembold,et al.  Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery , 2011 .

[88]  Paul D. Wagner,et al.  Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[89]  M. Um,et al.  Impacts of potential evapotranspiration on drought phenomena in different regions and climate zones. , 2019, The Science of the total environment.

[90]  Qi Gao,et al.  Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution , 2017, Sensors.

[91]  Bradley Z. Carlson,et al.  Monitoring the Seasonal Hydrology of Alpine Wetlands in Response to Snow Cover Dynamics and Summer Climate: A Novel Approach with Sentinel-2 , 2020, Remote. Sens..

[92]  S. Martinis,et al.  Large-scale surface water change observed by Sentinel-2 during the 2018 drought in Germany , 2020 .

[93]  Omid Ghorbanzadeh,et al.  Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data , 2020, Remote. Sens..

[94]  Antônio H. de C. Teixeira,et al.  Determining Regional Actual Evapotranspiration of Irrigated Crops and Natural Vegetation in the São Francisco River Basin (Brazil) Using Remote Sensing and Penman-Monteith Equation , 2010, Remote. Sens..

[95]  Nandin-Erdene Tsendbazar,et al.  Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[96]  Ramesh P. Singh,et al.  Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India , 2003 .

[97]  Shamsuddin Shahid,et al.  Changing Pattern of Droughts during Cropping Seasons of Bangladesh , 2018, Water Resources Management.

[98]  Christian Bernhofer,et al.  Evapotranspiration amplifies European summer drought , 2013 .

[99]  T. Tadesse,et al.  The Vegetation Drought Response Index (VegDRI): A New Integrated Approach for Monitoring Drought Stress in Vegetation , 2008 .

[100]  Piero Toscano,et al.  Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[101]  Feng Zhang,et al.  Estimation of Canopy Water Content by Means of Hyperspectral Indices Based on Drought Stress Gradient Experiments of Maize in the North Plain China , 2015, Remote. Sens..

[102]  Roberta E. Martin,et al.  Progressive forest canopy water loss during the 2012–2015 California drought , 2015, Proceedings of the National Academy of Sciences.

[103]  Martin Kappas,et al.  Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery , 2017, Sensors.

[104]  David P. Roy,et al.  Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices , 2010 .

[105]  J. Dracup,et al.  The Quantification of Drought: An Evaluation of Drought Indices , 2002 .

[106]  Jianxi Huang,et al.  Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation , 2019, Remote. Sens..

[107]  Fiona Cawkwell,et al.  Status of Phenological Research Using Sentinel-2 Data: A Review , 2020, Remote. Sens..

[108]  S. Arafat,et al.  Estimation of Evapotranspiration ETc and Crop Coefficient Kc of Wheat, in south Nile Delta of Egypt Using integrated FAO-56 approach and remote sensing data , 2012 .

[109]  S. Vicente‐Serrano.  Evaluating the Impact of Drought Using Remote Sensing in a Mediterranean, Semi-arid Region , 2007 .

[110]  Michael J. Hayes,et al.  Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness , 2007 .

[111]  V. Singh,et al.  A review of drought concepts , 2010 .

[112]  Roberta E. Martin,et al.  Landscape-scale variation in canopy water content of giant sequoias during drought , 2017, Forest Ecology and Management.

[113]  Francesco Vuolo,et al.  Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy , 2018, Remote Sensing of Environment.

[114]  R. Coluzzi,et al.  Exploring the Use of Sentinel-2 Data to Monitor Heterogeneous Effects of Contextual Drought and Heatwaves on Mediterranean Forests , 2020, Land.

[115]  Regina Below,et al.  Documenting Drought-Related Disasters , 2007 .

[116]  Zhe Zhu,et al.  Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data , 2020, Remote Sensing of Environment.

[117]  Vijay P. Singh,et al.  Assessment of drought vulnerability of the Tarim River basin, Xinjiang, China , 2015, Theoretical and Applied Climatology.

[118]  D. Wilhite,et al.  CHAPfER2UNDERSTANDING THE DROUGHT PHENOMENON:THE ROLE OF DEFINITIONS , 1985 .

[119]  Marco Heurich,et al.  Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[120]  Bingfang Wu,et al.  Agricultural drought mitigating indices derived from the changes in drought characteristics , 2020 .

[121]  J. S. Rubio-Asensio,et al.  A Novel ArcGIS Toolbox for Estimating Crop Water Demands by Integrating the Dual Crop Coefficient Approach with Multi-Satellite Imagery , 2018, Water.

[122]  Jianya Gong,et al.  Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data , 2018, Remote. Sens..

[123]  P. Pinter,et al.  Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index , 2003, Irrigation Science.

[124]  George P. Petropoulos,et al.  A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms , 2018, Environ. Model. Softw..

[125]  Francesco Marinello,et al.  Assessing the Feasibility of Using Sentinel-2 Imagery to Quantify the Impact of Heatwaves on Irrigated Vineyards , 2019, Remote. Sens..

[126]  George P. Petropoulos,et al.  Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning , 2017, Remote. Sens..

[127]  Martin Brandt,et al.  The forgotten land use class: Mapping of fallow fields across the Sahel using Sentinel-2 , 2020, Remote Sensing of Environment.

[128]  Bidisha Ghosh,et al.  Monitoring environmental supporting conditions of a raised bog using remote sensing techniques , 2018, Proceedings of the International Association of Hydrological Sciences.

[129]  Feng Gao,et al.  A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land , 2012, Remote. Sens..

[130]  Sergio M. Vicente-Serrano,et al.  To die or not to die: early warnings of tree dieback in response to a severe drought , 2015 .

[131]  Gerald Forkuor,et al.  Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso , 2018 .

[132]  W. Filek,et al.  The effect of drought stress on chlorophyll fluorescence in Lolium-Festuca hybrids , 2006, Acta Physiologiae Plantarum.

[133]  Lei Wang,et al.  Developing a fused vegetation temperature condition index for drought monitoring at field scales using Sentinel-2 and MODIS imagery , 2020, Comput. Electron. Agric..

[134]  M. R. Rahman,et al.  Meteorological drought in Bangladesh: assessing, analysing and hazard mapping using SPI, GIS and monthly rainfall data , 2016, Environmental Earth Sciences.

[135]  Karl Segl,et al.  Spectral harmonization and red edge prediction of Landsat-8 to Sentinel-2 using land cover optimized multivariate regressors , 2020, Remote Sensing of Environment.

[136]  J. Zhao,et al.  Higher temporal evapotranspiration estimation with improved SEBS model from geostationary meteorological satellite data , 2019, Scientific Reports.

[137]  Offer Rozenstein,et al.  A new approach for biocrust and vegetation monitoring in drylands using multi-temporal Sentinel-2 images , 2019, Progress in Physical Geography: Earth and Environment.

[138]  M. Palecki,et al.  THE DROUGHT MONITOR , 2002 .

[139]  Juan Manuel Sánchez,et al.  Estimating high resolution evapotranspiration from disaggregated thermal images , 2016 .

[140]  Brigitte Poulin,et al.  Mapping flooding regimes in Camargue wetlands using seasonal multispectral data , 2013 .

[141]  J. Marengo,et al.  Frequency, duration and severity of drought in the Semiarid Northeast Brazil region , 2018 .

[142]  Bin Chen,et al.  Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. , 2019, Science bulletin.

[143]  W. Liang,et al.  Spatial structure of surface soil water content in a natural forested headwater catchment with a subtropical monsoon climate , 2014 .

[144]  Mark A. Friedl,et al.  Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery , 2020, Remote Sensing of Environment.

[145]  G. Asner,et al.  Drought impacts on the Amazon forest: the remote sensing perspective. , 2010, The New phytologist.

[146]  Jing Chen,et al.  Retrieving seasonal variation in chlorophyll content of overstory and understory sugar maple leaves from leaf-level hyperspectral data , 2007 .

[147]  Elaine Wheaton,et al.  Drought as a natural disaster , 1995 .

[148]  Alan L. Flint,et al.  Use of the Priestley-Taylor evaporation equation for soil water limited conditions in a small forest clearcut , 1991 .

[149]  K. Price,et al.  Regional vegetation die-off in response to global-change-type drought. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[150]  P. Kempeneers,et al.  Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline , 2019, Remote sensing of environment.

[151]  Q. Tong,et al.  Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices , 2017 .

[152]  Bruce K. Wylie,et al.  Spatiotemporal Analysis of Landsat-8 and Sentinel-2 Data to Support Monitoring of Dryland Ecosystems , 2018, Remote. Sens..

[153]  Trisha Deevia Bhaga,et al.  Satellite monitoring of surface water variability in the drought prone Western Cape, South Africa , 2020 .

[154]  D. Wilhite.  Drought as a natural hazard : Concepts and definitions , 2000 .

[155]  F. Fassnacht,et al.  Monitoring Andean high altitude wetlands in central Chile with seasonal optical data: A comparison between Worldview-2 and Sentinel-2 imagery , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[156]  Andrea Nardini,et al.  Correlation of Field-Measured and Remotely Sensed Plant Water Status as a Tool to Monitor the Risk of Drought-Induced Forest Decline , 2020, Forests.

[157]  Joachim Hill,et al.  The Potential of EnMAP and Sentinel-2 Data for Detecting Drought Stress Phenomena in Deciduous Forest Communities , 2015, Remote. Sens..

[158]  Na Zhao,et al.  Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening , 2017, Remote. Sens..

[159]  L. Naidoo,et al.  Estimating soil moisture using Sentinel-1 and Sentinel-2 sensors for dryland and palustrine wetland areasa , 2020 .

[160]  Yuei-An Liou,et al.  Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review , 2014 .

[161]  J. M. Moore,et al.  Pixel block intensity modulation: Adding spatial detail to TM band 6 thermal imagery , 1998 .

[162]  Jan G. P. W. Clevers,et al.  Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[163]  Pierre Grussenmeyer,et al.  Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery , 2018, Remote Sensing of Environment.

[164]  Hector Nieto,et al.  Feasibility of Using the Two-Source Energy Balance Model (TSEB) with Sentinel-2 and Sentinel-3 Images to Analyze the Spatio-Temporal Variability of Vine Water Status in a Vineyard , 2020, Remote. Sens..

[165]  Luis Alonso,et al.  Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content , 2011, Sensors.

[166]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[167]  Gregoriy Kaplan,et al.  Estimating cotton water consumption using a time series of Sentinel-2 imagery , 2018 .

[168]  J. Im,et al.  Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions , 2016 .

[169]  Zhongxin Chen,et al.  Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation , 2019, Sensors.

[170]  Marcello Chiaberge,et al.  Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN) , 2019, Applied Sciences.

[171]  P. C. Nayak,et al.  Drought indicators-based integrated assessment of drought vulnerability: a case study of Bundelkhand droughts in central India , 2016, Natural Hazards.

[172]  Shengzhi Huang,et al.  Assessing socio-economic drought evolution characteristics and their possible meteorological driving force , 2019, Geomatics, Natural Hazards and Risk.

[173]  Philip Marzahn,et al.  Validation of Sentinel-2 fAPAR products using ground observations across three forest ecosystems , 2019, Remote Sensing of Environment.

[174]  M. Rautiainen,et al.  Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index , 2017 .