{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T20:45:23Z","timestamp":1773866723061,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T00:00:00Z","timestamp":1639958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Basemap and Planet Fusion\u2014derived from PlanetScope imagery\u2014represent the next generation of analysis ready datasets that minimize the effects of the presence of clouds. These datasets have high spatial (3 m) and temporal (daily) resolution, which provides an unprecedented opportunity to improve the monitoring of on-farm reservoirs (OFRs)\u2014small water bodies that store freshwater and play important role in surface hydrology and global irrigation activities. In this study, we assessed the usefulness of both datasets to monitor sub-weekly surface area changes of 340 OFRs in eastern Arkansas, USA, and we evaluated the datasets main differences when used to monitor OFRs. When comparing the OFRs surface area derived from Basemap and Planet Fusion to an independent validation dataset, both datasets had high agreement (r2 \u2265 0.87), and small uncertainties, with a mean absolute percent error (MAPE) between 7.05% and 10.08%. Pairwise surface area comparisons between the two datasets and the PlanetScope imagery showed that 61% of the OFRs had r2 \u2265 0.55, and 70% of the OFRs had MAPE &lt;5%. In general, both datasets can be employed to monitor OFRs sub-weekly surface area changes, and Basemap had higher surface area variability and was more susceptible to the presence of cloud shadows and haze when compared to Planet Fusion, which had a smoother time series with less variability and fewer abrupt changes throughout the year. The uncertainties in surface area classification decreased as the OFRs increased in size. In addition, the surface area time series can have high variability, depending on the OFR environmental conditions (e.g., presence of vegetation inside the OFR). Our findings suggest that both datasets can be used to monitor OFRs sub-weekly, seasonal, and inter-annual surface area changes; therefore, these datasets can help improve freshwater management by allowing better assessment and management of the OFRs.<\/jats:p>","DOI":"10.3390\/rs13245176","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T08:43:32Z","timestamp":1639989812000},"page":"5176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6136-8288","authenticated-orcid":false,"given":"Vinicius","family":"Perin","sequence":"first","affiliation":[{"name":"Center for Geospatial Analytics, North Carolina State University, 2800 Faucette Drive, Raleigh, NC 27606, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samapriya","family":"Roy","sequence":"additional","affiliation":[{"name":"Planet Labs Inc., San Francisco, CA 94107, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3337-9251","authenticated-orcid":false,"given":"Joe","family":"Kington","sequence":"additional","affiliation":[{"name":"Planet Labs Inc., San Francisco, CA 94107, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Harris","sequence":"additional","affiliation":[{"name":"Planet Labs Inc., San Francisco, CA 94107, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mirela G.","family":"Tulbure","sequence":"additional","affiliation":[{"name":"Center for Geospatial Analytics, North Carolina State University, 2800 Faucette Drive, Raleigh, NC 27606, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noah","family":"Stone","sequence":"additional","affiliation":[{"name":"Planet Labs Inc., San Francisco, CA 94107, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Torben","family":"Barsballe","sequence":"additional","affiliation":[{"name":"Planet Labs Inc., San Francisco, CA 94107, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michele","family":"Reba","sequence":"additional","affiliation":[{"name":"USDA-ARS Delta Water Management Research Unit, Arkansas State University, Jonesboro, AR 72467, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary A.","family":"Yaeger","sequence":"additional","affiliation":[{"name":"Center for Applied Earth Science and Engineering Research, The University of Memphis, 11 3675 Alumni Drive, Memphis, TN 38152, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,20]]},"reference":[{"key":"ref_1","unstructured":"(2021, September 08). Planet Team Planet Imagery Product Specifications. Available online: https:\/\/assets.planet.com\/docs\/Planet_Combined_Imagery_Product_Specs_letter_screen.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cooley, S.W., Smith, L.C., Stepan, L., and Mascaro, J. (2017). Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9121306"},{"key":"ref_3","first-page":"102218","article-title":"Evaluating the performance of high-resolution satellite imagery in detecting ephemeral water bodies over West Africa","volume":"93","author":"Mishra","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111437","DOI":"10.1016\/j.rse.2019.111437","article-title":"Water storage estimation in ungauged small reservoirs with the TanDEM-X DEM and multi-source satellite observations","volume":"235","author":"Vanthof","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e2021GL092556","DOI":"10.1029\/2021GL092556","article-title":"Effects of Using High Resolution Satellite-Based Inundation Time Series to Estimate Methane Fluxes From Forested Wetlands","volume":"48","author":"Hondula","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4233","DOI":"10.5194\/hess-23-4233-2019","article-title":"River-Ice and Water Velocities Using the Planet Optical Cubesat Constellation","volume":"23","author":"Altena","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111615","DOI":"10.1016\/j.rse.2019.111615","article-title":"Deriving High-Spatiotemporal-Resolution Leaf Area Index for Agroecosystems in the U.S. Corn Belt Using Planet Labs CubeSat and STAIR Fusion Data","volume":"239","author":"Kimm","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.rse.2018.02.067","article-title":"A Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) Utilizing Planet, Landsat and MODIS Data","volume":"209","author":"Houborg","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","first-page":"102260","article-title":"Fusion of Sentinel-2 and PlanetScope Time-Series Data into Daily 3 m Surface Reflectance and Wheat LAI Monitoring","volume":"96","author":"Sadeh","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Csillik, O., and Asner, G.P. (2020). Near-Real Time Aboveground Carbon Emissions in Peru. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0241418"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"17831","DOI":"10.1038\/s41598-019-54386-6","article-title":"Monitoring Tropical Forest Carbon Stocks and Emissions Using Planet Satellite Data","volume":"9","author":"Csillik","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Csillik, O., Kumar, P., and Asner, G.P. (2020). Challenges in Estimating Tropical Forest Canopy Height from Planet Dove Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12071160"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112586","DOI":"10.1016\/j.rse.2021.112586","article-title":"A Global Analysis of the Temporal Availability of PlanetScope High Spatial Resolution Multi-Spectral Imagery","volume":"264","author":"Roy","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112004","DOI":"10.1016\/j.rse.2020.112004","article-title":"Phenology of Short Vegetation Cycles in a Kenyan Rangeland from PlanetScope and Sentinel-2","volume":"248","author":"Cheng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112604","DOI":"10.1016\/j.rse.2021.112604","article-title":"Automatic Cloud and Cloud Shadow Detection in Tropical Areas for PlanetScope Satellite Images","volume":"264","author":"Wang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_16","unstructured":"(2021, September 08). Planet Team Planet Basemaps Product Specification. Available online: https:\/\/assets.planet.com\/products\/basemap\/planet-basemaps-product-specifications.pdf."},{"key":"ref_17","unstructured":"(2021, September 08). Planet Team Planet Fusion Monitoring Technical Specification. Available online: https:\/\/assets.planet.com\/docs\/Planet_fusion_specification_March_2021.pdf."},{"key":"ref_18","unstructured":"Kington, J.D., Jordahl, K.A., Kanwar, A.N., Kapadia, A., Sch\u00f6nert, M., and Wurster, K. (2019, January 9\u201313). IN13B-0716 Spatially and Temporally Consistent Smallsat-Derived Basemaps for Analytic Applications. Proceedings of the American Geophysical Union, Fall Meeting 2019, San Francisco, CA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e2020AV000321","DOI":"10.1029\/2020AV000321","article-title":"Drainage Canals in Southeast Asian Peatlands Increase Carbon Emissions","volume":"2","author":"Dadap","year":"2021","journal-title":"AGU Adv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1007\/s00338-020-02005-6","article-title":"A Global Coral Reef Probability Map Generated Using Convolutional Neural Networks","volume":"39","author":"Li","year":"2020","journal-title":"Coral Reefs"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108255","DOI":"10.1016\/j.agrformet.2020.108255","article-title":"Evaluation of Four Image Fusion NDVI Products against In-Situ Spectral-Measurements over a Heterogeneous Rice Paddy Landscape","volume":"297","author":"Kong","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Houborg, R., and McCabe, M. (2018). Daily Retrieval of NDVI and LAI at 3 m Resolution via the Fusion of CubeSat, Landsat, and MODIS Data. Remote Sens., 10.","DOI":"10.3390\/rs10060890"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106694","DOI":"10.1016\/j.agwat.2020.106694","article-title":"On-Farm Reservoir Monitoring Using Landsat Inundation Datasets","volume":"246","author":"Perin","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1016\/j.scitotenv.2018.06.188","article-title":"The Cumulative Impacts of Small Reservoirs on Hydrology: A Review","volume":"643","author":"Habets","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1080\/13241583.2015.1116182","article-title":"Advances in Assessing the Impact of Hillside Farm Dams on Streamflow","volume":"19","author":"Fowler","year":"2015","journal-title":"Australas. J. Water Resour."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.geomorph.2004.01.010","article-title":"The Role of Impoundments in the Sediment Budget of the Conterminous United States","volume":"71","author":"Renwick","year":"2005","journal-title":"Geomorphology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9","DOI":"10.23818\/limn.29.02","article-title":"Emerging Global Role of Small Lakes and Ponds: Little Things Mean a Lot","volume":"29","author":"Downing","year":"2010","journal-title":"Limnetica"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.4319\/lo.2006.51.5.2388","article-title":"The Global Abundance and Size Distribution of Lakes, Ponds, and Impoundments","volume":"51","author":"Downing","year":"2006","journal-title":"Limnol. Oceanogr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4207","DOI":"10.5194\/hess-18-4207-2014","article-title":"Small Farm Dams: Impact on River Flows and Sustainability in a Context of Climate Change","volume":"18","author":"Habets","year":"2014","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1111\/j.1752-1688.1989.tb05678.x","article-title":"The Impact of Stockwatering Ponds (Stockponds) On Runoff from Large Arizona Watersheds","volume":"25","author":"Mime","year":"1989","journal-title":"JAWRA J. Am. Water Resour. Assoc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jones, S.K., Fremier, A.K., DeClerck, F.A., Smedley, D., Pieck, A.O., and Mulligan, M. (2017). Big Data and Multiple Methods for Mapping Small Reservoirs: Comparing Accuracies for Applications in Agricultural Landscapes. Remote Sens., 9.","DOI":"10.3390\/rs9121307"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.jhydrol.2018.08.076","article-title":"Combining Landsat Observations with Hydrological Modelling for Improved Surface Water Monitoring of Small Lakes","volume":"566","author":"Ogilvie","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Perin, V., Tulbure, M.G., Gaines, M.D., Reba, M.L., and Yaeger, M.A. (2021). A Multi-Sensor Satellite Imagery Approach to Monitor on-Farm Reservoirs. Remote Sens. Environ., 112796.","DOI":"10.1016\/j.rse.2021.112796"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.5194\/hess-22-4349-2018","article-title":"Surface Water Monitoring in Small Water Bodies: Potential and Limits of Multi-Sensor Landsat Time Series","volume":"22","author":"Ogilvie","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"869","DOI":"10.13031\/aea.12352","article-title":"On-Farm Irrigation Reservoirs in Two Arkansas Critical Groundwater Regions: A Comparative Inventory","volume":"33","author":"Yaeger","year":"2017","journal-title":"Appl. Eng. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.agwat.2018.06.040","article-title":"Trends in the Construction of On-Farm Irrigation Reservoirs in Response to Aquifer Decline in Eastern Arkansas: Implications for Conjunctive Water Resource Management","volume":"208","author":"Yaeger","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_37","unstructured":"Shults, D.D., Nowlin, W.J., Yaeger, M.A., Massey, J.H., and Reba, M.L. (2020, January 13\u201316). A Spatiotemporal Anlysis Quantifying the Need for More On-Farm Reservoirs to Reduce Groundwater Use in the Cache and L\u2032Anguille River Regions in Northeaster AR. Proceedings of the ESRI User Conference, Online."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"6762","DOI":"10.1364\/AO.45.006762","article-title":"Validation of a Vector Version of the 6S Radiative Transfer Code for Atmospheric Correction of Satellite Data Part I: Path Radiance","volume":"45","author":"Kotchenova","year":"2006","journal-title":"Appl. Opt."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4455","DOI":"10.1364\/AO.46.004455","article-title":"Validation of a Vector Version of the 6S Radiative Transfer Code for Atmospheric Correction of Satellite Data Part II Homogeneous Lambertian and Anisotropic Surfaces","volume":"46","author":"Kotchenova","year":"2007","journal-title":"Appl. Opt."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Frantz, D. (2019). FORCE\u2014Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sens., 11.","DOI":"10.3390\/rs11091124"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1080\/01431169008955048","article-title":"Technical Note Description of a Computer Code to Simulate the Satellite Signal in the Solar Spectrum: The 5S Code","volume":"11","author":"Tanre","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-Based Cloud and Cloud Shadow Detection in Landsat Imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.rse.2018.04.046","article-title":"Improvement of the Fmask Algorithm for Sentinel-2 Images: Separating Clouds from Bright Surfaces Based on Parallax Effects","volume":"215","author":"Frantz","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_44","unstructured":"(2021, December 10). Planet Team Planet Basemaps: Comprehensive, High-Frequency Mosaics for Analysis. Available online: https:\/\/www.planet.com\/products\/basemap\/."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Doxani, G., Vermote, E., Roger, J.-C., Gascon, F., Adriaensen, S., Frantz, D., Hagolle, O., Hollstein, A., Kirches, G., and Li, F. (2018). Atmospheric Correction Inter-Comparison Exercise. Remote Sens., 10.","DOI":"10.3390\/rs10020352"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/S0034-4257(02)00091-3","article-title":"First Operational BRDF, Albedo Nadir Reflectance Products from MODIS","volume":"83","author":"Schaaf","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1080\/2150704X.2014.960606","article-title":"Analysis of Landsat-8 OLI Imagery for Land Surface Water Mapping","volume":"5","author":"Du","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1080\/01431161.2015.1009653","article-title":"An Automatic Method for Mapping Inland Surface Waterbodies with Radarsat-2 Imagery","volume":"36","author":"Li","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1007\/s12665-016-5686-2","article-title":"Assessing Methods of Identifying Open Water Bodies Using Landsat 8 OLI Imagery","volume":"75","author":"Liu","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2015.12.041","article-title":"Representative Lake Water Extent Mapping at Continental Scales Using Multi-Temporal Landsat-8 Imagery","volume":"185","author":"Sheng","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, R., Zhang, Q., Zhu, Y., Huang, B., and Lu, Z. (2019, January 11\u201313). An Automatic Thresholding Method for Water Body Detection from SAR Image. Proceedings of the 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Chongqing, China.","DOI":"10.1109\/ICSIDP47821.2019.9172964"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"DeVries, B., Huang, C., Lang, M., Jones, J., Huang, W., Creed, I., and Carroll, M. (2017). Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9080807"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"6445","DOI":"10.5194\/hess-21-6445-2017","article-title":"Monitoring Small Reservoirs\u2032 Storage with Satellite Remote Sensing in Inaccessible Areas","volume":"21","author":"Avisse","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_57","first-page":"036503","article-title":"Mapping Small and Medium-Sized Water Reservoirs Using Sentinel-1A: A Case Study in Chiapas, Mexico","volume":"14","author":"Henao","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Pena-Regueiro, J., Sebasti\u00e1-Frasquet, M.-T., Estornell, J., and Aguilar-Maldonado, J.A. (2020). Sentinel-2 Application to the Surface Characterization of Small Water Bodies in Wetlands. Water, 12.","DOI":"10.3390\/w12051487"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhao, S., Qin, X., Zhao, N., and Liang, L. (2017). Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening. Remote Sens., 9.","DOI":"10.3390\/rs9060596"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"111803","DOI":"10.1016\/j.rse.2020.111803","article-title":"Monthly Estimation of the Surface Water Extent in France at a 10-m Resolution Using Sentinel-2 Data","volume":"244","author":"Yang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"064011","DOI":"10.1088\/1748-9326\/aab5d3","article-title":"Lake Storage Variation on the Endorheic Tibetan Plateau and Its Attribution to Climate Change since the New Millennium","volume":"13","author":"Yao","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.scitotenv.2016.07.024","article-title":"Bathymetric Survey of Water Reservoirs in North-Eastern Brazil Based on TanDEM-X Satellite Data","volume":"571","author":"Zhang","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1080\/02626667.2010.484903","article-title":"Estimation Des Incertitudes Lors de La Simulation Des Impacts de Petites Retenues Agricoles Sur Les R\u00e9gimes d\u2032\u00e9coulement En Afrique Du Sud","volume":"55","author":"Hughes","year":"2010","journal-title":"Hydrol. Sci. J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1109\/TGRS.2019.2950705","article-title":"A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions","volume":"58","author":"Ahmad","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5176\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:49:55Z","timestamp":1760168995000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,20]]},"references-count":64,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245176"],"URL":"https:\/\/doi.org\/10.3390\/rs13245176","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,20]]}}}