{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T02:58:37Z","timestamp":1783565917919,"version":"3.55.0"},"reference-count":74,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Program of the National Natural Science Foundation of China","award":["20201321441"],"award-info":[{"award-number":["20201321441"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801161, 41801163"],"award-info":[{"award-number":["41801161, 41801163"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The increasingly frequent flooding imposes tremendous and long-lasting damages to lives and properties in impoverished rural areas. Rapid, accurate, and large-scale flood mapping is urgently needed for flood management, and to date has been successfully implemented benefiting from the advancement in remote sensing and cloud computing technology. Yet, the effects of agricultural emergency response to floods have been limitedly evaluated by satellite-based remote sensing, resulting in biased post-flood loss assessments. Addressing this challenge, this study presents a method for monitoring post-flood agricultural recovery using Sentinel-1\/2 imagery, tested in three flood-affected main grain production areas, in the middle and lower Yangtze and Huai River, China. Our results indicated that 33~72% of the affected croplands were replanted and avoided total crop failures in summer 2020. Elevation, flood duration, crop rotation scheme, and flooding emergency management affect the post-flood recovery performance. The findings also demonstrate rapid intervention measures adjusted to local conditions could reduce the agricultural failure cost from flood disasters to a great extent. This study provides a new alternative for comprehensive disaster loss assessment in flood-prone agricultural regions, which will be insightful for worldwide flood control and management.<\/jats:p>","DOI":"10.3390\/rs14030690","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T22:16:18Z","timestamp":1643753778000},"page":"690","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Monitoring Post-Flood Recovery of Croplands Using the Integrated Sentinel-1\/2 Imagery in the Yangtze-Huai River Basin"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5836-3619","authenticated-orcid":false,"given":"Miao","family":"Li","sequence":"first","affiliation":[{"name":"Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China"},{"name":"Ministry of Education Ecological Field Station for East Asian Migratory Birds, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2240-5389","authenticated-orcid":false,"given":"Ying","family":"Tu","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2450-3318","authenticated-orcid":false,"given":"Zhehao","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China"},{"name":"Ministry of Education Ecological Field Station for East Asian Migratory Birds, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Field, C.B., Barros, V., Stocker, T.F., and Dahe, Q. (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.","DOI":"10.1017\/CBO9781139177245"},{"key":"ref_2","unstructured":"Centre for Research on the Epidemiology of Disasters CRED & UN office for Disaster Risk Reduction (2021, May 05). The Human Cost of Disasters: An Overview of the Last 20 Years (2000\u20132019). Available online: https:\/\/reliefweb.int\/sites\/reliefweb.int\/files\/resources\/Human%20Cost%20of%20Disasters%202000-2019%20Report%20-%20UN%20Office%20for%20Disaster%20Risk%20Reduction.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1038\/s41586-021-03695-w","article-title":"Satellite imaging reveals increased proportion of population exposed to floods","volume":"596","author":"Tellman","year":"2021","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1111\/j.1753-318X.2009.01031.x","article-title":"Impacts of the summer 2007 floods on agriculture in England","volume":"2","author":"Posthumus","year":"2009","journal-title":"J. Flood Risk Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6535","DOI":"10.3390\/rs70606535","article-title":"Rapid Assessment of Crop Status: An Application of MODIS and SAR Data to Rice Areas in Leyte, Philippines Affected by Typhoon Haiyan","volume":"7","author":"Boschetti","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.isprsjprs.2020.06.011","article-title":"Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine","volume":"166","author":"Singha","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Uddin, K., Matin, M.A., and Meyer, F.J. (2019). Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh. Remote Sens., 11.","DOI":"10.3390\/rs11131581"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5077","DOI":"10.3390\/rs70505077","article-title":"Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data","volume":"7","author":"Dao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.isprsjprs.2013.09.008","article-title":"Satellite-based investigation of flood-affected rice cultivation areas in Chao Phraya River Delta, Thailand","volume":"86","author":"Son","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","first-page":"4","article-title":"The Impact of Flooding on China\u2019s Agricultural Production and Food Security in 2020","volume":"2020","author":"He","year":"2020","journal-title":"Agric. Policy Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.isprsjprs.2017.11.006","article-title":"An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery","volume":"136","author":"Tong","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111664","DOI":"10.1016\/j.rse.2020.111664","article-title":"Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine","volume":"240","author":"DeVries","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chini, M., Pelich, R., Pulvirenti, L., Pierdicca, N., Hostache, R., and Matgen, P. (2019). Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test Case. Remote Sens., 11.","DOI":"10.3390\/rs11020107"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shen, X., Wang, D., Mao, K., Anagnostou, E., and Hong, Y. (2019). Inundation Extent Mapping by Synthetic Aperture Radar: A Review. Remote Sens., 11.","DOI":"10.3390\/rs11070879"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Psomiadis, E., Diakakis, M., and Soulis, K.X. (2020). Combining SAR and Optical Earth Observation with Hydraulic Simulation for Flood Mapping and Impact Assessment. Remote Sens., 12.","DOI":"10.3390\/rs12233980"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"R\u00e4ttich, M., Martinis, S., and Wieland, M. (2020). Automatic Flood Duration Estimation Based on Multi-Sensor Satellite Data. Remote Sens., 12.","DOI":"10.3390\/rs12040643"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s10333-015-0496-9","article-title":"Assessing the degree of flood damage to rice crops in the Chao Phraya delta, Thailand, using MODIS satellite imaging","volume":"14","author":"Kotera","year":"2016","journal-title":"Paddy Water Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1007\/s11434-016-1167-y","article-title":"Ten years after Hurricane Katrina: Monitoring recovery in New Orleans and the surrounding areas using remote sensing","volume":"61","author":"Li","year":"2016","journal-title":"Sci. Bull."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3457","DOI":"10.1080\/0143116021000021206","article-title":"Assessment of post-flooding conditions of rice fields with multi-temporal satellite SAR data","volume":"24","author":"Lee","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"641","DOI":"10.5194\/isprs-annals-V-3-2020-641-2020","article-title":"Data Processing Architectures for Monitoring Floods Using Sentinel-1","volume":"V-3-2020","author":"Wagner","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1080\/01431161.2017.1420938","article-title":"SAR-based detection of flooded vegetation\u2014A review of characteristics and approaches","volume":"39","author":"Tsyganskaya","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2018.2860054","article-title":"Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches","volume":"57","author":"Landuyt","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3755","DOI":"10.5194\/hess-19-3755-2015","article-title":"A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation","volume":"19","author":"Musa","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_24","first-page":"147","article-title":"Review of water body information extraction based on satellite remote sensing","volume":"60","author":"Dan","year":"2020","journal-title":"J. Tsinghua Univ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40362-017-0043-8","article-title":"Do Remote Sensing Mapping Practices Adequately Address Localized Flooding? A Critical Overview","volume":"5","author":"Malinowski","year":"2017","journal-title":"Springer Sci. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, W., DeVries, B., Huang, C., Lang, M.W., Jones, J.W., Creed, I.F., and Carroll, M.L. (2018). Automated Extraction of Surface Water Extent from Sentinel-1 Data. Remote Sens., 10.","DOI":"10.3390\/rs10050797"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2014.10.027","article-title":"Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification","volume":"158","author":"Dronova","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.rse.2017.04.009","article-title":"Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations","volume":"195","author":"Sagar","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rse.2019.04.010","article-title":"Coastline extraction from repeat high resolution satellite imagery","volume":"229","author":"Dai","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2016.03.031","article-title":"Continuous monitoring of coastline dynamics in western Florida with a 30-year time series of Landsat imagery","volume":"179","author":"Li","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Moya, L., Mas, E., and Koshimura, S. (2020). Learning from the 2018 Western Japan Heavy Rains to Detect Floods during the 2019 Hagibis Typhoon. Remote Sens., 12.","DOI":"10.3390\/rs12142244"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2021.05.019","article-title":"Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning","volume":"178","author":"Jiang","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","first-page":"102400","article-title":"Monitoring the summer flooding in the Poyang Lake area of China in 2020 based on Sentinel-1 data and multiple convolutional neural networks","volume":"102","author":"Dong","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.envsoft.2017.01.006","article-title":"Flood inundation modelling: A review of methods, recent advances and uncertainty analysis","volume":"90","author":"Teng","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"112095","DOI":"10.1016\/j.rse.2020.112095","article-title":"A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication","volume":"251","author":"Liu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s11069-017-2755-0","article-title":"Rapid flood inundation mapping using social media, remote sensing and topographic data","volume":"87","author":"Rosser","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_39","unstructured":"(2021, December 05). Flood-Mapping Tool Could Change How Agricultural Planning Works. Available online: https:\/\/www.openaccessgovernment.org\/flood-mapping-tool\/121488\/."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.rse.2017.09.032","article-title":"Automatic near real-time flood detection using Suomi-NPP\/VIIRS data","volume":"204","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"180309","DOI":"10.1038\/sdata.2018.309","article-title":"GFPLAIN250m, a global high-resolution dataset of Earth\u2019s floodplains","volume":"6","author":"Nardi","year":"2019","journal-title":"Sci. Data"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1002\/rra.3296","article-title":"Hydrologic scaling for hydrogeomorphic floodplain mapping: Insights into human-induced floodplain disconnectivity","volume":"34","author":"Nardi","year":"2018","journal-title":"River Res. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"W09409","DOI":"10.1029\/2005WR004155","article-title":"Investigating a floodplain scaling relation using a hydrogeomorphic delineation method","volume":"42","author":"Nardi","year":"2006","journal-title":"Water Resour. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.rse.2007.01.011","article-title":"Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS time-series imagery","volume":"109","author":"Sakamoto","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rahman, M.S., and Di, L. (2020). A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment. Agriculture, 10.","DOI":"10.3390\/agriculture10040131"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Li, S., Goldberg, M.D., Sjoberg, W., Zhou, L., Nandi, S., Chowdhury, N., Straka, W., Yang, T., and Sun, D. (2020). Assessment of the Catastrophic Asia Floods and Potentially Affected Population in Summer 2020 Using VIIRS Flood Products. Remote Sens., 12.","DOI":"10.3390\/rs12193176"},{"key":"ref_47","unstructured":"(2021, May 05). Eight Flood Storage Areas along the Main Stream of the Huai River are Subsiding Water, Available online: http:\/\/www.gov.cn\/xinwen\/2020-08\/01\/content_5531885.htm."},{"key":"ref_48","first-page":"4517","article-title":"Spatio-temporal distribution of multiple cropping systems in the Poyang Lake region","volume":"28","author":"Yan","year":"2008","journal-title":"Acta Ecol. Sin."},{"key":"ref_49","unstructured":"(2021, December 05). Sentinel-1. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-1."},{"key":"ref_50","unstructured":"(2021, May 22). Sentinel-1 Algorithms. Available online: https:\/\/developers.google.com\/earth-engine\/guides\/sentinel1."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1111\/jfr3.12303","article-title":"Multi-temporal synthetic aperture radar flood mapping using change detection","volume":"11","author":"Clement","year":"2018","journal-title":"J. Flood Risk Manag."},{"key":"ref_52","unstructured":"(2021, May 05). Sentinel-2 User Handbook. Available online: https:\/\/sentinel.esa.int\/documents\/247904\/685211\/Sentinel-2_User_Handbook."},{"key":"ref_53","unstructured":"(2020, December 03). A 30-m Planetary-Scale Cropping Intensity Dataset. Available online: http:\/\/www.geodata.cn\/data\/datadetails.html?dataguid=8950600&docid=96."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"RG2004","DOI":"10.1029\/2005RG000183","article-title":"The Shuttle Radar Topography Mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Moya, L., Endo, Y., Okada, G., Koshimura, S., and Mas, E. (2019). Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods. Remote Sens., 11.","DOI":"10.3390\/rs11192320"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Szeliski, R. (2010). Computer Vision: Algorithms and Applications, Springer Science & Business Media.","DOI":"10.1007\/978-1-84882-935-0"},{"key":"ref_57","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_58","doi-asserted-by":"crossref","unstructured":"Donchyts, G., Schellekens, J., Winsemius, H., Eisemann, E., and van de Giesen, N. (2016). A 30 m Resolution Surface Water Mask Including Estimation of Positional and Thematic Differences Using Landsat 8, SRTM and OpenStreetMap: A Case Study in the Murray-Darling Basin, Australia. Remote Sens., 8.","DOI":"10.3390\/rs8050386"},{"key":"ref_59","unstructured":"Donchyts, G. (2018). Planetary-Scale Surface Water Detection from Space. [Ph.D. Thesis, Delft University of Technology]."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.tree.2005.05.011","article-title":"Using the satellite-derived NDVI to assess ecological responses to environmental change","volume":"20","author":"Pettorelli","year":"2005","journal-title":"Trends Ecol. Evol."},{"key":"ref_61","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":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TGRS.1995.8746027","article-title":"A feedback based modification of the NDVI to minimize canopy background and atmospheric noise","volume":"33","author":"Liu","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","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_65","unstructured":"(2021, May 05). The Ongoing Reconstructions to Regulate the Huai River and Prevent Disasters Benefit Residents for a Long Time. Available online: http:\/\/www.ahnews.com.cn\/yaowen1\/pc\/con\/2020-08\/22\/496_238585.html."},{"key":"ref_66","unstructured":"(2021, April 05). Guidance on Early Rice Harvesting and Baking After Flooding, and Strengthening the Technology Application for Semilate and Late Rice to Garantee a Good Harvest, Available online: http:\/\/nync.jiangxi.gov.cn\/art\/2020\/7\/17\/art_28519_2620024.html."},{"key":"ref_67","unstructured":"(2021, April 04). Technical Measures to Cope with Rice Flooding in Anhui Province. Available online: http:\/\/www.aaas.org.cn\/4303171\/13583673.html."},{"key":"ref_68","unstructured":"Lang, M., Klijn, F., and Samuels, P. (2016, January 17\u201321). Comprehensive flood economic losses: Review of the potential damage and implementation of an agricultural impact model. Proceedings of the 3rd European Conference on Flood Risk Management, Lyon, France."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"102115","DOI":"10.1016\/j.scs.2020.102115","article-title":"Observing community resilience from space: Using nighttime lights to model economic disturbance and recovery pattern in natural disaster","volume":"57","author":"Qiang","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"100392","DOI":"10.1016\/j.wace.2021.100392","article-title":"Persistent impact of spring floods on crop loss in U.S. Midwest","volume":"34","author":"Shirzaei","year":"2021","journal-title":"Weather Clim. Extrem."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1080\/2150704X.2021.1890262","article-title":"Multi-temporal mapping of flood damage to crops using sentinel-1 imagery: A case study of the Sesia River (October 2020)","volume":"12","author":"Samuele","year":"2021","journal-title":"Remote Sens. Lett."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"3700","DOI":"10.1109\/JSTARS.2015.2440439","article-title":"Rapid Damage Assessment of Rice Crop After Large-Scale Flood in the Cambodian Floodplain Using Temporal Spatial Data","volume":"8","author":"Kwak","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"126054","DOI":"10.1016\/j.eja.2020.126054","article-title":"Flooding has more adverse effects on the stem structure and yield of spring maize (Zea mays L.) than waterlogging in Northeast China","volume":"117","author":"Tian","year":"2020","journal-title":"Eur. J. Agron."},{"key":"ref_74","unstructured":"(2021, May 05). The Climate Data Guide. NDVI: Normalized-Difference-Vegetation-Index: NOAA AVHRR. Available online: https:\/\/climatedataguide.ucar.edu\/climate-data\/ndvi-normalized-difference-vegetation-index-noaa-avhrr."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/690\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:12:19Z","timestamp":1760134339000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/690"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,1]]},"references-count":74,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030690"],"URL":"https:\/\/doi.org\/10.3390\/rs14030690","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,1]]}}}