{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T16:48:38Z","timestamp":1772902118418,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China Major Program","award":["42192580"],"award-info":[{"award-number":["42192580"]}]},{"name":"National Natural Science Foundation of China Major Program","award":["42192584"],"award-info":[{"award-number":["42192584"]}]},{"name":"National Natural Science Foundation of China Major Program","award":["41975036"],"award-info":[{"award-number":["41975036"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42192580"],"award-info":[{"award-number":["42192580"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42192584"],"award-info":[{"award-number":["42192584"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41975036"],"award-info":[{"award-number":["41975036"]}],"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>Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. Many studies have focused on flood impact assessments for administrative districts, but fewer have focused on postdisaster impact assessments for specific crops. Therefore, this study used remote sensing data, including the normalized difference vegetation index (NDVI), elevation data, slope data, and precipitation data, combined with crop growth period data to construct a crop flood damage assessment index (CFAI). First, the analytic hierarchy process (AHP) was used to assign weights to the impact parameters; then, the Weighted Composite Score Method was used to calculate the CFAI; and finally, the impact was classified as sub-slight, slight, moderate, sub-severe, or severe based on the magnitude of the CFAI. This method was used for the Missouri River floods of 2019 in the United States and the Henan flood of 2021 in China. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to compare the results. Compared with the DVDI, the CFAI underestimated the evaluation results. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments.<\/jats:p>","DOI":"10.3390\/rs16091527","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T03:23:47Z","timestamp":1714101827000},"page":"1527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["The Construction of a Crop Flood Damage Assessment Index to Rapidly Assess the Extent of Postdisaster Impact"],"prefix":"10.3390","volume":"16","author":[{"given":"Yaoshuai","family":"Dang","sequence":"first","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9083-0045","authenticated-orcid":false,"given":"Leiku","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0812-7164","authenticated-orcid":false,"given":"Jinling","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/S2095-3119(16)61499-5","article-title":"RF-CLASS: A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making","volume":"16","author":"Di","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.pce.2011.03.005","article-title":"Estimation of flood losses to agricultural crops using remote sensing","volume":"36","author":"Itzerott","year":"2011","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.agrformet.2019.02.002","article-title":"Remote-sensing disturbance detection index to identify spatio-temporal varying flood impact on crop production","volume":"269","author":"Chen","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1016\/j.gloenvcha.2011.04.005","article-title":"Climate change impacts on pricing long-term flood insurance: A comprehensive study for the Netherlands","volume":"21","author":"Aerts","year":"2011","journal-title":"Glob. Environ. Chang."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1175\/2010BAMS3092.1","article-title":"Have disaster losses increased due to anthropogenic climate change","volume":"92","author":"Bouwer","year":"2011","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_6","unstructured":"(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."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1038\/nclimate1911","article-title":"Global flood risk under climate change","volume":"3","author":"Hirabayashi","year":"2013","journal-title":"Nat. Clim. Change"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Di, S., Guo, L., and Lin, L. (2018, January 6\u20139). Rapid estimation of flood crop loss by using DVDI. Proceedings of the 2018 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Hangzhou, China.","DOI":"10.1109\/Agro-Geoinformatics.2018.8476083"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"5837","DOI":"10.1080\/01431160802029669","article-title":"Use of RADARSAT-1 data and a digital elevation model to assess flood damage and improve rice production in the lower part of the Chi River Basin, Thailand","volume":"29","author":"Waisurasingha","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.advwatres.2017.06.019","article-title":"Floods and food security: A method to estimate the effect of inundation on crops availability","volume":"110","author":"Pacetti","year":"2017","journal-title":"Adv. Water Resour."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/S0022-1694(03)00084-2","article-title":"A mathematical model for flood loss estimation","volume":"277","author":"Dutta","year":"2003","journal-title":"J. Hydrol."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s00254-006-0234-0","article-title":"A methodology using GIS, aerial photos and remote sensing for loss estimation and flood vulnerability analysis in the Supersano-Ruffano-Nociglia Graben, southern Italy","volume":"50","author":"Forte","year":"2006","journal-title":"Environ. Geol."},{"key":"ref_15","first-page":"126","article-title":"Damage estimation on agricultural crops by a flood","volume":"Volume 8174","year":"2011","journal-title":"Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII"},{"key":"ref_16","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.agrformet.2009.11.015","article-title":"Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas","volume":"150","author":"Quiring","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yu, G., Di, L., Zhang, B., Shao, Y., Shrestha, R., and Kang, L. (2013, January 12\u201316). Remote-sensing-based flood damage estimation using crop condition profiles. Proceedings of the 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Fairfax, VA, USA.","DOI":"10.1109\/Argo-Geoinformatics.2013.6621908"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Di, L., Yu, E., Shrestha, R., and Lin, L. (2018, January 22\u201327). DVDI: A new remotely sensed index for measuring vegetation damage caused by natural disasters. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518022"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1080\/01431160110040026","article-title":"Assessment of crop damage using space remote sensing and GIS","volume":"23","author":"Silleos","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shrestha, R., Di, L., Yu, G., Shao, Y., Kang, L., and Zhang, B. (2013, January 12\u201316). Detection of flood and its impact on crops using NDVI-Corn case. Proceedings of the 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Fairfax, VA, USA.","DOI":"10.1109\/Argo-Geoinformatics.2013.6621907"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4374","DOI":"10.1109\/JSTARS.2014.2334332","article-title":"Analysis of NDVI data for crop identification and yield estimation","volume":"7","author":"Huang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1007\/s00484-023-02478-4","article-title":"Modeling crop yield using NDVI-derived VGM metrics across different climatic regions in the USA","volume":"67","author":"Shammi","year":"2023","journal-title":"Int. J. Biometeorol."},{"key":"ref_27","unstructured":"Galphade, M., More, N., Wagh, A., and Nikam, V.B. (2022). Advances in Data Computing, Communication and Security: Proceedings of I3CS2021, Springer Nature Singapore."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/S2095-3119(16)61502-2","article-title":"Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer","volume":"16","author":"Shrestha","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3733","DOI":"10.5194\/nhess-12-3733-2012","article-title":"Comparative flood damage model assessment: Towards a European approach","volume":"12","author":"Jongman","year":"2012","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1061\/(ASCE)1527-6988(2006)7:2(72)","article-title":"HAZUS-MH flood loss estimation methodology. II. Damage and loss assessment","volume":"7","author":"Scawthorn","year":"2006","journal-title":"Nat. Hazards Rev."},{"key":"ref_31","unstructured":"Thieken, A., Ackermann, V., Elmer, F., Kreibich, H., Kuhlmann, B., Kunert, U., Maiwald, H., Merz, B., Piroth, K., and Schwarz, J. (2009, January 6\u20138). Methods for the evaluation of direct and indirect flood losses. Proceedings of the RIMAX Contributions at the 4th International Symposium on Flood Defence (ISFD4), Toronto, ON, Canada."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"311","DOI":"10.5194\/nhess-8-311-2008","article-title":"Assessing flood risk for a rural detention area","volume":"8","author":"Kuhlmann","year":"2008","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_33","unstructured":"Crow, H.A. (2014). Assessment of the FEMA HAZUS-MH 2.0 Crop Loss Tool Fremont County, Iowa 2011, University of Southern California."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.eja.2006.10.007","article-title":"A simple model of regional wheat yield based on NDVI data","volume":"26","author":"Moriondo","year":"2007","journal-title":"Eur. J. Agron."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.agrformet.2010.11.012","article-title":"Crop yield forecasting on the Canadian Prairies using MODIS NDVI data","volume":"151","author":"Mkhabela","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.agrformet.2014.06.007","article-title":"A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation","volume":"197","author":"Son","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"107124","DOI":"10.1016\/j.ecolind.2020.107124","article-title":"Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling","volume":"121","author":"Shammi","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1016\/j.procs.2015.07.081","article-title":"Criteria in AHP: A systematic review of literature","volume":"55","author":"Camanho","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_39","first-page":"83","article-title":"Decision making with the analytic hierarchy process","volume":"1","author":"Saaty","year":"2008","journal-title":"Int. J. Serv. Sci."},{"key":"ref_40","unstructured":"Jing, F., Yang, Y., Deng, S., and Chen, X. (2011). Simulation and Assessment of Flood Disasters. Geospat. Inf., 9."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, M., Zhang, T., Tu, Y., Ren, Z., and Xu, B. (2022). Monitoring Post-Flood Recovery of Croplands Using the Integrated Sentinel-1\/2 Imagery in the Yangtze-Huai River Basin. Remote Sens., 14.","DOI":"10.3390\/rs14030690"},{"key":"ref_42","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1527\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:33:34Z","timestamp":1760106814000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1527"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,26]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16091527"],"URL":"https:\/\/doi.org\/10.3390\/rs16091527","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,26]]}}}