{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T08:54:24Z","timestamp":1768035264242,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Thammasat University Research fund under the TSRI","award":["TUFF19\/2564"],"award-info":[{"award-number":["TUFF19\/2564"]}]},{"name":"Thammasat University Research fund under the TSRI","award":["TUFF24\/2565"],"award-info":[{"award-number":["TUFF24\/2565"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Floods and droughts cause catastrophic damage in paddy fields, and farmers need to be compensated for their loss. Mobile applications have allowed farmers to claim losses by providing mobile photos and polygons of their land plots drawn on satellite base maps. This paper studies diverse methods to verify those claims at a parcel level by employing (i) Normalized Difference Vegetation Index (NDVI) and (ii) Normalized Difference Water Index (NDWI) on Sentinel-2A images, (iii) Classification and Regression Tree (CART) on Sentinel-1 SAR GRD images, and (iv) a convolutional neural network (CNN) on mobile photos. To address the disturbance from clouds, we study the combination of multi-modal methods\u2014NDVI+CNN and NDWI+CNN\u2014that allow 86.21% and 83.79% accuracy in flood detection and 73.40% and 81.91% in drought detection, respectively. The SAR-based method outperforms the other methods in terms of accuracy in flood (98.77%) and drought (99.44%) detection, data acquisition, parcel coverage, cloud disturbance, and observing the area proportion of disasters in the field. The experiments conclude that the method of CART on SAR images is the most reliable to verify farmers\u2019 claims for compensation. In addition, the CNN-based method\u2019s performance on mobile photos is adequate, providing an alternative for the CART method in the case of data unavailability while using SAR images.<\/jats:p>","DOI":"10.3390\/rs14236095","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T03:00:36Z","timestamp":1669950036000},"page":"6095","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Parcel-Level Flood and Drought Detection for Insurance Using Sentinel-2A, Sentinel-1 SAR GRD and Mobile Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9346-4848","authenticated-orcid":false,"given":"Aakash","family":"Thapa","sequence":"first","affiliation":[{"name":"School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3452-8845","authenticated-orcid":false,"given":"Teerayut","family":"Horanont","sequence":"additional","affiliation":[{"name":"School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5331-9897","authenticated-orcid":false,"given":"Bipul","family":"Neupane","sequence":"additional","affiliation":[{"name":"Advanced Geospatial Technology Research Unit, Sirindhorn International Institute of Technology, Pathum Thani 12000, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"key":"ref_1","unstructured":"(2022, September 24). Rice Sector at a Glance, Available online: https:\/\/www.ers.usda.gov\/topics\/crops\/rice\/rice-sector-at-a-glance\/."},{"key":"ref_2","unstructured":"(2022, September 24). Disasters to Hit Rice Output. Available online: https:\/\/www.bangkokpost.com\/business\/1789579\/disasters-to-hit-rice-output."},{"key":"ref_3","unstructured":"(2022, August 23). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/."},{"key":"ref_4","unstructured":"(2022, August 23). Google Earth Engine. Available online: https:\/\/earthengine.google.com\/."},{"key":"ref_5","unstructured":"(2022, August 23). EarthExplorer, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_6","unstructured":"(2022, August 23). EO Browser. Available online: https:\/\/apps.sentinel-hub.com\/eo-browser\/."},{"key":"ref_7","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_8","first-page":"11","article-title":"Sentinel-1 GRD preprocessing workflow","volume":"18","author":"Filipponi","year":"2019","journal-title":"Multidiscip. Digit. Publ. Inst. Proc."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gascon, F., Bouzinac, C., Th\u00e9paut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S., and Gaudel-Vacaresse, A. (2017). Copernicus Sentinel-2A calibration and products validation status. Remote Sens., 9.","DOI":"10.3390\/rs9060584"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TGRS.2012.2235447","article-title":"Experimental evaluation of Sentinel-2 spectral response functions for NDVI time-series continuity","volume":"51","author":"Gonsamo","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","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_12","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_13","unstructured":"EESA (2015). Sentinel-2 User Handbook, EESA. ESA Standard Document."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cavallo, C., Papa, M.N., Gargiulo, M., Palau-Salvador, G., Vezza, P., and Ruello, G. (2021). Continuous monitoring of the flooding dynamics in the Albufera Wetland (Spain) by Landsat-8 and Sentinel-2 datasets. Remote Sens., 13.","DOI":"10.3390\/rs13173525"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.rse.2019.01.019","article-title":"Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region","volume":"223","author":"Astola","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_16","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_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","unstructured":"(2022, August 28). Earth Engine Data Catalog. Available online: https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/sentinel\/."},{"key":"ref_19","first-page":"2021","article-title":"What is Synthetic Aperture Radar","volume":"27","author":"Herndon","year":"2020","journal-title":"Retrieved January"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1002\/widm.8","article-title":"Classification and regression trees","volume":"1","author":"Loh","year":"2011","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1038\/nbt1206-1565","article-title":"What is a support vector machine?","volume":"24","author":"Noble","year":"2006","journal-title":"Nat. Biotechnol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Thapa, A., Neupane, B., and Horanont, T. (2022, January 2\u20137). Object vs Pixel-based Flood\/Drought Detection in Paddy Fields using Deep Learning. Proceedings of the 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI), Kanazawa, Japan.","DOI":"10.1109\/IIAIAAI55812.2022.00095"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Arbel\u00e1ez, P., Hariharan, B., Gu, C., Gupta, S., Bourdev, L., and Malik, J. (2012, January 16\u201321). Semantic segmentation using regions and parts. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248077"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Roy Choudhury, A., Vanguri, R., Jambawalikar, S.R., and Kumar, P. (2018, January 16). Segmentation of brain tumors using DeepLabv3+. Proceedings of the International MICCAI Brainlesion Workshop, Granada, Spain.","DOI":"10.1007\/978-3-030-11726-9_14"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","article-title":"Places: A 10 million image database for scene recognition","volume":"40","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Neupane, B., Horanont, T., and Aryal, J. (2021). Deep learning-based semantic segmentation of urban features in satellite images: A review and meta-analysis. Remote Sens., 13.","DOI":"10.3390\/rs13040808"},{"key":"ref_28","unstructured":"(2022, September 11). MaliSorn. Available online: https:\/\/farminsure.infuse.co.th\/."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A review of vegetation indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Aryal, J., Sitaula, C., and Aryal, S. (2022). NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia. Land, 11.","DOI":"10.3390\/land11030351"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gessesse, A.A., and Melesse, A.M. (2019). Temporal relationships between time series CHIRPS-rainfall estimation and eMODIS-NDVI satellite images in Amhara Region, Ethiopia. Extreme Hydrology and Climate Variability, Elsevier.","DOI":"10.1016\/B978-0-12-815998-9.00008-7"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gupta, V.D., Areendran, G., Raj, K., Ghosh, S., Dutta, S., and Sahana, M. (2021). Assessing habitat suitability of leopards (Panthera pardus) in unprotected scrublands of Bera, Rajasthan, India. Forest Resources Resilience and Conflicts, Elsevier.","DOI":"10.1016\/B978-0-12-822931-6.00026-5"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_35","unstructured":"(2022, September 11). NDWI: Normalized Difference Water Index. Available online: https:\/\/eos.com\/make-an-analysis\/ndwi\/."},{"key":"ref_36","unstructured":"(2022, September 11). Sentinel-1 SAR GRD: C-Band Synthetic Aperture Radar Ground Range. Available online: https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/COPERNICUS_S1_GRD."},{"key":"ref_37","unstructured":"(2022, September 11). Interferometric Wide Swath. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/user-guides\/sentinel-1-sar\/acquisition-modes\/interferometric-wide-swath."},{"key":"ref_38","unstructured":"(2022, September 11). Stripmap. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/user-guides\/sentinel-1-sar\/acquisition-modes\/stripmap."},{"key":"ref_39","unstructured":"(2022, September 11). Extra Wide Swath. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/user-guides\/sentinel-1-sar\/acquisition-modes\/extra-wide-swath."},{"key":"ref_40","unstructured":"(2022, September 11). Acquisition Modes. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/user-guides\/sentinel-1-sar\/acquisition-modes."},{"key":"ref_41","unstructured":"(2022, September 11). Machine Learning in Earth Engine. Available online: https:\/\/developers.google.com\/earth-engine\/guides\/machine-learning."},{"key":"ref_42","unstructured":"(2022, September 11). Supervised Classification. Available online: https:\/\/developers.google.com\/earth-engine\/guides\/classification."},{"key":"ref_43","unstructured":"(2022, September 11). Unsupervised Classification (Clustering). Available online: https:\/\/developers.google.com\/earth-engine\/guides\/clustering."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hardy, A., Ettritch, G., Cross, D.E., Bunting, P., Liywalii, F., Sakala, J., Silumesii, A., Singini, D., Smith, M., and Willis, T. (2019). Automatic detection of open and vegetated water bodies using Sentinel 1 to map African malaria vector mosquito breeding habitats. Remote Sens., 11.","DOI":"10.3390\/rs11050593"},{"key":"ref_45","first-page":"168","article-title":"Remote sensing monitoring of flood and disaster analysis in Shouguang in 2018 from Sentinel - IB SAR data","volume":"30","author":"Luan","year":"2021","journal-title":"J. Nat. Disasters"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (2017). Classification and Regression Trees, Routledge.","DOI":"10.1201\/9781315139470"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.31557\/APJCP.2019.20.6.1887","article-title":"Classification of skin disease using ensemble data mining techniques","volume":"20","author":"Verma","year":"2019","journal-title":"Asian Pac. J. Cancer Prev."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"108152","DOI":"10.1109\/ACCESS.2019.2931922","article-title":"Moving object detection method via ResNet-18 with encoder\u2013decoder structure in complex scenes","volume":"7","author":"Ou","year":"2019","journal-title":"IEEE Access"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_50","unstructured":"(2022, September 11). Sentinel-2 Cloud Masking with s2cloudless. Available online: https:\/\/developers.google.com\/earth-engine\/tutorials\/community\/sentinel-2-s2cloudless."},{"key":"ref_51","first-page":"012064","article-title":"Using NDVI algorithm in Sentinel-2A imagery for rice productivity estimation (Case study: Compreng sub-district, Subang Regency, West Java)","volume":"Volume 481","author":"Khoirunnisa","year":"2020","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.9790\/0990-0120110","article-title":"NDVI differencing and post-classification to detect vegetation changes in Halabja City, Iraq","volume":"1","author":"Mansor","year":"2013","journal-title":"IOSR J. Appl. Geol. Geophys."},{"key":"ref_53","unstructured":"Simonetti, E., Simonetti, D., and Preatoni, D. (2014). Phenology-Based Land Cover Classification Using Landsat 8 Time Series, European Commission Joint Research Center."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/02757259409532206","article-title":"Speckle filtering of synthetic aperture radar images: A review","volume":"8","author":"Lee","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_55","unstructured":"ESA (2021). Sentinel High Level Operations Plan (HLOP). ESA Unclassified, ESA."},{"key":"ref_56","unstructured":"QGIS Development Team (2022, November 29). QGIS Geographic Information System, Open Source Geospatial Foundation; QGIS Development Team: 2009. Available online: https:\/\/qgis.org\/en\/site\/."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2013.07.015","article-title":"Mapping flooding regimes in Camargue wetlands using seasonal multispectral data","volume":"138","author":"Davranche","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"L06407","DOI":"10.1029\/2006GL029127","article-title":"A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States","volume":"34","author":"Gu","year":"2007","journal-title":"Geophys. Res. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6095\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:32:12Z","timestamp":1760146332000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6095"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":58,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14236095"],"URL":"https:\/\/doi.org\/10.3390\/rs14236095","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,1]]}}}