{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T22:42:16Z","timestamp":1777934536786,"version":"3.51.4"},"reference-count":23,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T00:00:00Z","timestamp":1606348800000},"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>On 10 August 2020, a series of intense and fast-moving windstorms known as a derecho caused widespread damage across Iowa\u2019s (the top US corn-producing state) agricultural regions. This severe weather event bent and flattened crops over approximately one-third of the state. Immediate evaluation of the disaster\u2019s impact on agricultural lands, including maps of crop damage, was critical to enabling a rapid response by government agencies, insurance companies, and the agricultural supply chain. Given the very large area impacted by the disaster, satellite imagery stands out as the most efficient means of estimating the disaster impact. In this study, we used time-series of Sentinel-1 data to detect the impacted fields. We developed an in-season crop type map using Harmonized Landsat and Sentinel-2 data to assess the impact on important commodity crops. We intersected a SAR-based damage map with an in-season crop type map to create damaged area maps for corn and soybean fields. In total, we identified 2.59 million acres as damaged by the derecho, consisting of 1.99 million acres of corn and 0.6 million acres of soybean fields. Also, we categorized the impacted fields to three classes of mild impacts, medium impacts and high impacts. In total, 1.087 million acres of corn and 0.206 million acres of soybean were categorized as high impacted fields.<\/jats:p>","DOI":"10.3390\/rs12233878","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T09:04:15Z","timestamp":1606381455000},"page":"3878","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Evaluating the Impact of the 2020 Iowa Derecho on Corn and Soybean Fields Using Synthetic Aperture Radar"],"prefix":"10.3390","volume":"12","author":[{"given":"Mehdi","family":"Hosseini","sequence":"first","affiliation":[{"name":"NASA-Harvest, Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3259-7759","authenticated-orcid":false,"given":"Hannah R.","family":"Kerner","sequence":"additional","affiliation":[{"name":"NASA-Harvest, Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6418-289X","authenticated-orcid":false,"given":"Ritvik","family":"Sahajpal","sequence":"additional","affiliation":[{"name":"NASA-Harvest, Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Estefania","family":"Puricelli","sequence":"additional","affiliation":[{"name":"NASA-Harvest, Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Hsiang","family":"Lu","sequence":"additional","affiliation":[{"name":"NASA-Harvest, Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0183-5435","authenticated-orcid":false,"given":"Afolarin Fahd","family":"Lawal","sequence":"additional","affiliation":[{"name":"NASA-Harvest, Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael L.","family":"Humber","sequence":"additional","affiliation":[{"name":"NASA-Harvest, Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary","family":"Mitkish","sequence":"additional","affiliation":[{"name":"NASA-Harvest, Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seth","family":"Meyer","sequence":"additional","affiliation":[{"name":"Food and Agricultural Policy Research Institute, University of Missouri, Columbia, MO 65211, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Inbal","family":"Becker-Reshef","sequence":"additional","affiliation":[{"name":"NASA-Harvest, Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,26]]},"reference":[{"key":"ref_1","unstructured":"USDA-NASS (2020, September 20). Iowa Ag News\u20132019 Crop Production, Available online: https:\/\/www.nass.usda.gov\/Statistics_by_State\/Iowa\/Publications\/Crop_Report\/2020\/IA-Crop-Production-Annual-01-20.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7112","DOI":"10.1080\/01431161.2020.1754494","article-title":"Synthetic Aperture Radar (SAR) Image Processing for Operational Space-Based Agriculture Mapping","volume":"41","author":"Davidson","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/LGRS.2011.2165272","article-title":"First demonstration of agriculture height retrieval with PolInSAR airborne data","volume":"9","author":"Irena","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alonso-Gonz\u00e1lez, A., Jagdhuber, T., and Hajnsek, I. (2015, January 22\u201324). Agricultural monitoring with polarimetric SAR time series. Proceedings of the 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), Annecy, France.","DOI":"10.1109\/Multi-Temp.2015.7245798"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kontgis, C., Warren, M.S., Skillman, S.W., Chartrand, R., and Moody, D.I. (2017, January 27\u201329). Leveraging Sentinel-1 time-series data for mapping agricultural land cover and land use in the tropics. Proceedings of the 9th International Workshop on the Analysis of Multi Temporal Remote Sensing Images (MultiTemp), Brugge, Belgium.","DOI":"10.1109\/Multi-Temp.2017.8035199"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_7","first-page":"101933","article-title":"Synthetic Aperture Radar and Optical Satellite Data for Estimating the Biomass of Corn","volume":"83","author":"Hosseini","year":"2019","journal-title":"Int. J. Earth Obs. Geoinf."},{"key":"ref_8","first-page":"101893","article-title":"An investigation of inversion methodologies to retrieve the Leaf Area Index of corn from C-Band backscatter","volume":"82","author":"Mandal","year":"2019","journal-title":"Int. J. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.rse.2015.09.002","article-title":"Estimation of Leaf Area Index (LAI) in corn and soybeans using multi-polarization C- and L-band radar data","volume":"170","author":"Hosseini","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bell, J., Gebremichael, E., Molthan, A., Schultz, L., Meyer, F., and Shrestha, S. (August, January 28). Synthetic Aperture Radar and Optical Remote Sensing of Crop Damage Attributed to Severe Weather in the Central United States. Proceedings of the IGARSS 2019, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899775"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Surek, G., and Nador, G. (2015). Monitoring of Damage in Sunflower and Maize Parcels Using Radar and Optical Time Series Data. J. Sens., 2015.","DOI":"10.1155\/2015\/548506"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111804","DOI":"10.1016\/j.rse.2020.111804","article-title":"Understanding wheat lodging using multi-temporal Sentinel-1 and Sentinel-2 data","volume":"243","author":"Chauhan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_13","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_14","unstructured":"Young, F., Chandler, O., and Apan, A. (2004, January 7\u201310). Crop Hail Damage: Insurance Loss Assessment using Remote Sensing. Proceedings of the Remote Sensing and Photogrammetry Society Conference, Aberdeen, UK."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"517","DOI":"10.5589\/m03-068","article-title":"Applying polarimetric radar imagery for mapping the productivity of wheat crops","volume":"30","author":"McNairn","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"69","DOI":"10.5589\/m11-023","article-title":"The sensitivity of RADARSAT-2 polarimetric SAR data to corn and soybean leaf area index","volume":"37","author":"Jiao","year":"2009","journal-title":"Can. J. Remote Sens."},{"key":"ref_18","unstructured":"Ban, Y. (2016). A Review of Multitemporal Synthetic Aperture Radar (SAR) for Crop Monitoring. Multitemporal Remote Sensing: Methods and Applications, Springer. Chapter 15."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_20","unstructured":"Kerner, H., Becker-Reshef, I., Estefania, I.P., Barker, B., Sahajpal, R., Skakun, S., Gray, P., and Hosseini, M. (2020, January 23\u201327). Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization. Proceedings of the SIGKDD ACM Conference on Knowledge Discovery and Data Mining Workshops, San Diego, CA, USA."},{"key":"ref_21","unstructured":"USDA-NASS (2020, September 20). Crop Production Report. 11 December 2018. Available online: https:\/\/downloads.usda.library.cornell.edu\/usda-esmis\/files\/tm70mv177\/r781wm151\/vm40xw490\/crop1218.pdf."},{"key":"ref_22","unstructured":"USDA-NASS (2020, September 20). Crop Production Report. 9 October 2020. Available online: https:\/\/downloads.usda.library.cornell.edu\/usda-esmis\/files\/tm70mv177\/ng452781q\/mk61s709f\/crop1020.pdf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Vreugdenhil, M., Wagner, W., Bauer-Marschallinger, B., Pfeil, I., Teubner, I., Rudiger, C., and Strauss, P. (2018). Sensitivity of Sentinel-1 Backscatter to Vegetation Dynamics: An Austrian Case Study. 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