Flood Mapping and Assessment of Crop Damage Based on Multi-Source Remote Sensing: A Case Study of the “7.27” Rainstorm in Hebei Province, China
Flooding is among the world’s most destructive natural disasters. From 27 July to 1 August 2023, Zhuozhou City and surrounding areas in Hebei Province experienced extreme rainfall, severely impacting local food security. To swiftly map the spatial and temporal distribution of the floodwaters and ass...
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MDPI AG
2025-03-01
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| author | Chenhao Wen Zhongchang Sun Hongwei Li Youmei Han Dinoo Gunasekera Yu Chen Hongsheng Zhang Xiayu Zhao |
| author_facet | Chenhao Wen Zhongchang Sun Hongwei Li Youmei Han Dinoo Gunasekera Yu Chen Hongsheng Zhang Xiayu Zhao |
| author_sort | Chenhao Wen |
| collection | DOAJ |
| description | Flooding is among the world’s most destructive natural disasters. From 27 July to 1 August 2023, Zhuozhou City and surrounding areas in Hebei Province experienced extreme rainfall, severely impacting local food security. To swiftly map the spatial and temporal distribution of the floodwaters and assess the damage to major crops, this study proposes a water body identification method with a dual polarization band combination for synthetic-aperture radar (SAR) data to solve the differences in water body feature recognition in SAR due to different polarization modes. Based on the SAR water body extent, the flood inundation extent was mapped with GF-6 optical data. In addition, Landsat-8 data were used to generate information on significant crops in the study area, while Sentinel-2 data and the Google Earth Engine (GEE) platform were used to classify the extent of crop damage. The results indicate that the flood inundated 700.51 km<sup>2</sup>, 14.10% of the study area. Approximately 40,700 hectares (ha) or 8.46% of the main crops were affected, including 33,700 ha of maize, 4300 ha of vegetables, and 2800 ha of beans. Moderate crop damage was the most widespread, affecting 37.62% of the crops, while very extreme damage was the least, affecting 5.10%. Zhuozhou City experienced the most significant impact, with 13,700 ha of crop damage, accounting for 33.70% of the total. This study provides a computational framework for rapid flood monitoring using multi-source remote sensing data, which also serves as a reference for post-disaster recovery, agricultural production, and crop risk assessment. |
| format | Article |
| id | doaj-art-7d168fb47b474630b7ea5439b7bc04fc |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-7d168fb47b474630b7ea5439b7bc04fc2025-08-20T02:58:56ZengMDPI AGRemote Sensing2072-42922025-03-0117590410.3390/rs17050904Flood Mapping and Assessment of Crop Damage Based on Multi-Source Remote Sensing: A Case Study of the “7.27” Rainstorm in Hebei Province, ChinaChenhao Wen0Zhongchang Sun1Hongwei Li2Youmei Han3Dinoo Gunasekera4Yu Chen5Hongsheng Zhang6Xiayu Zhao7School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaSchool of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaDepartment of Geography, The University of Hong Kong, Pokfulam, Hong Kong, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaFlooding is among the world’s most destructive natural disasters. From 27 July to 1 August 2023, Zhuozhou City and surrounding areas in Hebei Province experienced extreme rainfall, severely impacting local food security. To swiftly map the spatial and temporal distribution of the floodwaters and assess the damage to major crops, this study proposes a water body identification method with a dual polarization band combination for synthetic-aperture radar (SAR) data to solve the differences in water body feature recognition in SAR due to different polarization modes. Based on the SAR water body extent, the flood inundation extent was mapped with GF-6 optical data. In addition, Landsat-8 data were used to generate information on significant crops in the study area, while Sentinel-2 data and the Google Earth Engine (GEE) platform were used to classify the extent of crop damage. The results indicate that the flood inundated 700.51 km<sup>2</sup>, 14.10% of the study area. Approximately 40,700 hectares (ha) or 8.46% of the main crops were affected, including 33,700 ha of maize, 4300 ha of vegetables, and 2800 ha of beans. Moderate crop damage was the most widespread, affecting 37.62% of the crops, while very extreme damage was the least, affecting 5.10%. Zhuozhou City experienced the most significant impact, with 13,700 ha of crop damage, accounting for 33.70% of the total. This study provides a computational framework for rapid flood monitoring using multi-source remote sensing data, which also serves as a reference for post-disaster recovery, agricultural production, and crop risk assessment.https://www.mdpi.com/2072-4292/17/5/904floodsagricultural lossesmulti-source remote sensingGF dataSuper Typhoon Doksuri |
| spellingShingle | Chenhao Wen Zhongchang Sun Hongwei Li Youmei Han Dinoo Gunasekera Yu Chen Hongsheng Zhang Xiayu Zhao Flood Mapping and Assessment of Crop Damage Based on Multi-Source Remote Sensing: A Case Study of the “7.27” Rainstorm in Hebei Province, China Remote Sensing floods agricultural losses multi-source remote sensing GF data Super Typhoon Doksuri |
| title | Flood Mapping and Assessment of Crop Damage Based on Multi-Source Remote Sensing: A Case Study of the “7.27” Rainstorm in Hebei Province, China |
| title_full | Flood Mapping and Assessment of Crop Damage Based on Multi-Source Remote Sensing: A Case Study of the “7.27” Rainstorm in Hebei Province, China |
| title_fullStr | Flood Mapping and Assessment of Crop Damage Based on Multi-Source Remote Sensing: A Case Study of the “7.27” Rainstorm in Hebei Province, China |
| title_full_unstemmed | Flood Mapping and Assessment of Crop Damage Based on Multi-Source Remote Sensing: A Case Study of the “7.27” Rainstorm in Hebei Province, China |
| title_short | Flood Mapping and Assessment of Crop Damage Based on Multi-Source Remote Sensing: A Case Study of the “7.27” Rainstorm in Hebei Province, China |
| title_sort | flood mapping and assessment of crop damage based on multi source remote sensing a case study of the 7 27 rainstorm in hebei province china |
| topic | floods agricultural losses multi-source remote sensing GF data Super Typhoon Doksuri |
| url | https://www.mdpi.com/2072-4292/17/5/904 |
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