A Comprehensive Evaluation of Flooding’s Effect on Crops Using Satellite Time Series Data

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-03, Vol.15 (5), p.1305
• Main Authors: Miao, Shuangxi, Zhao, Yixuan, Huang, Jianxi, Li, Xuecao, Wu, Ruohan, Su, Wei, Zeng, Yelu, Guan, Haixiang, Abd Elbasit, Mohamed A. M., Zhang, Junxiao
• Format: Article
• Language: English
• Subjects: Agricultural industry; Agricultural land; Analysis; China; Clouds; corn; crop distribution; Crops; Damage assessment; Datasets; disaster level; Disasters; double Gaussian; Emergency management; Emergency preparedness; Evaluation; Flood damage; Flood management; Flooding; Floods; Precipitation; Rain; Remote sensing; Resilience; Satellite imagery; South Korea; Storm damage; Synthetic aperture radar; Time series; Vegetation index
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15051305

In July 2021, a flooding event, which attracted the attention of the whole country and even the world, broke out in Henan, resulting in dramatic losses across multiple fields (e.g., economic and agricultural). The basin at the junction of Hebi, Xinxiang, and Anyang was the most affected region, as the spread of water from the Wei river submerged surrounding agricultural land (e.g., corn-dominated). To comprehensively evaluate the flooding impacts, we proposed a framework to detect the flooding area and evaluated the degree of loss using satellite time series data. First, we proposed a double-Gaussian model to adaptively determine the threshold for flooding extraction using Synthetic Aperture Radar (SAR) data. Then, we evaluated the disaster levels of flooding with field survey samples and optical satellite images. Finally, given that crops vary in their resilience to flooding, we measured the vegetation index change before and after the flooding event using satellite time series data. We found the proposed double-Gaussian model could accurately extract the flooding area, showing great potential to support in-time flooding evaluation. We also showed that the multispectral satellite images could potentially support the classification of disaster levels (i.e., normal, slight, moderate, and severe), with an overall accuracy of 88%. Although these crops were temporarily affected by this flooding event, most recovered soon, especially for the slightly and moderately affected regions. Overall, the distribution of resilience of these affected crops was basically in line with the results of classified disaster levels. The proposed framework provides a comprehensive aspect to the retrospective study of the flooding process on crops with diverse disaster levels and resilience. It can provide rapid and timely flood damage assessment and support emergency management and disaster verification work.