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However, the impact of waterlogging on winter wheat yield on a large scale, compared with drought caused by water deficit, remains unclear. In this study, we assessed the waterlogging damage to winter wheat yield using the downscaled and fused TRMM 3B42 from 1998 to 2014. First, we downscaled the TRMM 3B42 with area-to-point kriging (APK) and fused it with rain gauge measurements using geographically weighted regression kriging (GWRK). Then, we calculated the accumulated number of rainy days (ARD) of different continuous rain processes (CRPs) with durations ranging from 5 to 15 days as a waterlogging indicator. A quadratic polynomial model was used to fit the yield change rate (YCR) and the waterlogging indicator, and the waterlogging levels (mild, moderate, and severe) based on the estimated YCR from the optimal model were determined. Our results showed that downscaling the TRMM 3B42 using APK improved the limited accuracy, while GWRK fusion significantly increased the precision of quantitative indicators, such as R (from 0.67 to 0.84), and the detectability of precipitation events, such as the probability of detection (POD) (from 0.60 to 0.78). Furthermore, we found that 67% of the variation in the YCR could be explained by the ARD of a CRP of 11 days, followed by the ARD of a CRP of 13 days (R2 of 0.65). During the typical wet growing season of 2001\u20132002, the percentages of mild, moderate, and severe waterlogged pixels were 5.72%, 2.00%, and 0.63%, respectively. Long time series waterlogging spatial mapping can clearly show the distribution and degree of waterlogging, providing a basis for policymakers to carry out waterlogging disaster prevention and mitigation strategies.<\/jats:p>","DOI":"10.3390\/rs15102573","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T02:27:04Z","timestamp":1684204024000},"page":"2573","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Mapping Waterlogging Damage to Winter Wheat Yield Using Downscaling\u2013Merging Satellite Daily Precipitation in the Middle and Lower Reaches of the Yangtze River"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0536-8176","authenticated-orcid":false,"given":"Weiwei","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China"}]},{"given":"Yuanyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang Carbon Neutral Innovation Institute, Zhejiang University of Technology, Hangzhou 310014, China"}]},{"given":"Weiwei","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0226-8492","authenticated-orcid":false,"given":"Ran","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou 310018, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4627-6021","authenticated-orcid":false,"given":"Jingfeng","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Agricultural Remote Sensing and Information Systems, Hangzhou 310058, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"024016","DOI":"10.1088\/1748-9326\/abd500","article-title":"Maize yield loss risk under droughts in observations and crop models in the United States","volume":"16","author":"Leng","year":"2021","journal-title":"Environ. 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