Mapping Waterlogging Damage to Winter Wheat Yield Using Downscaling–Merging Satellite Daily Precipitation in the Middle and Lower Reaches of the Yangtze River

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-05, Vol.15 (10), p.2573
• Main Authors: Liu, Weiwei, Chen, Yuanyuan, Sun, Weiwei, Huang, Ran, Huang, Jingfeng
• Format: Article
• Language: English
• Subjects: Agricultural development; Agricultural production; Chlorophyll; Climate change; Crop yield; daily precipitation fusion; Damage; Datasets; Drought; Emergency preparedness; Extreme weather; Global warming; Growing season; Mapping; Mathematical analysis; Polynomials; Precipitation; Rain; Rain and rainfall; Rain gauges; Remote sensing; Rivers; Seasons; Statistical analysis; Triticum aestivum; TRMM 3B42; TRMM satellite; Vegetation; Water deficit; Waterlogging; waterlogging mapping; Wheat; Winter wheat; winter wheat yield
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15102573

Excessive water and water deficit are two important factors that limit agricultural development worldwide. 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 2001–2002, 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.