Unraveling the spatial-temporal patterns of typhoon impacts on maize during the milk stage in Northeast China in 2020

Abstract

Typhoons are increasing in inland areas and affecting agriculture under climate change. Assessing the large-scale impact of typhoons on crops is essential for improving agricultural resilience. However, typhoon impacts are typically intricate and uncertain, depending on the different stages of crop growth and the indicators used for assessment. Here, we leveraged satellite data to examine the spatial-temporal patterns of typhoon-induced impacts on maize during the milk stage in Northeast China in 2020. We found that land surface water index (LSWI) showed a significantly higher negative relative change of 161.5% in regions severely affected by typhoons, surpassing that of normalized difference vegetation index (NDVI, 19.7%), near-infrared reflectance of vegetation (NIRV, 31.7%), and enhanced vegetation index (EVI, 25.2%), suggesting the outperformance of LSWI in monitoring the impact of typhoons on maize. Based on LSWI, we revealed the spatial-temporal distribution of typhoon impacts. Specifically, significant impacts on maize growth were observed in central Northeast China, particularly in Heilongjiang (49% of maize fields) and Jilin provinces (53% of maize fields). Furthermore, week-long lagging typhoon impacts on maize growth were generally found but lasted until the end of the maize growing season. We also found the typhoon impacts were partially mitigated by spatial and temporal compensations. Regions with above-average maize growth offset the losses experienced in areas severely affected by typhoons. Additionally, better maize growth in the early stages enhanced its resilience against subsequent typhoon impacts. Despite the mitigating effects on maize production, typhoons may increase the risk of maize mold, which warrants further investigation. Given the projected increase in future climate extremes, this study provides valuable insights into the rapid assessment of typhoon impacts using satellite data and cloud computing and supports decision-making in crop management for sustainable agricultural production.