Spatio-temporal variations and drought of spring maize in Northeast China since 2002

Abstract A lot of maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), however, this area is highly susceptible to drought. NDVI, LSWI, LST, and TVDI datasets from 2002 to 2020 were studied using the 8-day surface reflectance (SR) and land surface temperature (LST) of MODIS in this study. Spring maize distribution data were extracted using a decision tree classification method to reveal spatio-temporal patterns. The occurrences of mild, moderate, and severe droughts were investigated under spatio-temporal variations. The overall accuracy of verifying the spring maize distribution in 2018–2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In the ‘Liandaowan’ region of Northeast China, the government directed to reduce the planting area in 2015. Distribution variations were characterized by continuous growth in the pre-adjustment stage (2002–2014), adjustment and reduction during the in-adjustment stage (2015–2017), and optimization and recovery in the post-adjustment stage (2018–2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas, accounting for 33.62% and 19.83%, respectively. There were more droughts in the pre-adjustment stage in the expanded planting area with a gradual decrease in the latter two stages. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize lays the foundation for improved strategies to maintain field area and improve grain yield. This method could be easily applied to the study of other areas and could be combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy.