Drought Monitoring of Spring Maize in the Songnen Plain Using Multi-Source Remote Sensing Data

Abstract

Agricultural production is highly susceptible to the impact of drought. How to improve agricultural drought-monitoring capability has always been a research hotspot. Based on multi-source remote-sensing data, a novel comprehensive drought index (CDI) for spring maize was developed using the random forest model, and its feasibility was tested by using agricultural drought indices and agricultural statistics in this study. Then, the spatiotemporal characteristics of spring maize drought in the Songnen Plain from 2001 to 2018 were evaluated using the CDI. The results showed that: (1) the CDI effectively monitored spring maize drought in the Songnen Plain, outperforming other drought indices. (2) The monitoring results indicated that spring maize in the Songnen Plain was affected by large-scale droughts in 2001, 2004, 2007, and 2017, which was consistent with national drought disaster statistics. (3) By changing the drought barycenter, the drought barycenter of spring maize generally tended to the south and west of the Songnen Plain, so drought-prevention measures should be strengthened in these areas in the future. While factors affecting crop yield extended beyond drought, the variations in spring maize yield indirectly reflected the effectiveness of drought monitoring in this study.