Accurately mapping global wheat production system using deep learning algorithms
Yuchuan Luo, Zhao Zhang, Juan Cao, Liangliang Zhang, Jing Zhang, Jichong Han, Huimin Zhuang, Fei Cheng, Fulu Tao
Abstract
Assessing global food security and developing sustainable production systems need spatially explicit information on crop harvesting areas and yields; however the available datasets are spatially and temporally coarse. Here, we developed a general framework, Global Wheat Production Mapping System (GWPMS), to map the spatial distribution of wheat harvesting area and estimate yield using data-driven models across eight major wheat-producing countries worldwide. We found GWPMS could not only generate robust wheat maps with R2 consistently greater than 0.8, but also successfully captured a substantial fraction of yield variations with an average of 76%. The developed long short-term memory model outperformed other machine learning algorithms because it characterized the nonlinear and cumulative impacts of meteorological factors on yield. Using the derived wheat maps improved R2 by 6.7% compared to a popularly used dataset. GWPMS is able to map spatial distribution of harvesting areas in a scalable way and further estimate gridded-yield robustly, and it can be applied globally using publicly available data. GWPMS and the resultant datasets will greatly accelerate our understanding and studies on global food security.