Remote sensing enhanced multiple cropping systems zoning: A case study in China

Multiple cropping at broad geographic scales is jointly shaped by human and biophysical factors. While existing studies typically map the actual occurrence of multiple cropping or evaluate its climatic potential, few integrate these perspectives to support spatial optimization and agricultural decision-making. To address this gap, we developed a zoning framework for multiple cropping systems in China based on remote sensing data and probability-density analysis. First, we utilized the multiple cropping systems data, which is a third-level remote sensing-derived dataset generated from MODIS time-series imagery through multiple cropping index processing. Then the Kernel Density Estimation method was employed to calculate the probability density of each system. Finally, the boundaries of different multiple cropping zones were delineated and optimized, providing a precise representation of spatial distribution. We defined Double-cropping system, Seasonal-fallowed system, Single-cropping system, and Annual-fallowed system for the zoning scheme and found that these four types of multiple cropping exhibited good aggregation patterns across China. These patterns reveal distinct spatial and temporal disparities, which are shaped by historically multiple cropping differences, leading to spatially discrete zones. In addition, human activities have exerted an increasingly influence on the migration and formation of multiple cropping systems. This study demonstrates the value of enhancing agricultural zoning through remote sensing and provides evidence to support region-specific management strategies aimed at improving resource efficiency, economic returns, and crop production. • We present a case in which remote sensing data supports and enhances the refinement of agricultural zoning. • We developed the zoning scheme considering both and cropping and fallowing practices. • The density estimation method quantitatively characterizes the spatial distribution of the multiple cropping system data. • The remote sensing perspective enables the identification of long-term trends in China's cropping system transformations.