Global synergy cropland map

Information on global cropland distribution is critical for agricultural monitoring and food security. We propose a new self-adapting statistics allocation model (SASAM) that fuses multiple existing maps with national and subnational statistics to develop new global cropland map. This synergy method is based on the agreement between input cropland maps, and is independent of training samples. The statistics of cropland area are used as a standard to rank the input cropland maps and build a score table to indicate the agreement among the input datasets. Statistics of national, first and second subnational levels are allocated to the pixels with higher agreement scores, and the multi-level allocation results are then integrated to obtain the extent of cropland. We applied SASAM to produce a global cropland synergy map with a spatial resolution of 500 m circa 2010. The synergy cropland map is a critical input for the Spatial Production Allocation Model (SPAM) to make global crop distribution (https://doi.org/10.7910/DVN/PRFF8V). It will be the vital baseline information for global land modelling, food production estimation, and food security monitoring to meet the Sustainable Development Goals pertaining to agriculture adopted by the United Nations.