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Maps of different kinds of crops offer information about both crop distribution and crop mix, which support analyses on food security, environmental change, and climate change. Despite the growing capability for mapping specific crops, the majority of studies have focused on a few dominant crops, whereas maps with a greater diversity of crops lack research. Combining cropping seasons derived from MODIS EVI data, regional crop calendar data, and agricultural statistical surveys, we developed an allocation model to map 14 major crops at a 1 km resolution across China for the years 2000, 2010, and 2015. The model was verified based on the fitness between the area of the three typical combinations of region, crop/crop group derived from remote sensing data, and statistical data. The R2, indicating fitness, ranged from 0.51 to 0.75, with a higher value for the crops distributed in plain regions and a lower value in regions with topographically diverse landscapes. Within the same combination of region and crop/crop group, the larger harvest area a province has, the higher its fitness, suggesting an overall reliable result at the national level. A comparison of paddy rice between our results and the National Land Use/Cover Database of China showed a relatively high R2 and slope of fitness (0.67 and 0.71, respectively). Compared with the commonly used average allocation model, and without lending cropping season information, the diversity index of the results from our model is about 30% higher, indicating crop maps with greater spatial details. According to the spatial distribution analysis of the four main crops, the grids showing decreased trends accounted for 74.92%, 57.32%, and 59.00% of the total changed grid for wheat, rice, and soybean crops, respectively, while accounting for only 37.71% for maize. The resulting data sets can be used to improve assessments for nutrient security and sustainability of cropping systems, as well as their resilience in a changing climate.

Mapping Crop Distribution Patterns and Changes in China from 2000 to 2015 by Fusing Remote-Sensing, Statistics, and Knowledge-Based Crop Phenology