ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021

Abstract

Soybean, an essential food crop, has witnessed a steady rise in demand in recent years. There is a lack of high-resolution annual maps depicting soybean-planting areas in China, despite China being the world's largest consumer and fourth-largest producer of soybean. To address this gap, we developed the novel Regional Adaptation Spectra-Phenology Integration method (RASP) based on Sentinel-2 remote sensing images from the Google Earth Engine (GEE) platform. We utilized various auxiliary data (e.g., cropland layer, detailed phenology observations) to select the specific spectra and indices that differentiate soybeans most effectively from other crops across various regions. These features were then input for an unsupervised classifier (K-means), and the most likely type was determined by a cluster assignment method based on dynamic time warping (DTW). For the first time, we generated a dataset of soybean-planting areas across China, with a high spatial resolution of 10 m, spanning from 2017 to 2021 (ChinaSoyArea10m). The R2 values between the mapping results and the census data at both the county and prefecture levels were consistently around 0.85 in 2017–2020. Moreover, the overall accuracy of the mapping results at the field level in 2017, 2018, and 2019 was 77.08 %, 85.16 %, and 86.77 %, respectively. Consistency with census data was improved at the county level (R2 increased from 0.53 to 0.84) compared to the existing 10 m crop-type maps in Northeast China (Crop Data Layer, CDL) based on field samples and supervised classification methods. ChinaSoyArea10m is very spatially consistent with the two existing datasets (CDL and GLAD (Global Land Analysis and Discovery) maize–soybean map). ChinaSoyArea10m provides important information for sustainable soybean production and management as well as agricultural system modeling and optimization. ChinaSoyArea10m can be downloaded from an open-data repository (DOI: https://doi.org/10.5281/zenodo.10071427, Mei et al., 2023).