Development of a 10 m Resolution Maize and Soybean Map Over China

Spatially explicit crop information is essential for food security and agricultural sustainability. However, high-resolution crop type maps are unavailable in most countries. In this study, we developed an experimental workflow and produced the first 10 m resolution maize and soybean map over China. To support crop mapping, we implemented a stratified, two-stage, cluster sampling design and collected ground data over the entire country in 2019. We developed a multi-scale, multi-temporal procedure for mapping, in which field data were used as training to map maize and soybean over the first-stage samples (10 km × 10 km equal-area blocks) with PlanetScope and Sentinel-2 data. Then, the classified blocks were used as training to map maize and soybean over the country with wall-to-wall Sentinel-2 data and random forests. We used all available Sentinel-2 surface reflectance data acquired between April and October, 2019 and created monthly image composites as inputs for random forests. We derived in-season maize and soybean area estimates using the statistical field survey data and a regression estimator. Utilizing the probability output layer of random forests models, we found and applied empirical probability thresholds that matched map-based and sample-based area estimates. Maize area was estimated to be 330,609 ± 34,109 km2, and soybean area was estimated to be 78,107 ± 12,969 km2. Validated using the field samples as reference, our crop type map had an overall accuracy of 91.8 ± 1.2%. The user’s and producer’s accuracy for the maize class were 93.9 ± 2.5% and 79.2 ± 3.6%, respectively, and for the soybean class were 63.6 ± 12.1% and 61.9 ± 11.8%, respectively. Our map-based maize and soybean area estimates had close agreements with government reports at the provincial and prefectural levels, with r2 of 0.90 and 0.92 for maize, and 0.93 and 0.94 for soybean, respectively. Our experimental workflow can generate harmonized results for crop area estimation and crop type mapping simultaneously, within the growing season, and independent from government statistics. As Sentinel-2 data are being acquired consistently and very-high-resolution commercial satellite data are increasingly available, our established workflow may be applied in an operational setting for annual crop type mapping in China and other countries.

Li, H., Song, X.P., Hansen, M.C., Becker-Reshef, I., Adusei, B., Pickering, J., Wang, L., Wang, L., Lin, Z., Zalles, V., Potapov, P., Stehman, S.V. & Justice, C.O. (2022). Development of a 10 m Resolution Maize and Soybean Map Over China. AGU Fall Meeting, Abstract GC23A-03, December 11-15, Chicago, Illinois, USA.