10-m Crop Mapping Using Satellite Data, Field Survey and Machine Learning over North America

Spatially explicit crop maps are essential for agricultural management and food security. However, timely producing high-resolution crop maps over large areas with diverse crop types and heterogeneous fields remains a challenge. To date, only a few operational crop mapping programs exist, such as the Cropland Data Layer program in the US and the Annual Crop Inventory program in Canada. Although these 30-m maps have overall accuracies above 85% for major crops, they are produced and delivered publicly several months after harvest and have relatively lower accuracies over fragmented landscapes. In this study, we used an operational workflow to produce 10-m maps for maize and soybean over North America, from 2019 to 2022. From April to November each year, we downloaded Sentinel-2 surface reflectance data and corrected the bidirectional reflectance distribution function (BRDF) effects to generate nadir BRDF-adjusted reflectance. We then created 10-day composites and gridded the data in non-overlapping 1° × 1° tiles. We implemented temporal linear interpolation on the pixel basis to fill the data gaps in the composites. We collected a large amount of field data as reference and trained multi-year random forests models for maize and soybean, respectively. We validated the crop maps using a probability field sample as reference. Results showed that our map in 2022 achieved an overall accuracy of 95.5 ± 0.4%. The user’s and producer’s accuracy for maize were 93.7 ± 1.5% and 82.6 ± 2.4%, and for soybean were 91.8 ± 1.9% and 84.7 ± 1.8%, respectively. Our developed workflow can produce high-resolution crop maps at the continental scale. We anticipate that this framework is applicable for operational annual crop mapping in other large areas.

Li, H., Song, X.P., Adusei, B., Pickering, J. and Hansen, M. (2023). 10-m Crop Mapping Using Satellite Data, Field Survey and Machine Learning over North America. AGU Fall Meeting, Abstract GC53H-0902, December, San Franscico, California, USA.