10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product

•10 m crop type mapping using Sentinel-2 time series surface reflectance and NDVI.•Using 30 m cropland data layer (CDL) to derive training and evaluation samples.•94% & 83% random forest accuracy for South Dakota and California tiles.•10 m map has less aliasing effect and defines better boundaries & linear features. The 30 m resolution U.S. Department of Agriculture (USDA) crop data layer (CDL) is a widely used crop type map for agricultural management and assessment, environmental impact assessment, and food security. A finer resolution crop type map can potentially reduce errors related to crop area estimation, field size characterization, and precision agriculture activities that requires crop growth information at scales finer than crop field. This study is to develop a method for crop type mapping using Sentinel-2 10 m bands (i.e., red, green, blue, and near-infrared) and to examine the benefit of the derived 10 m crop type map. The crop type mapping was conducted for two study areas with significantly different field sizes and crop types in South Dakota and California, respectively. The Sentinel-2 10 m surface reflectance and the derived normalized difference vegetation index (NDVI) acquired in the 2019 growing season were used to generate monthly median composites as classification input. The training and evaluation samples were derived from CDL by (i) finding good quality 30 m CDL pixels and (ii) identifying a single representative Sentinel-2 10 m pixel time series for each 30 m good quality CDL pixel. The random forest algorithm was trained using 80% of the samples and evaluated using the 20% remaining samples, and the results showed high overall accuracies of 94% and 83% for South Dakota and California study areas, respectively. The major crops in both study areas obtained high user’s and producer’s accuracies (>87%). There is a good agreement between the class proportions in the 10 m crop type map and 30 m CDL for both study areas with R2 ≥ 0.94 and root mean square error (RMSE) ≤ 3%. More importantly, compared to the 30 m CDL, the 10 m crop type map has much less salt-pepper and crop boundary-aliasing effects and defines better the small surface features (e.g., small fields, roads, and rivers). The potential of the method for large area 10 m crop type mapping is discussed.