Abstract
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.
| Original language | English |
|---|---|
| Article number | 102692 |
| Journal | International Journal of Applied Earth Observation and Geoinformation |
| Volume | 107 |
| DOIs | |
| State | Published - Mar 2022 |
Bibliographical note
Publisher Copyright:© 2022 The Authors
Funding
The ESA Sentinel program management and staff, are thanked for the free provision of the Sentinel-2 surface reflectance data. This research was funded by the South Dakota State University Wadsworth research award.
| Funders | Funder number |
|---|---|
| South Dakota State University Wadsworth | |
| Ecological Society of America |
Keywords
- Cropland data layer
- High resolution
- Land cover mapping
- Machine learning
- Sentinel-2
- Time series
ASJC Scopus subject areas
- Global and Planetary Change
- Earth-Surface Processes
- Computers in Earth Sciences
- Management, Monitoring, Policy and Law