Mapping Cover Crops and Winter Land Cover in Michigan Using Sentinel-1 and Sentinel-2 Imagery and Google Earth Engine

In temperate climates, diversifying rotations with overwintering cover crops provides many benefits, including reducing nutrient losses, restoring soil organic matter, and managing weeds. However, there is limited understanding of where and when cover crops have been planted, especially relative to harvested winter crops, such as wheat and alfalfa. In this study, we use Sentinel-1 and Sentinel-2 satellite data to map winter land cover, including cover crops, across three sites in the Lower Peninsula of Michigan using random forest models. Our results show overall moderate accuracy (60–80%) across all three sites, with individual-level accuracies varying by region and land cover type. Generally, models that combined Sentinel-1 and Sentinel-2 bands, polarizations, and indices performed better than models that relied on one sensor alone. F1 scores for cover crop mapping were moderate, with the highest accuracies achieved for mapping any cover crop (0.77) compared to individual cover crop species—cereal rye (0.72) or ryegrass (0.50). Considering which bands and time periods were the most important for the classification, we found that vegetation indices developed using the red edge bands in the earlier part of the growing season were particularly important for classification accuracy. This work suggests that readily available Sentinel-1 and Sentinel-2 satellite data can be used to accurately map winter land cover, including cover crops, in the US Midwest.