Cover cropping is one of the best management practices for improving soil quality and reducing nutrient losses to water resources following the summer growing season. As a result, several cost-share programs have been established to encourage adoption of cover crops throughout the Western Lake Erie basin, an impaired watershed that is most prone to harmful algal blooms. However, there is limited information on tracking the spatial extent and temporal trends of cover crops at a landscape scale; current estimates are based on surveys. Remotely sensed imagery provides a cost-effective approach to document spatial and temporal trends of cover crops in a timely manner. The objective of this research is to assess the temporal and spatial patterns of winter cover crops in the Western Lake Erie basin using satellite imagery. Images from Sentinel 2A and Sentinel 2B satellites were classified into cover crops and no cover crops using a machine learning technique; Random Forest based supervised classification in the Google Earth Engine (GEE) platform. The results show high amounts of cover crops during 2015/16 and 2018/19 while relatively low amounts during 2016/17 and 2017/18 winter seasons. This trend was evaluated against the average nutrient concentration for winter months in the Maumee River. Increased cover cropping was observed to be related to lower nutrient concentration in the river and vice versa. This suggests that cover cropping is an effective and practical means of addressing the widespread water quality problem because of intensified agriculture.