# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 6 Conclusions  
Mapping termination dates for cover crops within the season (or near-real-time) using remote sensing has been challenging in the past due to the lack of high temporal and spatial resolution data. In this paper, we used experimental VENuS (2 day) time-series data to develop a new within-season termination (WIST) algorithm for detecting cover crop termination dates. The WIST approach searches for downtrends of Vegetation Index time-series data and estimates termination date and uncertainty during a senescent period. The WIST algorithm employs a local moving window approach and can run in the near-real-time mode using any period of an imagery time-series. Results from the Beltsville Agricultural Research Center experimental fields in 2019 and 2020 show that termination dates can be reliably mapped using high temporal and spatial resolution time-series data. The VENuS time-series data with 2 days repeat produced accurate results with a mean absolute difference of 2 days. The Sentinel-2 (5 day) time-series also detected correct termination dates but gave 7% missing and 10% false detections. The average uncertainty of detected terminations was 4 days for VENuS and 6 days for Sentinel-2. A simulated mapping of termination in near-real-time shows that the average lag times of detection were about 4 days for VENuS and 8 days for Sentinel-2 after actual termination or harvest, not including latency of satellite data. This study demonstrates the potential to map cover crop termination within the season using high temporal and spatial resolution data for cover crop monitoring and agroecosystem services. Further work is needed for operational applications using routine satellite observations such as the Harmonized Landsat and Sentinel-2 (HLS) dataset.  
Conceptualization, F.G., W.D.H., and M.C.A.; Data curation, F.G.; Formal analysis, F.G. and M.C.A.; Funding acquisition, F.G. and M.C.A.; Investigation, F.G.; Methodology, F.G.; Project administration, F.G.; Software, F.G.; Validation, F.G.; Visualization, F.G.; Writing--original draft, F.G.; Writing--review & editing, F.G., M.C.A., and W.D.H. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Aeronautics and Space Administration (NASA) Land Cover and Land Use MuSLI program (NNH17 ZDA001N-LCLUC), and the U.S. Geological Survey (USGS) Landsat Science Team program.  
The VENuS data were acquired and provided by the Centre National d'Etudes Spatiales (CNES), France, and the Israeli Space Agency. The Sentinel-2 data were produced by the European Space Agency (ESA). This research was a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. USDA is an equal opportunity provider and employer Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.