# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 5 Discussion  
### Performance and Comparison  
Our results show that cover crop termination dates can be reliably detected using high temporal and spatial resolution satellite imagery. The proposed WIST algorithm uses the SG filter to generate daily NDVI and uses the MACD approach to detect a downward trend. Once the downtrend is confirmed by the momentum of MACD and the amplitude of NDVI, the termination date is determined using the original observations during the senescent period. The two dates with the most significant decreasing rate are selected to estimate the termination date and its uncertainty. Many phenology algorithms have been developed in recent years. Most of them require multiple years of observations to generate annual phenology products. The annual phenology product for the target year could lag for several months to a year. The WIST algorithm can run in near-real-time whenever satellite data become available since both the SG filter and the MACD approach use a local moving-window technique. In our study, the average lag times of the WIST approach were about 4 days for VEN\(\mu\)S and 8 days for Sentinel-2, not including the satellite data latency. The global phenology products focus on major phenological metrics. The remote sensing phenological metrics are not the same as the crop physiological stage. Cover crop harvest/termination dates could be any day between the senescent onset and dormancy dates from remote sensing. The WIST algorithm can estimate cover crop termination dates directly. In addition, the WIST algorithm can detect all termination dates during the season or year, while most remote sensing phenology algorithms only detect a few seasonal cycles. For example, MODIS and VIIRS phenology products save two seasonal cycles in the annual phenology products [23]. For cover crops, as well as perennial hay crops such as alfalfa, the capability to map multiple terminations is desirable. It is only recently that high temporal and spatial resolution satellite data, such as from Sentinel-2 and PlanetScope, have become available. Before the advent of Sentinel-2 and the HLS dataset, we used the data fusion approach using Landsat and MODIS/VIIRS [36] to generate high temporal and spatial resolution data to map crop phenology [25] and estimate crop yield [37]. Even for the daily satellite observations, clouds and cloud shadows affect the number of usable observations in the optical remote sensing. Cloud occurrence varies with location, date, and time. In BARC, the daily average cloud-free observations during the termination period were 14.8% for VEN\(\mu\)S and 10.5% for Sentinel-2, which is typical for a temperate zone. The 2 days repeat VEN\(\mu\)S observations (14.8% clear pixels) detected all termination events. The 4-5-day Sentinel-2 time-series (10.5% clear pixels) detected over 90% of termination events but had 10% false detections. Results may degrade over a cloudy region where the daily clear observations are less than 10%. The harmonized Landsat and Sentinel-2 (HLS) data product provides 3-4 days surface reflectance at 30 m spatial resolution. The global HLS data product will be routinely available starting in late 2020, with an expected data latency of about 5 days [38]. If we add 8 days of lag time from the WIST algorithm to confirm the downtrend based on the Sentinel-2 time-series, we may expect that cover crop termination dates to be detected within 2 weeks. In this study, we used the 10 m resolution VEN\(\mu\)S (aggregated) and Sentinel-2 data, because our experimental fields are relatively large (>2 ha, 200 10 m pixels). The 5 m resolution VEN\(\mu\)S data were not assessed but could be valuable for smaller fields. The 30 m resolution HLS data are detailed enough for application to most large commercial agricultural fields in the United States [39] where the farm field sizes typically are much larger than in our study area. HLS data have been used in mapping vegetation and crop phenology alone [24], or together with other coarse resolution data [40; 41].