# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 5 Discussion
### Constraints and Limitations  
In the WIST algorithm, we used the momentum of MACD and the amplitude of NDVI to determine a significant downward trend. The thresholds were set to 0.01 for MACD and 0.15 for NDVI in this paper. These thresholds need to be assessed and potentially refined for other regions and cover crop types. A higher threshold requires a more significant downward trend and thus reduces false detection. This works better for green cover crops but may miss terminations for the sparse or less green cover crops when the change of NDVI after termination is not substantial. A lower threshold can detect more downward trends but may cause false detections. To determine appropriate thresholds for a cover crop in a region, an option is to save the momentum of MACD and the amplitude of NDVI for each pixel/field on each downtrend and then let users determine the thresholds using local ground data obtained afterward or a parameter optimization technique based on a history of recorded terminations for different cover types. Cloudy or cloud-shadowed pixels leaked from imperfect cloud detection algorithms may affect the performance of the WIST algorithm. In this paper, noise has been reduced using two procedures. The first procedure implemented in the SG filter is to remove apparent spikes based on the fitting error (more than four standard deviations in this paper). The second procedure uses an average value in a moving window to determine the downward trend and the senescent period, avoiding the noise from a single day. However, several cases are a challenge for the WIST approach. First, multiple noise spikes (e.g., undetected clouds or cloud shadows) from successive days can cause false detection, such as the first two terminations shown in Figure 9a. The procedures introduced in this paper cannot handle multiple noise spikes in a row, a challenging case for any gap-filling and smoothing algorithm. Second, NDVI after termination may drop significantly, and it may be wrongly interpreted as a noise signal. In that case, the noise threshold in the SG filter needs to be relaxed more than in standard processing. Third, mis-registration in the time-series imagery can cause false detections near field boundaries. Estimation using field objects can reduce noise and geolocation errors by excluding pixels near the field boundaries, assuming the entire field was harvested on the same day. Finally, the WIST algorithm requires high-quality remote sensing data with a good cloud mask and geolocation accuracy. Satellite observations in a time-series are not equally distributed due to clouds. The WIST algorithm estimates the termination date using two clear observations with the most significant decreasing rate during the senescent period. The actual termination date can be any date between the two observations. When the gaps between the two observations are large, the uncertainty of estimation will be more significant. Frequent satellite observations can produce a more accurate estimation with higher confidence (smaller uncertainty). In our study area, the 2 days repeat VEN\(\upmu\)S time-series produced accurate estimates with a mean uncertainty of 4 days compared to 6 days for the 4-5 days repeat Sentinel-2. The largest observed uncertainty of termination estimations was 8 days for VEN\(\upmu\)S and 20 days for Sentinel-2. Temporal frequency is crucial for the estimation of cover crop termination. The WIST approach is more effective when the cover crops are terminated while still green. The approach is not valid for summer grain crops or winter grain crops such as wheat and barley, harvested at the end of the growing season when the plants dry down and leaves turn brown or yellow. In those cases, the change of NDVI before and after harvest may not be the most significant one during the senescent period.