# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 1 Introduction
Planet data are acquired globally from different sensors but are costly, and the high-level harmonized surface reflectance data have not yet been released. Global Landsat and Sentinel-2 data are publicly available at no cost, but the acquired images have coarser resolution and less frequent than Planet and VEN\(\mu\)S.  
Second, current remote sensing phenology mapping approaches are not suitable for cover crop termination detection since they were not designed for the near-real-time application. Most remote sensing phenology algorithms require an entire year or multiple years of remote sensing time-series data. For example, the TIMESAT software requires 2 or more years of Vegetation Index (VI) data to build a continuous time-series to determine phenological metrics [20]. The Moderate Resolution Imaging Spectroradiometer (MODIS) land cover dynamic product (MCD12Q2) provides annual global land surface phenology metrics at 500 m spatial resolution [21]. Three consecutive years of MODIS time-series are used to produce vegetation phenology metrics for the middle (product) year [22]. The global land surface phenology product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) inherited the MODIS curvature phenology algorithm from Collection 5 [23] and used a 2-year window of VIIRS data. Similarly, the HLS phenology product also uses 2 years of HLS data, i.e., the year of interest with 6 month buffers prepended and appended [24]. For operational winter cover crop monitoring, termination dates need to be detected within weeks or even days, in near-real-time,based on a partial year of observations, to confirm that farmers are adhering to cost-share program management guidelines and reduce the need for field visits by cost-share program staff. Third, remote sensing phenology is different from physiological vegetation growth stages. Remote sensing dormancy (end of the season) is related to crop harvest but is different. The differences between dormancy and harvest dates depend on the remote sensing algorithms used and may vary from days to weeks [25]. Threshold-based approaches use different thresholds to determine critical phenological dates, and therefore, the differences between remote sensing phenological dates and crop physiological dates depend on the thresholds used [25]. Remote sensing senescence onset and dormancy dates may relate to cover crop termination or harvest. However, the actual termination or harvest dates could occur any time between senescence and dormancy in remote sensing time-series, and the range of dates (uncertainty) will be large from standard remote sensing phenology data products. Fourth, remote sensing observations in a time-series are not equally distributed. Some periods may suffer large temporal gaps without any observations due to clouds. A within-season mapping algorithm using a partial year of observations is more vulnerable to these large gaps than are algorithms that use multiple years of time-series data. Even though a large gap may be filled by using a curve fitting approach [26; 27], the uncertainty of the estimation will be considerable. Uncertainty in date estimation is critical information and needs to be provided for decision making. However, the uncertainties of phenological dates are not available in standard remote sensing phenology products. In a previous study, we developed a within-season emergence (WISE) detection approach using high temporal and spatial resolution remote sensing data [19]. The WISE approach uses a modified Savitzky-Golay (SG) filter method to fill temporal gaps and then uses a Moving Average Convergence Divergence (MACD) and VI-test approach to detect the green-up dates in an uptrend. Since both the SG filter and MACD approaches employ a local moving window method, the WISE approach can utilize any period of an imagery time-series during the early growth stages to detect crop emergence.