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Crop phenological phases have traditionally been observed from the ground, which is a labor-intensive and time-consuming activity that also lacks spatial variability due to the sparse and limited network of ground data, if any are available. In view of this, remote sensing can provide a low-cost avenue to systematically monitor and detect phenological phases from space. The most common approach for retrieving vegetation phenology from remotely sensed time series is the dynamic threshold method. However, only a few number of studies have attempted to calibrate and optimize these derived phenological metrics to relate them to actual crop growth stages. Accordingly, this study attempted to develop a framework to optimize the derivation of phenological phases for three major crops (winter wheat, corn and sugar beet) in Germany by investigating the optimal thresholds and comparing the performances with ground-truth observation data. To this end, the Normalized Difference Vegetation Index (NDVI) time series covering Germany and for two cropping seasons 2019 and 2020 were obtained and derived from a 10 x 10 km tiling grid of Sentinel-2 analysis ready data using a specific decentralized cloud platform that combines both a set of satellite imagery (petabytes of data) with huge analysis capabilities on a very large scale. Since cloud contamination is typically the major drawback for estimating phenology with optical satellite data, the study suggests first a new smoothing and gap filling method (UE-Whittaker) that is based on both envelope detection and the Whittaker filter and that, in the end, constructs high-quality NDVI time series that are suitable for phenological analysis. Based on these generated time series, the estimation of various phenological phases of crops as well as threshold optimisation and calibration were carried out as a second step. In which we traverse the thresholds from 0 to 1 with an increment of 0.01 for each specific phase and finds the optimal threshold when the lowest error value is obtained between the satellite-derived DOY and the observed DOY in ground data from the year 2019. Later on, these optimum thresholds were used to derive phenological phases from the next year, and the results of the calculation of the root-mean-square error (RMSE) and the mean absolute error (MAE) between the ground reported in-situ phenology observations and those derived from satellite data reveal that they ranged typically between 3 days and 2 weeks for nearly all the phenological phases. The findings demonstrate how calibrating and optimising the derivation of different phenological phases of crops using only optical data could produce a timely and accurate information on crop growth and its condition for a large area which can be used in agricultural management, crop yield estimation, and several other related applications.
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