Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression

Matias Salinero-Delgado

Using Google Earth Engine (GEE) a new Sentinel-2 (S2) phenology end-to-end processing chain was developed. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS)




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