Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations

The aim of this research is to explore the analysis methods allowing a synergetic use information exchange between Earth Observation (EO) data and growth models in order provide high spatial temporal resolution actual evapotranspiration predictions. An assimilation method based on Ensemble Kalman Filter algorithm allows for combining Sentinel-2 with new version Simple Algorithm For Yield (SAFY_swb) that considers effect water balance yield estimates daily trend (ET). Our study relevant context demonstrating effectiveness necessity satellite missions such as Land Surface Temperature Monitoring (LSTM), agriculture. proposed addresses problem both from point view, providing maps areas interest main biophysical quantities vegetation (LAI, biomass, Evapotranspiration), simulation basis aforementioned variables. efficiency was initially evaluated synthetic, large heterogeneous dataset, reaching values 70% even measurement errors assimilated variable. Subsequently, tested case central Italy, Actual Evapotranspiration relative RMSE 18%. novelty proposing solution partially solves problems related synergistic EO crop models, difficult calibration initial parameters, lack frequent high-resolution or computational cost methods. It opens way future developments, simultaneous multiple variables, deeper investigations using more specific datasets exploiting advanced tools.