Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data

The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines potentials entropy, anisotropy, alpha angle derived from a dual-polarimetric decomposition Sentinel-1 crop development. temporal profiles these analysed for wheat barley in vegetation periods 2017 2018 13 fields two test sites Northeast Germany. relation between polarimetric observed field is investigated using linear exponential regression models that evaluated coefficient determination (R2) root mean square error (RMSE). performance single furthermore compared those multiple models, including VV VH polarisation as well entropy alpha. Characteristic reflecting main phenological changes plants meteorological differences years both types. perform best growth stages tillering booting. highest R2 values reached plant height related anisotropy with 0.64 0.61, respectively. VH, VV, outperform most cases. (0.76), wet biomass (0.7), dry water content (0.69) improve slightly by up 0.05. Additionally, RMSE around 10% lower models. results indicate capability serving meaningful input prediction parameters. their development dependent on conditions. Knowledge about parameter phenology important farmers variability during period adapt optimize management.