LAUSR

Since the late 1970s, remote sensing techniques have been proven to be suitable for characterizing and monitoring plants and crops. In particular, synthetic aperture radar (SAR) missions contribute considerably to this prediction effort. However, the main issue when using SAR image series together with field observations is the scarcity of data due to the difficulty of acquiring field measurements. This research aimed to contribute to solving this problem with an alternative statistical model that can overcome the lack of a long, robust series of field-based ground truth observations. The main novelty of this research is the evaluation of the potential of a panel data approach to radar remote sensing imagery for predicting crop biophysical variables. For this purpose, RADARSAT-2 imagery was acquired over the study area in central Spain. Simultaneously, a field campaign was deployed to estimate crop parameters in the same area and to validate the results of the modelling. The analysis of the influence of the crop type on the incidence angle and the polarimetric parameters showed a strong influence of the co-polar channels (HH, VV), the entropy (H) and the coherence between the co-polar channels (γHHVV), with the differences being higher at 25°. The panel data analysis method demonstrated that good predictions, with R2 greater than 0.78, were achieved for all biophysical variables analysed in this study. Overall, this novel statistical approach with remote sensing data showed great applicability for the prediction of crop variables, even with a short series of observations.