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 (γ_HHVV), 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.

自1970年代后期以来，已证明遥感技术适用于表征和监测植物和农作物。特别是合成孔径雷达（SAR）任务对这种预测工作做出了很大贡献。但是，将SAR图像序列与现场观测一起使用时的主要问题是由于难以获得现场测量值而导致数据稀缺。这项研究旨在通过替代统计模型来解决该问题，该模型可以克服缺乏长期，可靠的一系列基于现场的地面真相观测的问题。这项研究的主要新颖之处在于对雷达遥感图像的面板数据方法预测作物生物物理变量的潜力进行了评估。为此，在西班牙中部的研究区域内获取了RADARSAT-2图像。同时，开展了一场野战，以估计同一地区的作物参数并验证建模结果。作物类型对入射角和极化参数的影响分析表明，同相通道（HH，VV），熵（H）和同相通道之间的相干性（γ）具有很大的影响_HHVV），差异在25°时更高。面板数据分析方法表明，对于本研究中分析的所有生物物理变量，均实现了良好的预测，R ²大于0.78。总体而言，这种新颖的具有遥感数据的统计方法显示出了对作物变量的预测的巨大适用性，即使进行了一系列简短的观察。