Predicting C-band backscattering coefficient using the water cloud model and optical vegetation indices

The assessment of satellite products over agricultural areas has several challenges that get more complex in developing countries, particularly when exploiting active observation. The use of free-open access satellite images such as Sentinel-1 (S-1) and Sentinel-2 (S-2) images as data sources is considered a great alternative to minimize the high cost and time consumption of the field measurements. The composition of the rainfed agricultural zones depends on the vegetation and soil layers, in that sense, the ability to detect changes in surface parameters is related to the adequate representation of these components. The selection of vegetation descriptors to represent the vegetation changes is one of the main steps for accurate simulations over vegetated areas. Therefore, it is essential to assess the impact of the different vegetation indices (VIs) derived from optical information when included in active models. In this study, the Normalized Difference Vegetation Index (NDVI) and Specific Leaf Area Vegetation Index (SLAVI) were compared with the Vegetation Water Content calculated based on NDVI values (VWC-ndvi). In this paper, the Water Cloud Model (WCM) was used based on in-situ information collected during a field experiment conducted in a rain-fed agricultural area located in Central Mexico (THEXMEX-19). It was concluded that the use of the VW C-ndvi obtained 0.97, -0.11 dB, and 0.49 dB for correlation coefficient (r), Bias, and root mean squared difference (RMSD), respectively. Based on the results, this work highly recommends the use of the WCM and Oh model when evaluating different combinations of parameters for operational use, especially for agricultural purposes.