Analyses of the Impact of Soil Conditions and Soil Degradation on Vegetation Vitality and Crop Productivity Based on Airborne Hyperspectral VNIR–SWIR–TIR Data in a Semi-Arid Rainfed Agricultural Area (Camarena, Central Spain)

Soils are an essential factor contributing to the agricultural production of rainfed crops such as barley and triticale cereals. Changing environmental conditions inadequate land management endangering soil quality productivity and, in turn, crop affected. Advances hyperspectral remote sensing great use for spatial characterization monitoring degradation status, well its impact on growth productivity. In this study, airborne data covering visible, near-infrared, short-wave infrared, thermal infrared (VNIR–SWIR–TIR, 0.4–12 µm) were acquired a Mediterranean area central Spain used analyze differences vegetation vitality grain yield relation status. Specifically, leaf index (LAI), water stress (CWSI), biomass derived from discussed regarding their relationship classification erosion accumulation stages (SEAS) based previous analyses during bare conditions. LAI harvested could be estimated by PLS regression situ reference (R2 0.83, r 0.91, RMSE 0.2 m2 m−2 R2 0.85, 0.92, 0.48 t ha−1 yield). addition, successfully predicted canopy spectral signal vegetated fields using random forest machine learning approach. Overall accuracy was achieved above 71% combining VNIR–SWIR–TIR reflectance emissivity growing season with topographic information after reducing redundancy dataset. The results show that traits spatially related soil’s shallow highly eroded soils, sandy zones being associated areas low LAI, yield, high stress. Overall, study illustrate enormous potential imaging spectroscopy combined analysis plant-soil system frame monitoring.