LAI and harvested crop biomass yield could be well estimated by PLS regression based on the hyperspectral and in situ reference data (R 2 of 0.83, r of 0.91, and an RMSE of 0.2 m 2 m −2 for LAI and an R 2 of 0.85, r of 0.92, and an RMSE of 0.48 t ha −1 for grain yield). Specifically, leaf area index (LAI), crop water stress index (CWSI), and the biomass of the crop yield are derived from the remote sensing data and discussed regarding their spatial differences and relationship to a classification of erosion and accumulation stages (SEAS) based on previous remote sensing analyses during bare soil conditions. In this study, hyperspectral airborne data covering the visible, near-infrared, short-wave infrared, and thermal infrared (VNIR–SWIR–TIR, 0.4–12 µm) were acquired in a Mediterranean agricultural area of central Spain and used to analyze the spatial differences in vegetation vitality and grain yield in relation to the soil degradation status. Advances in hyperspectral remote sensing are of great use for the spatial characterization and monitoring of the soil degradation status, as well as its impact on crop growth and agricultural productivity.
Changing environmental conditions and inadequate land management are endangering soil quality and productivity and, in turn, crop quality and productivity are affected. Soils are an essential factor contributing to the agricultural production of rainfed crops such as barley and triticale cereals.
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