Soil quality assessment with imaging spectroscopy under land use changes

Global population growth has resulted in land-use (LU) changes in many natural ecosystems, causing deteriorated environmental conditions that impact soil quality. This rapid growth in the global population caused many natural ecosystems to be transformed into human-dominated ones. Such LU dynamics require greater resource exploitation, commonly resulting in degraded environmental conditions that are acknowledged in the soil quality. The effects on the soil are even more acute in water-scarce and limited resources environments such as drylands. Therefore, developing appropriate approaches for soil quality assessment and function evaluation is necessary since the soils in those areas are usually undeveloped and retain lower organic matter capacity. The research aim was to apply, measure, and evaluate soil properties based on the imaging spectroscopy (IS) differences between natural and human-dominated LU practices in the dryland environment of the Negev Desert, Israel.  A flight campaign of the AisaFENIX hyperspectral airborne sensor was used to develop an IS prediction model for the SQI on a regional scale. The spectral signatures extracted from the hyperspectral image were well separable among the four LUs using the partial least squares-discriminant analysis (PLS-DA) classification method (OA = 95.31%, Kc = 0.90). The correlation was performed using multivariate support vector machine regression (SVM-R) models between the spectral data, the measured soil indicators, and the overall SQI. The SVM-R models were significantly correlated for several soil properties, including the overall SQI (R2adjVal = 0.87), with the successful prediction of the regional SQI mapping (R2adjPred = 0.78). Seven individual soil properties, including fractional sand and clay, SOM, pH, EC, SAR, and P, were successfully used for developing prediction maps. Applying IS, and statistically integrative methods for comprehensive soil quality assessments enhances the accuracy of predicting soil health and evaluating degradation processes in arid environments. This study establishes a precise tool for sustainable and efficient land management and could be an example for future potential IS earth-observing space missions for soil quality assessment studies and applications.