Looking for Optimal Maps of Soil Properties at the Regional Scale

Abstract

Around 70% of surface in Extremadura, Spain, faces a critical risk of degradation processes, highlighting the necessity for regional-scale soil property mapping to monitor degradation trends. This study aimed to generate the most reliable soil property maps, employing the most accurate methods for each case. To achieve this, six different machine learning (ML) techniques were tested to map nine soil properties across three depth intervals (0–5, 5–10 and > 10 cm). Additionally, 22 environmental covariates were utilized as inputs for model performance. Results revealed that the Random Forest (RF) model exhibited the highest precision, followed by Cubist, while Support Vector Machine showed effectiveness with limited data availability. Moreover, the study highlighted the influence of sample size on model performance. Concerning environmental covariates, vegetation indices along with selected topographic indices proved optimal for explaining the spatial distribution of soil physical properties, whereas climatic variables emerged as crucial for mapping the spatial distribution of chemical properties and key nutrients at a regional scale. Despite providing an initial insight into the regional soil property distribution using ML, future work is warranted to ensure a robust, up-to-date, and equitable database for accurate monitoring of soil degradation processes arising from various land uses.