Soil compaction resulting from heavy machinery use in agricultural and forestry operations poses a significant threat to sustainable agriculture. Advancements in remote sensing technology have enabled the acquisition of vegetation, water, and soil spectral indices, which offer valuable insights into soil properties. This study focuses on estimating the pre-compression stress (Pc) by developing pedotransfer functions (PTFs) using Sentinel-2 satellite-derived spectral indices and soil properties (clay, CaCo3, and bulk density) as inputs. Two machine learning methods, Random Forest (RF) and Boosted Regression Tree (BRT), are employed for the estimation. A total of 140 surface soil samples were collected randomly from agricultural areas in Qazvin province, Iran. The results indicate that the BRT method outperforms the RF method in terms of accuracy. The estimation of Pc achieved better results when the Redness Index (RI) was used as the soil spectral index and the Surface Water Capacity Index (SWCI) was employed as the water spectral index, along with the soil properties as inputs for PTF3 and PTF11. In the training and testing steps, the root mean square error (RMSE) decreased from 0.100 and 0.114 (kPa) in PTF1 to 0.071 and 0.098 in PTF3, and 0.072 and 0.097 in PTF11, respectively. These outcomes demonstrate the practical applicability of estimating Pc through the integration of soil properties and spectral indices. The findings highlight the potential of remote sensing technologies, such as spectral indices, as effective and cost-efficient tools for studying soil compaction. The proposed methodology contributes to our understanding of soil compaction processes and provides valuable insights for developing sustainable land management strategies. This study has implications for the agricultural sector and offers practical solutions to mitigate soil compaction and its detrimental effects on agricultural productivity.
