An integrated approach for estimating soil health: Incorporating digital elevation models and remote sensing of vegetation

Abstract

The deterioration of soil health (SH) in agricultural lands is a global challenge that poses a threat to food and resource security. We developed a practical framework to facilitate the large-scale SH assessment in agricultural fields of northwestern Iran. A total of 350 soil samples were collected and soil properties were determined. Eight linear and non-linear Soil Health Indexes (SHIs) were developed. Digital Elevation Model (DEM) and multiple remote sensing indexes were obtained from satellite images. SHI prediction models were developed using an integrated approach and through a model selection procedure, the most relevant indexes were identified. The results showed significant (P < 0.05) positive correlation between the IHI-LT and elevation (r = 0.56), Vegetation Health Index (VHI) (r = 0.69), and Surface Water Condition Index (SWCI) (r = 0.79). The multiple regression model including the above indexes strongly explained the spatial variability of the Integrated Soil Health Index (IHI) with both total (LT) and minimum (LM) dataset approaches (R2 = 0.72; AIC = −1607.27; RMSE = 0.03; ρc = 0.65). The developed models can be utilized for large-scale assessment of soil health conditions, reducing the cost and effort of conventional ground-truth soil sampling and analysis. Furthermore, this approach may aid in monitoring and mitigating the soil degradation in agricultural lands.