Assessing soil quality dynamics in northern Iran: a spatial modeling approach to predict and project future trends

Abstract

The soil in Northern Iran exhibits significant variability, showing notable responses to variations in climatic and environmental conditions. In a representative area covering 936.7 km², we measured 18 soil properties at 73 sampling locations. These properties were then screened using Principal Component Analysis (PCA) which identified five PCs with eigenvalues greater than 1.0 and correlation analysis to construct the Nemro Soil Quality Index (SQI) with a mean of 0.27 ± 0.04. The predictability of SQI was modeled using the Generalized Additive Model (R² = 0.669, explained deviance = 69.7%), indicating that elevated Normalized Difference Vegetation Index (NDVI; p value = 0.000) and daytime Land Surface Temperature (LST; p value = 0.000) enhance SQI, while higher slopes (p value = 0.020) have a diminishing effect. The model was also utilized to illustrate potential future alterations in SQI for the year 2040. For this purpose, the 2040 MODIS data (NDVI and LST) were projected using various regression models (0.09 < R² < 0.69) applied to historical mean annual MODIS data spanning from 2002 to 2022. The majority of projected changes in SQI exhibited a negative trend, primarily attributed to the depletion of vegetation cover at the peripheries of forest borders. These findings underscore the imperative need for strategic future management plans, emphasizing the preservation of soil in marginal forest lands.