High-resolution baseline digital mapping of soil fertility in the Euphrates basin, western Iraq

Digital Soil Mapping is a vital tool used to produce outputs for Digital Soil Assessment, facilitating analyses and recommendations for various environmental practices. Despite its significance, DSM has not been widely applied to soil fertility assessment due to the challenges associated with integrating multiple soil properties into a single map. Therefore, this study aimed to develop a new digital soil fertility map using higher-resolution data. The research was conducted in the Al-Anbar district, located in the Euphrates basin in western Iraq. Spectral indices and signature data from Sentinel-2 were utilized for soil sampling, supplemented by local ground measurements. Principal Component Analysis (PCA) was then employed to identify the most relevant soil fertility indicators. Subsequently, a digital soil map was generated using a Multiple Linear Regression (MLR) model. The results indicated that soil fertility was significantly represented by soil total nitrogen and phosphorus content. Moreover, the MLR model for soil fertility included soil moisture (SM), brightness index (BI), colour index (VI), green chlorophyll index (CIgreen), and B8A. Our approach demonstrated the potential of remote sensing and multiple linear modelling for soil fertility mapping, with an RMSE, R², and MAE of 1.11, 0.98, and 0.68, respectively. These findings suggest that integrating remote sensing with multiple linear regression modelling provides an effective method for accurately estimating soil properties over large areas, offering considerable benefits in terms of cost, efficiency, coverage, and the ability to monitor changes over time.