A PCA-based standardized spectral index from Sentinel-2 for modeling soil macronutrients in the Miandoab region, Iran

Spatial monitoring of soil macronutrients-especially total nitrogen (TN), available phosphorus (AP), and available potassium (AK)-using remote sensing technologies is a promising approach to promote precision agriculture. The aim of this study is to evaluate the effectiveness of Sentinel-2 satellite data and selected spectral indices in modeling the concentrations of these important soil macronutrients on agricultural land. A total of 181 soil samples were collected from a depth of 0-30 cm and analyzed in the laboratory using standard methods to determine nitrogen, phosphorus, and potassium content. Twelve common spectral indices and a newly proposed index-the standardized spectral reflectance index (SSRI), derived from the first principal component of a PCA-were extracted from the Sentinel-2 data. Linear regression modeling revealed that TN provided the most accurate predictions (R² = 0.77, RMSE = 0.04%, MSE = 0.01, RPIQ = 2.43 (good predictive performance)), followed by AK (R² = 0.72, RMSE = 166.49 ppm, MSE = 0.27, RPIQ = 2.21 (good predictive performance)), while AP showed relatively weak model performance (no significant regression and no predictive RPIQ), probably due to its limited spectral expression in remote sensing data. The newly introduced SSRI outperformed the conventional indices in nitrogen modeling, which is an important novelty of this study. Overall, the results indicate that the integration of Sentinel-2 data with optimized spectral indices provides a feasible and effective approach for the indirect estimation of TN and, to a lesser extent, AK. The application of this method has the potential to reduce reliance on costly field sampling, improve fertilizer management, and contribute to the sustainability of agricultural systems. It is recommended that multi-temporal Sentinel-2 imagery be used in future studies to refine SSRI extraction and provide a rational and innovative method for estimating these critical soil macronutrients.