Application of remote sensing methods for statistical estimation of organic matter in soils.

Maintaining soil fertility and optimizing land management strategies on a global scale necessitates a comprehensive understanding of soil's physicochemical properties. This study proposes the deployment of remote sensing techniques as a more efficient, accurate, and cost-effective alternative for exploring the properties of chernozem soils and for predicting soil organic matter (SOM) in specific regions of Kyiv, Ukraine. The spectral properties of the chernozem soils were examined by Landsat 5 TM and 8 OLI satellite imagery. A mosaic of the mean spectral reflectance values for the study period (1986-2015) was created on the Google Earth Engine. The construction of the predictive model employed reflectance values across six bands, a range of vegetation indices (RNDSI, NDWI, NDBI, BAEI, MSAVI, and NDVI), topographic data (slope), and climatic parameters (temperature, precipitation, soil moisture) as regressors. The optimal model was subsequently determined using the Forward stepwise method, which is predicated on F-test and Akaike criteria. This model yielded notable metrics of Multiple R = 0.651, R2 = 0.424, and Adjusted R2 = 0.397. The most significant predictors within the model were determined to be RED, NIR, slope, and soil moisture. Upon validation, the model displayed no evidence of heteroscedasticity, multicollinearity, or potential outliers, thereby underscoring its reliability and robustness. The proposed model serves as a valuable tool, offering both reliability and precision in predicting SOM in specific regions of Ukraine. [ABSTRACT FROM AUTHOR]