TL;DR
This study shows that using AEO and SHAP analysis with CatBoost enhances SWRC prediction accuracy, and shows that soil suction stood out as the key variable, with a SHAP mean nearly four times greater than the porosity.
This study aimed to evaluate the potential of spectral data in the Vis–NIR–SWIR region, combined with terrain and soil variables, for predicting soil organic carbon (SOC) using different machine learning algorithms, and to analyze the impact of these estimates on nitrogen recommendations for maize ( Zea mays...
Biochar is a porous, carbon-rich soil amendment that can enhance soil water retention capacity by modifying pore structure and physicochemical properties. Understanding the soil−water characteristic curve (SWCC) of biochar-amended soils is essential for evaluating their hydrological behavior and promoting the applicati...
Soil organic matter (SOM) is a key indicator of land degradation and soil functioning. It plays an essential role in nutrient cycling, soil structure, and long‐term agroecosystem resilience. The variations of surface cover, soil moisture and soil texture heterogeneity will affect the accuracy of SOM remote sensing...
Soil organic carbon (SOC) is a key indicator of cropland quality, soil fertility, and the carbon sequestration potential of agroecosystems. Accurate characterization of its spatial distribution is essential for black soil conservation and regional soil carbon management. However, regional-scale SOC prediction is often...
The neural network model is the best machine learning tool for predicting soil water in clay and sandy soils under semi-arid agroecological conditions.
Accurate prediction of the groundwater quality is essential for safeguarding public health from contaminated water and to guide water management decisions, especially in areas in which rapid population, industrial and agricultural growth lead to pollution and groundwater is the primary source of water. Traditional stat...
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