Comparison of environmental, remote sensing, and combined variables for predicting and mapping soil organic matter in the Huang-Huai-Hai Plain

Machine Learning-Based Prediction of Soil Moisture in Sikkim's High-Rainfall Zones Using Multimodal Remote Sensing Data

The strong predictive performance demonstrated the effectiveness of integrating high-resolution Sentinel-2 imagery data with ANN for accurate, scalable, and efficient soil moisture estimation in high-relief, data-sparse environments.

Integrating factor analysis and support vector machine for digital mapping of soil quality using DEM-derived geomorphometric variable

Prediction of Soil Infiltration Rate From Soil Color Parameters in a Brown Forest Soil of Transitional Climate Zone

Aim of the study: Forest vegetation exerts a significant influence on the physical and chemical characteristics of brown forest soils in transitional climate zones. Among soil physical properties, the infiltration rate (IR) is a key indicator of soil-water interactions, hydrological functioning, and ecosystem stability...

Crop-field scale prediction of soil organic carbon in a complex terrain and its implications for nitrogen recommendation

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...

Spectral indicators for Assessing the extent of Land use and its Relation to Organic carbon in Desert palm orchards soils in Karbala- Iraq

Remote sensing techniques involve acquiring and interpreting information about objects or areas from a distance, typically using satellite or aerial imagery. This study aimed to monitor land cover changes in central Iraq and investigate the impact of the age of organic carbon in the soil on vegetation dynamics using re...

Enhancing soil organic carbon mapping in Northeast China by incorporating laboratory-based soil hyperspectral measurements into spatial covariates and machine learning