Large-scale modeling of environmental variables is an increasingly complex but necessary
task. In this paper, we review the literature on using machine learning to cope with …

Optimisation of sampling design (methods chosen to select the samples) and sample size
(number of samples) remains a key challenge in digital soil mapping, especially in the area …

Petroleum hydrocarbon pollution causes significant damage to soil, so accurate prediction
and early intervention are crucial for sustainable soil management. However, traditional soil …

The scientific evaluation of land potential for different uses is essential for sustainable land
development. Our study attempts to quantify this potential for agricultural purposes at a …

Substrate properties are pivotal in shaping porewater chemistry and groundwater quality,
serving as the primary driving factors. While spatial analysis of geochemical distribution has …

Agriculture consumes the largest amount of water resources in the world; for this reason,
developing technologies aimed at efficiently using these resources for food production is …

This study introduces a framework for forecasting soil nitrogen content, leveraging multi-
modal data, including multi-sensor remote sensing images and advanced machine learning …

Evaluating the spatial variability of soil properties is an important prerequisite for precision
agriculture. This research was conducted on 84 soil samples from different areas of …

Optimisation of sampling design (methods chosen to select the samples) and sample size
(number of samples) remains a key challenge in digital soil mapping, especially in the area …