Critical knowledge gaps and research priorities in global soil salinity
Approximately 1 billion ha of the global land surface is currently salt-affected, representing
about 7% of the earth's land surface. Whereas most of it results from natural geochemical …
about 7% of the earth's land surface. Whereas most of it results from natural geochemical …
Machine learning for digital soil map**: Applications, challenges and suggested solutions
The uptake of machine learning (ML) algorithms in digital soil map** (DSM) is
transforming the way soil scientists produce their maps. Within the past two decades, soil …
transforming the way soil scientists produce their maps. Within the past two decades, soil …
Global predictions of primary soil salinization under changing climate in the 21st century
Soil salinization has become one of the major environmental and socioeconomic issues
globally and this is expected to be exacerbated further with projected climatic change …
globally and this is expected to be exacerbated further with projected climatic change …
Selecting appropriate machine learning methods for digital soil map**
Digital soil map** (DSM) increasingly makes use of machine learning algorithms to
identify relationships between soil properties and multiple covariates that can be detected …
identify relationships between soil properties and multiple covariates that can be detected …
Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates
In the digital soil map** (DSM) framework, machine learning models quantify the
relationship between soil observations and environmental covariates. Generally, the most …
relationship between soil observations and environmental covariates. Generally, the most …
Digital map** of soil properties using multiple machine learning in a semi-arid region, central Iran
Abstract Knowledge about distribution of soil properties over the landscape is required for a
variety of land management applications and resources, modeling, and monitoring …
variety of land management applications and resources, modeling, and monitoring …
Estimating soil salinity from remote sensing and terrain data in southern **
Abstract Machine-learning is the automated process of uncovering patterns in large datasets
using computer-based statistical models, where a fitted model may then be used for …
using computer-based statistical models, where a fitted model may then be used for …
Updated soil salinity with fine spatial resolution and high accuracy: The synergy of Sentinel-2 MSI, environmental covariates and hybrid machine learning approaches
Soil salinization is the main source of global soil degradation. It has impeded progress
towards sustainable development goals (SDGs) by threatening 20% of irrigated areas …
towards sustainable development goals (SDGs) by threatening 20% of irrigated areas …
Assessing the effects of slope gradient and land use change on soil quality degradation through digital map** of soil quality indices and soil loss rate
Slope gradient and land use change are known to influence soil quality and the assessment
of soil quality is important in determining sustainable land-use and soil-management …
of soil quality is important in determining sustainable land-use and soil-management …