[HTML][HTML] Machine learning and soil sciences: A review aided by machine learning tools
The application of machine learning (ML) techniques in various fields of science has
increased rapidly, especially in the last 10 years. The increasing availability of soil data that …
increased rapidly, especially in the last 10 years. The increasing availability of soil data that …
Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review
This article reviews the current research and applications of various digital soil mapping
(DSM) techniques used to map Soil Organic Carbon (SOC) concentration and stocks …
(DSM) techniques used to map Soil Organic Carbon (SOC) concentration and stocks …
Predicting and mapping of soil organic carbon using machine learning algorithms in Northern Iran
Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding
the chemical, physical, and biological functions of the soil. This study proposes machine …
the chemical, physical, and biological functions of the soil. This study proposes machine …
Remote sensing techniques for soil organic carbon estimation: A review
Towards the need for sustainable development, remote sensing (RS) techniques in the
Visible-Near Infrared–Shortwave Infrared (VNIR–SWIR, 400–2500 nm) region could assist …
Visible-Near Infrared–Shortwave Infrared (VNIR–SWIR, 400–2500 nm) region could assist …
[HTML][HTML] Presenting logistic regression-based landslide susceptibility results
L Lombardo, PM Mai - Engineering geology, 2018 - Elsevier
A new work-flow is proposed to unify the way the community shares Logistic Regression
results for landslide susceptibility purposes. Although Logistic Regression models and …
results for landslide susceptibility purposes. Although Logistic Regression models and …
A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based phenological variables
The spatial distribution of soil organic carbon (SOC) serves as critical geographic
information for assessing ecosystem services, climate change mitigation, and optimal …
information for assessing ecosystem services, climate change mitigation, and optimal …
High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia
Efficient and effective modelling methods to assess soil organic carbon (SOC) stock are
central in understanding the global carbon cycle and informing related land management …
central in understanding the global carbon cycle and informing related land management …
Multi-algorithm comparison for predicting soil salinity
Soil salinization is one of the most predominant processes responsible for land degradation
globally. However, monitoring large areas presents significant challenges due to strong …
globally. However, monitoring large areas presents significant challenges due to strong …
[HTML][HTML] A deep learning method to predict soil organic carbon content at a regional scale using satellite-based phenology variables
Obtaining the spatial distribution information of soil organic carbon (SOC) is significant to
quantify the carbon budget and guide land management for migrating carbon emissions …
quantify the carbon budget and guide land management for migrating carbon emissions …
Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of …
SOC is the most important indicator of soil fertility and monitoring its space-time changes is a
prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we …
prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we …