Highlighting the role of agriculture and geospatial technology in food security and sustainable development goals
Food security is a global challenge that aligns with several Sustainable Development Goals
(SDGs), including SDG 1‐“No Poverty”, SDG 2‐“Zero Hunger,” SDG 3‐“Good Health and …
(SDGs), including SDG 1‐“No Poverty”, SDG 2‐“Zero Hunger,” SDG 3‐“Good Health and …
Soil inorganic carbon, the other and equally important soil carbon pool: distribution, controlling factors, and the impact of climate change
Soil inorganic carbon (SIC) contributes to up to half of the terrestrial C stock and is especially
significant in arid and semi-arid environments, yet has not been explored as much as soil …
significant in arid and semi-arid environments, yet has not been explored as much as soil …
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 …
Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil
Soil organic carbon (SOC) is an important indicator of soil quality and directly determines
soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary …
soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary …
An advanced soil organic carbon content prediction model via fused temporal-spatial-spectral (TSS) information based on machine learning and deep learning …
X Meng, Y Bao, Y Wang, X Zhang, H Liu - Remote Sensing of Environment, 2022 - Elsevier
Abstract Knowledge of the soil organic carbon (SOC) content is critical for environmental
sustainability and carbon neutrality. With the development of remote sensing data and …
sustainability and carbon neutrality. With the development of remote sensing data and …
Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran
The cuurent study was performed to predict spatial distribution of some heavy metals (Ni, Fe,
Cu, Mn) in western Iran, using environmental covariates and applying two machine learning …
Cu, Mn) in western Iran, using environmental covariates and applying two machine learning …
Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison
There is a worldwide need for detailed spatial information to support soil map**, mainly in
the tropics, where main agricultural areas are concentrated. In this line, satellite images are …
the tropics, where main agricultural areas are concentrated. In this line, satellite images are …
Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images
It is important to predict the spatial distribution of SOC accurately for migrating carbon
emission and sustainable soil management. Environmental variables influence the accuracy …
emission and sustainable soil management. Environmental variables influence the accuracy …
Satellite imagery to map topsoil organic carbon content over cultivated areas: an overview
There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper
horizons or plough layer for enabling decision support and land management, while …
horizons or plough layer for enabling decision support and land management, while …
Enhancing the accuracy of machine learning models using the super learner technique in digital soil map**
Digital soil map** approaches predict soil properties based on the relationships between
soil observations and related environmental covariates using techniques such as machine …
soil observations and related environmental covariates using techniques such as machine …