[HTML][HTML] SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty
SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution
(250 m cell size) using state-of-the-art machine learning methods to generate the necessary …
(250 m cell size) using state-of-the-art machine learning methods to generate the necessary …
Prediction of soil organic carbon and the C: N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and …
T Zhou, Y Geng, C Ji, X Xu, H Wang, J Pan… - Science of the Total …, 2021 - Elsevier
Soil organic carbon (SOC) and soil carbon-to-nitrogen ratio (C: N) are the main indicators of
soil quality and health and play an important role in maintaining soil quality. Together with …
soil quality and health and play an important role in maintaining soil quality. Together with …
High-resolution digital map** of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning …
Soil organic carbon (SOC) and soil total nitrogen (STN) are important indicators of soil
health and play a key role in the global carbon and nitrogen cycles. High-resolution radar …
health and play a key role in the global carbon and nitrogen cycles. High-resolution radar …
Using deep learning for digital soil map**
Digital soil map** (DSM) has been widely used as a cost-effective method for generating
soil maps. However, current DSM data representation rarely incorporates contextual …
soil maps. However, current DSM data representation rarely incorporates contextual …
Monitoring changes in global soil organic carbon stocks from space
Soils are under threat globally, with declining soil productivity and soil health in many
places. As a key indicator of soil functioning, soil organic carbon (SOC) is crucial for …
places. As a key indicator of soil functioning, soil organic carbon (SOC) is crucial for …
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 …
SoilGrids 2.0: producing quality-assessed soil information for the globe
SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution
(250 metres cell size) using state-of-the-art machine learning methods to generate the …
(250 metres cell size) using state-of-the-art machine learning methods to generate the …
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 …
Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science
The availability and accessibility of multispectral and radar satellite remote sensing (SRS)
imagery are at an unprecedented high. These data have both become standard source of …
imagery are at an unprecedented high. These data have both become standard source of …
Cause-effect relationships using structural equation modeling for soil properties in arid and semi-arid regions
Predicting soil properties and evaluating their functions along with their related driving
factors is useful for providing useful geographical information for soil management, which is …
factors is useful for providing useful geographical information for soil management, which is …