[HTML][HTML] Soil erosion modelling: A bibliometric analysis
Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and
organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable …
organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable …
Open remote sensing data in digital soil organic carbon mapping: a review
This review focuses on digital soil organic carbon (SOC) mapping at regional or national
scales in spatial resolutions up to 1 km using open data remote sensing sources …
scales in spatial resolutions up to 1 km using open data remote sensing sources …
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 …
Random forest spatial interpolation
For many decades, kriging and deterministic interpolation techniques, such as inverse
distance weighting and nearest neighbour interpolation, have been the most popular spatial …
distance weighting and nearest neighbour interpolation, have been the most popular spatial …
[HTML][HTML] Comparing the prediction performance, uncertainty quantification and extrapolation potential of regression kriging and random forest while accounting for soil …
B Takoutsing, GBM Heuvelink - Geoderma, 2022 - Elsevier
Geostatistics and machine learning have been extensively applied for modelling and
predicting the spatial distribution of continuous soil variables. In addition to providing …
predicting the spatial distribution of continuous soil variables. In addition to providing …
Smart-Map: An Open-Source QGIS Plugin for Digital Mapping Using Machine Learning Techniques and Ordinary Kriging
Machine Learning (ML) algorithms have been used as an alternative to conventional and
geostatistical methods in digital mapping of soil attributes. An advantage of ML algorithms is …
geostatistical methods in digital mapping of soil attributes. An advantage of ML algorithms is …
[HTML][HTML] Large scale mapping of soil organic carbon concentration with 3D machine learning and satellite observations
Canada has extensive forests and peatlands that play key roles in global carbon cycle.
Canadian soils and peatlands are assumed to store approximately 20% of the world's soil …
Canadian soils and peatlands are assumed to store approximately 20% of the world's soil …
Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm
Fire is among the most dangerous and devastating natural hazards in forest ecosystems
around the world. The development of computational ensemble models for improving the …
around the world. The development of computational ensemble models for improving the …
Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning
B Kasraei, B Heung, DD Saurette, MG Schmidt… - … Modelling & Software, 2021 - Elsevier
Digital soil mapping (DSM) techniques have provided soil information that has
revolutionized soil management across multiple spatial extents and scales. DSM …
revolutionized soil management across multiple spatial extents and scales. DSM …
[HTML][HTML] Spatial statistics and soil mapping: A blossoming partnership under pressure
GBM Heuvelink, R Webster - Spatial statistics, 2022 - Elsevier
For the better part of the 20th century pedologists mapped soil by drawing boundaries
between different classes of soil which they identified from survey on foot or by vehicle …
between different classes of soil which they identified from survey on foot or by vehicle …