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Exploring machine learning models for soil nutrient properties prediction: A systematic review
Agriculture is essential to a flourishing economy. Although soil is essential for sustainable
food production, its quality can decline as cultivation becomes more intensive and demand …
food production, its quality can decline as cultivation becomes more intensive and demand …
Open remote sensing data in digital soil organic carbon map**: a review
This review focuses on digital soil organic carbon (SOC) map** 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 …
Machine learning approaches for prediction of fine-grained soils liquefaction
Since soil liquefaction is a dimension that increases the amount and severity of losses in an
earthquake, it is vital to estimate the liquefaction potential accurately. Traditionally, several …
earthquake, it is vital to estimate the liquefaction potential accurately. Traditionally, several …
Integrating active and passive remote sensing data for map** soil salinity using machine learning and feature selection approaches in arid regions
The prevention of soil salinization and managing agricultural irrigation depend greatly on
accurately estimating soil salinity. Although the long-standing laboratory method of …
accurately estimating soil salinity. Although the long-standing laboratory method of …
[HTML][HTML] Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks
X Ge, J Ding, D Teng, B **
A common practice in digital soil map** (DSM) is to incorporate many environmental
covariates into a machine-learning algorithm to predict the spatial patterns of soil attributes …
covariates into a machine-learning algorithm to predict the spatial patterns of soil attributes …
Spatial prediction of soil aggregate stability and soil organic carbon in aggregate fractions using machine learning algorithms and environmental variables
Abstract Knowledge about the spatial variability of soil aggregate stability indices, soil
organic carbon (SOC) in various aggregate sizes, and aggregation across the landscape is …
organic carbon (SOC) in various aggregate sizes, and aggregation across the landscape is …
[HTML][HTML] Improving model parsimony and accuracy by modified greedy feature selection in digital soil map**
In the context of increasing soil degradation worldwide, spatially explicit soil information is
urgently needed to support decision-making for sustaining limited soil resources. Digital soil …
urgently needed to support decision-making for sustaining limited soil resources. Digital soil …
Prediction of soil salinity parameters using machine learning models in an arid region of northwest China
Accurate estimation of soil ions composition is of great significance for preventing soil
salinization and guiding crop irrigation. The traditional laboratory measurement of ions …
salinization and guiding crop irrigation. The traditional laboratory measurement of ions …
Global soil salinity estimation at 10 m using multi-source remote sensing
Salinization is a threat to global agricultural and soil resource allocation. Current
investigations of global soil salinity are limited to coarse spatial resolution of the available …
investigations of global soil salinity are limited to coarse spatial resolution of the available …