Current use of land surface curvatures (LSCs) is in a confused state as a large gap has
opened up between applications and theoretical work. LSCs exercise important control on …

Soil organic matter (SOM) has often been separated into operational physical fractions, such
as particulate organic matter (POM) and mineral-associated organic matter (MAOM), to …

Brazil has extensive forests and savannas on deep weathered soils and plays a key role in
the discussions about carbon sequestration, but the distribution of soil organic carbon (SOC) …

In the digital soil map** (DSM) framework, machine learning models quantify the
relationship between soil observations and environmental covariates. Generally, the most …

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 …

Topographic variability is one of the main factors that affect soil properties and crop
production. In this work, we reviewed the concept and significance of various topographic …

Digital map** of soil organic carbon (SOC) is crucial to evaluate its spatial variability and
also to assess environmental factors controlling it at field scale. The current study was …

This study was conducted to evaluate the efficacy of the ensemble machine learning model
to predict the spatial variation of soil organic carbon (SOC) concentration in a deciduous …

Soil organic matter (SOM) is a heterogeneous material consisting of different functional
groups depending on soil texture and moisture regime. The lability of SOM (rapidity of …

Soil organic carbon (SOC) is a reliable indicator of soil productivity and land management.
Despite the widespread use of remote sensing data in estimating SOC variation, the …