This study aimed to develop a strategy to identify the relationship between soil information, such as texture, fertility and pedological classes, through vegetation spectral sensing obtained by a satellite sensor.
In the prediction of θFC, the most important soil information was PSD or texture class, depending on the type of information available for prediction (Tables 4b, 5).
Most pedotransfer functions (PTFs) have adopted soil texture information as the main predictor to estimate soil hydraulic properties, whether inputs are defined in terms of the relative proportion of different grain size particles or texture-based classifications.
This was performed based on the soil-environment model (case-based reasoning approach) using a 10-m LiDAR Digital Elevation Model (DEM) and derivatives such as slope, surface curvature, smooth multi-path wetness index, slope position classification in combination with landcover, mode of deposition and spatial cases of soil texture information.
Soil texture data was converted to a CEC value (mmol kg-1) using a linear relationship [ 23].
SEC has very high correlation with soil texture, DEM contain valuable information on soil moisture regime and landscape genesis, and AP carry information on topsoil total organic carbon, TOC.
OC content, total N content, soil texture, and soil pH were measured as described by Will et al. [45].
The explanatory variables include the soil order classification, texture, and parsed information from the S-map functional horizon description.
The soil texture and effective soil depth in grid cell x defines AWCx.
It resembles potting soil in texture and odour and may be used as a soil conditioner or mulch.
