Quantifying the ability of environmental parameters to predict soil texture fractions using regression-tree model with GIS and LIDAR data: The case study of Denmark

Soil texture is an important soil characteristic that drives crop production and field management, and is the basis for environmental monitoring (including soil quality and sustainability, hydrological and ecological processes, and climate change simulations). The combination of coarse sand, fine sand, silt, and clay in soil determines its textural classification. This study used Geographic Information Systems (GIS) and regression-tree modeling to precisely quantify the relationships between the soil texture fractions and different environmental parameters on a national scale, and to detect the most important parameters that can be used as weighted input data in soil environmental prediction models. Seven primary terrain parameters (elevation, slope gradient, slope aspect, plan curvature, profile curvature, flow direction, flow accumulation) and one compound topographic index (CTI) were generated from a Digital Elevation Model (DEM) acquired using airborne LIDAR (Light Detection and Ranging) systems. They were used along with digital data collected from other sources (existing maps and available pluviometric stations), i.e. parent materials, landscape types, geographic regions, yearly precipitation, seasonal precipitation to statistically explain soil texture fractions field/laboratory measurements (45,224 sampling sites) in the area of interest (Denmark). The developed strongest relationships were associated with clay and silt, variance being equal to 60%, followed by coarse sand (54.5%) and fine sand (52%) as the weakest relationship. This study also showed that parent materials (with a relative importance varying between 47% and 100%), geographic regions (31–100%) and landscape types (68–100%) considerably influenced all soil texture fractions, which is not the case for climate and DEM parameters. Yearly and seasonal precipitation had a significant impact on clay and silt; elevation had higher influence on coarse sand (13%), fine sand (12%) and clay (10%) where; slope gradient influenced silt (11.5%); slope aspect (14%) and CTI (9%) influenced fine sand; and profile/plan curvatures and flow direction/accumulation did not interfere in the building of the soil texture regression trees and associated relationships. The latter can be extrapolated to other areas sharing similar geo-environmental conditions.