Use of Topographic Models for Mapping Soil Properties and Processes

Abstract

Landscape topography is an important driver of landscape distributions of soil properties and processes due to its impacts on gravity-driven overland and intrasoil lateral transport of water and nutrients. Rapid advancements in aerial, space, and geographic technologies have led to large scale availability of digital elevation models (DEMs), which have proven beneficial in a wide range of applications by providing detailed topographic information. In this report, we presented a summary of recent topography-based soil studies and reviewed five main groups of topographic models in geospatial analyses widely used for soil sciences. We then compared performances of two types of topography-based models—topographic principal component regression (TPCR) and TPCR-kriging (TPCR-Kr)—to ordinary kriging (OKr) models in mapping spatial patterns of soil organic carbon (SOC) density and redistribution (SR) rate. The TPCR and OKr models were calibrated at an agricultural field site that has been intensively sampled, and the TPCR and TPCR-Kr models were evaluated at another field of interest with two sampling transects. High-resolution topographic variables generated from light detection and ranging (LiDAR)-derived DEMs were used as inputs for the TPCR model building. Both TPCR and OKr models provided satisfactory results on SOC density and SR rate estimations during model calibration. The TPCR models successfully extrapolated soil parameters outside of the area in which the model was developed but tended to underestimate the range of observations. The TPCR-Kr models increased the accuracies of estimations due to the inclusion of residual kriging calculated from observations of transects for local correction. The results suggest that even with low sample intensives, the TPCR-Kr models can reduce estimation variances and provide higher accuracy than the TPCR models. The case study demonstrated the feasibility of using a combination of linear regression and spatial correlation analysis to localize a topographic model and to improve the accuracy of soil property predictions in different regions.

Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil

“…Similar conclusions were reported by Song et al [61], who noted that local environmental attributes play a less significant role than other predictors on a flat terrain system. Li et al [62] inferred that environmental attributes could capture large-scale influences of soil transport but not those occurring at a flat topographic condition. Thus, the over-employment of environmental factors in small-scale flat terrain areas reduces the prediction accuracy and increases the calculation complexity.…”

Soil Aggregate Stability Mapping Using Remote Sensing and GIS-Based Machine Learning Technique

“…Many other studies confirmed the significant role of topography and its derivatives (elevation, slope and TWI) in the spatial distribution of SAS (Cantón et al, 2009;Tang et al, 2010;Nsabimana et al, 2020). Furthermore, we cannot forget the effect of the topography on other soil properties (Kumar and Singh, 2016;Li et al, 2020;Maleki et al, 2020;Tajik el al., 2020) and (Celik, 2005;Jin et al, 2008;Mahmoudabadi et al, 2017;Maynard and Levi, 2017), which will undoubtedly affect the distribution of soil stability. Besides all above results, we cannot forget the role of remote sensing parameters, which have shown their importance during this study.…”

Digital mapping of soil texture classes for efficient land management in the Piedmont plain of Iran

“…Increasing As and tanβ causes the water flow rate and the amount of water to increase and, consequently, increases the erosive power of the water. It explains potential flow erosion and related land processes (Kalambukattu et al, 2018;Li et al, 2020) (1) et al, 2020) MSAVI minimizes the effect of bare soil on the Soil-Adjusted Vegetation Index (SAVI). MSAVI is calculated as a ratio between the R and NIR values with an inductive L function applied to maximize the reduction in soil effects on the vegetation signal Green Normalized Difference Vegetation Index (GNDVI) (Gitelson et al, 1996) Compared with the NDVI index, GNDVI is more sensitive to chlorophyll concentration.…”