Location: Hydrology and Remote Sensing LaboratoryTitle: Use of topographic models for mapping soil properties and processes
|LI, X. - US Department Of Agriculture (USDA)
|DU, L. - US Department Of Agriculture (USDA)
|LEE, S. - University Of Maryland
Submitted to: Soil Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/12/2020
Publication Date: 5/15/2020
Citation: Li, X., McCarty, G.W., Du, L., Lee, S. 2020. Use of topographic models for mapping soil properties and processes. Soil Systems. 4:32. https://doi.org/10.3390/soilsystems4020032.
Interpretive Summary: A study of landscape topography is an assessment of the current terrain features and a representation of the landform. Because topography reflects elevation changes within detailed landform features over a region, it can significantly impact soil processes. Rapid advancements in aerial, space, and geographic technologies have led to large scale availability of digital elevation models (DEMs) providing detailed topographic information for topographic model generation. We compared performances of two types of topography-based models using advanced statistical methods. We found that the combined use of these models allows for local calibration of a general regional topographic model. This approach leads to generation of accurate soil maps with a very limited number of new samples from a location under study.
Technical 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 nutrient. 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 present a summary of recent topography-based soil studies and review five main groups of topographic models used 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 with 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 LiDAR–derived DEMs were used as inputs for 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 demonstrates 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.