Predicting soil properties in 3D: Should depth be a covariate?

Yuxin Ma*, Budiman Minasny, Alex McBratney, Laura Poggio, Mario Fajardo

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Soil is a three-dimensional volume with property variability in all three dimensions. In Digital Soil Mapping (DSM), the variation of soil properties down a profile is usually harmonised by the use of the equal-area spline depth function approach. Soil observations at various depth intervals are harmonised to pre-determined depth intervals. To create maps of soil at the defined depth intervals, 2.5D model produces maps of individual depth intervals separately. Those maps can be reconstructed to produce a continuous depth function for each predicted location. More recently, several studies propose that soil property at any depth can be mapped using a model incorporating depth along with spatial covariates as predictor variables, creating a ‘3D’ model. The aim of this study is to evaluate the proposition that soil properties can be predicted at any depth. This study compares the 2.5D model and 3D model in two areas. The first test is on a 1500 km2 area in Edgeroi, New South Wales (NSW), Australia, mapping soil organic carbon (SOC, %), carbon storage (kg m−2), pH (H2O), clay content (%), and cation exchange capacity (CEC, mg/kg) based on depth-interval observations. The second study area in the Lower Hunter Valley has SOC observations at every 2 cm increment from a 210 km2 area. 2.5D and 3D models were tested in both study areas using four machine learning techniques: Cubist regression tree, Quantile Regression Forest (QRF), Artificial Neural Network (ANN), and 3D Generalised Additive Model (GAM). Results show that, in terms of R2 and RMSE, 2.5D and 3D models using different machine learning models produce comparable results on the validation of depth interval observations. The 3D tree-based models produce “stepped” prediction of properties with depth. Results on the Hunter Valley area with point observations show that the 3D model cannot replicate field point observations. 3D soil mapping on point depth observation has lower accuracy and larger uncertainty compared to the 2.5D model. For future DSM studies, 3D soil mapping with depth as a covariate requires caution with respect to the prediction method and the requirements of the results.
Original languageEnglish
Article number114794
JournalGeoderma
Volume383
DOIs
Publication statusPublished - 1 Feb 2021

Keywords

  • 3D mapping
  • Average prediction interval
  • Digital soil mapping
  • Tree-based model

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