An error budget for soil salinity mapping using different ancillary data
J. Huang A , E. Zare A , R. S. Malik B and J. Triantafilis A C
+ Author Affiliations
- Author Affiliations
A School of Biological, Earth and Environmental Sciences, University of New South Wales, High Street, Kensington, NSW 2052, Australia.
B Department of Agriculture and Food, Dore Street, Katanning, WA 6317, Australia.
C Corresponding author. Email: j.triantafilis@unsw.edu.au
Soil Research 53(5) 561-575 https://doi.org/10.1071/SR15043
Submitted: 10 February 2015 Accepted: 23 March 2015 Published: 20 August 2015
Abstract
Secondary soil salinisation occurs as a function of human interaction with the landscape. Increasing salinity is a major constraint to crop yield. The electrical conductivity of a saturated soil-paste extract (ECe, dS m–1) defines the level of salinity in soil. In order to manage salinity, farmers need to map its variation. However, ECe determination is time-consuming and expensive. Digital mapping of ECe is possible by using ancillary data such as easy-to-obtain digital elevation model, gamma-ray spectrometry and electromagnetic (EM) induction data. In this paper, we used these ancillary data and empirical best linear unbiased prediction (E-BLUP) to make a digital map of ECe. In this regard, we found that elevation, radioelement of thorium (Th) and logEM38-v were the most statistically useful ancillary data. We also developed an error-budget procedure to quantify the relative contributions that model, input (for all the ancillary datasets), and combined and individual covariate (for each of the ancillary datasets) error made to the prediction error of our map of ECe. The error-budget procedure used ordinary kriging, E-BLUP and conditional simulation to produce numerous realisations of the data and their underlying errors. Results show that the combined error of model error and input error was ~4.44 dS m–1. Compared with the standard deviation of observed soil ECe (3.61 dS m–1), the error was large. Of this error, most was attributable to the input error (1.38 dS m–1), which is larger than the model error (0.02 dS m–1). In terms of the input error, we determined that the larger standard deviation is attributable to the lack of ancillary data, namely the ECa in areas adjacent to the Darling River and on the aeolian dune where data collection was difficult owing to dense native vegetation.
Additional keywords: electromagnetic induction, digital elevation model, γ-ray spectrometry, salinity, uncertainty.
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