An Error Budget for Mapping Field-Scale Soil Salinity at Various Depths using Different Sources of Ancillary Data

Authors

Jingyi Huang, University of New South Wales
E G. Barrett-Lennard, Department of Primary Industries and Regional DevelopmentFollow
Tanya Kilminster, Department of Primary Industries and Regional DevelopmentFollow
Aidan Sinnott, Precision Agronomics Australia
John Triantafillis, University of New South WalesFollow

Document Type

Article

Publication Date

10-23-2015

Journal Title

Soil Science Society of America Journal

ISSN

0361-5995

Keywords

soil salinity, modelling

Disciplines

Natural Resources and Conservation | Natural Resources Management and Policy

Abstract

To manage soil salinity, farmers need to map its variation, often quantified as the electrical conductivity of a saturated soil-paste extract (ECe, dS m-1). However, ECe determination is time-consuming and expensive. Previous studies have evaluated the use of digital elevation models (DEMs, i.e., elevation), airborne γ-ray (γ-ray) spectrometry (i.e., K, U, and Th) and electromagnetic (EM, i.e., EM38 and EM34) data to map ECe at the district scale. Herein we use similar ancillary data set and empirical best linear unbiased prediction (E-BLUP) to make maps of ECe at different depth intervals (0–0.25, 0.25–0.50, and 0.50–0.75 m) at the field scale. The ancillary data was collected using a ground-based mobile sensing system which included; a GPS which provided spatial coordinates (Easting, Northing), a RS700 (γ-ray) mobile spectrometer, and a DUALEM-1. An error budget procedure was conducted to quantify the model, input, and individual covariate errors of ECe. Results show that while none of the γ-ray data were significant, scaled Easting, elevation, and 1-m horizontal coplanar (1mHcon) were optimal for mapping ECe at 0 to 0.25 m, while 1mHcon was optimal at 0.25 to 0.50 m and 0.50 to 0.75 m. Among all the individual covariate errors for mapping 0- to 0.25-m ECe, elevation (0.40 dS m-1) was smallest, followed by 1mHcon (2.23 dS m-1). To reduce error, additional soil samples are necessary to prevent the edge effect of the kriging process. Additionally, inversion of EM data could be used to improve ECe mapping considering the relatively large model error associated with EM data in the subsoil of inverted salinity profiles.

Recommended Citation

Huang, J, Barrett-Lennard, E G, Kilminster, T, Sinnott, A, and Triantafillis, J. (2015), An Error Budget for Mapping Field-Scale Soil Salinity at Various Depths using Different Sources of Ancillary Data. Soil Science Society of America Journal, 79 (6), 1717-1728.
https://library.dpird.wa.gov.au/nrm_research/23