Simplifying field-scale assessment of spatiotemporal changes of soil salinity

Elia Scudiero; Todd H Skaggs; Dennis L Corwin

doi:10.1016/j.scitotenv.2017.02.136

Simplifying field-scale assessment of spatiotemporal changes of soil salinity

Sci Total Environ. 2017 Jun 1:587-588:273-281. doi: 10.1016/j.scitotenv.2017.02.136. Epub 2017 Feb 27.

Authors

Elia Scudiero¹, Todd H Skaggs², Dennis L Corwin²

Affiliations

¹ University of California Riverside, Department of Environmental Sciences, Riverside, CA, USA; USDA-ARS, United States Salinity Laboratory, Riverside, CA, USA. Electronic address: elia.scudiero@ars.usda.gov.
² USDA-ARS, United States Salinity Laboratory, Riverside, CA, USA.

PMID: 28256315
DOI: 10.1016/j.scitotenv.2017.02.136

Abstract

Monitoring soil salinity (EC_e) is important for planning and implementing agronomic and irrigation practices. Salinity can be measured through soil sampling directed by geospatial measurements of apparent soil electrical conductivity (EC_a). Using data from a long-term (1999-2012) monitoring study at a 32.4-ha saline field located in California, USA, two established field-scale approaches to map and monitor soil salinity using EC_a are reviewed: one that relies on a single EC_a survey to identify locations that can be repeatedly sampled to infer the frequency distribution of EC_e; and another based on repeated EC_a surveys that are calibrated, each time, to EC_e estimation using ground-truth data from soil samples. The reviewed approaches are very accurate and reliable, but require extensive soil sampling. Subsequently, we propose a novel approach - temporal analysis of covariance (t-ANOCOVA) modeling - that results in accurate spatiotemporal salinity estimations using EC_a surveys with a significant reduction in the number of soil samples needed for calibration of EC_a to EC_e. In this modeling framework, the EC_e-EC_a relationship is described with a log-transformed linear function. The regression slope indicates the magnitude of the contribution of EC_e to EC_a and is assumed to remain constant over time, while the intercept represents the secondary factors influencing EC_a that are not related to EC_e (e.g., soil tillage). Once the t-ANOCOVA slope is established for a field, in subsequent surveys as few as three soil samples are used to estimate a time-specific t-ANOCOVA intercept so that EC_a measurements can be converted to EC_e estimations. Our results suggest that this approach is reliable at low salinity values (i.e., where common crops can grow). The t-ANOCOVA approach requires further validation before real-world implementations, but represents a significant step towards the use of EC_a mobile sensor technology for inexpensive soil salinity monitoring at high temporal resolution.

Keywords: Electromagnetic induction; Mapping; Monitoring; Soil salinity; Soil spatial variability.

Published by Elsevier B.V.