title: Simplifying field-scale assessment of spatiotemporal changes of soil salinity.
abstract: Monitoring soil salinity (ECe) 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 (ECa). 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 ECaare reviewed: one that relies on a single ECasurvey to identify locations that can be repeatedly sampled to infer the frequency distribution of ECe; and another based on repeated ECasurveys that are calibrated, each time, to ECeestimation 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 ECasurveys with a significant reduction in the number of soil samples needed for calibration of ECato ECe. In this modeling framework, the ECe-ECarelationship is described with a log-transformed linear function. The regression slope indicates the magnitude of the contribution of ECeto ECaand is assumed to remain constant over time, while the intercept represents the secondary factors influencing ECathat are not related to ECe(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 ECameasurements can be converted to ECeestimations. 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 ECamobile sensor technology for inexpensive soil salinity monitoring at high temporal resolution.
authors.affiliation: 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.
authors.forename: Elia
authors.initials: E
authors.lastname: Scudiero
authors.affiliation: USDA-ARS, United States Salinity Laboratory, Riverside, CA, USA.
authors.forename: Todd H
authors.initials: TH
authors.lastname: Skaggs
authors.affiliation: USDA-ARS, United States Salinity Laboratory, Riverside, CA, USA.
authors.forename: Dennis L
authors.initials: DL
authors.lastname: Corwin
keywords: Electromagnetic induction; Mapping; Monitoring; Soil salinity; Soil spatial variability