Submitted to: Science of the Total Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/16/2017
Publication Date: 2/27/2017
Citation: Scudiero, E., Skaggs, T.H., Corwin, D.L. 2017. Simplifying field-scale assessment of spatiotemporal changes of soil salinity. Science of the Total Environment. 587:273-281. doi: 10.1016/j.scitotenv.2017.02.136.
Interpretive Summary: In arid and semi-arid regions with intensively managed irrigated agriculture, soil salinization is a well-known hazard that can over time reduce the productivity of farmland. Left unchecked, soil salinity may increase to the point that lands become unsuitable for farming. Efforts to combat and mitigate salinity damage are hindered by a lack of data on the extent and variability of soil salinity at both farm and regional scales. In this study, we propose a low-cost approach to monitor spatiotemporal changes of soil salinity over multiple fields, with sub-field resolution, using near-ground sensor measurements of apparent electrical conductivity (ECa) and soil samples. The key feature of this methodology is the low number of soil samples needed to translate the ECa measurements into soil salinity (ECe) estimations, once an initial ECa-ECe relationship is established for a selected farm. This work has implications for managing agricultural lands and irrigation waters, and will be of interest to land resource managers, agricultural consultants, extension specialists, farmers, scientists and researchers working on remote sensing and land management, and the Natural Resource Conservation Service.
Technical Abstract: Monitoring soil salinity (ECe) is important to properly plan agronomic and irrigation practices. Salinity can be readily 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 ECa are reviewed: one that relies on a single ECa survey to identify locations that can be repeatedly sampled to infer the frequency distribution of ECe; and another based on repeated ECa surveys that are calibrated, each time, to ECe 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 ECa surveys with a significant reduction in the number of soil samples needed for calibration of ECa to ECe. In this modeling framework, the ECe-ECa relationship is described with a lognormal linear function. The regression slope indicates the magnitude of the contribution of ECe to ECa and is assumed to remain constant over time, while the intercept represents the secondary factors influencing ECa that 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 ECa measurements can be converted to ECe 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 ECa mobile sensor technology for inexpensive soil salinity monitoring at high temporal resolution.