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 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 log-transformed 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.
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