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Soil moisture is an important component of the hydrological cycle and a key mediator between land surface and atmospheric interactions. Although substantial progress has been made in remote sensing of soil moisture at different spatial scales, the shallow penetration depth of remote sensors greatly limits their utility for applications in meteorological modelling and hydrological studies where the critical variable of interest is the root-zone soil moisture content. Therefore, this study assesses the relationship between soil moisture at the surface (10 cm) and in lower soil layers (20, 40, 60, 80, 100, and 120 cm) under varying climates, soils, and crop types. Cross-correlation analysis is applied to daily in-situ soil moisture measurements from 4712 locations in agricultural lands across the contiguous United States. Our analysis demonstrates that zero-day lag always produced the highest correlation between 10 cm soil moisture and soil moisture in the lower layers. In addition, a positive and strong relationship between 10 and 20 cm soil moisture (r = 0.84) was observed, while the relationships between 10 and 40 cm soil moisture were moderate (r = 0.52). The decline in cross-correlation continued to the deeper soil layers, which indicated that, on a daily timescale, the surface soil moisture gradually becomes decoupled with soil moisture at greater depths. Therefore, our research suggests that the estimation of soil moisture in the soil layers up to 40 cm based on surface soil moisture is most promising. However, the influence of climate, crop type, and soil texture on the strength of relationships between surface and lower layers makes the prediction difficult. The comparatively weak relationship between precipitation and soil moisture (0.09-0.32), as well as the relationship between reference evapotranspiration (ETo) and soil moisture (-0.19-0.18), in this study can be attributed to scale mismatching from different data sources., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., (Published by Elsevier B.V.)