Staff Research Highlight - Quantifying the potential of using Soil Moisture Active Passive (SMAP) soil moisture variability to predict subsurface water dynamics
Nayak, A. K., Xu, X., Frey, S. K., Khader, O., Erler, A. R., Lapen, D. R., Russell, H. A. J., & Sudicky, E. A. (2025). Quantifying the potential of using Soil Moisture Active Passive (SMAP) soil moisture variability to predict subsurface water dynamics. In Hydrology and Earth System Sciences (Vol. 29, Issue 1, pp. 215–244). Copernicus GmbH. https://doi.org/10.5194/hess-29-215-2025
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We’re pleased to highlight this publication co-authored by several Aquanty staff including Steven K. Frey, Bruce Xu, Omar Khader, Andre R. Erler, and Edward A. Sudicky, which investigates the potential for using near-surface soil moisture measurements from the Soil Moisture Active Passive (SMAP) satellite system to predict subsurface soil moisture and groundwater storage dynamics. This research offers valuable insights into how satellite-based soil moisture data can inform large-scale hydrological modelling and support improved water resource management.
“Unlike loosely or sequentially coupled groundwater–surface water models, HGS is a fully integrated model, providing the simultaneous solution of the channel, surface, and subsurface flow regimes at each time step.”
Figure 1. (a) Location of the South Nation Watershed (SNW). (b) The SNW land cover map (source: Agriculture and Agri-Food Canada's Annual Crop Inventory 2015) overlaid with location of the 9 km grids (boxes for the pixels and black dots for the centers) for the SMAP SM product used in this study. (c) Soil map for the SNW (source: Soil Landscapes of Canada version 3.2, Agriculture and Agri-Food Canada), along with the in situ SM monitoring sites. (d) Surface elevation along with location of streamflow gauges. (e) Tile drains installed for the SNW (provided by the South Nation Conservation Authority), along with location of groundwater level monitoring wells.
In this research highlight, the study evaluates the link between SMAP-derived surface soil moisture (SSM) and subsurface hydrologic conditions in the South Nation Watershed (SNW) in eastern Ontario, Canada. Researchers compared SMAP measurements with results from a high-resolution, fully integrated surface-subsurface model built using HydroGeoSphere (HGS). The model simulates variably saturated groundwater flow and accounts for key hydrologic processes such as infiltration, overland flow, and groundwater recharge. SMAP data were further processed using the Soil Water Index (SWI) approach, which applies an exponential filter to surface moisture data to estimate moisture content at deeper soil layers.
HGS was central to this analysis, offering a comprehensive simulation of water movement through soil layers down to approximately 35 meters. The model incorporated over 1.2 million three-dimensional finite elements and provided daily transient simulations between 2008 and 2017. This level of spatial and temporal detail allowed researchers to directly compare model outputs to SMAP satellite data and in-situ soil moisture measurements across various depths.
The study found strong correlations between SMAP SSM variability and simulated subsurface soil moisture at multiple depths, with optimal time lags of ~1 day for the 25–50 cm soil layer, ~6 days for the 50–100 cm layer, and ~11 days for groundwater storage. These findings suggest that surface soil moisture data from satellites can serve as useful predictors of deeper soil and groundwater conditions. The study also determined optimal characteristic time lengths (Topt) for the SWI method, ranging from 21 days for the 0–25 cm layer to 38 days for the 0–100 cm layer, values consistent with similar studies in other agricultural regions.
By integrating SMAP satellite data with fully integrated modelling using HydroGeoSphere, this research bridges the gap between remote sensing and physics-based hydrological modelling. It highlights the potential for using satellite-derived SSM and derived indices like SWI to estimate subsurface hydrologic behavior with practical implications for large-scale water resource monitoring. The results not only validate the use of SMAP for monitoring root zone moisture and groundwater trends, but also demonstrate the utility of HGS for capturing the complexity of coupled surface and subsurface hydrological processes across diverse soil and climate conditions.
Abstract:
Advances in satellite Earth observation have opened up new opportunities for global monitoring of soil moisture (SM) at fine to medium resolution, but satellite remote sensing can only measure the near-surface soil moisture (SSM). As such, it is critically important to examine the potential of satellite SSM measurements to derive the water resource variations in deeper subsurface. This study compares the SSM variability captured by the Soil Moisture Active and Passive (SMAP) satellite and the Soil Water Index (SWI) derived from SMAP SSM with subsurface SM and groundwater (GW) dynamics simulated by a high-resolution fully integrated surface water–groundwater model over an agriculturally dominated watershed in eastern Canada across two spatial scales, namely SMAP product grid (9 km) and watershed (∼4000 km2). SMAP measurements compare well with the hydrologic simulations in terms of SSM variability at both scales. Simulated subsurface SM and GW storage show lagged and smoother characteristics relative to SMAP SSM variability with an optimal delay of ∼1 d for the 25–50 cm SM, ∼6 d for the 50–100 cm SM, and ∼11 d for the GW storage for both scales. Modeled subsurface SM dynamics agree well with the SWI derived from SMAP SSM using the classic characteristic time lengths (15 d for the 0–25 cm layer and 20 d for the 0–100 cm layer). The simulated GW storage showed a slightly delayed variation relative to the derived SWI. The quantified optimal characteristic time length Topt for SWI estimation (by matching the variations in SMAP-derived SWI and modeled root zone SM) is comparable to Topt obtained in other agricultural regions around the world. This work demonstrates SMAP SM measurements as a potentially useful aid when predicting root zone SM and GW dynamics and validating fully integrated hydrologic models across different spatial scales. This study also provides insights into the dynamics of near-surface–subsurface water interaction and the capabilities and approaches of satellite-based SM monitoring and high-resolution fully integrated hydrologic modeling.