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Water 2019, 11(3), 542; https://doi.org/10.3390/w11030542

Comparative Analysis of High-Resolution Soil Moisture Simulations from the Soil, Vegetation, and Snow (SVS) Land Surface Model Using SAR Imagery Over Bare Soil

1
Science and Technology Branch, Environment and Climate Change Canada, Dorval, QC H9P 1J3, Canada
2
Science and Technology Branch, Agriculture and Agri-Food Canada, Winnipeg, MB R3C 3G7, Canada
3
Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
*
Author to whom correspondence should be addressed.
Received: 28 December 2018 / Revised: 27 February 2019 / Accepted: 12 March 2019 / Published: 15 March 2019
(This article belongs to the Section Hydrology)
PDF [1515 KB, uploaded 15 March 2019]

Abstract

Soil moisture is a key variable in Earth systems, controlling the exchange of water and
energy between land and atmosphere. Thus, understanding its spatiotemporal distribution and
variability is important. Environment and Climate Change Canada (ECCC) has developed a new
land surface parameterization, named the Soil, Vegetation, and Snow (SVS) scheme. The SVS land
surface scheme features sophisticated parameterizations of hydrological processes, including water
transport through the soil. It has been shown to provide more accurate simulations of the temporal
and spatial distribution of soil moisture compared to the current operational land surface scheme.
Simulation of high resolution soil moisture at the field scale remains a challenge. In this study, we
simulate soil moisture maps at a spatial resolution of 100 m using the SVS land surface scheme over
an experimental site located in Manitoba, Canada. Hourly high resolution soil moisture maps were
produced between May and November 2015. Simulated soil moisture values were compared with
estimated soil moisture values using a hybrid retrieval algorithm developed at Agriculture and
Agri-Food Canada (AAFC) for soil moisture estimation using RADARSAT-2 Synthetic Aperture
Radar (SAR) imagery. Statistical analysis of the results showed an overall promising performance
of the SVS land surface scheme in simulating soil moisture values at high resolution scale.
Investigation of the SVS output was conducted both independently of the soil texture, and as a
function of the soil texture. The SVS model tends to perform slightly better over coarser textured
soils (sandy loam, fine sand) than finer textured soils (clays). Correlation values of the simulated
SVS soil moisture and the retrieved SAR soil moisture lie between 0.753–0.860 over sand and 0.676-
0.865 over clay, with goodness of fit values between 0.567–0.739 and 0.457–0.748, respectively. The
Root Mean Square Difference (RMSD) values range between 0.058–0.062 over sand and 0.055–0.113
over clay, with a maximum absolute bias of 0.049 and 0.094 over sand and clay, respectively. The
unbiased RMSD values lie between 0.038–0.057 over sand and 0.039–0.064 over clay. Furthermore,
results show an Index of Agreement (IA) between the simulated and the derived soil moisture
always higher than 0.90.
Keywords: Synthetic Aperture Radar; land surface scheme; soil moisture Synthetic Aperture Radar; land surface scheme; soil moisture
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Dabboor, M.; Sun, L.; Carrera, M.L.; Friesen, M.; Merzouki, A.; McNairn, H.; Powers, J.; Bélair, S. Comparative Analysis of High-Resolution Soil Moisture Simulations from the Soil, Vegetation, and Snow (SVS) Land Surface Model Using SAR Imagery Over Bare Soil. Water 2019, 11, 542.

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