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Water 2016, 8(7), 311; doi:10.3390/w8070311

Development of Dynamic Ground Water Data Assimilation for Quantifying Soil Hydraulic Properties from Remotely Sensed Soil Moisture

School of Agricultural Civil & Bio-Industrial Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea
Department of Regional Infrastructures Engineering, Kangwon National University, 1 Kangwondaehak Street, Chuncheon 200-701, Korea
Climate Research Department, APEC Climate Center, 12 Centum 7-ro, Haeundae-gu, Busan 612-020, Korea
Water Resources Research Center, K-water Institute, 125, Yuseong-daero, 1698 beon-gil, Yuseong-gu, Daejeon 34045, Korea
Author to whom correspondence should be addressed.
Academic Editors: Alexander Löw and Jian Peng
Received: 18 May 2016 / Revised: 15 July 2016 / Accepted: 19 July 2016 / Published: 22 July 2016
(This article belongs to the Special Issue Remote Sensing of Soil Moisture)
View Full-Text   |   Download PDF [2881 KB, uploaded 22 July 2016]   |  


Several inversion modeling-based approaches have been developed/used to extract soil hydraulic properties (α, n, θres, θsat, Ksat) from remotely sensed (RS) soil moisture footprints. Hydrological models with shallow ground water (SGW) table depths in soils simulate daily root zone soil moisture dynamics based on the extracted soil parameters. The presence of SGW table depths in soils significantly influences model performances; however, SGW table depths are usually unknown in the field, thus, unknown SGW table depths might cause uncertainties in the model outputs. In order to overcome these drawbacks, we developed a dynamic ground water (DGW) data assimilation approach that can consider SGW table depths across time for quantifying effective soil hydraulic properties in the unsaturated zone. In order to verify the DGW data assimilation scheme, numerical experiments comprising synthetic and field validation experiments were conducted. For the numerical studies, the Little Washita (LW) watershed in Oklahoma and Olney (OLN)/Bondville (BOND) sites in Illinois were selected as different hydroclimatic regions. For the synthetic conditions, we tested the DGW scheme using various soil textures and vegetation covers with fixed and dynamically changing SGW table depths across time in homogeneous and heterogeneous (layered) soil columns. The DGW-based soil parameters matched the observations under various synthetic conditions better than those that only consider fixed ground water (FGW) table depths in time. For the field validations, our proposed data assimilation scheme performed well in predicting the soil hydraulic properties and SGW table depths at the point, airborne sensing, and satellite scales, even though uncertainties exist. These findings support the robustness of our proposed DGW approach in application to regional fields. Thus, the DGW scheme could improve the availability and applicability of pixel-scale soil moisture footprints based on satellite platforms. View Full-Text
Keywords: dynamic ground water; fixed ground water; remote sensing; shallow ground water; soil hydraulic properties; SWAP dynamic ground water; fixed ground water; remote sensing; shallow ground water; soil hydraulic properties; SWAP

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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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Shin, Y.; Lim, K.J.; Park, K.; Jung, Y. Development of Dynamic Ground Water Data Assimilation for Quantifying Soil Hydraulic Properties from Remotely Sensed Soil Moisture. Water 2016, 8, 311.

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