Soil moisture profile estimation by combining P-band SAR polarimetry with hydrological and multi-layer scattering models

  • An approach for estimating vertically continuous soil moisture profiles under varying vegetation covers by combining remote sensing with soil (hydrological) modeling is proposed. The approach uses decomposed soil scattering components, after the removal of the vegetation scattering components from fully polarimetric P-band SAR observations. By comparing these with hydrological simulations, soil moisture profiles from the soil surface until a soil depth of 30 cm (assumed average P-band penetration depth) are estimated. Here, the hydrological model HYDRUS-1D, as a representative of any soil hydrological model, is employed to simulate an ensemble of realistic soil moisture profiles, which are used for a multi-layer soil scattering model to obtain forward modeled soil scattering components. Compared to the decomposed SAR-based soil scattering components, the most appropriate soil moisture profile from the ensemble is estimated. The approach is able to provide physically (hydraulic) moreAn approach for estimating vertically continuous soil moisture profiles under varying vegetation covers by combining remote sensing with soil (hydrological) modeling is proposed. The approach uses decomposed soil scattering components, after the removal of the vegetation scattering components from fully polarimetric P-band SAR observations. By comparing these with hydrological simulations, soil moisture profiles from the soil surface until a soil depth of 30 cm (assumed average P-band penetration depth) are estimated. Here, the hydrological model HYDRUS-1D, as a representative of any soil hydrological model, is employed to simulate an ensemble of realistic soil moisture profiles, which are used for a multi-layer soil scattering model to obtain forward modeled soil scattering components. Compared to the decomposed SAR-based soil scattering components, the most appropriate soil moisture profile from the ensemble is estimated. The approach is able to provide physically (hydraulic) more meaningful soil moisture profile shapes than currently existing profile estimation approaches, like polynomial fitting to few measurements at discrete soil depths. Results are presented across eight in situ measuring stations in the U.S. within six test sites of NASA's Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission between 2013 and 2015. In-depth analyzes and validations with in situ measured soil moisture information demonstrate the feasibility of the proposed approach. Overall, estimated soil moisture profiles at the different sites match the varying local climate, vegetation cover, and soil conditions. Coefficients of determination between estimated and in situ measured soil moisture values vary between 0.48 and 0.92, while unbiased errors range from 1.4 vol% to 3.7 vol%, and Fréchet distances (analyzing the similarity of profile shapes) vary between 0.1 and 0.2 [−].show moreshow less

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Metadaten
Author:Anke FluhrerORCiDGND, Thomas JagdhuberORCiDGND, Carsten Montzka, Maike Schumacher, Hamed Alemohammad, Alireza Tabatabaeenejad, Harald KunstmannORCiDGND, Dara Entekhabi
URN:urn:nbn:de:bvb:384-opus4-1120491
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/112049
ISSN:0034-4257OPAC
Parent Title (English):Remote Sensing of Environment
Publisher:Elsevier BV
Type:Article
Language:English
Year of first Publication:2024
Publishing Institution:Universität Augsburg
Release Date:2024/03/18
Tag:Computers in Earth Sciences; Geology; Soil Science
Volume:305
First Page:114067
DOI:https://doi.org/10.1016/j.rse.2024.114067
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Geographie
Fakultät für Angewandte Informatik / Institut für Geographie / Lehrstuhl für Physische Geographie mit Schwerpunkt Klimaforschung
Fakultät für Angewandte Informatik / Institut für Geographie / Lehrstuhl für Regionales Klima und Hydrologie
Dewey Decimal Classification:9 Geschichte und Geografie / 91 Geografie, Reisen / 910 Geografie, Reisen
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)