Parametric exponentially correlated surface emission model for L-band passive microwave soil moisture retrieval |
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| Institution: | 1. Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China;2. Jiangsu Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China;3. Guangzhou Institute of Geography, Guangzhou 510070, China;4. Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA;1. School of Physics, UNSW, 2052, Sydney, NSW, Australia;2. School of Physics, The University of Sydney, 2006, Sydney, NSW, Australia;1. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;2. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya (UPC), IEEC/UPC and SMOS Barcelona Expert Center (SMOS-BEC), 08034 Barcelona, Spain;3. Agrosphere Institute, Forschungszentrum Jülich, 52428 Jülich, Germany;4. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA |
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| Abstract: | Surface soil moisture is an important parameter in hydrology and climate investigations. Current and future satellite missions with L-band passive microwave radiometers can provide valuable information for monitoring the global soil moisture. A factor that can play a significant role in the modeling and inversion of microwave emission from land surfaces is the surface roughness. In this study, an L-band parametric emission model for exponentially correlated surfaces was developed and implemented in a soil moisture retrieval algorithm. The approach was based on the parameterization of an effective roughness parameter of Hp in relation with the geometric roughness variables (root mean square height s and correlation length l) and incidence angle. The parameterization was developed based on a large set of simulations using an analytical approach incorporated in the advanced integral equation model (AIEM) over a wide range of geophysical properties. It was found that the effective roughness parameter decreases as surface roughness increases, but increases as incidence angle increases. In contrast to previous research, Hp was found to be expressed as a function of a defined slope parameter m = s2/l, and coefficients of the function could be well described by a quadratic equation. The parametric model was then tested with L-band satellite data in soil moisture retrieval algorithm over the Little Washita watershed, which resulted in an unbiased root mean square error of about 0.03 m3/m3 and 0.04 m3/m3 for ascending and descending orbits, respectively. |
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| Keywords: | Soil moisture L-band Roughness parameterization Exponential correlation SMOS SMAP |
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