Considering combined or separated roughness and vegetation effects in soil moisture retrievals |
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| Institution: | 1. Centre d’Etudes Spatiales de la BIOsphère (CESBIO – Uninversité Fédérale de Toulouse, CNES, CNRS, IRD), UMR5126, BPI 2801, 31401 Toulouse Cedex 9, France;2. Centre INRA Bordeaux, Aquitaine, UR1263 ISPA, F-33140 Villenave d’Ornon, Centre INRA Bordeaux, Aquitaine, France;3. NASA, Goddard Space Flight Center, Greenbelt, MD 20771, USA;1. School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China;2. Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China;1. Instituto Geofísico del Perú, Calle Badajoz #169, Mayorazgo IV Etapa, Ate Vitarte, Lima, Peru;2. Univ. Grenoble Alpes, IRD, CNRS, G-INP, IGE (UMR 5001), 38000 Grenoble, France;3. Paris University and Laboratory of Climate and Oceanography - LOCEAN (Sorbonne University, IRD, MNHN, CNRS), 4 Place Jussieu, 75252 Paris Cedex 05, France;4. IRD, Géoscience Environnement Toulouse (GET-CNRS, IRD, Université de Toulouse), Toulouse, France;1. College of Science, Guilin University of Technology, Guilin 541004, China;2. Center for Data Analysis and Algorithm Technology, Guilin University of Technology, Guilin 541004, China;3. College of Marine Sciences, Beibu Gulf University, Qinzhou 535011, China |
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| Abstract: | For more than six years, the Soil Moisture and Ocean Salinity (SMOS) mission has provided multi angular and full-polarization brightness temperature (TB) measurements at L-band. Geophysical products such as soil moisture (SM) and vegetation optical depth at nadir (τnad) are retrieved by an operational algorithm using TB observations at different angles of incidence and polarizations. However, the quality of the retrievals depends on several surface effects, such as vegetation, soil roughness and texture, etc. In the microwave forward emission model used in the retrievals (L-band Microwave Emission Model, L-MEB), soil roughness is modelled with a semi-empirical equation using four main parameters (Qr, Hr, Nrp, with p = H or V polarizations). At present, these parameters are calibrated with data provided by airborne studies and in situ measurements made at a local scale that is not necessarily representative of the large SMOS footprints (43 km on average) at global scale. In this study, we evaluate the impact of the calibrated values of Nrp and Hr on the SM and τnad retrievals based on SMOS TB measurements (SMOS Level 3 product) over the Soil Climate Analysis Network (SCAN) network located in North America over five years (2011–2015). In this study, Qr was set equal to zero and we assumed that NrH = NrV. The retrievals were performed by varying Nrp from ?1 to 2 by steps of 1 and Hr from 0 to 0.6 by steps of 0.1. At satellite scale, the results show that combining vegetation and roughness effects in a single parameter provides the best results in terms of soil moisture retrievals, as evaluated against the in situ SM data. Even though our retrieval approach was very simplified, as we did not account for pixel heterogeneity, the accuracy we obtained in the SM retrievals was almost systematically better than those of the Level 3 product. Improved results were also obtained in terms of optical depth retrievals. These new results may have key consequences in terms of calibration of roughness effects within the algorithms of the SMOS (ESA) and the SMAP (NASA) space missions. |
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| Keywords: | Soil moisture Soil roughness SMOS L-band Retrievals Optical vegetation depth |
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