Uncertainty in Soil Moisture Retrievals: an Ensemble Approach using SMOS L-Band Microwave Data
| Autor: | Amen Al Yaari, Michael H. Cosh, Steven Chan, Alexander Gruber, Jean-Pierre Wigneron, Gabrielle De Lannoy, Rolf H. Reichle, Robin van der Schalie, Jan J. Quets |
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| Přispěvatelé: | Department of Earth and Environmental Sciences, Université Catholique de Louvain = Catholic University of Louvain (UCL), Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), NASA Jet Propulsion Laboratory, United States Department of Agriculture (USDA), Global Modeling and Assimilation Office (GMAO), NASA Goddard Space Flight Center (GSFC), VanderSat B.V., Partenaires INRAE |
| Rok vydání: | 2019 |
| Předmět: |
L band
010504 meteorology & atmospheric sciences [SDV]Life Sciences [q-bio] 0208 environmental biotechnology Bayesian probability Soil Science 02 engineering and technology 01 natural sciences Article Atmospheric radiative transfer codes Range (statistics) Sensitivity (control systems) Computers in Earth Sciences radiative transfer model uncertainty retrieval 0105 earth and related environmental sciences Remote sensing parameters Anomaly (natural sciences) ensemble Geology 020801 environmental engineering Brightness temperature [SDE]Environmental Sciences Environmental science Satellite |
| Zdroj: | Remote Sens Environ Remote Sensing of Environment Remote Sensing of Environment, Elsevier, 2019, 229, pp.133-147. ⟨10.1016/j.rse.2019.05.008⟩ |
| ISSN: | 0034-4257 1879-0704 |
| DOI: | 10.1016/j.rse.2019.05.008⟩ |
| Popis: | The uncertainty of surface soil moisture (SM) retrievals from satellite brightness temperature (TB) observations depends primarily on the choice of radiative transfer model (RTM) parameters, prior SM information and TB inputs. This paper studies the sensitivity of several established and experimental SM retrieval products from the Soil Moisture Ocean Salinity (SMOS) mission to these choices at 11 reference sites, located in 7 watersheds across the United States (US). Different RTM parameter sets cause large biases between retrievals. Whereas typical RTM parameter sets are calibrated for SM retrievals, it is shown that a parameter set carefully optimized for TB forward modeling can also be used for retrieving SM. It is also shown that the inclusion of dynamic prior SM estimates in a Bayesian retrieval scheme can strongly improve SM retrievals, regardless of the choice of RTM parameters. The second part of this paper evaluates the ensemble uncertainty metrics for SM retrievals obtained by propagating a wide range of RTM parameters through the RTM, and the relationship with time series metrics obtained by comparing SM retrievals with in situ data. As expected for bounded variables, the total spread in the ensemble SM retrievals is smallest for wet and dry SM values and highest for intermediate SM values. After removal of the strong long-term SM bias associated with the RTM parameter values for individual ensemble members, the remaining anomaly ensemble SM spread shows higher values when SM deviates further from its long-term mean SM. This reveals higher-order biases (e.g. differences in variances) in the retrieval error, which should be considered when characterizing retrieval error. The time-average anomaly ensemble SM spread of 0.037 m3/m3 approximates the actual time series unbiased root-mean-square-difference of 0.042 m3/m3 between ensemble mean retrievals and in situ data across the reference sites. ispartof: REMOTE SENSING OF ENVIRONMENT vol:229 pages:133-147 ispartof: location:United States status: published |
| Databáze: | OpenAIRE |
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