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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #363496

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Uncertainty in soil moisture retrievals: an ensemble approach using SMOS L-band microwave data

item QUETS, J. - Leuven University
item DELANNOY, G. - Leuven University
item AL YAARI, A. - Inra, Génétique Animale Et Biologie Intégrative , Jouy-En-josas, France
item CHAN, S. - Jet Propulsion Laboratory
item Cosh, Michael
item GRUBER, A. - Leuven University
item REICHLE, R. - Goddard Space Flight Center
item VAN DER SCHALIE, R. - Netherlands Institute Of Ecology
item WIGNERON, J. - Institut National De La Sante Et De La Recherche Medicale (INSERM)

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/6/2019
Publication Date: 5/15/2019
Citation: Quets, J., Delannoy, G., Al Yaari, A., Chan, S., Cosh, M.H., Gruber, A., Reichle, R., Van Der Schalie, R., Wigneron, J. 2019. Uncertainty in soil moisture retrievals: an ensemble approach using SMOS L-band microwave data. Remote Sensing of Environment. 229:133-147.

Interpretive Summary: Improving the accuracy of satellite retrievals of soil moisture is a common goal in the remote sensing community. At the heart of soil moisture remote sensing is the radiative transfer model which incorporates several parameters which have a large impact on the accuracy of the products being produced. This study analyzes how adding prior soil moisture status can improve the radiative transfer model product accuracy, as well as how the parameter sets in the radiative transfer modeling influence overall accuracy. The results of this study help to improve the methodologies for parameterizing the land surface in these modeling schemes, as well as provide guidance on future implementation of retrieval models. This will be beneficial to atmospheric and weather modelers as the ultimate products will be of higher quality.

Technical Abstract: 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 (quasi-)operational 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 the long-term mean SM. This reveals higher-order biases (e.g. differences in variances) in the retrieval error, which should be accounted for 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.