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Open AccessArticle

Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy

by 1,2, 3,*, 4,5, 1 and 1
1
School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
2
Geomatics Center of Huzhou, Huzhou 313000, China
3
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
4
QiuZhen College, Huzhou University, Huzhou 313000, China
5
Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1556; https://doi.org/10.3390/rs12101556
Received: 8 April 2020 / Revised: 28 April 2020 / Accepted: 11 May 2020 / Published: 14 May 2020
(This article belongs to the Special Issue Microwave Remote Sensing for Hydrology)
Hydrological models play an essential role in data assimilation (DA) systems. However, it is a challenging task to acquire the distributed hydrological model parameters that affect the accuracy of the simulations at a grid scale. Remote sensing data provide an ideal observation for DA to estimate parameters and state variables. In this study, a special assimilation scheme was proposed to jointly estimate parameters and soil moisture (SM) by assimilating brightness temperature (TB) from the Soil Moisture and Ocean Salinity (SMOS) mission. Variable infiltration capacity (VIC) hydrological model and L-band microwave emission of the biosphere model (L-MEB) are coupled as model and observation operators, respectively. The scheme combines two stages of estimators, one for the static model parameters and the other for the dynamic state variables. The estimators approximate the posterior probability distribution of an unknown target through sequential Monte Carlo (SMC) sampling. Markov chain Monte Carlo (MCMC) and immune evolution strategy are embedded in both stages to solve particle impoverishment problems. To evaluate the effectiveness of the scheme, the estimated SM sets are compared with in-situ observations and SMOS products in Maqu on the Tibetan Plateau. Specifically, the root mean square error decreased from 0.126 to 0.087 m3m−3 for surface SM, with a slight impact on the root zone. The temporal correlation between DA results and in-situ measurements increased to 0.808 and 0.755 for surface SM (+0.057) and root zone SM (+0.040), respectively. The results demonstrate that assimilating TB has tremendous potential as an approach to improve the estimation of distributed model parameters and SMs of surface and root zone at a grid scale, and the immune evolution strategy is effective for increasing the accuracy of approximation in sampling. View Full-Text
Keywords: SMOS; data assimilation; soil moisture; immune evolution; particle filter; remote sensing SMOS; data assimilation; soil moisture; immune evolution; particle filter; remote sensing
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MDPI and ACS Style

Ju, F.; An, R.; Yang, Z.; Huang, L.; Sun, Y. Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy. Remote Sens. 2020, 12, 1556. https://doi.org/10.3390/rs12101556

AMA Style

Ju F, An R, Yang Z, Huang L, Sun Y. Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy. Remote Sensing. 2020; 12(10):1556. https://doi.org/10.3390/rs12101556

Chicago/Turabian Style

Ju, Feng; An, Ru; Yang, Zhen; Huang, Lijun; Sun, Yaxing. 2020. "Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy" Remote Sens. 12, no. 10: 1556. https://doi.org/10.3390/rs12101556

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