Assimilation of microwave brightness temperatures for soil moisture estimation using particle filter

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Bi, H.Y.^1,2 ; Ma, J.W.^1 ; Qin, S.X.^1,2 ; Zeng, J.Y.^1,2

Centre for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China^1

University of the Chinese Academy of Sciences, Beijing, 100049, China^2

关键词: Brightness temperatures;    Ground observations;    Infiltration capacity;    Microwave brightness temperature;    Non-Gaussian systems;    Radiative transfer model;    Soil moisture estimation;    Surface soil moisture;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012126/pdf
DOI  :  10.1088/1755-1315/17/1/012126

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Soil moisture plays a significant role in global water cycles. Both model simulations and remote sensing observations have their limitations when estimating soil moisture on a large spatial scale. Data assimilation (DA) is a promising tool which can combine model dynamics and remote sensing observations to obtain more precise ground soil moisture distribution. Among various DA methods, the particle filter (PF) can be applied to non-linear and non-Gaussian systems, thus holding great potential for DA. In this study, a data assimilation scheme based on the residual resampling particle filter (RR-PF) was developed to assimilate microwave brightness temperatures into the macro-scale semi-distributed Variance Infiltration Capacity (VIC) Model to estimate surface soil moisture. A radiative transfer model (RTM) was used to link brightness temperatures with surface soil moisture. Finally, the data assimilation scheme was validated by experimental data obtained at Arizona during the Soil Moisture Experiment 2004 (SMEX04). The results show that the estimation accuracy of soil moisture can be improved significantly by RR-PF through assimilating microwave brightness temperatures into VIC model. Both the overall trends and specific values of the assimilation results are more consistent with ground observations compared with model simulation results.