Land surface temperature (LST) is one of the key states of the Earth surface system. Remote sensing has the capability to obtain high-frequency LST observations with global coverage. However, mainly due to cloud cover, there are always gaps in the remotely sensed LST product, which hampers the application of satellite-based LST in data-driven modeling of surface energy and water exchange processes. We explored the suitability of the data interpolating empirical orthogonal functions (DINEOF) method in moderate resolution imaging spectroradiometer LST reconstruction around Ali on the Tibetan Plateau. To validate the reconstruction accuracy, synthetic clouds during both daytime and nighttime are created. With DINEOF reconstruction, the root mean square error and bias under synthetic clouds in daytime are 4.57 and −0.0472 K, respectively, and during the nighttime are 2.30 and 0.0045 K, respectively. The DINEOF method can well recover the spatial pattern of LST. Time-series analysis of LST before and after DINEOF reconstruction from 2002 to 2016 shows that the annual and interannual variabilities of LST can be well reconstructed by the DINEOF method.
Besides uncertainties introduced by atmospheric forcing and initial states, land surface simulation results are mainly
determined by model structure and related model parameters. Traditional data assimilation approaches, as they only
focus on mathematically updating the simulated states when observations become available, have little intrinsic
improvement in the model performance. Model parameter optimization will lead to reduced biases in simulation results
and then a better forecasting skill can be expected. Therefore, calibrating model parameters and updating states
simultaneously in the framework of sequential model-data fusion would be valuable for uncertainty quantification. A
dual state-parameter estimation land data assimilation system is implemented in this paper by coupling the Variable
Infiltration Capacity(VIC) land surface model, the Tau-Omega Radiative Transfer Model(RTM) and Sampling
Importance Resampling Particle Filter(SIR-PF) algorithm. Passive microwave brightness temperature observations from
Passive/Active L and S band (PALS) sensor in SMEX02 are assimilated and the results demonstrate that both soil
moisture states and model lumped parameters can be estimated simultaneously.