The first approach assimilates microwave data through an optimization-assimilation system (so-called LDAS-UT). The land data assimilation system adopts a dual-pass data assimilation framework; its first pass estimates time-invariant parameter values through fitting TB data in a long-term window and its second pass estimates land state through assimilating TB data in a short-term window. The principle behind this algorithm is the fact that model parameters have a persistent impact on state variables (such as soil moisture), and therefore, a long time window is needed to achieve a stable parameter estimation. By contrast, soil moisture may change drastically at a shorter time scale. The LDAS assimilates AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) brightness temperature (TB) data and its performance is validated against three soil moisture networks in the Tibetan Plateau and Mongolian Plateau, where current remote sensing of soil moisture usually performs not well.
The second approach simulates soil moisture with a land surface model that is calibrated through above microwave data assimilation. Soil moisture simulations need effective soil parameter values. Conventional approaches may obtain soil parameter values at point scale, but they are costly and not efficient at grid scale (~ 10 km) of current climate models. This study explores the effectiveness of the parameters estimated though the first pass of the LDAS. The estimated parameter values are evaluated against intensive measurements of soil parameters and soil moisture in the three networks of the Tibetan Plateau and the Mongolian Plateau. The results indicate that this satellite data-based approach can improve the data quality of soil porosity, a key parameter for soil moisture modeling, and LSM simulations with the estimated parameter values reasonably reproduce the measured soil moisture. This demonstrates it is feasible to calibrate LSMs for soil moisture simulations at grid scale by assimilating microwave satellite data, although more efforts are expected to improve the robustness of the model calibration.
Supplementary URL: http://www.sciencedirect.com/science/article/pii/S002216941500966X