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Summary Soil moisture is a key variable in climate system, and its accurate simulation needs effective soil parameter values. Conventional approaches may obtain soil parameter values at point scale, but they are costly and not efficient at grid scale (10–100 km) of current climate models. This study explores the possibility to estimate soil parameter values by assimilating AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) brightness temperature (TB) data. In the assimilation system, the TB is simulated by the coupled system of a land surface model (LSM) and a radiative transfer model (RTM), and the simulation errors highly depend on parameters in both the LSM and the RTM. Thus, sensitive soil parameters may be inversely estimated through minimizing the TB errors. A crucial step for the parameter estimation is made to suppress the contamination of uncertainties in atmospheric forcing data. The effectiveness of the estimated parameter values is evaluated against intensive measurements of soil parameters and soil moisture in three grasslands 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. [ABSTRACT FROM AUTHOR]

Summary: A mesoscale Tibetan Plateau Soil Moisture/Temperature Monitoring Network (SMTMN) has been established to study large-scale soil–vegetation–atmosphere interactions and to validate satellite soil moisture products. Soil moisture at four layers (0–5cm, 10cm, 20cm, and 40cm) of 30 sites was monitored since July, 2010. This paper firstly introduces the network and then presents preliminary spatiotemporal analyses based on the in situ soil moisture measurements in SMTMN. Three temporal scales (half-hourly, daily, and 10-days) and three time periods corresponding to typical soil wetness conditions, including frozen soil in winter times, are discussed. Primary findings are: (a) generally 13 randomly distributed sites in the study domain are required (i.e. number of required sites) to estimate areal mean soil moisture with correlation coefficient ⩾0.99 and root mean square difference ⩽0.02m3 m−3. This provides guidance for future soil moisture network establishment in similar regions; (b) both number of required sites and the most representative site are insensitive to temporal scales while conversely sensitive to soil wetness conditions; (c) the combination of a few optimally-selected sites can give more robust estimate of areal mean soil moisture than a single site does because the former contains more information on spatial heterogeneity. These findings can provide not only a practical compromise between maintenance cost and risk on reliability for an existing soil moisture network, but also insights for soil moisture upscaling studies and satellite soil moisture products validations. [ABSTRACT FROM AUTHOR]