Accuracy issues associated with satellite remote sensing soil moisture data and their assimilation

Satellite remote sensing is widely used for monitoring the changing planet Earth. Many remote sensing data products are being generated and used every day. Among these data products are the microwave remote sensing data of land surface soil moisture. Soil moisture often limits the exchanges of water and energy between atmosphere and land surface, controls the partitioning of rainfall among evaporation, infiltration and runoff, and impacts vegetation photosynthetic rate and soil microbiologic respiratory activities. Their accuracy plays essential role for the success of their applications. Accurate measurement of this variable across the global land surface is thus required for global water, energy and carbon cycle sciences and many civil and military applications. Currently available satellite soil moisture data products have been generated from the low frequency channel observations of the currently flying microwave sensors (the TRMM Microwave Imager-TMI; Aqua Advanced Microwave Scanning Radiometer-AMSR-E, and Navel Research Lab's WindSat). However, because of several accuracy issues all of these soil moisture data have not yet been used in operational applications. The most apparent accuracy issue is that the soil moisture data retrievals from the three different sensors are significantly different from each other even when they are retrieved with the same algorithm. This might have been caused by the calibration errors in their brightness temperatures. A Simultaneous Conical-scanning Overpass (SCO) method is tested to address this issue. Secondly, satellite sensor footprints are usually several orders larger than the local points where in situ soil moisture measurements for validation are obtained. How to appropriately compare the satellite soil moisture retrievals of large spatial areas with the in situ measurements becomes an important issue. A point-to-pixel mapping approach is examined for a solution of this issue. The third issue is how to handle biases of the soil moisture retrievals from land surface model (LSM) simulations when they are assimilated into the LSM. Existing solutions for this issue are summarized and whether these error-handling strategies are effective or reliable are discussed. Finally general conclusions of this study are presented for users who are interested in satellite soil moisture data assimilation.