Application of SMOS Soil Moisture and Brightness Temperature at High Resolution With a Bias Correction Operator

作者：Kornelsen Kurt C^*; Davison Bruce^*; Coulibaly Paulin^*

来源：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(4): 1590-1605.

DOI：10.1109/JSTARS.2015.2474266

• Data assimilation
• land surface
• microwave radiometry
• soil moisture

The assimilation of soil moisture and brightness temperature (TB) are expected to improve the modeling of land surface processes, but are only available at a resolution that is far coarser than the scale of many hydrological processes. Due to systematic differences between model states and satellite observations, a bias correction operator is a necessary step in land data assimilation schemes and was evaluated as a method to disaggregate coarse-scale satellite observations to fine-scale model grid cells (similar to 800 m). This was done by coupling the Modelisation Environmentale Communautaire-Surface Hydrology (MESH) Hydrological Land-Surface Scheme to the Community Microwave Emissions Model (CMEM) to simulate soil moisture and TB. By comparison, MESH-CMEM was found to be in good agreement with observations from the Soil Moisture and Ocean Salinity (SMOS) satellite at the scale of SMOS data products (R approximate to 0.55), with simulated TB being better correlated than soil moisture retrievals. Following bias correction, TB and soil moisture retrievals at 800-m resolution had comparable performance to coarse-resolution SMOS data. Bias correction of TB was more reliable than soil moisture. These findings indicate that both TB and soil moisture retrievals can be assimilated in a land surface model at moderate-to-high resolution with a simple observation operator.

• 出版日期2016-4
• 单位Saskatoon