Articles | Volume 25, issue 3
Research article
29 Mar 2021
Research article |  | 29 Mar 2021

The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis

Jianxiu Qiu, Jianzhi Dong, Wade T. Crow, Xiaohu Zhang, Rolf H. Reichle, and Gabrielle J. M. De Lannoy

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Cited articles

Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P., and Smets, B.: GEOV1: LAI, FAPAR Essential Climate Variables and FCOVER global time series capitalizing over existing products. Part 1: Principles of development and production, Remote Sens. Environ., 137, 299–309,, 2013. 
Bolten, J. D. and Crow, W. T.: Improved prediction of quasi-global vegetation conditions using remotely-sensed surface soil moisture, Geophys. Res. Lett., 39, L19406,, 2012. 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,, 2001. 
Chan, S., Njoku, E. G., and Colliander A.: SMAP L1C radiometer half-orbit 36 km EASE-Grid brightness temperatures, version 3, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA,, 2016. 
Chen, F., Crow, W. T., Starks, P. J., and Moriasi, D. N.: Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture, Adv. Water Resour., 34, 526–536,, 2011. 
Short summary
The SMAP L4 dataset has been extensively used in hydrological applications. We innovatively use a machine learning method to analyze how the efficiency of the L4 data assimilation (DA) system is determined. It shows that DA efficiency is mainly related to Tb innovation, followed by error in precipitation forcing and microwave soil roughness. Since the L4 system can effectively filter out precipitation error, future development should focus on correctly specifying the SSM–RZSM coupling strength.