Refinement of SMOS Multiangular Brightness Temperature Toward Soil Moisture Retrieval and Its Analysis Over Reference Targets

作者:Zhao, TJ (Zhao, Tianjie)[ 1,2,3 ] ; Shi, JC (Shi, Jiancheng)[ 1,2,3 ] ; Bindlish, R (Bindlish, Rajat)[ 4 ] ; Jackson, TJ (Jackson, Thomas J.)[ 4 ] ; Kerr, YH (Kerr, Yann H.)[ 5 ] ; Cosh, MH (Cosh, Michael H.)[ 4 ] ; Cui, Q (Cui, Qian)[ 1,2,3 ] ; Li, YQ (Li, Yunqing)[ 1,2,3 ] ; Xiong, C (Xiong, Chuan)[ 1,2,3 ] ; Che, T (Che, Tao)[ 6 ]

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

卷: 8  期: 2  页: 589-603

DOI: 10.1109/JSTARS.2014.2336664

出版年: FEB 2015

摘要

Soil moisture ocean salinity (SMOS) mission has been providing L-band multiangular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations also allow for the retrieval of additional parameters beyond soil moisture, but these are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio-frequency interference (RFI) issues and aliasing at lower look angles increase the uncertainty of observations and thereby affect the soil moisture retrieval that utilizes observations at specific angles. In this study, we proposed a two-step regression approach that uses a mixed objective function based on SMOS L1c data products to refine characteristics of multiangular observations. The approach was found to be robust by validation using simulations from a radiative transfer model, and valuable in improving soil moisture estimates from SMOS. In addition, refined brightness temperatures were analyzed over three external targets: Antarctic ice sheet, Amazon rainforest, and Sahara desert, by comparing with WindSat observations. These results provide insights for selecting and utilizing external targets as part of the upcoming soil moisture active passive (SMAP) mission.

通讯作者地址: Zhao, TJ (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Beijing Normal Univ, Beijing 100101, Peoples R China

[ 3 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China

[ 4 ] ARS, HRSL, USDA, Beltsville, MD USA

[ 5 ] Ctr EtudesSpatiales Biosphere CESBIO, F-31401 Toulouse, France

[ 6 ] Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Peoples R China