Parametric exponentially correlated surface emission model for L-band passive microwave soil moisture retrieval

作者:Zhao, TJ (Zhao Tianjie)[ 1,2 ] ; Shi, JC (Shi Jiancheng)[ 1,2 ] ; Rajat, B (Rajat, Bindlish)[ 3 ] ; Thomas, J (Thomas, Jackson)[ 3 ] ; Michael, C (Michael, Cosh)[ 3 ] ; Jiang, LM (Jiang Lingmei)[ 4 ] ; Zhang, ZJ (Zhang Zhongjun)[ 5 ] ; Lan, HM (Lan Huimin)[ 5 ]

PHYSICS AND CHEMISTRY OF THE EARTH

卷: 83-84  页: 65-74

DOI: 10.1016/j.pce.2015.04.001

出版年: 2015

摘要

Surface soil moisture is an important parameter in hydrology and climate investigations. Current and future satellite missions with L-band passive microwave radiometers can provide valuable information for monitoring the global soil moisture. A factor that can play a significant role in the modeling and inversion of microwave emission from land surfaces is the surface roughness. In this study, an L-band parametric emission model for exponentially correlated surfaces was developed and implemented in a soil moisture retrieval algorithm. The approach was based on the parameterization of an effective roughness parameter of Hp in relation with the geometric roughness variables (root mean square height s and correlation length l) and incidence angle. The parameterization was developed based on a large set of simulations using an analytical approach incorporated in the advanced integral equation model (AIEM) over a wide range of geophysical properties. It was found that the effective roughness parameter decreases as surface roughness increases, but increases as incidence angle increases. In contrast to previous research, Hp was found to be expressed as a function of a defined slope parameter m = s(2)/l, and coefficients of the function could be well described by a quadratic equation. The parametric model was then tested with L-band satellite data in soil moisture retrieval algorithm over the Little Washita watershed, which resulted in an unbiased root mean square error of about 0.03 m(3)/m(3) and 0.04 m(3)/m(3) for ascending and descending orbits, respectively. (C) 2015 Elsevier Ltd. All rights reserved.

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

[ 3 ] ARS, Hydrol & Remote Sensing Lab, USDA, Beltsville, MD 20705 USA

[ 4 ] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China

[ 5 ] Beijing Normal Univ, Sch Informat Sci & Technol, Beijing 100875, Peoples R China