作者:Zhang, XT (Zhang, Xiaotong)[ 1 ] ; Liang, SL (Liang, Shunlin)[ 1,2 ] ; Song, Z (Song, Zhen)[ 1 ] ; Niu, HL (Niu, Hailin)[ 3 ] ; Wang, GX (Wang, Guoxin)[ 1 ] ; Tang, WJ (Tang, Wenjun)[ 4 ] ; Chen, ZQ (Chen, Zhuoqi)[ 3 ] ; Jiang, B (Jiang, Bo)[ 1 ]

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

卷: 54  期: 2  页: 1156-1169

DOI: 10.1109/TGRS.2015.2475615

出版年: FEB 2016

摘要

Incident solar radiation (R-s) over the Earth's surface plays an important role in determining the Earth's climate and environment. Generally, R-s can be obtained from direct measurements, remotely sensed data, or reanalysis and general circulation model (GCM) data. Each type of product has advantages and limitations: the surface direct measurements provide accurate but sparse spatial coverage, whereas other global products may have large uncertainties. Ground measurements have been normally used for validation and occasionally calibration, but transforming their "true values" spatially to improve the satellite products is still a new and challenging topic. In this paper, an improved thin-plate smoothing spline approach is presented to locally "calibrate" the Global LAnd Surface Satellite (GLASS) R-s product using the reconstructed R-s data from surface meteorological measurements. The influence of surface elevation on R-s estimation was also considered in the proposed method. The point-based surface reconstructed R-s was used as the response variable, and the GLASS R-s product and the surface elevation data at the corresponding locations as explanatory variables to train the thin-plate spline model. We evaluated the performance of the approach using the cross-validation method at both daily and monthly time scales over China. We also validated the estimated R-s based on the thin-plate spline method using independent ground measurements and independent satellite estimates of R-s. These validation results indicated that the thin-plate smoothing spline method can be effectively used for calibrating satellite-derived R-s products using ground measurements to achieve better accuracy.

通讯作者地址: Zhang, XT; Liang, SL (通讯作者)

Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

通讯作者地址: Liang, SL (通讯作者)

Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA.

通讯作者地址: Niu, HL (通讯作者)

Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China.

通讯作者地址: Tang, WJ (通讯作者)

Chinese Acad Sci, Key Lab Tibetan Environm Changes & Land Surface P, Inst Tibetan Plateau Res, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA

[ 3 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China

[ 4 ] Chinese Acad Sci, Key Lab Tibetan Environm Changes & Land Surface P, Inst Tibetan Plateau Res, Beijing 100875, Peoples R China