Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method

作者:Yang, L (Yang, Lu)[ 1 ] ; Zhang, XT (Zhang, Xiaotong)[ 1 ] ; Liang, SL (Liang, Shunlin)[ 2 ] ; Yao, YJ (Yao, Yunjun)[ 1 ] ; Jia, K (Jia, Kun)[ 1 ] ; Jia, AL (Jia, Aolin)[ 1 ]

REMOTE SENSING

卷: 10  期: 2

文献号: 185

DOI: 10.3390/rs10020185

出版年: FEB 2018

文献类型:Article

摘要

Downward shortwave radiation (DSR) is an essential parameter in the terrestrial radiation budget and a necessary input for models of land-surface processes. Although several radiation products using satellite observations have been released, coarse spatial resolution and low accuracy limited their application. It is important to develop robust and accurate retrieval methods with higher spatial resolution. Machine learning methods may be powerful candidates for estimating the DSR from remotely sensed data because of their ability to perform adaptive, nonlinear data fitting. In this study, the gradient boosting regression tree (GBRT) was employed to retrieve DSR measurements with the ground observation data in China collected from the China Meteorological Administration (CMA) Meteorological Information Center and the satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) at a spatial resolution of 5 km. The validation results of the DSR estimates based on the GBRT method in China at a daily time scale for clear sky conditions show an R-2 value of 0.82 and a root mean square error (RMSE) value of 27.71 Wm(-2) (38.38%). These values are 0.64 and 42.97 Wm(-2) (34.57%), respectively, for cloudy sky conditions. The monthly DSR estimates were also evaluated using ground measurements. The monthly DSR estimates have an overall R-2 value of 0.92 and an RMSE of 15.40 Wm(-2) (12.93%). Comparison of the DSR estimates with the reanalyzed and retrieved DSR measurements from satellite observations showed that the estimated DSR is reasonably accurate but has a higher spatial resolution. Moreover, the proposed GBRT method has good scalability and is easy to apply to other parameter inversion problems by changing the parameters and training data.

通讯作者地址: Zhang, XT (通讯作者)

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

地址:

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

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