Retrieval of 30-m-Resolution Leaf Area Index From China HJ-1 CCD Data and MODIS Products Through a Dynamic Bayesian Network
作者:Qu, YH (Qu, Yonghua)[ 1,2 ] ; Zhang, YZ (Zhang, Yuzhen)[ 3 ] ; Xue, HZ (Xue, Huazhu)[ 4 ]
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷: 7  期: 1  页: 222-228
DOI: 10.1109/JSTARS.2013.2259472
出版年: JAN 2014

摘要
The leaf area index (LAI) is a characteristic parameter of vegetation canopies. This parameter is significant in research on global climate change and ecological environments. The China HJ-1 satellite has a revisit cycle of four days and provides CCD (HJ-1 CCD) data with a resolution of 30 m. However, the HJ-1 CCD is incapable of obtaining observations at multiple angles. This is problematic because single-angle observations provide insufficient data for determining the LAI. This article proposes a new method for determining the LAI using the HJ-1 CCD data. The proposed method uses background knowledge of the dynamic land surface processes that is extracted from MODerate resolution Imaging Spectroradiometer (MODIS) LAI data with a resolution of 1 km. The proposed method was implemented in a dynamitic Bayesian network scheme by integrating an LAI dynamic process model and a canopy reflectance model with the remotely sensed data. The validation was conducted using field LAI data collected in the Guantao County of the Hebei Province in China. The results showed that the determination coefficient between the estimated and the measured LAI was 0.791, and the RMSE was 0.61. The results suggest that this algorithm can be widely applied to determine high-resolution leaf area indexes using data from the China HJ-1 satellite even if the information from single-angle observations are insufficient for quantitative application.

通讯作者地址: Qu, YH (通讯作者)
Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Chinese Acad Sci, Inst Remote Sensing Applicat, Beijing 100875, Peoples R China
[ 3 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
[ 4 ] Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Henan, Peoples R China