当前位置： 首页»科研成果» 2014 张玉珍与合作者在IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING发表论文 全球院

 

Yonghua Qu, Yuzhen Zhang, and Huazhu Xue

 

State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University. He is also with the Institute of Remote Sensing Applications of the Chinese Academy of Sciences, Beijing, 100875, China;

College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China;

School of Surveying and Land Information Engineering, Henan Polytechnic University, Henan Jiaozuo 454000, China.

 

ABSTRACT

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.

 

KEY WORDS: Bayesian method, HJ-1 CCD, leaf area index, MODIS

 

PUBLISHED BY: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (1): 222-228.

 

SOURCE: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6524002