A self-adaptive approach of retrieving leaf area index based on the imaging geometry model of HJ-1A multispectral camera

Quanjun Jiao; Bing Zhang; Wenjuan Zhang; Di Wu; Yuangfeng Wu

doi:10.1117/12.816010

29 December 2008 A self-adaptive approach of retrieving leaf area index based on the imaging geometry model of HJ-1A multispectral camera

Quanjun Jiao, Bing Zhang, Wenjuan Zhang, Di Wu, Yuangfeng Wu

Author Affiliations +

Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 72850B (2008) https://doi.org/10.1117/12.816010
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China

Abstract

Leaf Area Index (LAI) is an important parameter in terrestrial ecosystem. Wide width of HJ-1A CCD image causes varied view angels. It is rather important to analyze HJ-1A CCD camera's geometrical features to improve the accuracy of LAI production. This paper focuses on the imaging geometry characteristics of HJ-1A multi-spectral CCD camera, and then brings a self-adaptive approach of retrieving vegetation parameter through analyzing the influence of sensor's imaging geometry factor to vegetation parameter retrieval. This self-adopt approach couples remote sensing imaging process model with vegetation information extraction model and studies systemically on the vegetation information extraction general modeling, data simulation with the remote sensing image features, analyzing the influence of imaging factor to vegetation information extraction and optimizing vegetation information extraction model. The validation result shows that the method in this paper is much better than the simple semi-empirical model no consider of imaging geometrical factor of HJ-1A CCD camera.

Citation Download Citation

Quanjun Jiao, Bing Zhang, Wenjuan Zhang, Di Wu, and Yuangfeng Wu "A self-adaptive approach of retrieving leaf area index based on the imaging geometry model of HJ-1A multispectral camera", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72850B (29 December 2008); https://doi.org/10.1117/12.816010

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