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Proceedings Paper

New approaches on quantitative remote sensing for retrieving variable parameters of land surfaces
Author(s): Xiaowen Li; Jindi Wang; Liming He; Hao Zhang
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Paper Abstract

Quantitative remote sensing and GIS technologies are providing opportunities for Bioproduction and natural resources monitoring. Some gross primary production (GPP) and net primary production (NPP) models take ecological structure parameters (Land cover, LAI, and biomass) retrieved from remote sensing data as inputs to get information on ecosystem exchange on a global scale. Our recent research focuses on key problems in retrieving variable parameters of land surfaces from remote sensing observations, such as, the observing scale is different from the measuring scale of ground truth, the parameters which application requires may not be the same as the parameters of current physical models, the uncertainty of retrieved parameters by model inversion. In this paper, we present our new approaches on scaling effect modeling, physical model inversion by using prior knowledge, field experiments and setup of the spectrum knowledge base of typical objects, and the applications of the quantitative remote sensing research achievements in ecosystem assessment and monitoring, such as, in GPP and NPP of forest, energy exchange of crop field and grassland, water cycle, climate change. Some new modeling ideas and parameters retrieving results will be shown in this paper, as well as some remote sensing application samples.

Paper Details

Date Published: 9 November 2004
PDF: 11 pages
Proc. SPIE 5544, Remote Sensing and Modeling of Ecosystems for Sustainability, (9 November 2004); doi: 10.1117/12.563269
Show Author Affiliations
Xiaowen Li, Beijing Normal Univ. (China)
Institute of Remote Sensing Applications, CAS (China)
Jindi Wang, Beijing Normal Univ. (China)
Liming He, Beijing Normal Univ. (China)
Hao Zhang, Beijing Normal Univ. (China)


Published in SPIE Proceedings Vol. 5544:
Remote Sensing and Modeling of Ecosystems for Sustainability
Wei Gao; David R. Shaw, Editor(s)

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