New approaches on quantitative remote sensing for retrieving variable parameters of land surfaces

Xiaowen Li; Jindi Wang; Liming He; Hao Zhang

doi:10.1117/12.563269

9 November 2004 New approaches on quantitative remote sensing for retrieving variable parameters of land surfaces

Xiaowen Li, Jindi Wang, Liming He, Hao Zhang

Proceedings Volume 5544, Remote Sensing and Modeling of Ecosystems for Sustainability; (2004) https://doi.org/10.1117/12.563269
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

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.

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Xiaowen Li, Jindi Wang, Liming He, and Hao Zhang "New approaches on quantitative remote sensing for retrieving variable parameters of land surfaces", Proc. SPIE 5544, Remote Sensing and Modeling of Ecosystems for Sustainability, (9 November 2004); https://doi.org/10.1117/12.563269

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KEYWORDS

Remote sensing

Data modeling

Expectation maximization algorithms

Databases

Statistical analysis

Fractal analysis

Ecosystems

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