Statistics – Applications
Scientific paper
Jan 2002
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002esasp.475..211r&link_type=abstract
In: Proceedings of the Third International Symposium on Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Appl
Statistics
Applications
Process Modelling
Scientific paper
Retrieval of bio- and geophysical parameters from remote sensing data is an important field of research, and the prospect of extracting such information in an operational manner with a high degree of accuracy is somewhat of a holy grail and a strong driver of current scientific work. Meaningful parameter retrieval however requires not only the availability of appropriate sensors and inversion algorithms, but also that the data that are to be utilized are acquired in a planned and systematic manner. Regional extrapolation of locally developed retrieval algorithms is imperative if the applications are to be more than of mere academic interest, and spatially consistent data over large areas thus become a requirement. The terrestrial parameters that we are attempting to characterize and quantify are furthermore in a state of constant change as a result of both human-induced and natural events, and unless we take the temporal dynamics of these phenomena into account, we will lack the temporal context and our measurements will merely constitute snap-shots in time. Providing systematic, repetitive observations over large areas is potentially one of the strengths of remote sensing technology, and one where it could provide substantial support to both scientific and commercial applications. However, high resolution remote sensing data are generally not acquired systematically, neither in time nor in space, and this is considered a serious impediment extensive use of the technology, and for the development of operational applications. In this paper, various aspects of requirements for systematic data acquisitions are discussed, with emphasis on the needs for regional scale parameter retrieval, relevant in the context of climate change research and terrestrial carbon cycle science.
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