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A large number of quantitative retrieval models have been proposed in recent years, and there is continuous momentum in proposing new ones. Building a model, from design through to implementation stages, involves a process of knowledge collection, organization and transmission. In this paper we introduce the SECI model to manage the conversion of qualitative remote sensing knowledge and propose a...
The Remote Sensing Quantitative Retrieval is not only a Compute-intensive problem, but also a Data-intensive problem. One of the most effective solutions is Grid Computing platform built on the basis of network, which is a scalable virtual unified platform with unlimited computing power and storage capacity. The RSSN (Remote Sensing Information Service Grid Node) is a PC cluster for Remote Sensing...
The algorithm is based on the assumption that TOA reflectance increase with the aerosol load as well as the surface reflectance at same time gradually changes on different days within 14 days. Then the surface reflectance is derived from FengYun-2D (FY2D) measurements every 1 hour as the second darkest of reflectance for each time of day to minimize the effect of geometry change and cloud. The “true...
Cloud computing, a long-held dream of computing as a utility, has prosperous potential of transforming a large part of the information technology (IT) industry. It has successfully become a new business model in a short time. Now, many companies have launched their own products. Nevertheless, the current commercial mode does not meet the request of applications of remote sensing quantitative inversion.
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