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Abstract
In Earth science, information science, space science, and other disciplines, scientists use the land surface parameter inversion method in their work, applying this to the atmosphere, vegetation, soil, drought, and so on. Multidisciplinary experts sometimes collaborate on a particular application. However, these remote sensing models do not have a unified method of description and management and cannot effectively achieve the sharing of models and data resources. It is also hard to meet user demand for global data and models in the current state, especially in the face of global problems and long-term series problems. In this paper, we examine the scientific questions of the computability and scalability of remote sensing models. This paper adopts a data dependency approach to describe a remote sensing model and implements a hierarchical unified description and management method using modelling based on four layers: a data-processing view, an atomic model view, an on-demand resource package view, and a workflow view. We choose three typical remote sensing models for disaster monitoring as use cases and describe the practical application process of the proposed method. The results demonstrate the advantages and powerful capabilities of this efficient method.
Highlights
Mathematical Problems in Engineering and cooperatively process the different data and model information resources, meaning that the data provided may not be sufficient, and the required information may not be provided in a timely manner
E development of remote sensing parameter inversion techniques has led to the establishment of many inversion models, such as snow parameters inversion model [12], forest reflectance inversion model [13], and surface water inversion model [14], that are used around the globe. ere is a lack of correlation between these models, and their maximum potential cannot be achieved without proper management. e remote sensing model has characteristics of diversity and heterogeneity, making it difficult for users to autonomously organise and design the surface information processing flow to and allow it to run automatically
E Open Geospatial Consortium (OGC) has carried out in-depth research in the field of spatial information sharing and services and has developed a series of geospatial data interoperability specifications that provide a unified application framework for the design and development of geospatial information integration. ese works lay the foundation for research into intelligent service models with massive remote sensing data [24,25,26,27]
Summary
It was shown that each task is described by data and processing resources in the description of the remote sensing model. Unlike the common application-based model classification, which represents the level of the data products and the production process of each level of these products, in this paper, the common remote sensing model is divided into five different types according to the calculation characteristics: the numerical, iterative, statistical, neighborhood, and frequency domain calculations. Based on the description of the previous layer, users combine the metadata information of the atomic model defined in the previous step to select appropriate data and computing resources. Models that are appropriate to the actual computing environment, such as distributed computing, grid computing, and cloud e atomic model
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