Abstract
MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument onboard NASA’s Terra (launched in 1999) and Aqua (launched in 2002) satellite missions as part of the more extensive Earth Observation System (EOS). By measuring the reflection and emission by the Earth-Atmosphere system in 36 spectral bands from the visible to thermal infrared with near-daily global coverage and high-spatial-resolution (250 m ~ 1 km at nadir), MODIS is playing a vital role in developing validated, global, interactive Earth system models. MODIS products are processed into three levels, i.e., Level-1 (L1), Level-2 (L2) and Level-3 (L3). To shift the current static and “one-size-fits-all” data provision method of MODIS products, in this paper, we propose a service-oriented flexible and efficient MODIS aggregation framework. Using this framework, users only need to get aggregated MODIS L3 data based on their unique requirements and the aggregation can run in parallel to achieve a speedup. The experiments show that our aggregation results are almost identical to the current MODIS L3 products and our parallel execution with 8 computing nodes can work 88.63 times faster than a serial code execution on a single node.
Highlights
MODIS L3 products in both aggregation results and in aggregation processes; (4) the computation experiments done in this paper are based on the full capability of the services, while the conference paper only experimented with a cloud fraction calculation
While the development of remote sensing techniques greatly improves our understanding of the Earth, it brings two challenges
The current static and “one-size-fits-all” data provision method by most remote sensing data providers such as NASA Distributed Active Archive Center (DAAC) is difficult to meet increasing diverse usage requirements from the community. To address these two challenges, we propose a service-oriented flexible and efficient MODIS aggregation framework
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. It offers flexible visualizations on various L3 variables based on customizable requirements including input variables, output statistics (such as minimum, maximum and time average), time range and spatial area. As it is not an L2-to-L3 aggregation system, there are limitations compared with what our algorithm provides. MODIS L3 products in both aggregation results and in aggregation processes; (4) the computation experiments done in this paper are based on the full capability of the services, while the conference paper only experimented with a cloud fraction calculation.
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