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Article

Efficient and Flexible Aggregation and Distribution of MODIS Atmospheric Products Based on Climate Analytics as a Service Framework

1
Department of Physics, University of Maryland Baltimore County, Baltimore, MD 21250, USA
2
Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD 21250, USA
3
Department of Information Systems, University of Maryland Baltimore County, Baltimore, MD 21250, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Juan M. Haut
Remote Sens. 2021, 13(17), 3541; https://doi.org/10.3390/rs13173541
Received: 6 July 2021 / Revised: 20 August 2021 / Accepted: 2 September 2021 / Published: 6 September 2021
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. View Full-Text
Keywords: big climate data; flexible data aggregation; climate analytics as a service big climate data; flexible data aggregation; climate analytics as a service
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MDPI and ACS Style

Zheng, J.; Huang, X.; Sangondimath, S.; Wang, J.; Zhang, Z. Efficient and Flexible Aggregation and Distribution of MODIS Atmospheric Products Based on Climate Analytics as a Service Framework. Remote Sens. 2021, 13, 3541. https://doi.org/10.3390/rs13173541

AMA Style

Zheng J, Huang X, Sangondimath S, Wang J, Zhang Z. Efficient and Flexible Aggregation and Distribution of MODIS Atmospheric Products Based on Climate Analytics as a Service Framework. Remote Sensing. 2021; 13(17):3541. https://doi.org/10.3390/rs13173541

Chicago/Turabian Style

Zheng, Jianyu, Xin Huang, Supriya Sangondimath, Jianwu Wang, and Zhibo Zhang. 2021. "Efficient and Flexible Aggregation and Distribution of MODIS Atmospheric Products Based on Climate Analytics as a Service Framework" Remote Sensing 13, no. 17: 3541. https://doi.org/10.3390/rs13173541

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