Next Article in Journal
Evaluation of a Statistical Approach for Extracting Shallow Water Bathymetry Signals from ICESat-2 ATL03 Photon Data
Next Article in Special Issue
Optimizing Urban LiDAR Flight Path Planning Using a Genetic Algorithm and a Dual Parallel Computing Framework
Previous Article in Journal
Motion Phase Compensation Methods for Azimuth Ambiguity Suppression in HRWS SAR
Previous Article in Special Issue
Sentinel-1 Big Data Processing with P-SBAS InSAR in the Geohazards Exploitation Platform: An Experiment on Coastal Land Subsidence and Landslides in Italy
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
Journals
Remote Sensing
Volume 13
Issue 17
10.3390/rs13173541
remotesensing-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Article

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

by
Jianyu Zheng
1,2,
Xin Huang
3,
Supriya Sangondimath
3,
Jianwu Wang
3,* and
Zhibo Zhang
1,2
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
(This article belongs to the Special Issue Big Data in Earth Observation: A New Computing Paradigm for Remote Data Analysis)
View Full-Text Download PDF
Browse Figures

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. 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
►▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
SciFeed

Share and Cite

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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Map by Country/Region

1
Back to TopTop