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Description

This repository does NASA MODIS data aggregation from level 2 to level 3 in a flexible and parallel approach. Please check documents in examples folder to see examples on how to use our software.

Installation

Conda environment setup

conda create -n MODIS_Aggregation -c conda-forge python=3.7 libnetcdf netCDF4 h5py

>> git clone https://github.com/big-data-lab-umbc/MODIS_Aggregation.git
>> cd MODIS_Aggregation
>> python setup.py install

The code is tested with Python 3.7

Usage examples

Please check the examples folder to see sample codes to import the library and use its functions for specific aggregation requirements. The examples mainly demonstrate how to conduct local execution, Dask-based distributed execution and MPI-based distributed execution. Besides these core usage examples, we also show examples for result comparison and service based deployment.

Before running the aggregation example, please read the README.md in the examples folder for the instruction on downloading the Level-2 input data.

To run the aggregation example in local environment:

>> cd examples/local_execution
>> sh MODIS_Aggregation_Local_Commands.sh

Team members

  • Faculty: Dr. Jianwu Wang, Department of Information Systems, UMBC
  • Faculty: Dr. Zhibo Zhang, Department of Physics, UMBC
  • PhD student: Jianyu Zheng, Department of Physics, UMBC
  • PhD student: Chamara Rajapakshe, Department of Physics, UMBC
  • PhD student: Pei Guo, Department of Information Systems, UMBC
  • PhD student: Xin Huang, Department of Information Systems, UMBC
  • MS student: Supriya Sangondimath, Department of Information Systems, UMBC
  • MS student: Savio Kay, Department of Information Systems, UMBC
  • MS student: Deepak Prakash, Department of Information Systems, UMBC
  • MS student: Lakshmi Priyanka Kandoor, Department of Information Systems, UMBC

Publications

  • Jianyu Zheng, Xin Huang, Supriya Sangondimath, Jianwu Wang, and Zhibo Zhang. 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, 2021.
  • Jianwu Wang, Xin Huang, Jianyu Zheng, Chamara Rajapakshe, Savio Kay, Lakshmi Kandoor, Thomas Maxwell, Zhibo Zhang. Scalable Aggregation Service for Satellite Remote Sensing Data. In Proceedings of the 20th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2020), pages 184-199, Springer, 2020.

Acknowledgement

The project is mainly funded by NASA CMAC program