ACCESS to Terra Data Fusion Products

Principal Investigator (PI): Larry Di Girolamo: University of Illinois at Urbana-Champaign
Co-Investigators (Co-PI): Yan Liu, John Towns, Shaowen Wang, Guangyu Zhao: UIUC; Muqun Wang: HDF Group

NASA’s Terra spacecraft has been orbiting the Earth since 1999. The satellite orbits the Earth 16 times each day, allowing its five instruments—Advanced Spaceborne Thermal Emission and Reflection (ASTER), Measurement of Pollution in the Troposphere (MOPITT), Multi-angle Imaging SpectroRadiometer (MISR), Clouds and the Earth's Radiant Energy System (CERES), and Moderate Resolution Imaging Spectroradiometer (MODIS)—to collect data about the Earth. Each of these instruments provides valuable information about the Earth and its atmosphere.

Terra satellite and areas of coverage

Image of NASA's Terra satellite showing the different areas of coverage provided by its five instruments: ASTER, MOPITT, MISR, CERES, and MODIS. Screenshot from NASA's Terra Satellite: ACCESS to Terra Data Fusion Products.

The significant added value of the Terra mission, however, comes from a combination of different Terra instrument observations of the Earth system over time. However, combining instrument data is challenging. Each instrument’s data resides at different locations in different file formats with different granularities and different grids/projections. And, there is a lot of data—about 2TB per day for nearly 20 years. These challenges often prevent scientists from taking full advantage of Terra data sets.

Led by Larry Di Girolamo, the team from the University of Illinois at Urbana-Champaign developed a system that efficiently generates and delivers mission-scale Terra data fusion products that can be used to address a wide range of Earth science questions. This project combines Terra observations from different instruments which enables scientists to derive new Terra products that better examine changes in the Earth and its atmosphere over the course of the Terra mission.

Di Girolamo’s team produced both a basic fusion product and an advanced fusion toolkit that allows users to resample and reproject radiance fields from one Terra instrument onto grids used by another Terra instrument, to a regularly spaced grid, or to a user-specified grid.

As a use case, the team developed the Climate Marble, which will appear in the planetarium dome show “Birth of Planet Earth” produced by Thomas Lucas Productions, Spitz Creative Media, and the National Center for Supercomputing Applications's Advanced Visualization Lab. The team is also creating an interactive web interface that will make Climate Marble available to the public in early 2019. Publications on the Climate Marble, Earth’s spectral and textural trends as observed from Terra, and new global retrievals of cloud properties from Terra instruments are examples of studies currently underway by Di Girolamo’s team.


Terra Data Fusion moved the 2.4 PB file-based data set containing fused data generated from all the data collected by all the instruments on the Terra Satellite from 2000–2015 to the AWS cloud. The data will be available to researchers in 2020.

Terra Fusion site

In the News

Blue Waters Supercomputer Processes New Data for NASA’s TERRA Satellite


Di Girolamo, L., et al. (2017). NASA Terra Satellite: ACCESS to Terra Data Fusion Products.

Fu, D. (2018). Examination of the behavior of MODIS-retrieved cloud droplet effective radius through MISR-MODIS data fusion (M.Sc. Thesis). University of Illinois at Urbana-Champaign.

Fu, D., Di Girolamo, Liang, L. & Zhao, G. (2019). Regional biases in Moderate Resolution Imaging Spectroradiometer (MODIS) marine liquid water cloud drop effective radius deduced through fusion with Multi-angle Imaging SpectroRadiometer (MISR). Journal of Geophysical Research, Atmospheres. doi:10.1029/2019JD031063 (in press).

Wang, Y., Yang, P., Hioki, S., King, M.D., Baum, B.A., Di Girolamo, L. & Fu, D. (2019). Ice cloud optical thickness, effective radius, and ice water path inferred from fused MISR and MODIS measurements based on a pixel-level optimal ice particle roughness model. Journal of Geophysical Research, Atmospheres. doi:10.1029/2019JD030457 (in press)

Wang, Y., Hioki, S., Yang, P., King, M.D., Di Girolamo, L., Fu, D. & Baum, B.A. (2018). Inference of an optimal ice particle model through latitudinal analysis of MISR and MODIS data. Remote Sensing, 10(12). doi:10.3390/rs10121981

Zhan, Y., Di Girolamo L., Davies, R., & Moroney, C. (2018). Instantaneous top-of-atmosphere albedo comparison between CERES and MISR over the Arctic. Remote Sensing, 10(12), 1882. doi:10.3390/rs10121882

Zhao, G., M. Yang, Y. Gao, Y. Zhan, H.-K. Lee, & Di Girolamo, L. (2019). PYTAF: a python tool for spatially resampling Earth observation data. Earth Science Informatics (submitted).

Page Last Updated: Feb 27, 2020 at 5:12 PM EST