A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments
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Updated
Mar 25, 2026 - Python
PACE launched into Earth orbit on 2024-02-08 to extend and improve NASA’s over 20-year record of global ocean biology (especially the tiny plants and algae that sustain marine food webs), aerosols (tiny particles suspended in the air), and clouds.
Two instruments fly on the satellite:
Sunlight interacting with substances present in open water, such as the green photosynthetic pigment found in phytoplankton and land plants, gives the ocean its dynamic and informative color.
Measuring polarization states of UV-to-shortwave light at various angles provides new information on the atmosphere and clouds, such as particle size and composition.
A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments
A demonstration and practice project for the PACE Data Hackweek
Python methods to estimate phytoplankton pigment concentrations from PACE satellite remote sensing reflectance data.
Unified algorithm combining DT, DB, and OMI UV aerosol algorithm to be applied to PACE OCI sensor.
assess the impact of 2024 Central American fires on surface ocean properties in the Gulf of Mexico
Launchpad for Sarasota Science and Technology Society (STS) Processing of STELLA Spectrometer, Landsat and PACE Ocean Data
Creating tutorials and tools for helping end users work with PACE data.
We have created a Jupyter Notebook to use with NASA PACE data employing HyperCoast to download the data and then view and process these hyperspectral data using traditional python code. We have also attempted to calculate chlorophyll a too in this notebook that is CoLab ready.
Extending the long-running CyAN CI project to the newly launched NASA PACE/OCI
PACE Hackweek 2024 project: FLARP (FLuorescence Analysis and Research with PACE)