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Land Surface Learning Resources

Webinars, tutorials, data recipes and more to help you work with Land Surface.
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PRISM Level 2A Reflectance showing spectra over bloom of Noctiluca scintillans in Cape Town, South Africa

Water Quality Mapping with PRISM L2A Reflectance

In this tutorial, we use PRISM Level 2A surface reflectance from the BioSCape campaign for studying blooms of Noctiluca scintillans and turbidity.
External Resource
Jupyter Notebook
Oct. 6, 2026
MASTER Level 1A emissivity across six thermal infrared bands

Access and Visualization of MASTER L2 Land Surface Temperature and Emissivity

In this tutorial we discover and access MASTER Level 2 data for NASA GEMx campaign and visualize land surface temperature and emissivity.
External Resource
Jupyter Notebook
Sept. 23, 2026
MASTER Level 1B calibrated radiance

Access and Visualize MASTER L1B Radiance Data

In this tutorial we discover and access MASTER Level 1B (L1B) calibrated radiance data from a Western Diversity Time Series (WDTS) campaign.
External Resource
Jupyter Notebook
Sept. 23, 2026
HyTES Level 1 Radiance Spectra over land, inshore, and water samples

Access and Visualize HyTES Data

Discover, access, and visualize Hyperspectral Thermal Emission Spectrometer (HyTES) L1 Radiance and L2 land surface temperature and emissivity data
External Resource
Jupyter Notebook
Aug. 17, 2026
Three-panel figure comparing EMIT and PACE OCI observations over a wildfire region in southeastern Australia. The left panel shows a high-resolution stretched RGB EMIT image with detailed terrain, vegetation, burn scars, and surrounding agricultural fields. The center panel displays surface reflectance spectra for a selected location (approximately 142.3°E, 37.0°S), comparing EMIT pre-fire and post-fire observations (solid green and orange lines) with corresponding PACE OCI pre-fire and post-fire observatio

Processing PACE and EMIT Data

This tutorial will demonstrate how to preprocess the EMIT and PACE reflectance data so that they can be used together to investigate science questions. Using the emit_tools and pace_tools modules, the data are orthorectified, geolocated, merged where needed, and resampled onto a common grid, enabling direct comparison of observations from the two sensors.
External Resource
Jupyter Notebook
July 14, 2026
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