Release of Getting Started with Cloud-Native HLS Data in Python Tutorial

November 23, 2020

On October 5, 2020, the LP DAAC released the PROVISIONAL daily 30 meter (m) global Harmonized Landsat Sentinel-2 (HLS) Sentinel-2 Multi-spectral Instrument Surface Reflectance (HLSS30) Version 1.5 data to the public. The limited sample of provisional data are currently available in the LP DAAC Cumulus cloud archive and are stored as Cloud Optimized GeoTIFFs (COG). This release provides the science community with a unique opportunity to provide feedback on the data prior to a vetted, science quality, data release.

To aid users in accessing and working with the newly released HLSS30 Version 1.5 data, The LP DAAC is pleased to announce the availability of a Jupyter Notebook tutorial called “Getting Started with Cloud-Native HLS Data in Python”. The tutorial is available as a Jupyter Notebook and an HTML output, and demonstrates how to extract an EVI time series from Harmonized Landsat and Sentinel-2 (HLS) data in the cloud using CMR's SpatioTemporal Asset Catalog (CMR-STAC). The tutorial follows a use case centered on observing the seasonal progression of an agricultural field in northern California, United States. In addition to learning how to work with HLS data, this tutorial provides an introduction to STAC, how to query a STAC API to find data for a desired spatiotemporal region of interest, how to access subsets of COGs in the cloud (and how to export your own COGs!), and how to visualize and calculate statistics for a time series of HLS data using xarray.

Check out the E-learning page for the Getting Started with Cloud-Native HLS Data in Python tutorial, or access the tutorial repository directly at: