SuPER Excited! The HLS Subsetting, Processing, and Exporting Reformatted (HLS SuPER) Data Prep Script has been Released

February 18, 2021
By: LP DAAC

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. On January 21, 2021, the LP DAAC released the PROVISIONAL daily 30  m global HLS Landsat 8 Operational Land Imager (OLI) Surface Reflectance and Top of Atmosphere Brightness (HLSL30) Version 1.5 data. 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 HLS Version 1.5 data, The LP DAAC is pleased to announce the availability of a data prep script called the “HLS Subsetting, Processing and Exporting Reformatted (HLS SuPER)” data prep script. The HLS SuPER script is a command line executable Python script that finds, accesses, and processes HLS data according to input criteria. Users can specify their desired region of interest (ROI) – via GeoJSON, Shapefile, or bounding box – as well as their temporal period, desired bands/layers of interest, and acceptable cloud cover amount. Additionally, the script clips the desired layers to the ROI, does basic quality filtering and scaling operations, and saves the data in the user’s desired output file format (COG, NetCDF-4, and zarr are available).

Check out the Data Prep Script page or access the script repository directly at: https://git.earthdata.nasa.gov/projects/LPDUR/repos/hls-super-script/browse.

P.S. We have also updated the “Getting Started with Cloud-Native HLS Data in Python” Jupyter Notebook tutorial to include HLSL30 availability! Check it out at: https://lpdaac.usgs.gov/resources/e-learning/getting-started-cloud-native-hls-data-python/.