From Raw Pixels to Products: Building a Landsat Sea Surface Temperature Product Cookbook

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Where do you start if the data product you need doesn’t exist yet? Designing a scientific algorithm is already a challenge — making it automated, open, reusable, and cloud-ready is even harder. This project creates a cookbook that guides users through building a data product algorithm using best-in-class open methods, with Landsat sea surface temperature (SST) as the case study. It demystifies complex steps like thermal infrared atmospheric correction and avoids dependence on proprietary radiative transfer models.

This Project Pythia Cookbook covers how to create a data product from a raw image to data production.

Motivation

• Streamlines some of the biggest time sinks in science: data access and generation
• Makes complex thermal correction and retrieval methods more accessible
• Provides a full, reproducible workflow — from data download to robust error quantification — for training new users and accelerating applied research

Authors

Tasha Snow, Genevieve Clow, Kathryn Moore, Arman Oliazadeh, Jianwen Du.

Contributors

Structure

This cookbook is broken into seven chapters: data access, data prepreprocessing, building a brightness temperature correction, retrieving a corrected value, calibrating, validating with in situ data, and producing the data file.

Section 1 Data Access

Teaches how to most effectively find and access your datasets from a local laptop or the cloud.

Section 2 Data Preprocessing

Learn about the different steps involved in data preprocessing as the data is prepared to be used in conjunction with other datasets and eventually corrected and applied within a retrieval.

Section 5 Calibration

Learn why we would want to calibrate our new dataset and how to do so.

Section 6 Validation

Learn about what data to use for validation, why we want to validate, and potential models we can use for validation.

Running the Notebooks

You can either run the notebook using Binder or on your local machine.

Running on Binder

The simplest way to interact with a Jupyter Notebook is through Binder, which enables the execution of a Jupyter Book in the cloud. The details of how this works are not important for now. All you need to know is how to launch a Pythia Cookbooks chapter via Binder. Simply navigate your mouse to the top right corner of the book chapter you are viewing and click on the rocket ship icon, (see figure below), and be sure to select “launch Binder”. After a moment you should be presented with a notebook that you can interact with. I.e. you’ll be able to execute and even change the example programs. You’ll see that the code cells have no output at first, until you execute them by pressing {kbd}Shift+{kbd}Enter. Complete details on how to interact with a live Jupyter notebook are described in Getting Started with Jupyter.

Note, not all Cookbook chapters are executable. If you do not see the rocket ship icon, such as on this page, you are not viewing an executable book chapter.

Running on Your Own Machine

If you are interested in running this material locally on your computer, you will need to follow this workflow:

(Replace "cookbook-example" with the title of your cookbooks)

1. Clone the https://github.com/ProjectPythia/cookbook-example repository:

    git clone https://github.com/ProjectPythia/cookbook-example.git

2. Move into the cookbook-example directory

   cd cookbook-example

3. Create and activate your conda environment from the environment.yml file

   conda env create -f environment.yml
   conda activate cookbook-example

4. Move into the notebooks directory and start up Jupyterlab

   cd notebooks/
   jupyter lab