1.1 Getting started

1.1 Getting started#

How to use this book#

Our goal is for this book to be helpful to users across a range of backgrounds and with different needs and interests. It is designed to be a comprehensive walk-through of scientific workflows using earth observation data in Python. At the same time, we recognize some users may be interested in following individual segments and a more modular structure; follow the book from start to finish or check out the individual sections that interest you. Check out the tutorials overview page to see which topics are covered in different notebooks.

For an introduction and background on data cubes and cloud-optimized geoscience, head to the background section; otherwise, head straight to the tutorials.

Running tutorial notebooks on your own

Head to the software page for detailed instructions on how to get started running the tutorial notebooks on your own.

Data used in this book#

Most of the examples in this book use data accessed programmatically from cloud-object storage. Where relevant, we include intermediate objects from the workflows in this book’s Github repository to remove the need for computationally-intensive operations in the tutorials.

Several notebooks in the second tutorial use Sentinel-1 data processed by the Alaska Satellite Facility and downloaded locally. See the tutorial introduction for more details on downloading the data and different options for using the tutorial. For more background on all of the datasets used in this book, see Tutorial Data.

Important

The datasets used in these tutorials can be complicated to work with and require significant background knowledge in order to understand their limitations and how best to interpret them. It is the responsibility of the user to understand the physical principles that underpin remote sensing datasets and how they should be used and interpreted. See the Tutorial Data section for a detailed discussion of these datasets and links to important background information.

Who is this book for?#

This book is for anyone interested in working with earth observation data in Python. We try to provide relevant domain and background information where necessary, and we direct the reader to helpful resources that provide more robust discussions of important concepts when appropriate.

We provide all the resources necessary to work through the Jupyter Notebooks in these tutorials on your own machine. Some experience working with Python will be helpful. However, the tutorials are designed to be beginner-friendly.

If you’d like a more thorough background on working with geospatial data in Python

Check out An Introduction to Earth and Environmental Data Science, which is based on a semester-long Columbia University course titled Research Computing in Earth Science.

The tutorials in this book include discussions of different types of geospatial data, especially data cubes. We include some background information; however, if these terms are new to you, we recommend checking out the following resources before getting started:

Overview of book#

Part 2: Background#

Background on data cubes and an introduction to array-based geoscience data and how it is represented in Xarray and Python. This section provides an overview and explanations of topics that will be referenced in the applied tutorials.

Tutorials#

Each tutorial focuses on a different type of remote sensing dataset and demonstrates how to assess and work through the nuances, details, and challenges that can arise from each. A common characteristic of each dataset emphasized throughout the notebooks is working with larger-than-memory datasets on the computational resources of a standard laptop.

Part 3: ITS_LIVE ice velocity data tutorial \(\tiny \nearrow\)#

This tutorial focuses on ITS_LIVE, a NASA MEASURES project and publicly accessible dataset stored in an AWS S3 repo as Zarr data cubes.

Part 4: Sentinel-1 RTC imagery tutorial \(\tiny \nearrow\)#

This tutorial focuses on another satellite dataset: Sentinel-1 Radiometric Terrain Corrected imagery. Sentinel-1 is a satellite-based imaging radar. More specifically, it is a synthetic aperture radar (SAR). SAR sensors look to the side rather than straight-down like conventional optical and infrared satellite sensors. This side-looking geometry causes geometric distortions that must be addressed prior to analysis. SAR data undergoes different types of processing for different scientific applications. Part 4 demonstrates how to access this data from two publicly available online repositories: Alaska Satellite Facility and Microsoft Planetary Computer. These notebooks demonstrate the different ways to read this data and prepare it for analysis, as well as an initial comparison of the two datasets.

Part 5: Conclusion \(\tiny \nearrow\)#

A summary of the lessons learned throughout the tutorials and synthesis of these ideas into suggestions and best practices for developing scientific workflows analyzing n-dimensional earth observation data.

About this book#

These tutorials were initially developed while Emma Marshall interned with the Summer Internships in Parallel Computational Sciences (SIParCS) program at the National Center for Atmospheric Research (NCAR). Jessica Scheick, Scott Henderson, and Deepak Cherian were internship supervisors for this project. The internship was also supported by a NASA Open Source Tools, Frameworks, and Libraries program (Award 80NSSC22K0345), with a specific focus on developing educational resources for working with cloud-hosted data using Xarray. Tutorial development continued after the conclusion of the SIParCS internship when Emma Marshall returned to the University of Utah as a Ph.D. student, where she was supported by a FINESST Fellowship Grant (80NSSC22K1536).

Contents