Tutorial to learn how to access and analyze time series of Satellite and Satellite-based Data using Python and JupyterLab in the Cloud
This tutorial aims to provide scientists (or anybbody) who want to use satellite data with the necessary tools for obtaining, temporally analyzing, and visualizing these data using the Cloud. Note: This in not a tutorial on Python per se - there are a myriad of resources for that. The purpose of this tutorial is to learn, through examples, only the necessary Python code and tools required to extract and do simple temporal analysis of satellite data.
We want you to get your toes wet, get to see and use the power of Python, and then maybe you will want to learn more. For that, we encourage you to visit the links on the Resources section at the end of each chapter.
This project, supported by the Better Scientific Software foundation, and the tutorial in oceanography that we use as basis here was originally funded by NASA.
This tutorial is developed to run and access satellite data on the Cloud.
To launch the tutorial:
- Click on the binder icon below. It will redirect you to an online environment with the tutorial.
- It might take some time to load the first time, but eventually you'll be promted with a Jupyter environment, listing the Chapters of this tutorial on your web browser (See Chapter 2 for a brief guide on Jupyter Notebook).
- Double click on the Chapter you want to work on. It will open in a new tab.
- At the end of the session, quit the session (top right of the page).
- You can access the tutorial (repeating this same procedure) as many times as you want.
This tutorial is divided into Chapters that provide the necessary tools as building blocks. These chapters are stand-alone, so can be skipped if you are familiar with the particular tool presented.
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Introduction to Python for Earth Science: Basic concepts about Python
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Introduction to Jupyter Lab: How to use the web interface JupyterLab
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Python Basics: Basic of Python
4a. Python Tools: xarray, the library that makes satellite data analysis easy
4b. Plotting Tools: Python plotting libraries
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Satellite Cloud Data: Background information on Cloud access and data
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Ocean Data Example: First cloud data acquisition and analysis on ocean surface temperature data
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Atmospheric Data Example: Acquisition and analysis of satellite-based data wind data from the cloud
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Land Data Example: Acquisition and analysis of vegetation data from online data
The tutorials can also be cloned from this repository and run locally on your computer (you would need good access ttot the internet and make sure your libraries are update and compatible). To get instructions of how to install Python, Jupyter Notebooks, clone the tutorials from Github, and to access the data on the cloud, see here.
Developed by: Marisol García-Reyes (marisolgr@faralloninstitute.org)
Funded by: Better Scientific Software (BSSw) Fellowship Program
Modified from 'Python for Oceanographers' by: Chelle Gentemann and Marisol García-Reyes. Access here, and 'Pangeo Tutorial for AGU Oceans Sciences 2020' here.