Python for Remote Sensing: Analysis, Visualization, and Workflow for Earth Scientists

Instructor: Rebekah Esmaili

Overview

Are you tired of wrestling with satellite-based datasets? In this tutorial, you will get a crash course on using Python and other modern tools to analyze your satellite datasets which can speed up the learning curve and improve research efficiency. We’ll cover the basics of the NetCDF and HDF self-describing data formats, display data FAST using Panoply, and demo basic analysis using Python. Additionally, we share some of the best practices such as using version control and clean coding guidelines. This session will be beneficial for both experienced programmers who want overview of modern tools and also those just getting started with scientific programming. Examples will focus on NOAA and NASA datasets, the presented tools and techniques can be applied to other scientific datasets across other disciplines. No prior experience required.

Objectives

This course is designed to introduce earth scientists to modern programming tools and techniques to view and analyze data. The primary goal is for attendees to:

  • Understand the basic structure of array-oriented scientific datasets
  • Practice importing data into Python using NetCDF APIs
  • Perform basic analysis using Python
  • Gain familiarity with good coding practices, e.g. version control, clean coding, and project development
  • Introduce the helpful resources to attendees if they get stuck

Agenda

All times in Mountain Standard Time (MST)
8:00amMeet and greet/computing environment set-up
8:15amBasic Python and Jupyter Notebooks
9:00amUnderstanding and viewing to scientific data formats
9:30amImporting scientific data files
10:00amVisualizing satellite datasets
10:45amPerforming common remote sensing tasks with Python, Version Control
11:00amAdjourn

Pre-workshop set-up

Please download and/or install the following before the session begins:

Resources

Inclusivity

It is my intent to create a learning environment that is respectful of diversity: gender, sexuality, disability, age, socioeconomic status, ethnicity, race, and culture. Your suggestions are encouraged and appreciated.