Introduction to Python for Satellite Remote Sensing


I have begun a course on using Python for analyzing satellite remote sensing data. The course will cover everything from downloading the data to implementing machine learning techniques.

Python is an increasingly popular language for satellite remote sensing applications due to its versatility and ease of use. With Python, users can access and manipulate data from a variety of sources, including satellites, aerial photographs, and ground-based sensors. Additionally, it's simple syntax allows for rapid development of algorithms that can automate tedious tasks such as image classification. Python also provides powerful visualization libraries, allowing users to easily visualize their data and results. Finally, its open-source nature allows for broad collaboration and sharing of code between researchers. In short, Python is a great tool for satellite remote sensing applications. With it, one can quickly develop sophisticated algorithms and visualize their results with ease.

Python has gained widespread acceptance in the remote sensing community due to its many advantages. It's relatively easy to learn, and has a wealth of libraries that extend its capabilities into areas such as image processing, machine learning, and data analysis. Additionally, it can be used for a wide variety of applications such as land cover mapping, digital terrain modeling, and feature detection. All of these capabilities make Python a versatile tool for any remote sensing project.

  1. Introduction Page 1Set up your work environment Page 2
    1. Installing Conda Distribution Page 3
  2. Obtaining Earth Science Data Page 4
    1. Download data from NOAA AWS Page 5
    2. Download data from NASA LAADS DAAC Page 6
  3. Extracting Earth Science Data with python Page 7
    1. Reading an HDF4 file Page 8
      1. Using pyhdf library Page 9
      2. Using xarray library (case 1) Page 10
    2. Reading a netCDF file Page 11
      1. Using netCDF4 library Page 12
      2. Using xarray library (case 2) Page 13
    3. Reading a GRIB weather data file Page 14
  4. Find all VIIRS granules containing a specific location Page 15