Earth Observation using Python A Practical Programming Guide

by Esmaili, Rebekah B.

• ISBN13: 9781119606888
• ISBN10: 1119606888
• Edition: 1st
• Format: Hardcover
• Copyright: 2021-08-24
• Publisher: American Geophysical Union

Summary

Learn basic Python programming to create functional and effective visualizations from earth observation satellite data sets

Thousands of satellite datasets are freely available online, but scientists need the right tools to efficiently analyze data and share results. Python has easy-to-learn syntax and thousands of libraries to perform common Earth science programming tasks.

Earth Observation Using Python: A Practical Programming Guide presents an example-driven collection of basic methods, applications, and visualizations to process satellite data sets for Earth science research.

• Gain Python fluency using real data and case studies
• Read and write common scientific data formats, like netCDF, HDF, and GRIB2
• Create 3-dimensional maps of dust, fire, vegetation indices and more
• Learn to adjust satellite imagery resolution, apply quality control, and handle big files
• Develop useful workflows and learn to share code using version control
• Acquire skills using online interactive code available for all examples in the book

The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

Find out more about this book from this Q&A with the Author 

Author Biography

Rebekah B. Esmaili, Science and Technology Corp., Maryland, USA

Table of Contents

Section I Overview of Modern Satellite Datasets

1. Brief History of Earth Observing Satellites
2. Overview of Python
2.1. Why Python?
2.2. Useful Packages in Remote Sensing
2.2.1. NumPy
2.2.2. pandas
2.2.3. Matplotlib
2.2.4. Cartopy
2.2.5. xarray�@

3. Understanding self-describing data formats
3.1. Overview of formats
3.2. Appropriate use of data
3.2.1. Processing levels
3.2.2. Product Maturity
3.2.3. Quality Flags
3.2.4. Experimental products
3.2.5. Validation�@

4. Overview of satellite missions and products
4.1. Satellite Platforms
4.1.1. LEO satellites
4.1.2. GEO satellites
4.1.3. Deep Space
4.1.4. CubeSats
4.2. Brief catalog of current satellite missions and products 4.2.1. Data Portals
4.2.2. Meteorology and Atmospheric Science 4.2.2.1. Geostationary Satellites 4.2.2.1.1. GOES-R
4.2.2.1.2. METEOSAT
4.2.2.1.3. HIMAWARI
4.2.2.2. JPSS Series: Suomi-NPP and NOAA-20
4.2.2.3. Hyperspectral Soundings
4.2.3. Hydrology
4.2.3.1. GPM
4.2.3.2. SMAP
4.2.4. Oceanography
4.2.4.1. SST
4.2.4.2. Ocean Color

Section II Visualizing Data

5. Basic Python Syntax
5.1. Start Guide
5.1.1. Installation
5.1.2. Development Environments
5.1.3. Setting up Jupyter Notebooks
5.1.4. "Hello World" in Python
5.2. Variable Assignment and Arithmetic
5.3. Array and Matrix Operations
5.4. Time Series Sata
5.5. Loops and List Comprehensions
5.6. Logical Expressions

6. Importing Datasets

6.1. Common Formats
6.1.1. NetCDF
6.1.2. HDF
6.1.3. GRIB2
6.1.4. BUFR
6.1.5. geoTIFF
6.2. Importing Data using NumPy and xarray�@

7. Plotting data
7.1. 2D and 3D Visualizations using Matplotlib
7.1.1. Simple Plots
7.1.1.1. Line and Scatter
7.1.1.2. Mesh Plots
7.1.1.3. Countour Plots
7.1.1.4. Faceting Plots
7.1.2. Time Series
7.1.3. Skew-T
7.2. Geo 2D: Mapping Data using Cartopy
7.3. N-D data 7.3.1. Time
7.3.2. Hyperspectral

8. Routine Data Operations 8.1. Resizing, cropping, and thinning data

8.2. Re-gridding
8.3. Atmospheric Corrections
8.4. Masking Data
8.5. Compositing and Combining Multiple Satellite Images
8.6. Matching with Surface Observations

Section III: Best Coding Practices

9. Workflow
9.1. Isolating Environments
9.2. Individual vs. Team
9.3. Local, Server, or Cloud Computing
9.4. Production Rules and Guidelines

10. Scripting with Python

11. Skill Sharing and Reproducible Research
11.1. Clean Coding Techniques
11.2. Documentation
11.3. Version Control and Code/Data Sharing