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 

Table of Contents:
Foreword Introduction 1 A Tour of Current Satellite Missions and Products 1.1 History of Computational Scientific Visualization 1.2 Brief catalog of current satellite products 1.2.1 Meteorological and Atmospheric Science 1.2.2 Hydrology 1.2.3 Oceanography and Biogeosciences 1.2.4 Cryosphere 1.3 The Flow of Data from Satellites to Computer 1.4 Learning using Real Data and Case Studies 1.5 Summary 1.6 References 2 Overview of Python 2.1 Why Python? 2.2 Useful Packages for Remote Sensing Visualization 2.2.1 NumPy 2.2.2 Pandas 2.2.3 Matplotlib 2.2.4 netCDF4 and h5py 2.2.5 Cartopy 2.3 Maturing Packages 2.3.1 xarray 2.3.2 Dask 2.3.3 Iris 2.3.4 MetPy 2.3.5 cfgrib and eccodes 2.4 Summary 2.5 References 3 A Deep Dive into Scientific Data Sets 3.1 Storage 3.1.1 Single-values 3.1.2 Arrays 3.2 Data Formats 3.2.1 Binary 3.2.2 Text 3.2.3 Self-describing data formats 3.2.4 Table-Driven Formats 3.2.5 geoTIFF 3.3 Data Usage 3.3.1 Processing Levels 3.3.2 Product Maturity 3.3.3 Quality Control 3.3.4 Data Latency 3.3.5 Re-processing 3.4 Summary 3.5 References 4 Practical Python Syntax 4.1 "Hello Earth" in Python 4.2 Variable Assignment and Arithmetic 4.3 Lists 4.4 Importing Packages 4.5 Array and Matrix Operations 4.6 Time Series Data 4.7 Loops 4.8 List Comprehensions 4.9 Functions 4.10 Dictionaries 4.11 Summary 4.12 References 5 Importing Standard Earth Science Datasets 5.1 Text 5.2 NetCDF 5.3 HDF 5.4 GRIB2 5.5 Importing Data using xarray 5.5.1 netCDF 5.5.2 GRIB2 5.5.3 Accessing datasets using OpenDAP 5.6 Summary 5.7 References 6 Plotting and Graphs for All 6.1 Univariate Plots 6.1.1 Histograms 6.1.2 Barplots 6.2 Two Variable Plots 6.2.1 Converting Data to a Time Series 6.2.2 Useful Plot Customizations 6.2.3 Scatter Plots 6.2.4 Line Plots 6.2.5 Adding data to an existing plot 6.2.6 Plotting two side-by-side plots 6.2.7 Skew-T Log-P 6.3 Three Variable Plots 6.3.1 Filled Contour 6.3.2 Mesh Plots 6.4 Summary 6.5 References 7 Creating Effective and Functional Maps 7.1 Cartographic Projections 7.1.1 Projections 7.1.2 Plate Carrée 7.1.3 Equidistant Conic 7.1.4 Orthographic 7.2 Cylindrical Maps 7.2.1 Global plots 7.2.2 Changing projections 7.2.3 Regional Plots 7.2.4 Swath Data 7.2.5 Quality Flag Filtering 7.3 Polar Stereographic Maps 7.4 Geostationary Maps 7.5 Plotting datasets using OpenDAP 7.6 Summary 7.7 References 8 Gridding Operations 8.1 Regular 1D grids 8.2 Regular 2D grids 8.3 Irregular 2D grids 8.3.1 Resizing 8.3.2 Regridding 8.3.3 Resampling 8.4 Summary 8.5 References 9 Meaningful Visuals through Data Combination 9.1 Spectral and Spatial Characteristics of Different Sensors 9.2 Normalized Difference Vegetation Index (NDVI) 9.3 Window Channels 9.4 RGB 9.4.1 True Color 9.4.2 Dust RGB 9.4.3 Fire/Natural RGB 9.5 Matching with Surface Observations 9.5.1 With user-defined functions 9.5.2 With Machine Learning 9.6 Summary 9.7 References 10 Exporting with Ease 10.1 Figures 10.2 Text Files 10.3 Pickling 10.4 NumPy binary files 10.5 NetCDF 10.5.1 Using netCDF4 to create netCDF files 10.5.2 Using Xarray to create netCDF files 10.5.3 Following Climate and Forecast (CF) metadata conventions 10.6 Summary 11 Developing a Workflow 11.1 Scripting with Python 11.1.1 Creating scripts using text editors 11.1.2 Creating scripts from Jupyter Notebooks 11.1.3 Running Python scripts from the command line 11.1.4 Handling output when scripting 11.2 Version Control 11.2.1 Code Sharing though Online Repositories 11.2.2 Setting-up on GitHub 11.3 Virtual Environments 11.3.1 Creating an environment 11.3.2 Changing environments from the command line 11.3.3 Changing environments in Jupyter Notebook 11.4 Methods for code development 11.5 Summary 11.6 References 12 Reproducible and Shareable Science 12.1 Clean Coding Techniques 12.1.1 Stylistic conventions 12.1.2 Tools for Clean Code 12.2 Documentation 12.2.1 Comments and docstrings 12.2.2 README file 12.2.3 Creating useful commit messages 12.3 Licensing 12.4 Effective Visuals 12.4.1 Make a Statement 12.4.2 Undergo Revision 12.4.3 Are Accessible and Ethical 12.5 Summary 12.6 References Conclusion A Installing Python A.1 Download and Install Anaconda A.2 Package management in Anaconda A.3 Download sample data for this book B Jupyter Notebooks B.1 Running on a Local Machine (New Coders) B.2 Running on a Remote Server (Advanced) B.3 Tips for Advanced Users B.3.1 Customizing Notebooks with Configuration Files B.3.2 Starting and Ending Python Scripts B.3.3 Creating Git Commit templates C Additional Learning Resources D Tools D.1 Text Editors and IDEs D.2 Terminals E Finding, Accessing, and Downloading Satellite Datasets E.1 Ordering data from NASA EarthData E.2 Ordering data from NOAA/CLASS F Acronyms Acknowledgements