Details

Earth Observation Using Python


Earth Observation Using Python

A Practical Programming Guide
Special Publications, Band 75 1. Aufl.

von: Rebekah B. Esmaili

143,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 04.08.2021
ISBN/EAN: 9781119606895
Sprache: englisch
Anzahl Seiten: 304

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Beschreibungen

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

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