{"product_id":"earth-observation-using-python-isbn-9781119606888","title":"Earth Observation Using Python","description":"\u003cp\u003e\u003cb\u003eLearn basic Python programming to create functional and effective visualizations from earth observation satellite data sets\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThousands 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eEarth Observation Using Python: A Practical Programming Guide \u003c\/i\u003epresents an example-driven collection of basic methods, applications, and visualizations to process satellite data sets for Earth science research.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eGain Python fluency using real data and case studies\u003c\/li\u003e \u003cli\u003eRead and write common scientific data formats, like netCDF, HDF, and GRIB2\u003c\/li\u003e \u003cli\u003eCreate 3-dimensional maps of dust, fire, vegetation indices and more\u003c\/li\u003e \u003cli\u003eLearn to adjust satellite imagery resolution, apply quality control, and handle big files\u003c\/li\u003e \u003cli\u003eDevelop useful workflows and learn to share code using version control\u003c\/li\u003e \u003cli\u003eAcquire skills using online interactive code available for all examples in the book\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe 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.\u003cbr\u003e\u003cbr\u003eFind out more about this book from this \u003ca href=\"https:\/\/eos.org\/editors-vox\/a-new-practical-guide-to-using-python-for-earth-observation\"\u003eQ\u0026amp;A with the Author\u003c\/a\u003e \u003c\/p\u003e \u003cp\u003eForeword\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 A Tour of Current Satellite Missions and Products\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 History of Computational Scientific Visualization\u003c\/p\u003e \u003cp\u003e1.2 Brief catalog of current satellite products\u003c\/p\u003e \u003cp\u003e1.2.1 Meteorological and Atmospheric Science\u003c\/p\u003e \u003cp\u003e1.2.2 Hydrology\u003c\/p\u003e \u003cp\u003e1.2.3 Oceanography and Biogeosciences\u003c\/p\u003e \u003cp\u003e1.2.4 Cryosphere\u003c\/p\u003e \u003cp\u003e1.3 The Flow of Data from Satellites to Computer\u003c\/p\u003e \u003cp\u003e1.4 Learning using Real Data and Case Studies\u003c\/p\u003e \u003cp\u003e1.5 Summary\u003c\/p\u003e \u003cp\u003e1.6 References\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Overview of Python\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Why Python?\u003c\/p\u003e \u003cp\u003e2.2 Useful Packages for Remote Sensing Visualization\u003c\/p\u003e \u003cp\u003e2.2.1 NumPy\u003c\/p\u003e \u003cp\u003e2.2.2 Pandas\u003c\/p\u003e \u003cp\u003e2.2.3 Matplotlib\u003c\/p\u003e \u003cp\u003e2.2.4 netCDF4 and h5py\u003c\/p\u003e \u003cp\u003e2.2.5 Cartopy\u003c\/p\u003e \u003cp\u003e2.3 Maturing Packages\u003c\/p\u003e \u003cp\u003e2.3.1 xarray\u003c\/p\u003e \u003cp\u003e2.3.2 Dask\u003c\/p\u003e \u003cp\u003e2.3.3 Iris\u003c\/p\u003e \u003cp\u003e2.3.4 MetPy\u003c\/p\u003e \u003cp\u003e2.3.5 cfgrib and eccodes\u003c\/p\u003e \u003cp\u003e2.4 Summary\u003c\/p\u003e \u003cp\u003e2.5 References\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 A Deep Dive into Scientific Data Sets\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Storage\u003c\/p\u003e \u003cp\u003e3.1.1 Single-values\u003c\/p\u003e \u003cp\u003e3.1.2 Arrays\u003c\/p\u003e \u003cp\u003e3.2 Data Formats\u003c\/p\u003e \u003cp\u003e3.2.1 Binary\u003c\/p\u003e \u003cp\u003e3.2.2 Text\u003c\/p\u003e \u003cp\u003e3.2.3 Self-describing data formats\u003c\/p\u003e \u003cp\u003e3.2.4 Table-Driven Formats\u003c\/p\u003e \u003cp\u003e3.2.5 geoTIFF\u003c\/p\u003e \u003cp\u003e3.3 Data Usage\u003c\/p\u003e \u003cp\u003e3.3.1 Processing Levels\u003c\/p\u003e \u003cp\u003e3.3.2 Product Maturity\u003c\/p\u003e \u003cp\u003e3.3.3 Quality Control\u003c\/p\u003e \u003cp\u003e3.3.4 Data Latency\u003c\/p\u003e \u003cp\u003e3.3.5 Re-processing\u003c\/p\u003e \u003cp\u003e3.4 Summary\u003c\/p\u003e \u003cp\u003e3.5 References\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Practical Python Syntax\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 \"Hello Earth\" in Python\u003c\/p\u003e \u003cp\u003e4.2 Variable Assignment and Arithmetic\u003c\/p\u003e \u003cp\u003e4.3 Lists\u003c\/p\u003e \u003cp\u003e4.4 Importing Packages\u003c\/p\u003e \u003cp\u003e4.5 Array and Matrix Operations\u003c\/p\u003e \u003cp\u003e4.6 Time Series Data\u003c\/p\u003e \u003cp\u003e4.7 Loops\u003c\/p\u003e \u003cp\u003e4.8 List Comprehensions\u003c\/p\u003e \u003cp\u003e4.9 Functions\u003c\/p\u003e \u003cp\u003e4.10 Dictionaries\u003c\/p\u003e \u003cp\u003e4.11 Summary\u003c\/p\u003e \u003cp\u003e4.12 References\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Importing Standard Earth Science Datasets\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Text\u003c\/p\u003e \u003cp\u003e5.2 NetCDF\u003c\/p\u003e \u003cp\u003e5.3 HDF\u003c\/p\u003e \u003cp\u003e5.4 GRIB2\u003c\/p\u003e \u003cp\u003e5.5 Importing Data using xarray\u003c\/p\u003e \u003cp\u003e5.5.1 netCDF\u003c\/p\u003e \u003cp\u003e5.5.2 GRIB2\u003c\/p\u003e \u003cp\u003e5.5.3 Accessing datasets using OpenDAP\u003c\/p\u003e \u003cp\u003e5.6 Summary\u003c\/p\u003e \u003cp\u003e5.7 References\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Plotting and Graphs for All\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Univariate Plots\u003c\/p\u003e \u003cp\u003e6.1.1 Histograms\u003c\/p\u003e \u003cp\u003e6.1.2 Barplots\u003c\/p\u003e \u003cp\u003e6.2 Two Variable Plots\u003c\/p\u003e \u003cp\u003e6.2.1 Converting Data to a Time Series\u003c\/p\u003e \u003cp\u003e6.2.2 Useful Plot Customizations\u003c\/p\u003e \u003cp\u003e6.2.3 Scatter Plots\u003c\/p\u003e \u003cp\u003e6.2.4 Line Plots\u003c\/p\u003e \u003cp\u003e6.2.5 Adding data to an existing plot\u003c\/p\u003e \u003cp\u003e6.2.6 Plotting two side-by-side plots\u003c\/p\u003e \u003cp\u003e6.2.7 Skew-T Log-P\u003c\/p\u003e \u003cp\u003e6.3 Three Variable Plots\u003c\/p\u003e \u003cp\u003e6.3.1 Filled Contour\u003c\/p\u003e \u003cp\u003e6.3.2 Mesh Plots\u003c\/p\u003e \u003cp\u003e6.4 Summary\u003c\/p\u003e \u003cp\u003e6.5 References\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Creating Effective and Functional Maps\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Cartographic Projections\u003c\/p\u003e \u003cp\u003e7.1.1 Projections\u003c\/p\u003e \u003cp\u003e7.1.2 Plate Carrée\u003c\/p\u003e \u003cp\u003e7.1.3 Equidistant Conic\u003c\/p\u003e \u003cp\u003e7.1.4 Orthographic\u003c\/p\u003e \u003cp\u003e7.2 Cylindrical Maps\u003c\/p\u003e \u003cp\u003e7.2.1 Global plots\u003c\/p\u003e \u003cp\u003e7.2.2 Changing projections\u003c\/p\u003e \u003cp\u003e7.2.3 Regional Plots\u003c\/p\u003e \u003cp\u003e7.2.4 Swath Data\u003c\/p\u003e \u003cp\u003e7.2.5 Quality Flag Filtering\u003c\/p\u003e \u003cp\u003e7.3 Polar Stereographic Maps\u003c\/p\u003e \u003cp\u003e7.4 Geostationary Maps\u003c\/p\u003e \u003cp\u003e7.5 Plotting datasets using OpenDAP\u003c\/p\u003e \u003cp\u003e7.6 Summary\u003c\/p\u003e \u003cp\u003e7.7 References\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Gridding Operations\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Regular 1D grids\u003c\/p\u003e \u003cp\u003e8.2 Regular 2D grids\u003c\/p\u003e \u003cp\u003e8.3 Irregular 2D grids\u003c\/p\u003e \u003cp\u003e8.3.1 Resizing\u003c\/p\u003e \u003cp\u003e8.3.2 Regridding\u003c\/p\u003e \u003cp\u003e8.3.3 Resampling\u003c\/p\u003e \u003cp\u003e8.4 Summary\u003c\/p\u003e \u003cp\u003e8.5 References\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Meaningful Visuals through Data Combination\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Spectral and Spatial Characteristics of Different Sensors\u003c\/p\u003e \u003cp\u003e9.2 Normalized Difference Vegetation Index (NDVI)\u003c\/p\u003e \u003cp\u003e9.3 Window Channels\u003c\/p\u003e \u003cp\u003e9.4 RGB\u003c\/p\u003e \u003cp\u003e9.4.1 True Color\u003c\/p\u003e \u003cp\u003e9.4.2 Dust RGB\u003c\/p\u003e \u003cp\u003e9.4.3 Fire\/Natural RGB\u003c\/p\u003e \u003cp\u003e9.5 Matching with Surface Observations\u003c\/p\u003e \u003cp\u003e9.5.1 With user-defined functions\u003c\/p\u003e \u003cp\u003e9.5.2 With Machine Learning\u003c\/p\u003e \u003cp\u003e9.6 Summary\u003c\/p\u003e \u003cp\u003e9.7 References\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Exporting with Ease\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Figures\u003c\/p\u003e \u003cp\u003e10.2 Text Files\u003c\/p\u003e \u003cp\u003e10.3 Pickling\u003c\/p\u003e \u003cp\u003e10.4 NumPy binary files\u003c\/p\u003e \u003cp\u003e10.5 NetCDF\u003c\/p\u003e \u003cp\u003e10.5.1 Using netCDF4 to create netCDF files\u003c\/p\u003e \u003cp\u003e10.5.2 Using Xarray to create netCDF files\u003c\/p\u003e \u003cp\u003e10.5.3 Following Climate and Forecast (CF) metadata conventions\u003c\/p\u003e \u003cp\u003e10.6 Summary\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Developing a Workflow\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Scripting with Python\u003c\/p\u003e \u003cp\u003e11.1.1 Creating scripts using text editors\u003c\/p\u003e \u003cp\u003e11.1.2 Creating scripts from Jupyter Notebooks\u003c\/p\u003e \u003cp\u003e11.1.3 Running Python scripts from the command line\u003c\/p\u003e \u003cp\u003e11.1.4 Handling output when scripting\u003c\/p\u003e \u003cp\u003e11.2 Version Control\u003c\/p\u003e \u003cp\u003e11.2.1 Code Sharing though Online Repositories\u003c\/p\u003e \u003cp\u003e11.2.2 Setting-up on GitHub\u003c\/p\u003e \u003cp\u003e11.3 Virtual Environments\u003c\/p\u003e \u003cp\u003e11.3.1 Creating an environment\u003c\/p\u003e \u003cp\u003e11.3.2 Changing environments from the command line\u003c\/p\u003e \u003cp\u003e11.3.3 Changing environments in Jupyter Notebook\u003c\/p\u003e \u003cp\u003e11.4 Methods for code development\u003c\/p\u003e \u003cp\u003e11.5 Summary\u003c\/p\u003e \u003cp\u003e11.6 References\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Reproducible and Shareable Science\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Clean Coding Techniques\u003c\/p\u003e \u003cp\u003e12.1.1 Stylistic conventions\u003c\/p\u003e \u003cp\u003e12.1.2 Tools for Clean Code\u003c\/p\u003e \u003cp\u003e12.2 Documentation\u003c\/p\u003e \u003cp\u003e12.2.1 Comments and docstrings\u003c\/p\u003e \u003cp\u003e12.2.2 README file\u003c\/p\u003e \u003cp\u003e12.2.3 Creating useful commit messages\u003c\/p\u003e \u003cp\u003e12.3 Licensing\u003c\/p\u003e \u003cp\u003e12.4 Effective Visuals\u003c\/p\u003e \u003cp\u003e12.4.1 Make a Statement\u003c\/p\u003e \u003cp\u003e12.4.2 Undergo Revision\u003c\/p\u003e \u003cp\u003e12.4.3 Are Accessible and Ethical\u003c\/p\u003e \u003cp\u003e12.5 Summary\u003c\/p\u003e \u003cp\u003e12.6 References\u003c\/p\u003e \u003cp\u003eConclusion\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Installing Python\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Download and Install Anaconda\u003c\/p\u003e \u003cp\u003eA.2 Package management in Anaconda\u003c\/p\u003e \u003cp\u003eA.3 Download sample data for this book\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Jupyter Notebooks\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Running on a Local Machine (New Coders)\u003c\/p\u003e \u003cp\u003eB.2 Running on a Remote Server (Advanced)\u003c\/p\u003e \u003cp\u003eB.3 Tips for Advanced Users\u003c\/p\u003e \u003cp\u003eB.3.1 Customizing Notebooks with Configuration Files\u003c\/p\u003e \u003cp\u003eB.3.2 Starting and Ending Python Scripts\u003c\/p\u003e \u003cp\u003eB.3.3 Creating Git Commit templates\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Additional Learning Resources\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD Tools\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eD.1 Text Editors and IDEs\u003c\/p\u003e \u003cp\u003eD.2 Terminals\u003c\/p\u003e \u003cp\u003e\u003cb\u003eE Finding, Accessing, and Downloading Satellite Datasets\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eE.1 Ordering data from NASA EarthData\u003c\/p\u003e \u003cp\u003eE.2 Ordering data from NOAA\/CLASS\u003c\/p\u003e \u003cp\u003e\u003cb\u003eF Acronyms\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAcknowledgements\u003c\/p\u003e \u003cp\u003e\u003cb\u003eRebekah Bradley Esmaili\u003c\/b\u003e, Atmospheric Scientist, Science and Technology Corp. (STC) and NOAA\/JPSS, University of Maryland, USA.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEarth Observation Using Python\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Practical Programming Guide \u003c\/p\u003e\u003cp\u003eThousands 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.  \u003c\/p\u003e\u003cp\u003e\u003ci\u003eEarth Observation Using Python: A Practical Programming Guide \u003c\/i\u003epresents an example-driven collection of basic methods, applications, and visualizations to process satellite data sets for Earth science research. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eVolume highlights Include:\u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eGain Python fluency using real data and case studies\u003c\/li\u003e\n\u003cli\u003eRead and write common scientific data formats, such as netCDF, HDF, and GRIB2\u003c\/li\u003e\n\u003cli\u003eCreate 3-dimensional maps of dust, fire, vegetation indices and more\u003c\/li\u003e\n\u003cli\u003eLearn to adjust satellite imagery resolution, apply quality control, and handle big files\u003c\/li\u003e\n\u003cli\u003eDevelop useful workflows and learn to share code using version control\u003c\/li\u003e\n\u003cli\u003eAcquire skills using online interactive code available for all examples in the book\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eThe 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.\u003c\/i\u003e\u003c\/p\u003e","brand":"American Geophysical Union","offers":[{"title":"Default Title","offer_id":47989096546533,"sku":"NP9781119606888","price":183.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119606888.jpg?v=1761782779","url":"https:\/\/k12savings.com\/products\/earth-observation-using-python-isbn-9781119606888","provider":"K12savings","version":"1.0","type":"link"}