rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Earth observation using Python
    sub title: a practical programming guide
  name:
    name part: Esmaili, Rebekah Bradley
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: nju
    date issued: 2021
    copyright date: 2021
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (ix, 287 pages : : illustrations (chiefly color), maps (chiefly color.)
  abstract: "Python is a modern programming language that has exploded in popularity both inside and outside of the Earth science community. Part of its appeal is it's easy-to-learn syntax and the thousands of available libraries which can be synthesized with core Python to do nearly any computing task imaginable. In particular, Python is useful for reading Earth-observing satellite datasets, which can be notoriously difficult to use due to the volume of information that results from the multitude of sensors, platforms, and spatio-temporal spacing. Python facilitates reading a variety of self-describing binary datasets that these observations are often encoded in. Using the same software, one can complete the entirerty of a research project and even produce plots. Within a notebook environment, the scientist can document and distribute the code which can improve efficiency and transparency within the Earth sciences community. Even with the right tools data are seldom ready off-the-shelf for analysis and research and requires a number of pre-processing steps to make the data useable. What steps to take and why are often except perhaps for data developers themselves. Data users often misunderstand concepts such as data quality, how to perform an atmospheric correction, or the complex regridding schemes necessary to compare data with different resolutions. Even to a technical user, the nuances can be frustrating and difficult to overcome. The consequence of this is that data remains unused, or worse, potentially misused"--
  table of contents: A tour of current satellite missions and products -- Overview of Python -- A deep dive into scientific data sets -- Practical Python syntax -- Importing standard earth science datasets -- Plotting and graphs for all -- Creating effective and functional maps -- Gridding operations -- Meaningful visuals through data combination -- Exporting with ease -- Developing a workflow -- Reproducible and shareable science.
  note: Rebekah Bradley Esmaili.
  note: Includes bibliographical references and index.
  subject:
    topic: Earth sciences
    topic: Data processing
  subject:
    topic: Remote sensing
    topic: Data processing
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Information visualization
  subject:
    topic: Artificial satellites in earth sciences
  subject:
    topic: Earth sciences
    topic: Methodology
  classification: QE48.8 .E85 2021
  classification: 550.285/5133
  related item:
    title info:
      title: Special publications ; 75
  related item:
    title info:
      title: Earth observation using Python
    name:
      name part: Esmaili, Rebekah Bradley.
    origin info:
      publisher: Hoboken, NJ : Wiley, [2021]
    identifier: (DLC) 2021001632
  related item:
    identifier: (DLC) 2021001631
  identifier: 9781119606925 (electronic bk.)
  identifier: 1119606926 (electronic bk.)
  identifier: 9781119606888
  identifier: 1119606888
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: NhCcYBP
    record creation date: 230502
    record change date: 20260214212157.8
    record identifier: in00004686693
    language of cataloging:
      language term: eng