Earth observation using python. A practical programming guide / Исследование Земли с использованием python. Практическое руководство по программированию

Python is a programming language that is rapidly growing in popularity. The number of users is large, although difficult to quantify; in fact, Python is currently the most tagged language on stackoverflow.com, a coding Q&A website with approximately 3 million questions a year. Some view this interest as hype, but there are many reasons to join the movement. Scientists are embracing Python because it is free, open source, easy to learn, and has thousands of add-on packages. Many routine tasks in the Earth sciences have already been coded and stored in off-the-shelf Python libraries. Users can download these libraries and apply them to their research rather than simply using older, more primitive functions. The widespread adoption of Python means scientists are moving toward a common programming language and set of tools that will improve code shareability and research reproducibility.

Among the wealth of remote sensing data available, satellite datasets are particularly voluminous and tend to be stored in a variety of binary formats. Some datasets conform to a “standard” structure, such as netCDF4. However, because of uncoordinated efforts across different agencies and countries, such standard formats bear their own inconsistencies in how data are handled and intended to be displayed. To address this, many agencies and companies have developed numerous “quick look” methods. For instance, data can be searched for and viewed online as Jpeg images, or individual files can be displayed with free, open-source software tools like Panoply (www.giss.nasa.gov/tools/panoply/) and HDFView (www.hdfgroup.org/downloads/hdfview/). <...>