5.1 Wrapping up#
It is a popular refrain, and a sentiment many analysts can likely relate to, “that 80% of data analysis is spent on the cleaning and preparing of data” [11, 15, 54]. This book focuses on the data cleaning and preparation steps of an analytical workflow that ingests satellite remote sensing time series datasets. We draw on the wealth of knowledge and research that attends to this topic in order to produce tutorials that demonstrate and explain these concepts in the context of cloud-optimized, publicly available array data and the software ecosystem built around the Xarray data model in Python.
In this chapter, you will find summaries of the concepts covered throughout the Jupyter Notebooks included in this book and a return to the introduction’s discussion of data cubes that synthesizes lessons learned in the tutorials.
Open source tools and packages#
We mainly use Xarray and tools within the Xarray ecosystem. There are many exciting open-source projects and tools related to Xarray data cubes that were not highlighted in this book. A few are:
5.2 Tutorials Summary
This book features two tutorials, each focuses on a different earth observation dataset and containing five notebooks that cover different steps of a typical workflow such as data access, manipulation and organization and visualization and exploratory analysis. In this section, you will find a few of the common topics throughout these notebooks and links to where they are addressed in each tutorial.
5.3 Data cubes revisited
Synthesizing lessons from tutorial examples to enumerate guidance and best-practices for working Xarray geospatial data cubes.