Authors: Eligio Maure, Patricia Simwa

6. Data Analysis in Python

6.1. Overview

In this section we introduce ocean colour data retrieval methods, mostly focusing on NASA and JAXA provided products. We then proceed to discuss how to read and visualise the obtained products. Since the book focuses on the use of ocean colour level-2 data, which are in the satellite view geometry, a discussion on how to project these swath images into an Earth map projection is given. Once the data is map projected then spatial and temporal analysis can be done with ease. In the subsequent parts we introduce how to do the temporal and spatial analysis. We close by discussing how the results can be saved to either text file (e.g., csv) or netCDF files.

6.2. Learning Outcomes

• Gain the ability to retrieve and map project a swath imagery

• Gain the ability to extract a pixel values from a swath

• Gain the ability to composite a series of swath imagery

• Perform time series analysis

6.3. How to Proceed

This chapter assumes familiarity with materials of:

• A Brief Introduction to Python

• Introduction to Ocean Colour

• Working with Map Projections in Python

This chapter will start with swath reprojection. Data retrieval, reading and visualisation have been introduced and discussed in Section 4. For the swath reprojection, the following modules are used.

Module Name	Description
Pyresample	Resampling geospatial image data in Python
pyproj	Python interface to cartographic projections and coordinate transformations library

Once the data has been reasample, we can either visualise the resampled data or the swath image using matplotlib. Cartopy helps in plotting projected swath imagery on a map. Some of the possible map projections to use with Cartopy were introduced in Section 5. Additional information can be obtained directly from the Cartopy homepage.