Time Series Analysis using Google Earth Engine and Google Colab

Introduction/Earth Engine Background

Google Earth Engine is a powerful and useful platform with many built-in tools to run geospatial analyses using Google's servers and computational infrastructure. Earth Engine hosts a number of datasets of various categories and applications including selections of Landsat collections from 1979 to present, Sentinel collections, MODIS collections, and a host of various data including DEMs, crop inventories, several radiance sensors, and many more vector and raster datasets. This data can be accessed, queried, and processed within a short timeframe and allows a user to select and process data into workable products within minutes. Google Earth Engine relies on user scripting to query datasets and process data into charts, graphs, maps, animations, applets, and any available products that Google's API offers. This tutorial will assume that you have an understanding of Python scripting and GIS experience, however all code will be provided. Earth Engine offers a 'Code Editor' and 'Explorer' to find, access, and manipulate data. The Code Editor is used in strictly JavaScript API/language, an example of which can be seen at Supervised Classifications using Google Earth Engine by Loucas Diamant-Boustead. Rather than using the Code Editor exclusively, Google also offers a Python API within the Google Colab environment. Colab allows for instant Python writing and execution within a web browser, with little setup required. There are plenty of Python libraries that can be used to assist in geospatial analysis, with the ability to create, query, and mutate dataframes (like Excel), perform spatial joins, perform statistical analysis, and to map and plot trends using Earth Engine datasets.

This tutorial will focus on using Google Colab as a platform to perform Earth Engine analyses. Python functions and processes will be displayed as code and explained at a basic level, with a focus placed on the meaning/reasoning behind the code, rather than a full explanation of how Python scripting works. Basic levels of knowledge and understanding of GIS, statistics, and Python is highly recommended for this tutorial. The subject of this walkthrough will focus on Google Earth Engine's Sentinel 5-P TROPOMI dataset, which is offered at a level 3 (pre-processed) product. Because it is pre-processed, its data products can be used directly to perform analysis. Google Earth Engine's Colab API will be utilized to analyze changes in atmospheric nitrogen dioxide (NO2) during the 2020 COVID pandemic (specifically, their related lockdowns) using TROPOMI products. An example of both statistical/quantitative analysis and visual/qualitative analysis will be provided and walked through.