Processing Copernicus Sentinel-2 Data Using Python

Learn how to use Python to access the Copernicus Dataspace Ecosystem, and process and analyze Sentinel-2 imagery

What you'll learn

Create a Copernicus Open Dataspace account

Install and setup Anaconda for Python development

Search, filter and download Copernicus Sentinel-2 data using the Python API

Analyze and process Copernicus Sentinel-2 data

Requirements

No programming experience or other prerequisites needed. You will learn everything as part of the course.

Description

The use of remote sensing data is growing, with the need to use such data for many applications ranging from the environment to agriculture, urban development, security and disaster management. This course is intended for beginners who would like to make their first acquaintance with remote sensing data, and learn how to use freely available tools such as Python to analyze and process freely available imagery from the Copernicus Sentinel-2 mission. No prerequisite knowledge is required.Through a step-by-step learning process, this course starts off with setting up a Copernicus Dataspace Ecosystem account, and installing a Python environment. Python is then used to make use of the Copernicus Dataspace Ecosystem API to search for, filter and download Sentinel-2 products. Also using Python, these products are then opened and the corresponding optical and near-infrared bands are analyzed and processed to create and RGB composite image, as well as calculate commonly used indices such as NDVI and NDWI. Basic correction methods such as normalization and brightness correction are also introduced.At the end of the course, a bonus application is presented, where a machine learning technique (clustering) is used to partition the content of the Sentinel-2 product into various categories to obtain an estimate for a land cover map.

Overview

Section 1: Introduction

Lecture 1 Welcome and introduction

Lecture 2 Setting up a Copernicus account

Lecture 3 Installing and setting up a Python environment

Section 2: Downloading Copernicus data

Lecture 4 Authentication

Lecture 5 Searching and filtering Copernicus Sentinel-2 acquisitions

Lecture 6 Downloading and unzipping the acquisition

Section 3: Opening and processing a Sentinel-2 acquisition

Lecture 7 Opening a Sentinel-2 acquisition using rasterio

Lecture 8 Analyzing the bands and creating an RGB composite image

Lecture 9 Normalization and brightness correction

Lecture 10 Creating an image subset using lat/lon coordinates

Section 4: Commonly used indices: NDVI and NDWI

Lecture 11 Computing and rendering NDVI

Lecture 12 Computing and rendering NDWI

Section 5: Exporting data

Lecture 13 Exporting to different file formats

Section 6: Additional content: land cover mapping

Lecture 14 Obtaining a land cover map using KMeans clustering

Section 7: Conclusion

Lecture 15 Conclusion

New remote sensing data users,Experienced GIS software users who want to start using Python to process remote sensing data,Existing Copernicus data users who want to learn how to use the new Data Space Ecosystem