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Methods of Time Series Analysis in Remote Sensing

Module 1 covers a range of basic principles and methods of remote sensing time series analysis that are applicable to data from different platforms and sensors. While the hands-on parts of the module focus on optical satellite imagery, many of the approaches you learn here will be helpful for working with other data types (such as close-range imagery time series or time series of 3D point clouds from photogrammetry or laser scanning). Hence, in this module you will learn about:

  • Principles of time series in general and remote sensing time series in specific
  • Major Earth observation missions, data archives and access options
  • Strategies and computing facilities for large remote sensing time series, including introductions with Python and with the Google Earth Engine
  • Classification approaches and methods for remote sensing time series
  • Trajectory-based views on remotely sensed variables
  • Approaches to land surface monitoring and change detection
  • Fusion of multi-modal remote sensing time series
  • Possibilities and best practices for validating your analyses with remote sensing time series

Structure overview

This module covers the following themes:

Prerequisites to perform this module

The following skills and background knowledge are required for this module:

  • Basics of statistics
  • Basics of geoinformation systems and handling raster/vector data
  • Some familiarity with QGIS
  • Basic programming skills in Python
  • Principles of remote sensing

Software

For the practical parts of this module (excercises and tutorials), you will need:

  • QGIS - In some of the Module 1 excercises, the graphical user interface of QGIS is used for visualization of data or for digitizing polygons (used to label training samples).
  • Google Earth Engine - For several tutorials and excercises of Module 1, a registered user account for this service is required (create one here, if you don't have one).
  • Python - You can use the package and environment management system Conda and the etrainee_m1.yml file to install the packages needed for the tutorials and excercises into a fresh Python environment. The yaml file can be downloaded here: etrainee_m1.yml.

Toolbox intro

Before you start with Theme 1 of Module 1, we recommend that you go through the Toolbox intro. There, you will learn how to

  • set up your working environment with Python and all required packages
  • create, modify and run interactive Jupyter Notebooks containing Python code
  • use Python for basic processing steps and visualization of geospatial data

Practical parts of this module (overview)

Module 1 contains at least one practical part (tutorial or excercise) per theme. The tutorials are linked as separate documents in the respective sections of a theme. Many of them are provided as Python Jupyter Notebooks and, if you download them, you can explore them interactively.

Mandatory parts

It is recommended to go through the following tutorials and excercises (one per theme). They are focusing on image time series analysis with Python's xarray package and the Google Earth Engine (GEE) Python API, and they (partly) build upon each other:

Optional parts

In case you want to explore further topics and methods, there are more tutorials and excercises available:

Data credits

Landsat imagery courtesy of the U.S. Geological Survey / Terms of use

Copernicus Sentinel data courtesy of the European Space Agency - ESA / Terms of use

Start the module

... by proceeding to the Toolbox intro or (if you are already familiar with Conda, Jupyter Notebooks and GeoPython) skip this and

... go directly to the first theme on Principles of remote sensing time series.