[Image credit: ESA / Astrium GmbH]
Welcome to the fascinating world of Earth observation!
Every point on the Earth is viewed from space several times per day! To make the most of this technological revolution, we must invest in know-how in handling and interpreting the massive data flow. Earth observation (EO), or the gathering of information about our planet’s physical, chemical and biological systems through the analysis electromagnetic data recorded by spaceborne and airborne instruments, is a rapidly growing discipline. In addition, most global environmental issues require global data sets which are only available through satellite instruments. The rapid expansion of the Earth observation sector urgently calls for experts who have sophisticated skills in processing and interpreting the data collected through the missions.
Teacher-in-charge: Miina Rautiainen (firstname.lastname@example.org)
PREREQUISITE: To attend this course, you must have completed either "Photogrammetry, laser scanning and remote sensing" (GIS-E1040) or "Earth observation" (ELEC-E4510).The course is organized fully online and starts on Tuesday 20 April 2021 at 10.15 am. All course participants are expected to attend the first session!
Course dates: April 20 - June 2, 2020
Credits: 5 credits (~135 hours of work in six weeks)
WEEK 1: THEORETICAL BACKGROUND
The course kick-off is on Tuesday 20 April at 10.15 am. We will go through course practicalities and assessment, and you will be divided into reading groups for an assignment (Assignment #6). During this week, you will study materials related to the theoretical basis of optical remote sensing (i.e., radiative transfer theory). You will review radiometric concepts (which should be familiar to you from your previous remote sensing studies) and apply them in calculations (Assignment #1). This week's podcast is about being self-employed in the Earth observation sector.
WEEK 2: MODELS
You will study materials related to spectral modeling and physically-based reflectance models. You will apply a simple physically-based reflectance model to simulate multispectral satellite data for a forest area (Assignment #2). This week's podcasts are about Landsat's international partners and open training data in remote sensing.
WEEK 3: INTERNET RESOURCES & CLOUD COMPUTING
You will learn about the wealth of online resources available for Earth observation enthusiasts and experts (Assignment #3). You will also learn to use cloud computing (Google Earth Engine) in the analysis of satellite data (Assignment #4). This week's podcasts are about using satellite data in a developing country and the open data policy of Landsat.
WEEK 4: SPECTRAL MEASUREMENTS
You will learn about analyses related to hyperspectral data, and how spectral libraries can be collected as part of field surveys (Assignment #5). This week's podcasts are about time series analysis of satellite data and the new Harmonized Landsat Sentinel (HSL) product.
WEEK 5-6: APPLICATIONS
You will learn about the role of satellite remote sensing in climate change research, with a focus on the global radiation budget and albedo. You will become acquainted with scientific literature on remote sensing (Assignment #6). You will participate in group work related to scientific research in remote sensing (related to Assignment #6 ).
LIST OF ASSIGNMENTS
The assignment will introduce you to basic calculations in spectral field measurements and will help you understand the relationships between different radiometric units commonly applied also in satellite and airborne remote sensing. (max 10 points)
The assignment will introduce you to spectral modeling using a physically-based reflectance model. You will learn how to simulate multi- and/or hyperspectral satellite data, and will also get experience in implementing a simple spectral simulation model. (max 20 points)
The assignment will introduce you to online resources and tools available for remote sensing specialists. You will answer a few questions related to specific online tools and resources. (max 10 points)
The assignment will introduce you to the use of cloud computing (Google Earth Engine) in e.g., time series analysis of global satellite products for extensive geographical areas. You will use your creativity and scientific research skills to study environmental changes based on optical satellite data. (max 20 points)
The assignment will provide you background information on field work in remote sensing and the use of a spectrometer in outdoor conditions. You will analyze a spectral library from Otaniemi. (max 20 points)
Assignment #6Podcast discussions: During weeks #1-4, you will also listen to professional remote sensing related podcasts. When we meet, we will have discussions about the podcasts and what you thought about them! You can earn bonus points by participating in the group discussion.
The assignment will introduce you to applications of spectral data in scientific literature. You will read a scientific article in small groups and prepare a presentation to explain the topic to your fellow students. (max 20 points)
Deadlines for assignments:
Assignment #1: DL Sunday 25 April
Assignment #2: DL Tuesday 4 May
Assignment #3: DL Sunday 9 May
Assignment #4: DL Sunday 16 May
Assignment #5: DL Sunday 23 May
Assignment #6: DL Tuesday 25 May (seminar presentations)
The course assessment is based on six assignments. This means that you need to dedicate a considerable amount of time to assignments during the course -- the expected workload of the course is 135 hours. The assignments have been designed so that they develop different skills Earth observation experts need: writing skills, data analysis skills, modeling skills, critical thinking skills, professional discussion skills, presentation skills, group work skills, … A minimum of 50 points (and all assignment reports handed in) is required to pass the course. There is a possibility to earn bonus points from small extra assignments and podcast discussions. If you hand in an assignment late, five points per day will be deducted from the points you would have obtained had you handed in the assignment on time.
Hyperspectral Data Analysis in R (the new hsdar package by Meyer & Lehnert, 2020)
"An R software package which focuses on the processing, analysis and simulation of hyperspectral (remote sensing) data. The package provides a new class (Speclib) to handle large hyperspectral datasets and the respective functions to create Speclibs from various types of datasets such as e.g., raster data or point measurements taken with a field spectrometer. "
Useful reading materials
"Computer processing of remotely-sensed images". 2011. By Mather & Koch. Wiley & Blackwell publication. 434 p. (Available as e-book in Aalto. A good basic text book.)
"Remote sensing of the environment". 2014 (or older editions). By Jensen. Pearson publications. (A good basic text book.)
"Remote sensing of vegetation: principles, techniques, and applications". 2010. By Jones & Vaughan. Oxford University Press. 353 p. (An excellent book for those interested in vegetation!)
"Hyperspectral remote sensing: principles and applications". 2008. By Borengasser, Hungate & Watkins. 119 p. (A basic text book for beginners.)
"Quantitative Remote Sensing of Land Surfaces". 2004. By Liang. Wiley & Sons, Inc. Publication. 533 p. (A more advanced text book.)
"Advances in Land Remote Sensing: System, Modeling, Inversion and Application". 2008. By Liang (ed.). Springer Science+Business Media. 497 p. (A more advanced book / collection of review articles by scientists.)
Scientific journals in remote sensing
If you are interested in the most recent developments in remote sensing data & methods, you need to start following scientific journals. The five most highly ranked scientific journals (according to ISI Thomson Reuters, latest listing) in this field are
1) Remote Sensing of Environment
2) ISPRS Journal of Photogrammetry and Remote Sensing
3) International Journal of Applied Earth Observation and Geoinformation
4) IEEE Transactions on Geoscience and Remote Sensing
5) Remote Sensing
Earth observation and sustainability
Free webinars & other self study materials