Water contamination weakens and destroys natural systems that support human health, food production, and biodiversity.
Managing water quality is a great challenge simply because many factors are contributing; the way we use water, practices that threaten water quality, the different pollutants and their sources and the forcing hydrology. Knowing the processes of these factors affecting water quality is not sufficient. It is essential to monitor these factors to understand how they combine and interact for different regions.
Current monitoring practices by water managers depend largely on labour-intensive measurements, which provide only a limited insight into the spatial and temporal distributions. On the other hand, Earth Observation technology offers a significant benefit due to its synoptic coverage and repeated surveillance. However, the added value of Earth Observation data is determined, primarily, by the ability to convert these data into accurate information on water quality variables.
This course will increase your knowledge and technical skills in using Earth Observation data to derive water quality indicators and assess their accuracy.
For whom is this course relevant?
Tertiary: at graduate and post-graduate levels.
- MSc
- PhD candidate
What is the course content?
The course builds upon the quartile one, the core modules, of the Master's Geo-information Science and Earth Observation and the Water Resources and Environmental Management 2.0 courses and contains four interwoven learning modules. The first three learning modules form the centre of this course, whereas the last module comprises a closure to this course:
- Basic principles of Remote Sensing of water quality
- Earth Observation data access and handling with Google Earth Engine (GEE) using Python in Jupyter Notebooks
- Earth Observation-based water quality mapping (turbidity and algae indicators in addition to oil spill mapping)
- End of course assignment is a case study whereby the participants apply the newly gained knowledge and integrate different Earth Observation data to produce water quality maps of areas of interest. Successful completion of the assignment is mandatory to receive study-load equipment certificate.
What is the programme structure?
The course follows the filliped classroom technique: the students are kindly requested to review the online videos before the live online sessions. During the live online sessions, the lecturers will answer questions, solve the exercises related to the concepts presented in the video lectures, and explain the Python-GEE exercises. The teaching method is based on learning-by-doing and practice. Sets of exercises, therefore, support each lecture. There are two types of activity in this course:
- Following the video lectures and attempting to solve the exercises embedded in the lectures. These exercises are paper (or Excel)-based, whereby the participants can enhance the learning experience of the physical concepts, e.g., solve simple problems or derive a value by applying sets of equations.
- Attend the live online sessions:
- ask questions and carry out discussions to enhance their understanding of the biophysical concepts presented in the video lectures
- solve the exercises embedded in the video lectures
- engage in the skill development exercises: these exercises use Python in the Jupyter Notebooks environment. They focus on providing the participant with the practical competency to process Earth Observation data with Google Earth Engine. indicators.
What will be achieved?
After completing this course your will be able to:
- recall pollution sources and pollutants in the marine environment
- summarise concepts of remote sensing of water quality
- work in a cloud computing environment using Python
- derive water quality indicators from Earth Observation data using in Google Earth Engine
Why this course?
Do you want to develop the skills necessary to use big satellite data to detect water pollution and monitor water quality? Then, this online course is what you need.
The high-level video lectures, self-study, online discussions and Python exercises allow for an enjoyable online social experience. Live-streamed talks and visualised tutorials using state-of-the-art analysis tools will give you the skills to complete your learning experience on real-life study cases.
Career perspectives
Acquire the skills to use Earth Observation data to monitor water quality and detect pollution in coastal and inland waters.
Online learning, what is it like?
The course offers:
- High-level video lectures, self-study and online discussions
- Live-streamed talks and visualised tutorials using state-of-the-art analysis tools.
In this respect, the course is mainly based on self-study and attending the discussion sessions, with an expected study load of about 4 to 6 hours a week.
About your Diploma
Upon successful completion of this course you will receive a Certificate which will include the name of the course.
Along with your Certificate you will receive a Course Record providing all the subjects studied as part of the course. It states: the course code, subject, ECTS credits, exam date, location and the mark awarded.
If you decide to follow a full Postgraduate or Master's course at ITC, and after approval of the Examination Board, you will be exempted from the course(s) you followed successfully as an online course.
Admission requirements
- A STEM diploma at a BSc level
- Affinity with statistical and mathematical analysis
- Affinity with python coding is a plus
Academic level and background
Applicants for an online course should have a Bachelor degree or equivalent from a recognised university in a discipline related to the course, preferably combined with working experience in a relevant field.
Documentation
The faculty accepts transcripts, degrees and diplomas in the following languages: Dutch, English, German, French and Spanish. It is at the discretion of the faculty to require additional English translations of all documents in other languages as well.
Language skills
Success in your studies requires a high level of English proficiency. Therefore, prospective students with an international (other than Dutch) degree must meet the English language requirement. As proof that you meet this requirement, you will be asked in the application procedure to upload one of the requested language certificates:
- IELTS (academic) with an overall band score of at least 6.0 (with a minimum sub-score of 6.0 for speaking and writing) and certificates not older than two years.
- TOEFL iBT (internet-based) with an overall score of 80 (with a minimum sub-score of 20 for speaking) and certificates not older than two years. Please note that the University of Twente does not accept the MyBest scores of the TOEFL test.
- Cambridge C1 Advanced Formerly known as; Cambridge English Advanced (CAE), obtained with an A, B or C grade.
- Cambridge C2 Proficiency Formerly known as; Cambridge English Proficiency (CPE) obtained with an A, B or C grade.
Only these internationally recognised test results are accepted. Without a valid certificate, we cannot process your application.
Other requirements
- Ensure you have obtained a valid English test result before the application deadline. If your application is accompanied by a language test score report with a test date after our application deadline, we will not process it. Therefore, make sure to do the test in advance, as it will take time for you to get the official certificate.
- When applying for a scholarship, the language requirements may be different because scholarship providers may have different requirements.
Exemptions
You are exempted from the English language requirement if you hold:
- a relevant bachelor's degree from an accredited academic institution in the Netherlands
- if you are a national of one of the countries in this list (PDF)
- a three-year bachelor's degree in Australia, Canada (English-speaking part), Ireland, New Zealand, UK or USA. When your awarding institution is in one of these countries, but your teaching institution was not, you are not exempted. The same rule applies to distance (online) education.
Computer skills
To follow online education you must have basic computer experience, regular access to internet, and e-mail. For some courses, additional computer skills are required (see description of specific course).
Technical requirements online education and assessment
For online education, we formulated guidelines to guarantee optimal performance. For online oral exams and proctoring during online assessments, the webcam and headset requirements need to be met.
GIS and remote sensing
Most online courses, except for the introductory course, require knowledge of, and skills in, working with GIS and/or digital image processing of remotely sensed data.
Candidates are asked to provide proof of identity during the registration process.