SOES6027 Global ocean monitoring
It will cover the techniques and data products used in the application of remote sensing to global ocean monitoring, and will include case studies which may vary from year to year. Techniques and data products: • Introduction to ocean monitoring: types of applications, operational requirements, international treaties/conventions/policies on global ocean monitoring. • International law and global monitoring. • Overview of available satellite techniques/products. • Space/time resolution and coverage. • Production of datasets: merging data from different sensors, (Inter)Calibration and validation, particularly for long time series, dealing with e.g. bias towards cloud-free conditions. • Use of other types of data (in situ, model). • Data processing for extracting signals; filters, transforms. • Synergy. Case Studies: • A small number of Case Studies, developed both during lectures and in the practicals. These may vary from year to year, but will cover topics such as: o Regional indicators - ENSO, NAO; o Primary productivity (carbon-cycle related); o Climate change; o Water quality and pollution.
Aims and Objectives
• To provide a grounding in the remote sensing techniques used in global ocean monitoring. • To provide an understanding of the data products and specific data characteristics required for this application, in particular the construction of long time series and global datasets from different missions. • To provide an awareness of the advantages and limitations of the use of remote sensing in global ocean monitoring. • To illustrate the role of remote sensing in global ocean monitoring through a series of case studies with different data requirements. • To prepare students for working in the operational use of satellite data.
Having successfully completed this module you will be able to:
- Appreciate the need for a multi-sensor approach to global ocean monitoring.
- Appreciate the role of other data complementary to remote sensing, including model and in situ data.
- Understand the importance of data analysis and processing techniques, together with validation and calibration in producing appropriate datasets for ocean monitoring.
- Identify the benefits for global environmental science arising from the availability of satellite datasets.
- Select the data products and characteristics best suited to specific applications.
- Communicating to a non-specialist audience.
- Technical writing.
- Time management.
- Information technology and image processing.
This will be an intensive short course of three weeks duration. Formal lectures: Lectures from NOCS-LSO staff and invited external speakers, through the use of PowerPoint presentations. Lectures are complemented by handout materials. Where relevant, lecturers own research experience in the appropriate fields is brought into the lecturing sessions. Computer practicals: Four computer practical are used to illustrate the concepts covered in the formal lectures. Computer practical use the software package MATLAB. Presentations: Students give presentations on key topics relevant to the course. Essay: Students undertake an essay on a key topic relevant to the course. A wide range of support can be provided for those students who have further or specific learning and teaching needs.
This module may be available to students on both the final year of four-year undergraduate programmes, and to postgraduate taught master's programmes.
Learning and Teaching
Teaching and learning methods
Project (50%): Based on a list provided in the first week of the course. An assessed essay outline will be included, which will be produced by the end of the first week. Tests Learning Outcomes 1,2,3,4,5 Presentation (20%): A 15 minute oral presentation will be given by each student in the final week of the course, the topic will be the same as that covered by the essay. Tests Learning Outcomes 1,2,3,4,5 Practical Report (30%): One practical write-up will be assessed for the practicals. Tests Learning Outcomes 2,3,5
|Total study time||150|
|Presentation (15 minutes)||20%|
|Project report (2500 words)||50%|