The Course is Highly Methods-Oriented
The goal of the course is to teach participants the basic skills needed to work independently to acquire, analyze and visualize data sets derived from a variety of satellite sensors (e.g., SeaWiFS, MODIS, MERIS, VIIRS, OLI on Landsat-8, OLCI on Sentinal-3, AVHRR, SeaWinds, SSM/I and AVISO Merged Altimetry).
Strong emphasis is given to ocean color remote sensing and to the use of NASA’s SeaDAS software to derive mapped imagery of geophysical parameters (e.g., chlorophyll or CDOM) from raw SeaWiFS, MODIS, MERIS and VIIRS data obtain through the Ocean Color Web Data Server. The course also covers image analysis methods to work with satellite imagery of 1) sea surface temperature, 2) ocean wind speed and 3) sea surface height.
An important feature of the course is to develop good Python programming skills that are needed to effectively make use of satellite image data to answer important oceanographic questions.
An equally important feature of the course involves running SeaDAS with pre-written Python scripts to batch process large quantities of raw SeaWiFS, MODIS, MERIS and VIIRS data to obtain high resolution mapped images of chlorophyll and other ocean-color related products.
Background lectures will cover the fundamentals of bio-optics, pigment algorithms, primary production algorithms and, to a lesser extent, the underlying physical principals leading to the measurement of sea surface temperature, ocean wind speed and ocean topography. See the course syllabus link for more details on the topics covered in this course.
The class it typically comprised of about 80% graduate students, 15% post-graduate professionals and 5% undergraduates. Participants come from universities, NOAA/NASA facilities and private companies. There is often fairly strong international representation.
About 80% of past course participants have had no prior programming experience so the programming effort will begin at a basic level. More advanced students having some prior programming experience will be given more challenging programming problems from which to learn.
Great effort is made to make the course fun and low-stress while at the same time challenging the participants to learn a very large amount of material in a short amount of time.