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Ocean colour radiometry is a technology, and a discipline of research, concerning the study of the interaction between the visible electromagnetic radiation coming from the sun and aquatic environments. In general, the term is used in the context of remote-sensing observations, often made from Earth-orbiting satellites. Using sensitive radiometers, such as those on-board satellite platforms, one can measure carefully the wide array of colors emerging out of the ocean. These measurements can be used to infer important information such as phytoplankton biomass or concentrations of other living and non-living material that modify the characteristics of the incoming radiation. Monitoring the spatial and temporal variability of algal blooms from satellite, over large marine regions up to the scale of the global ocean, has been instrumental in characterizing variability of marine ecosystems and is a key tool for research into how marine ecosystems respond to climate change and anthropogenic perturbations.
Source: Wikipedia Ocean Color
The "MERIS Algal bloom detection using BEAM" tutorial uses Terradue's Developer Cloud Sandbox service to implement a simple datapipeline workflow based on a band arithmetic expression to MERIS Level 1 data to visualy detect algal blooms and bin these products.
The tutorial builds upon the on the work done in the context of the ESA Cat-1 project “Production of global MERIS MCI composite images for detection of plankton blooms and other events” submitted by Dr. Jim Gower.
The tutorial is designed to run on a Developer Cloud Sandbox, that provides Earth Science data access services, and assistance tools for a user to implement, test and validate an application. It will go through two different lifecycle modes: Sandbox mode and Cluster mode. Used in Sandbox mode (single virtual machine), the service supports cluster simulation and user assistance functions in building the distributed application. Used in Cluster mode (collections of virtual machines), the service supports the deployment and execution of the application with the power of distributed computing processing over large datasets (leveraging the Hadoop Streaming MapReduce technology).
To get started with the tutorial implementation, jump on our Readme for this application.