. . "201800313" . "WRS_0005" . "7"^^ . "196"^^ . "10"^^ . "2023-07-06T22:00:00Z"^^ . "f2f" . "Learning outcomes 1, 2, 3: Participatory teaching with targeted individual assignments\nLearning outcome 4: Tutorial training and supervised practical\nLearning outcome 5: Group work supervision, question & answer sessions"@en . . . . . . . . "4"^^ . "4" . "2B " . "2023-04-23T22:00:00Z"^^ . "This course will focus on the combined use of satellite and in-situ observations and models for environmental monitoring of terrestrial and aquatic ecosystems. Current satellite and data technology permit observation and quantification of energy and water cycle components. Carbon, primary productivity in ecosystems and greenhouse gas emissions can also be monitored from space. The course will address the challenge of understanding how energy, water and carbon cycles interact and are coupled in ecosystems and at the boundaries between land, water, and atmosphere. Methods for retrieval of radiation, water and biogeochemical variables from satellite data will be reviewed, and an introduction to the use and evaluation of currently available satellite data related to the water, energy and the biogeochemical (BGC) cycles will be given.\nSimulation models of soil - vegetation (e.g. agriculture) and aquatic systems (e.g., lakes, wetlands and coastal zones) will be used for analysis, interpretation and systems modelling of water, energy and biogeochemical processes. Field work and visits to one or more of ITC’s in-situ monitoring sites (in urban, forest, coastal estuarine, and marine locations) are foreseen."@en . "Water and Carbon Dynamics in Ecosystems"@en . . "Water and Carbon Dynamics in Ecosystems"@en . "Water and Carbon Dynamics in Ecosystems"@en . . . "Learning outcome"@en . . "Perform a coupled water, energy, carbon dynamics analysis on an area of interest, using a blend of satellite, in-situ and global atmospheric and weather forecast model datasets."@en . "Perform a coupled water, energy, carbon dynamics analysis on an area of interest, using a blend of satellite, in-situ and global atmospheric and weather forecast model datasets."@en