. . . . . . "understand data model integration using satellite data"@en . . . . . . . . "select optimal object / pixel / time based mapping method for a given problem"@en . . . . . . . . "Model selection based on envisaged application and data availability/required"@en . . . . . . . "Understand basic concepts of modelling, model parameter and boundary conditions."@en . . . . . . . "Evaluate model performance and explain the sources of error and uncertainty "@en . . . . . . . . . . "model performance assessment"@en . "Modeling & Mapping: LU4"@en . . . . . . . . . . . . . "Model and model selection"@en . "Modeling & Mapping: LU1"@en . . . . . . . . . "satellite data-model integration"@en . "Modeling & Mapping: LU3"@en . . . . . . . . . . "Optimal modelbased mapping"@en . "Modeling & Mapping: LU5"@en . . . . . . . . "Model parameterisation"@en . "Modeling & Mapping: LU2"@en . . . . . . "Functional design"@en . "open science"@en . "reporting"@en . . "https://ltb.itc.utwente.nl/page/792/concept/152974" . . . . . . . . . "data driven models"@en . . . . "data availability"@en . . . . . . . . "map to model"@en . . . "https://ltb.itc.utwente.nl/page/792/concept/152732" . . "Remote sensing"@en . . . . . . . . . . . . . . . . . . . . . "model parameterisation"@en . . . "https://ltb.itc.utwente.nl/page/792/concept/152741" . . "Statistics"@en . . . . . . . . . . . . . "object based mapping method"@en . . . . . . . . . . . . . . . . . "presentation"@en . . . . . . "model performance"@en . . . . . . . "model selection"@en . . . . . . . . "model types"@en . . . . . . "evaluation"@en . . . . . . . . "application"@en . . . . . . . . "sources of error"@en . . . . . . . "time series"@en . . . . . . "use of model"@en . . . . . "mapping"@en . . . "https://ltb.itc.utwente.nl/page/792/concept/152957" . . "Satellite data"@en . . . . . . . . . . "spatio-temporal modelling"@en . . . . . . . "model"@en . . . . . . "model parameter"@en . . . . . . "model boundary conditions"@en . . . . . . "map results"@en . . . . . . "map boundary conditions"@en . . . . . . "pixel based mapping method"@en . . . . . . "data model integration"@en . . . . . . "model to map"@en . . . . . . "problem"@en . . . . . . "sources of uncertainty"@en . . . . . . "parameters"@en . . . . . . "modelling concepts"@en . . . . . . "model examples"@en . . . . . . . . "boundary conditions"@en . . . . . . "time based mapping method"@en . . . . . . "spatio-temporal mapping"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152942" . . "Data requirements"@en . . . . . . . . . . . . . . "data integration"@en . . . . . . . . . . . . . . "Natural Mapping & Management Generalist"@en . . . . . . . . . . . . . "Natural Modelling Vegetation Specialist"@en . . . . . . . . . . . . . "Water and vegetation modelling and management (WRS+NRM"@en . . . . . . . . . . . . . "Natural Monitoring Vegetation Expert"@en . . . . . . . . . . . . . "Natural Mapping & Modelling Conservationalist"@en . . . . . . . . . . . . . "Water and Health modelling and management (WRS+EOS)"@en . . . . . . . . . . . . . "Geological Remote Sensing/Earth Resources Security"@en . . . . . . . . . . . . . "Ocean, coastal – delta and inland water modelling and management"@en . . . . . . . . . . . . . "Inland water resources modelling and management"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "M-GEO 5.0"@en . . . . . . . . . . . . . . . . . . . . . . . . . "Resources Security"@en . . . "Course"@en . "5.0" . "140.0" . "10.0" . "4.0" . "3.0" . "[CONCEPT] Monitoring of physical and chemical atmospheric, land and ocean variables with remote sensing requires an understanding of Earth’s ecosystems. Understanding of systems can be based on expert knowledge, experimental relations or physical relations, and this understanding can be captured in a descriptive model. Models are to understand, detect, predict, and describe interactions within and between ecosystems and the atmosphere across scales that range from local to global.\n\nRemote sensing can be used for parameter input in models, but also for spatial and temporal interpolation or extrapolation. This course provides an introduction to knowledge-driven, data-driven and physical modelling, starting with appropriate model selection given a specific problem or data availability. The course therefore deals with basic concepts and boundary conditions. Much emphasis is on integration of remote sensing observations into models, and selecting optimal object / pixel / time based mapping method for a given problem "@en . "Modeling & Mapping"@en . "Modeling & Mapping"@en . "Modeling & Mapping"@e