Master’s degree1,500 h · Online · 11 modules

Master's in Space Oceanography

This master's brings space technology and marine science together so you can understand the ocean on a global scale. You will learn to analyze satellite data, model ocean processes and study ocean-atmosphere interaction, using remote sensing, GIS, numerical modeling and artificial intelligence to monitor sea surface temperature, salinity, currents and productivity, interpret satellite imagery and apply algorithms that extract reliable information on the state of the ocean and its role in the global climate.

Duration1,500 h

ModeOnline

Start1 December

Modules11

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About the program

What is the Master's in Space Oceanography?

This master's covers the full chain of space-based ocean science. You will start with the physics of satellite remote sensing, active and passive sensors, the electromagnetic spectrum and spatial, temporal and spectral resolution; move on to atmospheric correction, radiometric calibration and multispectral and hyperspectral image processing; retrieve sea surface temperature, chlorophyll, turbidity, suspended matter and ocean color; study eddies, thermal fronts, upwelling and sediment plumes; fuse optical, SAR, LiDAR, altimetry and HF radar data; build predictive models with machine learning, CNNs, RNNs and transformers for extreme events; integrate satellite products with numerical and data assimilation models, GIS and early warning systems; and work with the missions and data infrastructure of the field, from Copernicus Sentinel, SWOT, Jason, SMOS and SMAP to cloud platforms, big data and FAIR principles.

What sets this master's apart: it treats the satellite as a scientific instrument for understanding how the ocean works, not only as a source of images, so every technique leads to a physical or ecological process, from coastal dynamics and harmful algal blooms to storm surges, coral bleaching and climate variability; it combines classic remote sensing with multisensor fusion and AI-based forecasting; and it closes with a Final Project that integrates remote sensing and predictive modeling for the sustainable management of marine ecosystems.

Program typeMaster’s degree

ModeOnline

Duration1,500 hours

Modules11

Start1 December

Program highlights

Physics of satellite remote sensing: active and passive sensors, spectral bands, orbits and resolution.
Image processing: atmospheric and radiometric correction, cloud masking, classification and segmentation.
Retrieval of SST, chlorophyll, turbidity, ocean color and dynamic features such as eddies, fronts and upwelling.
Multisensor fusion of optical, SAR, LiDAR, altimetry and HF radar data for coastal and open-ocean monitoring.
AI forecasting with deep learning, CNNs, RNNs and transformers for extreme events and early warning systems.
Space missions and data management: Sentinel, SWOT, Jason, SMOS, SMAP, cloud platforms and FAIR data.

Syllabus

Syllabus of the Master's in Space Oceanography

11 modules. Select a module to see its content.

Module 1

Advanced satellite remote sensing for the dynamic analysis of marine ecosystems and integrated coastal management

1.1. Fundamentals of satellite remote sensing: physical principles, active and passive sensors, electromagnetic spectrum and spatial and temporal resolution
1.2. Advanced satellite data processing: atmospheric correction, radiometric calibration, segmentation and supervised and unsupervised classification
1.3. Detection and monitoring of oceanographic parameters: sea surface temperature, chlorophyll, turbidity, ocean color and phytoplankton concentration
1.4. Multispectral and hyperspectral analysis algorithms for identifying and assessing marine habitats and coastal biomes
1.5. Integration of satellite data with hydrodynamic models to understand coastal dynamics and transport and dispersion processes
1.6. Temporal and spatial monitoring of extreme marine events: red tides, upwelling, river discharge and coastal pollution
1.7. Development and use of geographic information systems (GIS) for the integrated management of marine protected areas and vulnerable coastal zones
1.8. Multisource analysis of satellite sensors (MODIS, Sentinel, VIIRS, Landsat) to assess marine biodiversity and productivity
1.9. Applications in integrated coastal management: spatial planning, environmental impact mitigation and conservation policies based on satellite evidence
1.10. Practical case studies: implementation and evaluation of coastal management plans using remote sensing in areas of high ecological and socioeconomic value

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 2

Multispectral satellite image processing for real-time ocean monitoring and modeling

2.1. Fundamentals of multispectral remote sensing: physical principles, radiative interaction and satellite data acquisition
2.2. Ocean satellite sensors: technical characteristics, spectral bands and spatial and temporal resolution
2.3. Radiometric and atmospheric correction: advanced methods to mitigate distortions in multispectral images
2.4. Digital image processing: filtering, enhancement, classification and spectral segmentation algorithms
2.5. Oceanographic biophysical modeling: multispectral correlation with parameters such as chlorophyll, suspended matter and surface temperature
2.6. Integration of multispectral data with hydrodynamic models for real-time ocean forecasting and monitoring
2.7. Applications in extreme event detection: multitemporal analysis to track red tides, spills and sediment flows
2.8. Implementation of early warning systems based on automated multispectral processing
2.9. Specialized software and GIS platforms for the analysis and visualization of multispectral satellite data
2.10. Case studies and validation: in situ calibration and verification against conventional oceanographic data

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 3

Advanced satellite remote sensing and multispectral processing techniques for the comprehensive monitoring and management of marine ecosystems

3.1. Fundamentals of satellite remote sensing: physical principles of remote detection, radiative interaction and active and passive sensors
3.2. Electromagnetic spectra applied to oceanography: analysis of visible, infrared and microwave bands to identify marine parameters
3.3. Satellite platforms for marine monitoring: technical features of Earth observation satellites, polar versus geostationary orbits, and their use in coastal and ocean ecosystems
3.4. Advanced multispectral image processing: radiometric and geometric calibration, atmospheric correction and multisensor data fusion to increase spatial and spectral resolution
3.5. Models and algorithms for information extraction: detection of chlorophyll, turbidity, sea surface temperature (SST) and biochemical composition from multispectral analysis
3.6. Integration of satellite data with GIS: production of high-precision thematic maps to assess and monitor marine ecosystems and vulnerable coastal zones
3.7. Detection and tracking of critical environmental events: identification and temporal analysis of harmful algal blooms, oil spills and thermal anomalies in coral ecosystems using satellite techniques
3.8. Application of artificial intelligence and machine learning: automatic classification and prediction of ecological states using multitemporal sets of satellite images
3.9. Monitoring of shoreline dynamics and morphodynamic change: use of multispectral and radar sensors to assess erosion, sedimentation and coastal processes under environmental stress
3.10. Practical case studies: application of advanced satellite methods in real marine management projects for sustainable decisions on conservation and resource use

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 4

Multisensor fusion (optical, SAR, LiDAR and high-frequency radar) and AI predictive modeling for operational monitoring, extreme event forecasting and integrated coastal management

4.1. Theoretical foundations of multisensor fusion: optical principles, synthetic aperture radar (SAR), atmospheric LiDAR and high-frequency (HF) radar
4.2. Advanced signal processing: algorithms for the spatial and temporal integration of heterogeneous remote sensing data
4.3. Radiometric and geometric calibration and correction on satellite and airborne platforms to ensure accurate data fusion
4.4. Physical and statistical modeling of ocean variables from multisensor data: surface temperature, salinity, turbidity and coastal current dynamics
4.5. Implementation of artificial intelligence: deep learning and convolutional neural networks for the automatic extraction and classification of patterns in multisensor images
4.6. Development of predictive models for extreme marine events: assessment and anticipation of storm surges, coastal storms and hydrodynamic anomalies
4.7. Integration of operational monitoring systems: real-time data flows, cloud computing platforms and early warning systems in coastal management
4.8. Advanced applications in integrated management: environmental zoning, marine resource planning and human impact assessment using combined datasets
4.9. Real case studies and validation of predictive models with in situ and satellite data: cross-calibration and uncertainty analysis
4.10. International protocols and standards for the transmission, security and storage of multisensor data in space oceanography

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 5

Fundamentals and applications of multispectral satellite sensors for the characterization and dynamic modeling of complex ocean processes

5.1. Physical and spectral principles of electromagnetic radiation in the multispectral range applied to oceanography
5.2. Design and technical features of satellite sensors: spectral, spatial and temporal resolution, radiometric calibration and in situ validation
5.3. Satellite data processing: atmospheric correction, cloud detection and removal, and fusion of multispectral and panchromatic images
5.4. Biophysical and biochemical models derived from multispectral sensors to estimate chlorophyll, suspended matter and sea surface temperature
5.5. Detection and tracking of dynamic ocean phenomena: eddies, thermal fronts, upwelling zones and sediment plumes through multispectral analysis
5.6. Integration of satellite data with numerical and data assimilation models to forecast and simulate complex ocean processes
5.7. Advanced supervised and unsupervised classification techniques to characterize marine habitats and monitor environmental change
5.8. Operational applications: water quality surveillance, red tide forecasting, assessment of human impacts and sustainable fisheries management
5.9. Use of complementary platforms: combining active sensors (LiDAR, SAR radar) with passive multispectral sensors for multifactor analysis
5.10. Current and future challenges in ocean satellite technology: miniaturization, satellite constellations and access to big data for real-time decisions

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 6

Advanced remote sensing technologies and AI predictive modeling for the integrated management of marine ecosystems and ocean processes

6.1. Fundamentals of space remote sensing applied to oceanography: physical principles, acquisition and processing of multispectral and hyperspectral satellite data
6.2. Advanced remote sensing systems: synthetic aperture radar (SAR), satellite altimetry, thermal radiometry and optical spectrometry for ocean monitoring
6.3. Preprocessing algorithms: atmospheric correction, radiometric calibration and georeferencing to ensure the quality and accuracy of ocean data
6.4. Mathematical and numerical models in oceanography: fluid dynamics and modeling of currents, waves and sediment transport
6.5. Integration of artificial intelligence and machine learning for predictive analysis: deep neural networks and supervised and unsupervised learning applied to ocean phenomena
6.6. Development and validation of predictive models for the integrated management of marine ecosystems: simulation of climate change and assessment of biodiversity and environmental risks
6.7. Geographic information systems (GIS) and ocean data visualization platforms: tools for real-time decision-making
6.8. Practical applications in coastal and maritime management: monitoring of protected areas, sustainable fisheries, natural disaster mitigation and pollution control
6.9. International case studies: use of advanced remote sensing and AI modeling in programs for the conservation and responsible use of the ocean
6.10. International regulations, standards and protocols for satellite data and their use in public marine policy

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 7

Advanced satellite remote sensing technologies and predictive modeling for the sustainable management of marine ecosystems

7.1. Fundamentals of satellite remote sensing: physical principles of radiation-ocean interaction and Earth-orbiting satellite platforms
7.2. Advanced instrumentation for marine observation: hyperspectral sensors, synthetic aperture radar (SAR), laser altimeters and multispectral sensors
7.3. Radiometric processing and correction of satellite images: removal of atmospheric noise, cross-calibration and precise georeferencing
7.4. Advanced algorithms for detecting ocean parameters: surface temperature, chlorophyll concentration, turbidity and surface currents
7.5. Predictive modeling of ecosystem dynamics with machine learning and deep learning: neural networks, spatial regression and satellite time series
7.6. Integration of in situ, satellite and numerical model data for a holistic assessment of marine ecosystems
7.7. Spatiotemporal analysis for the early detection of anomalous phenomena: algal blooms, coral bleaching events and point-source pollution
7.8. Development and implementation of early warning systems for the sustainable management and resilience of marine reserves and protected coastal zones
7.9. Specialized GIS tools for the management and advanced visualization of ocean data and predictive results
7.10. Case studies in environmental management and sustainability: assessment of climate change impacts and adaptive strategies based on remote sensing and modeling

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 8

Integration of multimodal satellite systems and artificial intelligence algorithms for real-time monitoring and predictive management of extreme ocean phenomena

8.1. Fundamentals and structure of multimodal satellite systems: orbits, constellations and sensor types for ocean observation
8.2. Principles of satellite data acquisition and processing for ocean monitoring: synthetic aperture radar (SAR), altimetry, spectroscopy and passive sensors
8.3. Design and integration of multimodal satellite platforms for the synergistic collection of physical, chemical and biological ocean variables
8.4. Advanced architectures for real-time data transmission and assembly: satellite communication protocols, multiple access and latency reduction
8.5. Introduction to artificial intelligence (AI) algorithms applied to space oceanography: supervised, unsupervised and deep learning for interpreting multimodal data
8.6. Development and training of predictive models based on convolutional (CNN) and recurrent (RNN) neural networks and transformers for the early detection of extreme ocean phenomena
8.7. AI-based fusion of multisensor and multimodal data to improve spatial and temporal resolution in ocean monitoring
8.8. Intelligent geospatial systems: integration of satellite data with numerical ocean models and in situ data to generate early warnings
8.9. Implementation of real-time monitoring platforms: dashboard design, data flow automation and advanced visualization
8.10. Predictive management of extreme ocean events: tsunamis, tropical storms, anomalous currents and upwelling phenomena

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 9

Multispectral satellite remote sensing, multisensor data and AI predictive modeling for environmental monitoring, coastal management and extreme event forecasting

9.1. Fundamentals of multispectral satellite remote sensing: physical principles, spectral bands and spatial resolution
9.2. Advanced satellite sensor technologies: comparison of optical, infrared, SAR radar and hyperspectral sensors
9.3. Multisensor data integration: fusion of satellite, in situ and airborne sensor information to improve accuracy and coverage
9.4. Multispectral image processing: radiometric and atmospheric correction, calibration, segmentation and feature extraction
9.5. Remote sensing applications in environmental monitoring: detection of chlorophyll, sea surface temperature, turbidity and pollutant tracking
9.6. Predictive modeling in oceanography: fundamentals of coupled numerical models for ocean and atmospheric dynamics
9.7. Implementation of artificial intelligence: convolutional neural networks and deep learning for automatic classification and detection in satellite data
9.8. Decision support systems for coastal management: integration of multitemporal data and spatial modeling for planning and risk mitigation
9.9. Forecasting and monitoring of extreme events: early warnings based on multivariate analysis and artificial intelligence applied to cyclones, storm surges and coastal phenomena
9.10. Advanced case studies: critical evaluation of international satellite remote sensing projects for marine management and natural disaster response

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 10

Space oceanography missions and data management

10.1. History of space oceanography missions
10.2. Copernicus Sentinel missions
10.3. SWOT and Jason missions
10.4. SMOS and SMAP salinity missions
10.5. Calibration and validation with in situ data
10.6. Oceanographic data formats and standards
10.7. Cloud-based access and processing platforms
10.8. Management of large data volumes
10.9. FAIR principles for open data
10.10. Space data exploitation project

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 11

Final Project

11.1. Fundamentals of satellite remote sensing in oceanography: physical principles of data acquisition, passive and active sensors, spatial, temporal and spectral resolution, radiometric calibration and atmospheric correction
11.2. Advanced satellite image processing: algorithms for detecting chlorophyll, sea surface temperature (SST) and suspended matter concentration, using platforms such as Sentinel, MODIS and VIIRS
11.3. Multisensor data integration: fusion of optical, synthetic aperture radar (SAR) and altimetry information for multidimensional ocean characterization
11.4. Predictive numerical modeling of marine dynamics: mathematical foundations, Navier-Stokes equations applied to geophysical fluids, hydrodynamic modeling and coupling with satellite observations
11.5. Simulation and validation of marine ecosystem models using satellite data: modeling of primary productivity, biogeochemical cycles and response to climate variability
11.6. Geographic information systems (GIS) applied to space oceanography: management, spatial analysis and advanced visualization of integrated ocean data for decision-making
11.7. Methods for the assessment and sustainable management of marine ecosystems based on remote sensing and predictive modeling: ecological indicators, detection of vulnerable zones and environmental impact analysis
11.8. Implementation of technology platforms for real-time monitoring: integration of geostationary and polar satellites, coastal stations and early warning systems
11.9. Development of the applied project: design and execution of a final project that integrates remote sensing and modeling for sustainable environmental management in marine protected areas
11.10. Ethical, legal and regulatory aspects of the use of satellite data and its application in public marine conservation policy

At this stage

136 hestimated workload

11 wkweeks at your pace

Plan your pace

Duration and format of the Master's in Space Oceanography

It is 1,500 hours. Tell us how many you can study each week and we will tell you when you will really finish.

Hours per week

4 h30 h

With 12 h per week

months to finish

125 weeks

weeks in total

This is the pace at which most students finish. It fits well with the installment plan.

Faculty

Academic leadership and faculty

The Navalis teaching team: working professionals who teach and supervise the programs.

Dr. Marina AlcoverAcademic Director and Head of Quality Assurance

Eng. Diego CifuentesSenior Lecturer in Naval Welding and Non-Destructive Testing; Head of QA/QC for Welded Joints

Capt. Javier Abaroa (MCA)Senior Lecturer in Ship Handling, Electronic Navigation (ECDIS/Radar/ARPA) and GMDSS; STCW Compliance and Bridge Resource Management Coordinator

Dr. Pau FerrerSenior Lecturer in Naval Architecture and Simulation; Director of the Hydrodynamics and CFD Laboratory

Dr. Nuria LlobregatSenior Lecturer in Marine Meteorology, SAR and Operational Safety; Navigation Risk Management Coordinator

Eng. Javier BañulsSenior Lecturer in Electrical Engineering, Automation and ETO Engineering; Electronic Integration and Engine Alarms Coordinator

Eng. Sofía MarquinaSenior Lecturer in Materials, Composites and Corrosion Protection; Coordinator of the Advanced Paints and Coatings Line

Eng. Tomás RieraSenior Lecturer in Propulsion, Alternative Energy and Hybrid Systems; Energy Performance and Vibration Coordinator

Objectives

Objectives of the Master's in Space Oceanography

01Model and forecast ocean dynamics by developing advanced numerical models, calibrated with satellite and in situ data, that simulate currents, temperature and salinity and make it possible to anticipate extreme events and their impact on coastal ecosystems.

02Analyze and understand ocean-atmosphere interactions, including the mechanisms of energy, mass and momentum transfer between ocean and atmosphere and their influence on weather and climate prediction.

03Apply remote sensing to study the ocean, identifying and analyzing patterns of sea surface temperature, currents and chlorophyll concentration from satellite data to understand ocean dynamics and their impact on climate and marine life.

04Develop new methods for space-based ocean analysis, implementing advanced geostatistical models that interpolate and predict ocean variables in data-sparse areas by combining remote sensing and in situ data to improve spatial and temporal resolution.

05Manage space oceanography research projects, planning and carrying out sampling and validation campaigns while ensuring data integrity and compliance with scientific protocols.

06Interpret satellite data to understand ocean processes, identifying patterns of surface temperature, currents and primary productivity to infer ocean dynamics and their effects on climate and marine life.

Careers

Career opportunities after the Master's in Space Oceanography

01Research scientistdevelopment of ocean models, analysis of satellite data and publication of scientific results.

02Operational oceanographersea state forecasting, monitoring of extreme events and support for navigation and maritime activities.

03Marine remote sensing specialistapplication of remote sensing techniques to ocean studies and development of image processing algorithms.

04Ocean data analystprocessing and analysis of large ocean datasets and development of visualization tools.

05Environmental consultant or marine resource managerimpact assessment of coastal and marine activities, mitigation design and sustainable resource planning.

06Oceanographic instrumentation technician or ocean science communicatorsupport for measurement campaigns and design of educational programs on the ocean.

Frequently asked questions

Questions about the Master's in Space Oceanography

Large-scale ocean processes observed with space technology, such as sea surface temperature, currents, sea level, salinity, winds and ocean color, to understand ocean dynamics and their interaction with the global climate.

Among others, Copernicus Sentinel, MODIS, VIIRS and Landsat for optical data, SAR sensors, the Jason and SWOT altimetry missions and the SMOS and SMAP salinity missions.

Yes, a working knowledge of Python or MATLAB is recommended, since you will process satellite imagery, build models and apply machine learning algorithms throughout the program.

Several modules apply machine learning and deep learning, including CNNs, RNNs and transformers, to classify satellite images, fuse multisensor data and forecast extreme ocean events.

No. Remote sensing is combined with numerical ocean modeling, data assimilation, GIS, early warning systems and the management of large ocean datasets under FAIR principles.

Online, with recorded classes, discussion forums and personalized tutoring, designed for both working professionals and recent graduates.

Through practical exercises with satellite data in each module, such as image processing, parameter retrieval, model validation and case studies, together with the Final Project.

A Final Project that integrates satellite remote sensing and predictive modeling to support the sustainable management of a marine ecosystem, such as a marine protected area.

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Online admission

Admission process

No waiting for a call back. If your documents are in order, you can enroll in the same session.

Entry requirements

Profile: aimed at physicists, mathematicians and engineers, oceanographers and climate scientists, aerospace professionals, environmental consultants and policy makers, and graduates in environmental science or geology who want to apply space technology to the study of the ocean; a degree in physics, mathematics, marine science, aeronautical, telecommunications or civil engineering or a related field is recommended, together with a solid grounding in mathematics, programming (Python, MATLAB) and data analysis.
Language: sufficient command of English to follow the classes and the technical documentation.
Technical resources: a computer able to handle large datasets, with a broadband internet connection and the remote sensing and GIS software indicated at the start of the program.
Admission: online application, review of your profile and a short interview with the academic team.

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