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

Master's in Satellite Ocean Observation

This master's gives you a complete view of marine remote sensing techniques and how they are used in day-to-day ocean monitoring. You will learn to interpret satellite data to track currents, temperature, water quality and marine life, master image processing, data fusion, big data and machine learning pipelines, and build predictive models and early warning services that support the sustainable management of ocean resources, coastal zones, fisheries and marine protected areas in a changing climate.

Duration1,500 h

ModeOnline

Start1 December

Modules11

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About the program

What is the Master's in Satellite Ocean Observation?

This master's is organized around turning satellite observations into operational ocean information. You will begin with the physics and types of satellite sensors, from radiometers and spectroradiometers to SAR, altimeters, hyperspectral sensors and satellite LiDAR, and the orbits and constellations that determine coverage and revisit; move on to preprocessing, calibration, atmospheric correction and validation with buoys, CTD profiles and Argo floats; retrieve sea surface temperature, chlorophyll, salinity, sea level, waves, wind, currents and sea ice; fuse satellite and in situ data and feed them into operational models and early warning systems; build big data infrastructures, cloud and NoSQL storage, real-time streaming pipelines and deep learning models; and apply all of it to coral reefs, upwelling zones, harmful algal blooms, oil spills, fisheries, coastal services, CubeSats and the Copernicus Marine Service.

What sets this master's apart: rather than focusing on ocean physics research, it prepares you to run satellite monitoring as a service, with automated, interoperable workflows, high-performance computing, dashboards and decision support systems for coastal managers, fisheries and conservation agencies; it gives particular weight to big data and AI pipelines for near-real-time detection of anomalies; and it closes with a Final Project that integrates satellite technology and predictive modeling to design indicators and tools for sustainable ocean conservation.

Program typeMaster’s degree

ModeOnline

Duration1,500 hours

Modules11

Start1 December

Program highlights

Satellite sensors and platforms: radiometers, SAR, altimeters, hyperspectral and LiDAR, polar and geostationary orbits, CubeSats.
Preprocessing and validation: calibration, atmospheric correction and comparison with buoys, CTD profiles and Argo floats.
Operational variables: SST, ocean color and chlorophyll, salinity, sea level, geostrophic currents, wind, waves and sea ice.
Multisensor fusion and operational modeling for early warning, coastal services and adaptive fisheries management.
Big data and AI: cloud and NoSQL infrastructure, streaming pipelines, CNNs, random forests, SVMs and HPC workflows.
Applications to coral bleaching, harmful algal blooms, spills, marine protected areas and the Copernicus Marine Service.

Syllabus

Syllabus of the Master's in Satellite Ocean Observation

11 modules. Select a module to see its content.

Module 1

Advanced satellite sensor technologies and digital processing for global ocean monitoring

1.1. Fundamentals of satellite sensors: physical principles, electromagnetic spectra and the properties of radiation in the visible, infrared and microwave ranges for ocean studies
1.2. Types of satellite sensors: classification, operation and specific applications of passive sensors (radiometers, spectroradiometers) and active sensors (synthetic aperture radar, altimeters)
1.3. Emerging technologies in ocean sensors: hyperspectral sensors, satellite LiDAR and combinations of multispectral sensors for an integrated view of the marine environment
1.4. Satellite platforms: detailed analysis of constellations and polar and geostationary orbits and their implications for the timing and spatial coverage of ocean data
1.5. Advanced data acquisition and preprocessing: radiometric and geometric correction, in situ calibration and multisensor fusion techniques to improve the quality and accuracy of observations
1.6. Digital signal processing models applied to marine satellite images: spatial filtering, texture analysis, pattern extraction and automatic detection of ocean phenomena
1.7. Quantitative monitoring of ocean variables: sea surface temperature (SST), chlorophyll concentration, sea surface height, waves, currents and salinity from satellite data
1.8. Interpretation and validation of satellite data through integration with ocean stations, buoys, CTD profiles and numerical ocean models
1.9. Advanced tools and software for satellite image analysis: GIS platforms, machine learning algorithms and artificial intelligence techniques for marine monitoring
1.10. Practical applications and case studies: tracking extreme events such as red tides and oil spills, and monitoring climate change through continuous satellite observation

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 2

Integrated analysis of satellite data for the assessment and sustainable management of marine ecosystems

2.1. Fundamentals of satellite remote sensing: physical principles, sensor types and orbital platforms
2.2. Spectral characteristics of the ocean: analysis of reflectance, fluorescence and thermal emission
2.3. Advanced satellite image processing: atmospheric correction, georeferencing and temporal mosaics
2.4. Detection and quantification of ocean parameters: surface temperature, chlorophyll, turbidity and suspended matter concentration
2.5. Interpretation models for water quality and the monitoring of biogeochemical events
2.6. Integration of in situ data with satellite observations for calibration and validation
2.7. Application of artificial intelligence and machine learning algorithms to satellite ocean data
2.8. Monitoring of marine ecosystems: coral reefs, upwelling zones and benthic habitats through remote observation
2.9. Temporal and spatial analysis for sustainable management: assessment of trends, extreme events and changes in biodiversity
2.10. Design of satellite-based early warning systems to mitigate environmental impacts and marine disasters

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 3

Satellite sensor technologies and digital processing for global ocean monitoring and the assessment of marine ecosystems

3.1. Fundamentals of satellite sensors: physical principles, sensor types (optical, radar, altimetry, spectrometry) and technical characteristics for ocean observation
3.2. Satellite platforms: polar, geostationary and sun-synchronous constellations, orbits, revisit times and global coverage
3.3. Data capture technologies: radiometry, interferometry and spectroscopy applied to ocean parameters
3.4. Digital image processing: radiometric and geometric calibration, atmospheric correction and multisensor fusion algorithms
3.5. Interpretation of satellite data for ocean variables: surface temperature, chlorophyll concentration, salinity and sea state
3.6. Monitoring of dynamic phenomena: analysis of currents, waves and eddies with advanced remote sensing techniques
3.7. Validation and correction models: use of ocean buoys, Argo profiles and in situ campaigns to ensure precision and accuracy
3.8. Integrated geographic information systems (GIS) and advanced visualization for the spatial and temporal analysis of marine ecosystems
3.9. Applications of artificial intelligence and machine learning: automatic classification, anomaly detection and forecasting of extreme ocean events
3.10. Case studies: monitoring of critical coastal zones, assessment of human impacts and sustainable management based on satellite data

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 4

Satellite and in situ multisensor fusion and operational modeling for forecasting ocean phenomena, warning services and coastal and fisheries applications

4.1. Fundamentals of multisensor fusion: physical principles, algorithms and techniques for integrating satellite and in situ data
4.2. Types of satellite sensors: optical sensors, synthetic aperture radar (SAR), altimeters, spectroradiometers and sea surface temperature (SST) sensors
4.3. In situ measurements: ocean buoys, CTD profiles, coastal radars and autonomous platforms
4.4. Advanced signal processing: normalization, time synchronization and atmospheric correction
4.5. Operational ocean modeling: mathematical foundations, hydrodynamic modeling, ocean-atmosphere coupling and data assimilation in numerical models
4.6. Forecasting of ocean phenomena: waves, currents, internal tides, extreme events and coastal processes
4.7. Development and validation of early warning systems: design, thresholds, protocols and operational feedback
4.8. Smart coastal services: real-time monitoring, sustainable resource management and environmental risk mitigation
4.9. Fisheries applications based on satellite observation: detection of aggregations, biomass estimation and adaptive fisheries management
4.10. Case studies and practical applications: multisensor integration for managing red tides, environmental surveillance and emergency response

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 5

Advanced integration of big data and machine learning for the dynamic interpretation of satellite ocean imagery

5.1. Fundamentals of big data in satellite oceanography: characteristics, sources and types of massive data used in ocean observation
5.2. Technology infrastructure for storage and processing: distributed architectures, cloud systems and NoSQL databases specialized in geospatial data
5.3. Advanced preprocessing and cleaning of satellite data: radiometric correction, noise filtering, handling of missing data and multisensor calibration
5.4. Supervised and unsupervised machine learning models for the classification and segmentation of ocean images: convolutional neural networks, random forests, SVMs and advanced clustering
5.5. Deep learning for the automatic detection of dynamic patterns on the ocean surface: extreme events, algal blooms, thermal fronts and surface currents
5.6. Real-time analysis pipelines: orchestration of satellite data flows, stream processing and predictive alerts based on adaptive algorithms
5.7. Multisource data integration: combining optical, radar and in situ data to improve the accuracy of predictive models and contextual interpretation
5.8. Advanced visualization of ocean big data: GIS tools, interactive 3D platforms and custom dashboards for scientific and operational decision-making
5.9. Model validation and evaluation: specific metrics for satellite image interpretation, cross-validation and uncertainty analysis in ocean forecasts
5.10. Real case studies and practical application: detailed analysis of recent ocean events with big data and machine learning, with an emphasis on their environmental and economic relevance

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 6

Innovative techniques and technologies in satellite sensors for ocean monitoring and sustainable management

6.1. Fundamentals of satellite remote sensing: physical and electromagnetic principles applied to ocean sensors
6.2. Types of satellite sensors: synthetic aperture radar (SAR), passive radiometers, altimeters and hyperspectral spectrometers
6.3. Innovations in optical sensors: multispectral detection and advanced techniques to discriminate organic matter and sediments
6.4. Monitoring of key ocean parameters: surface temperature, salinity, chlorophyll, turbidity and sea level
6.5. Advanced satellite signal processing: calibration algorithms, atmospheric correction and multisensor data fusion
6.6. Satellite platforms and emerging constellations: CubeSats, nanosatellites and continuous observation systems
6.7. Integration of satellite data with numerical ocean models for forecasting and sustainable management
6.8. Applications in coastal management and environmental protection: detection of spills, marine toxin events and tracking of sensitive habitats
6.9. International regulations and standards in satellite ocean observation: interoperability and open access to data
6.10. Development and use of specialized software platforms for the visualization, analysis and dissemination of satellite ocean information

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 7

Technological innovation in satellite sensors and predictive modeling for the conservation and sustainable management of marine ecosystems

7.1. Fundamentals of satellite sensors: physical and technological principles of remote detection across the electromagnetic spectrum applied to the ocean
7.2. Design and evolution of optical, multispectral, hyperspectral and synthetic aperture radar (SAR) sensors for marine monitoring
7.3. Advanced satellite data processing: atmospheric correction, radiometric calibration and filtering algorithms to improve information quality
7.4. Integration of satellite systems with in situ platforms and ocean data to validate and improve remote sensing products
7.5. Predictive modeling of ocean dynamics: development and implementation of numerical models based on satellite data to forecast physical and biogeochemical variables
7.6. Application of artificial intelligence and machine learning to the interpretation and multitemporal analysis of satellite series for the early detection of ecological anomalies
7.7. Advanced remote sensing techniques for tracking critical ocean phenomena: coral bleaching events, harmful algal blooms and pollutant spills
7.8. Methods for assessing human impact on marine ecosystems using indicators derived from satellite sensors and ecosystem modeling
7.9. Development of decision support systems for sustainable management based on near-real-time satellite data
7.10. Case studies of regional and global impact on marine conservation, using state-of-the-art satellite technology and integrated predictive models

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 8

Advanced processing and artificial intelligence algorithms to optimize satellite observation and management of marine ecosystems

8.1. Fundamentals of digital satellite signal processing: acquisition, preprocessing and radiometric calibration
8.2. Development and application of advanced artificial intelligence (AI) algorithms for the automatic detection and classification of ocean parameters
8.3. Convolutional neural networks (CNN) and deep learning for the segmentation and analysis of marine satellite images
8.4. Supervised and unsupervised learning techniques to identify biogeochemical and physical patterns in marine ecosystems
8.5. Optimization of predictive models with genetic algorithms and evolutionary methods in the sustainable management of ocean resources
8.6. Integration of multisensor data from optical, radar and altimetry satellites to improve spatial and temporal resolution
8.7. Use of artificial intelligence for the real-time monitoring of critical ocean phenomena such as fronts, upwelling and bleaching events
8.8. Assessment of satellite data quality and advanced atmospheric correction techniques to increase observation accuracy
8.9. Design of automated, interoperable workflows for the massive processing of satellite data in high-performance computing (HPC) environments
8.10. Analysis of case studies and integrated projects that apply artificial intelligence to the adaptive management of marine protected areas and the mitigation of environmental impacts

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 9

Advanced sensor, modeling and big data techniques for global monitoring and sustainable management of marine ecosystems

9.1. Fundamental principles of satellite observation: physical and technological concepts behind remote sensors applied to the ocean
9.2. Advanced types of satellite sensors: hyperspectral spectroradiometers, SAR radars, multi-mission altimeters and LiDAR systems for ocean detection
9.3. Satellite data processing and calibration: atmospheric, radiometric and geometric correction to ensure accuracy in time series
9.4. Ocean modeling: integration of satellite data with hydrodynamic, biochemical and climate models to simulate and forecast marine variables
9.5. Sensor fusion techniques: multiplatform and multispectral combination to improve the spatial and temporal resolution of monitoring
9.6. Big data in oceanography: data architectures, cloud storage and management systems for large volumes of ocean information
9.7. Application of artificial intelligence and machine learning to satellite data analysis: automatic detection of phenomena such as algal blooms, acidification events and currents
9.8. Real-time monitoring and operational control: platforms and sensor networks for the surveillance of vulnerable ecosystems and response to critical events
9.9. Advanced visualization tools and geographic information systems (GIS) for interpretation and decision-making in marine management
9.10. Case studies and integrated applications: sustainable management of marine protected areas, tracking of pollutants and fishery resources, natural disaster prevention and climate change

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 10

Altimetry, ocean color and operational applications

10.1. Radar altimetry and sea level
10.2. Dynamic topography and geostrophic currents
10.3. Sea surface temperature
10.4. Salinity from space
10.5. Ocean color and chlorophyll
10.6. Wind and waves from space
10.7. Sea ice from space
10.8. The Copernicus Marine Service
10.9. Applications in fisheries and navigation
10.10. Operational application project

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 11

Final Project

11.1. Design and structure of the final project: overall planning and definition of scientific and applied objectives
11.2. Satellite data sources: optical, radar, altimetry, spectroradiometer and multispectral systems for ocean monitoring
11.3. Advanced satellite data processing: atmospheric correction, radiometric calibration, data fusion and temporal-spectral analysis
11.4. Predictive modeling: numerical techniques and machine learning algorithms applied to ocean dynamics and biogeochemical variables
11.5. Integration of satellite data with hydrodynamic and ecological models to assess the health of marine ecosystems
11.6. Applications for sustainable management: tracking of habitat change, extreme climate events, species proliferation and water quality
11.7. Development of specific satellite indicators for marine conservation and the evaluation of environmental policy
11.8. Management of large data volumes: cloud platforms, big data and geographic information systems (GIS) applied to remote oceanography
11.9. Validation and verification of results: comparison with in situ data, uncertainty analysis and continuous improvement methods
11.10. Preparation of technical and outreach reports: professional structure, advanced data visualization and effective communication for scientific and policy audiences

At this stage

136 hestimated workload

11 wkweeks at your pace

Plan your pace

Duration and format of the Master's in Satellite Ocean Observation

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 Satellite Ocean Observation

01Manage satellite ocean data, processing and validating the information and integrating it into ocean models and visualization systems that support decision-making.

02Assess and forecast the impact of climate change on the ocean, analyzing ocean data and climate models to identify coastal risks and propose mitigation strategies adapted to each marine ecosystem.

03Develop and apply algorithms for the advanced analysis of satellite ocean imagery, implementing deep learning segmentation techniques that identify and classify ocean objects and phenomena with high accuracy.

04Design and validate numerical ocean circulation models, ensuring their stability and accuracy through calibration with observational data and sensitivity analysis to different parameters and forcing conditions.

05Implement and optimize coastal and ocean monitoring systems, integrating data from in situ sensors, satellites and numerical models to produce robust, representative information on the state of the ocean and the coast.

06Lead ocean remote sensing research and development projects, defining objectives, scope and resources and managing risks and multidisciplinary teams to deliver innovative results and high-quality scientific publications.

Careers

Career opportunities after the Master's in Satellite Ocean Observation

01Ocean data scientistanalysis of satellite data for climate studies, ocean currents and water quality.

02Coastal risk analystmodeling and forecasting of extreme events such as storm surges and coastal flooding.

03Environmental consultantassessment of the environmental impact of maritime activities and design of mitigation strategies.

04Officer in public agenciesmonitoring of illegal fishing, marine pollution control and management of marine protected areas.

05Marine remote sensing specialist or geospatial software developersatellite-based products and platforms for the maritime, energy and insurance industries.

06Researcher or Earth observation project managerdevelopment of new observation techniques and coordination of ocean monitoring programs.

Frequently asked questions

Questions about the Master's in Satellite Ocean Observation

Satellite remote sensing data such as sea surface temperature, ocean color, sea surface height, surface winds, currents, waves, salinity and sea ice, combined with in situ data from buoys, CTD profiles and Argo floats.

This program is oriented toward operational monitoring: building data pipelines, early warning systems, indicators and decision support tools for coastal, fisheries and conservation managers, with a strong emphasis on big data and AI.

Basic Python is recommended. You will use it with QGIS and specialized libraries to process imagery, build machine learning models and automate workflows.

Yes. You will work with Copernicus Marine products and other open satellite data services, and learn how they support applications in fisheries, navigation and coastal management.

Yes. Several modules apply supervised and unsupervised learning, CNNs, random forests, SVMs and evolutionary algorithms to classify images, detect anomalies and optimize predictive models.

Online in a flexible format, with practical projects based on real data and a focus on solving real problems in the sector.

Through practical work in each module, such as image processing, parameter retrieval, model validation and the design of monitoring systems, together with the Final Project.

A Final Project that integrates satellite technology and predictive modeling to develop indicators and tools for sustainable ocean conservation, presented in technical and outreach reports.

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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 oceanographers and climate scientists, environmental engineers and consultants, fisheries and aquaculture researchers, maritime industry professionals and graduates in marine science, geophysics or related fields; a degree in marine science, physics, mathematics or environmental, civil or computer engineering, or demonstrable experience in oceanography, remote sensing or geospatial analysis, is recommended, together with basic knowledge of calculus, linear algebra, statistics and Python programming.
Language: sufficient command of English to follow the classes and the technical documentation.
Technical resources: a computer with a camera and microphone, a stable broadband internet connection and data analysis software such as QGIS and Python with the relevant libraries.
Admission: online application, review of your profile and a short interview with the academic team.

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