class: center, middle, inverse, title-slide .title[ # Sentinel-1 ] .subtitle[ ## Earth Observations, Land and Ocean Monitoring ] .author[ ### Christy Choi ] .institute[ ### CASA0023 Learning Diary ] .date[ ### 2026/02/11 (updated: 2026-02-20) ] --- # What is Sentinel-1? .pull-left[ * A polar orbiting two satellite constellation that carries C-band synthetic aperture radar (C-SAR) * Able to acquire data under day-and-night, all weather and atmospheric conditions * Able to capture land, ice, and coastal zones data with high and medium resolutions * Launched by the European Space Agency (ESA) under the Copernicus Program to acquire environment and security data ] .pull-right[ <img src="../images/w2_images/sentinel-1.jpg" alt="" width="100%" style="display: block; margin: auto 0 auto auto;" /> <p style="font-size: 0.6em; text-align: center; margin-top: 0;"> Figure 1: Sentinel-1 (Source: <a href="https://sentiwiki.copernicus.eu/web/sentinel-1">SentiWiki Copernicus</a>) </p> ] --- # Mission of Sentinel-1 Launched on 5 December 2024, Sentinel-1C reestablished a two-satellite constellation orbiting 180° apart for extensive worldwide coverage by May 2025 with technical upgrades. SAR allows continuous monitoring and provides consistent long-term data for time-series analysis. ### Key Mission Objectives and Applications <div style="font-size: 10px; margin-top: -15px; margin-bottom: 15px; color: #555;"> Reference: <a href="https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/sentinel-1">NASA EARTHDATA</a> , <a href="https://sentiwiki.copernicus.eu/web/s1-applications#S1Applications-OverviewofS1Applications">SentiWiki Copernicus</a> </div> .panelset[ .panel[.panel-name[Land Monitoring] <div style="font-size: 14px;"> .pull-left-3[ #### Forestry * **Role**: Forest Type Classification, Biomass Estimation, Disturbance Detection * **Climate Change**: Trace Forest Carbon History, Estimate Carbon Emissions * **Land Cover**: Support forest management, Monitor illegal timber harvesting activities ] .pull-middle-3[ #### Agriculture * **Crop Production**: Conditions, Harvest Prediction, Monitor Seasonal Changes, Pastures Productivity, Drought Impacts * **Soil**: Soil Properties, Degradation, Land Productivity, Tillage Activities ] .pull-right-3[ #### Urban Deformation Mapping * **Land Subsidence**: Detect surface movements * **Infrastructure**: Detect Structural Damage to Infrastructures (e.g. Buildings) and Underground Conditions * **Disaster**: Assess Damage/ Devastation After Events (e.g. Explosion) ] <div style="clear:both"></div> </div> ] .panel[.panel-name[Maritime Monitoring] <div style="font-size: 14px;"> .pull-left-4[ #### Ice * **Identification**: Type, Thickness, Size, Density, Orientation, Location * **Navigation in Ice-Covered Zones** ] .pull-mid1-4[ #### Ship * **Cooperative System**: Uses AIS to Detect, Classify, Trace and Locate Vessels ] .pull-mid2-4[ #### Oil Pollution * **Prosecution**: Oil Detection from Illegal discharges, Spills Spread, Naturally Seepage with AIS information ] .pull-right-4[ #### Oceanography * **Weather Prediction**: Storm and Cyclone * **Coastal**: Marine Waves, Ocean Currents and Sea Surface Winds * **Wave Energy** ] <div style="clear:both"></div> </div> ] .panel[.panel-name[Emergency Management] <div style="font-size: 14px;"> .pull-left-3[ #### Flood Monitoring * **Hydrology**: Run-off and Inundation Analysis * **Vegetation**: Detect Flooded Vegetation ] .pull-middle-3[ #### Earthquake Analysis * **Earthquake Deformation Mapping** * **Identify Potential Risks**: Discover Active Fault Lines from Persistent Monitoring * **Large-scale Monitoring Earthquakes** ] .pull-right-3[ #### Landslide and Volcano Monitoring * **Identify Potential Warnings**: Monitor Surface Deformation, Identify First Signs of Volcanic Activity, Preceding Earthquakes, Pre-eruption Uplift, Quantify Ground Deformation, Post Eruption Volcanic Shrinkage ] <div style="clear:both"></div> </div> ] ] --- # Technical Specifications .pull-left[ .panelset[ .panel[.panel-name[Resolution] * Spatial Coverage: Worldwide * Spatial Resolution: 25x40m, 5x5m, and 5x20m * Spectral Resolution: C-band radar at 5.405 GHz * Temporal Coverage: * Global revisit bi-weekly (interval of 12 Days) * Repeat frequency of 6 days with two-satellites constellation * 175 orbits per cycle per satellite * Revisit rates higher in areas with higher latitude (e.g. < 1 day at Arctic, ~ 2 days at Europe) <div style="font-size: 10px; margin-top: 20px; color: #555;"> Reference: <a href="https://sentiwiki.copernicus.eu/web/sentiwiki">SentiWiki</a> , <a href="https://www.earthdata.nasa.gov/data/instruments/sentinel-1-c-sar#:~:text=Instrument%20Type,Frequently%20Asked%20Questions">NASA EARTHDATA</a> , <a href="https://www.sciencedirect.com/science/article/pii/S0034425712000600">Torres. et.al, 2012</a> </div>] .panel[.panel-name[Specifications] * Orbital Altitude: 693 km * Sensor Complement: C-SAR * Operative autonomy: 96 hours * Data Delivery: 24 hours * Lifespan: 7.25 years (consumables for 12 years) <div style="font-size: 10px; margin-top: 20px; color: #555;"> Reference: <a href="https://www.eoportal.org/satellite-missions/copernicus-sentinel-1#spacecraft">eoPortal</a> </div> ] ] ] .pull-right[ <img src="../images/w2_images/sentinel1_constellation.png" alt="" width="280px" style="display: block; margin: auto;" /> <p style="font-size: 0.6em; text-align: center; margin-top: -10px; margin-bottom: 20px;"> Figure 2: Sentinel-1 Constellation (Source: <a href="https://sentiwiki.copernicus.eu/web/s1-mission">SentiWiki Copernicus</a>) </p> <img src="../images/w2_images/Sentinel-1-Repeat-Coverage-Frequency-Geometry-2025_1.2.jpg" alt="" width="280px" style="display: block; margin: auto;" /> <p style="font-size: 0.6em; text-align: center; margin-top: -10px;"> Figure 3: Sentinel-1 Repeat Coverage Frequency (Source: <a href="https://sentiwiki.copernicus.eu/web/s1-mission">SentiWiki Copernicus</a>) </p> ] --- # Land Monitoring #### Crop Monitoring, Case Study of the Netherlands ([Khabbazan et al., 2019](https://www.mdpi.com/2072-4292/11/16/1887)) **Purpose** * Monitor growth and development of key Dutch crops during growing season **Data and Methods** * 60 Sentinel-1A/B SAR acquisitions, 6 days temporal resolution, IW 5x20m spatial resolution * Backscatter time series compared against imagery * Coherence Data compared to NDVI and ground observations * Hydrometeorological data * Weather: Precipitation, Temperature, Solar Radiation, Humidity, Wind Speed * Inception and Dew (water detected with rain events, and without rain) * Soil Moisture * Estimate key dates for emergence, closure and harvest **Key Takeaways** * SAR ensured quality, timely and reliability of data, meeting monitoring needs * Low frequency radar suitable for sensing soil moisture, water content and structural changes of crops * VH/VV ratios useful in reducing soil moisture influences, more sensitive in vegetation water content * Coherence Data is a more readily identifiable indicator for harvest * Address Netherlands' persistent cloud covers during growing seasons (Only 20% chance of clear satellite acquisition can be obtained usually) --- # Maritime Monitoring #### Maritime Surveillance at the Mediterranean Sea ([Santamaria et al., 2017](https://www.mdpi.com/2072-4292/9/7/678)) **Purpose** * Monitor human shipping activities at sea * Security: Irregular sea border crossing, smuggling illegal goods or substances * Safety: Search and Rescue, Shipping traffic * Environmental and sustainability: Fishing Control, Pollution **Data and Methods** * Total 11,647 Sentinel-1 IW acquisitions over 2 years, 6 days temporal resolution, 20x22m resolution * Ship detection: SUMO (automatic analysis for large set of images) * Storage and Processing Earth Observation Data: JEODPP * Integrated Maritime Surveillance Platform: Blue Hub * Recurrent Targets Analysis **Key Takeaways** * Access to unprecedented volumes of data enables comprehensive maritime surveillance * Observation periodicities of 1.5 to 4 days due to overlap swaths * Shipping activity mapping * Ability to detect non-cooperative vessels that is not visible on AIS * 60% found correlated with AIS, 40% non-correlated * However, Recurrent Target Analysis identified 20% of false alarms (15% fixed structures, 5% ambiguity) from SUMO detection, requiring filtering according to specific application needs --- # Emergency Response Management #### Flood Mapping for Tropical Storm Imelda ([Amer, 2025](https://www.mdpi.com/2072-4292/17/11/1869)) **Purpose** * Informing emergency response and risk mitigation **Data and Methods** * 10x10m spatial resolution * Used backscatter ratio and machine learning to classify water and land * Able to capture flooding, rainfall extent caused by the storm * Identify any terrain, land cover characteristics that influences the spatial distribution of floodings **Key Takeaways** * Able to provide accurate, timely information for disaster response * Advantageous during heavy rainfall events, where optical sensor are comparably ineffective * High accuracy for rapid responses with 96% and 94% of pre-flood and during-flood classification --- # Benefits and Limitations .panelset[ .panel[.panel-name[Benefits] <img src="../images/w2_images/huge.jpg" alt="" width="80%" style="display: block; margin: auto;" /> <p style="font-size: 0.6em; text-align: center; margin-top: -10px;"> Figure 4: (Left) Sentinel-1 C-Band Radar transmit signals to the Earth surface. (Right) Radar passes through clouds (red), and depicts signals reflected from Earth's surface back to the satellite. (Source: <a href="https://land.copernicus.eu/en/feature-articles/sentinel-1c-secures-the-future-of-radar-data">Copernicus</a>) </p> * Free and Open Data * Consistent, High-Resolution, Quality, Reliable Data * Accessibility: Timely and Accurate Data * Improved Technical Instruments: Upgraded radar imaging and design to withstand space debris * C-band radar * Ability to operate in darkness, penetrate through clouds, and vegetation but to a lesser extent * Able to deliver consistent and reliable data under conditions * Two-satellite constellation allow passing over same areas at the same time each orbital cycle <div style="font-size: 10px; margin-top: 20px; color: #555;"> Reference: <a href="https://land.copernicus.eu/en/feature-articles/sentinel-1c-secures-the-future-of-radar-data">Copernicus</a> </div> ] .panel[.panel-name[Limitations] * Extensive pre-processing required: Noise removal required before analysis ([Boemke, et.al, 2023](https://www.sciencedirect.com/science/article/pii/S1875963723000241)) * Temporal misalignment (gap): may risk failing to capture peak events in rapid-onset monsoon events ([Anucharn et.al ,2025](https://ijg.journals.publicknowledgeproject.org/index.php/journal/article/view/4165/2171)) * Able to detect medium to large patterns, smaller and denser patterns may be limited ([Khabbazan et al., 2019](https://www.mdpi.com/2072-4292/11/16/1887); [Santamaria et al., 2017](https://www.mdpi.com/2072-4292/9/7/678)) * SAR operates at shorter microwave wavelengths, exhibiting weaker penetration which interacts primarily with the upper canopy layer ([Gupta, et.al, 2026](https://www.sciencedirect.com/science/article/pii/S2666017226000155)) ]] --- # Reflection * Overall, SAR takes advantages over weather dependent optical satellites * Studies have demonstrated Sentinel-1's technical abilities monitor urban and natural environments, through integrated quantitative designs it allows EO data to support predictions and decision makings * Spatial resolution of Sentinel-1 is beneficial for large-scale but less on smaller scale applications, therefore suitability based on specific application needs should be carefully considered * Reliance on a single sensor choice may cause limitations. Therefore, multi-sensor integration such as optical imagery may be beneficial to look at * Studies mentioned about using Sentinel-1 SAR imagery with Google Earth Engine (GEE) for efficiency and consistency in data pre-processing. More on GEE will be covered in later chapters