Sentinel-3 Reference Guide

This reference guide provides detailed information about Sentinel-3 data parameters and options available through the Copernicus Data Space Ecosystem.

Overview

Sentinel-3 is a constellation of two polar-orbiting satellites (Sentinel-3A and Sentinel-3B) carrying multiple instruments for ocean and land monitoring. The mission provides systematic measurements of Earth’s oceans, land, ice, and atmosphere.

Key Features: - Ocean and Land Colour Instrument (OLCI) - 21 spectral bands - Sea and Land Surface Temperature Radiometer (SLSTR) - thermal and optical channels - SAR Radar Altimeter (SRAL) - precise height measurements - Synergy products combining OLCI and SLSTR data - 27-day repeat cycle with daily global coverage - Systematic global coverage for ocean and land applications

Search Parameters

Parameter Types

When searching for Sentinel-3 data, parameters are passed in two ways:

Direct Parameters: Passed directly to the search() method - collection_name - Mission/collection identifier - product_type - Product type (OL_1_EFR___, OL_2_LFR___, etc.) - orbit_direction - Orbit direction (ASCENDING/DESCENDING) - aoi_wkt - Area of interest in WKT format - start_date / end_date - Temporal range - top - Maximum number of results
Attributes: Passed in the attributes dictionary - processingLevel - Processing level (1, 2) - platform - Satellite platform (S3A, S3B) - instrument - Instrument type (OLCI, SLSTR, SRAL, SYNERGY, AUX) - orbitNumber - Absolute orbit number - sensorMode - Sensor mode (Earth Observation) - cloudCover - Cloud cover percentage - status - Product availability status - relativeOrbitNumber - Relative orbit number - processingBaseline - Processing baseline version - timeliness - Data delivery timeliness

Basic Parameters

Collection Name

Use 'SENTINEL-3' as the collection name.

results = searcher.search(collection_name='SENTINEL-3')

Geographic Parameters

Geometry

Region of Interest defined in Well Known Text (WKT) format with coordinates in decimal degrees (EPSG:4326).

# Polygon example
aoi_wkt = 'POLYGON((0 35, 10 35, 10 45, 0 45, 0 35))'
results = searcher.search(
    collection_name='SENTINEL-3',
    aoi_wkt=aoi_wkt
)

Product Parameters

Product Types

Sentinel-3 offers various product types organized by instrument:

OLCI (Ocean and Land Colour Instrument)

SLSTR (Sea and Land Surface Temperature Radiometer)

SRAL (SAR Radar Altimeter)

SYNERGY Products

# Search for OLCI Level 2 land products
results = searcher.search(
    collection_name='SENTINEL-3',
    product_type='OL_2_LFR___'
)

Processing Level

Available processing levels:

1 - Level 1 (TOA radiances, brightness temperatures)
2 - Level 2 (Geophysical products)

# Search for Level 2 products
results = searcher.search(
    collection_name='SENTINEL-3',
    attributes={'processingLevel': '2'}
)

Platform

Sentinel-3 constellation satellites:

S3A - Sentinel-3A (launched 2016)
S3B - Sentinel-3B (launched 2018)

# Search for Sentinel-3A data only
results = searcher.search(
    collection_name='SENTINEL-3',
    attributes={'platform': 'S3A'}
)

Instrument

Sentinel-3 instruments:

OLCI - Ocean and Land Colour Instrument
SLSTR - Sea and Land Surface Temperature Radiometer
SRAL - SAR Radar Altimeter
SYNERGY - Combined OLCI and SLSTR products
AUX - Auxiliary data files

# Search for OLCI instrument data
results = searcher.search(
    collection_name='SENTINEL-3',
    attributes={'instrument': 'OLCI'}
)

Sensor Mode

Sentinel-3 sensor mode:

Earth Observation - Standard Earth observation mode

# Search for Earth observation mode
results = searcher.search(
    collection_name='SENTINEL-3',
    attributes={'sensorMode': 'Earth Observation'}
)

Cloud Cover

Cloud Cover Percentage

Filter products by cloud cover percentage (0-100%). Applicable mainly to OLCI and SLSTR products.

# Search for products with less than 30% cloud cover
results = searcher.search(
    collection_name='SENTINEL-3',
    attributes={'cloudCover': '[0,30]'}
)

Orbit Parameters

Orbit Direction

ASCENDING - Satellite moving from south to north
DESCENDING - Satellite moving from north to south

results = searcher.search(
    collection_name='SENTINEL-3',
    orbit_direction='DESCENDING'
)

Orbit Number

Absolute orbit number (integer value or range).

# Single orbit
results = searcher.search(
    collection_name='SENTINEL-3',
    attributes={'orbitNumber': '12345'}
)

# Orbit range
results = searcher.search(
    collection_name='SENTINEL-3',
    attributes={'orbitNumber': '[12345,12350]'}
)

Relative Orbit Number

Relative orbit number (1-385 for Sentinel-3), representing the orbit within a repeat cycle.

# Search for relative orbit 100
results = searcher.search(
    collection_name='SENTINEL-3',
    attributes={'relativeOrbitNumber': '100'}
)

Quality and Timeliness

Timeliness

Data delivery timeliness categories:

NR - Near Real-Time
NT - Non Time-Critical
ST - Slow Time-Critical

# Search for near real-time data
results = searcher.search(
    collection_name='SENTINEL-3',
    attributes={'timeliness': 'NR'}
)

Processing Baseline

Processing baseline version (affects product quality and algorithms used).

# Search for specific processing baseline
results = searcher.search(
    collection_name='SENTINEL-3',
    attributes={'processingBaseline': '03.00'}
)

Status

Product availability status:

ONLINE - Immediately available for download
OFFLINE - Requires retrieval from long-term storage
ALL - Both online and offline products

# Search for immediately available products
results = searcher.search(
    collection_name='SENTINEL-3',
    attributes={'status': 'ONLINE'}
)

Practical Examples

Example 1: Ocean Color Monitoring

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for OLCI ocean color products
results = searcher.search(
    collection_name='SENTINEL-3',
    product_type='OL_2_WFR___',
    aoi_wkt='POLYGON((0 35, 10 35, 10 45, 0 45, 0 35))',  # Mediterranean
    start_date='2023-06-01',
    end_date='2023-06-30',
    attributes={
        'instrument': 'OLCI',
        'cloudCover': '[0,20]'
    }
)

print(f"Found {len(results)} ocean color products")

Example 2: Land Surface Temperature

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for SLSTR land surface temperature products
results = searcher.search(
    collection_name='SENTINEL-3',
    product_type='SL_2_LST___',
    aoi_wkt='POLYGON((10 40, 20 40, 20 50, 10 50, 10 40))',  # Central Europe
    start_date='2023-07-01',
    end_date='2023-07-31',
    attributes={
        'instrument': 'SLSTR',
        'timeliness': 'NT'
    }
)

print(f"Found {len(results)} land surface temperature products")

Example 3: Altimetry for Ocean Monitoring

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for SRAL ocean altimetry products
results = searcher.search(
    collection_name='SENTINEL-3',
    product_type='SR_2_WAT___',
    aoi_wkt='POLYGON((-10 30, 10 30, 10 50, -10 50, -10 30))',  # Atlantic
    start_date='2023-08-01',
    end_date='2023-08-31',
    attributes={
        'instrument': 'SRAL',
        'processingLevel': '2'
    }
)

print(f"Found {len(results)} ocean altimetry products")

Example 4: Synergy Products for Vegetation

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for SYNERGY vegetation products
results = searcher.search(
    collection_name='SENTINEL-3',
    product_type='SY_2_VG1___',
    aoi_wkt='POLYGON((0 40, 20 40, 20 60, 0 60, 0 40))',  # Europe
    start_date='2023-05-01',
    end_date='2023-05-31',
    attributes={
        'instrument': 'SYNERGY',
        'processingLevel': '2'
    }
)

print(f"Found {len(results)} vegetation products")

Example 5: Fire Detection

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for SLSTR fire products
results = searcher.search(
    collection_name='SENTINEL-3',
    product_type='SL_2_FRP___',
    aoi_wkt='POLYGON((-10 35, 5 35, 5 45, -10 45, -10 35))',  # Spain/Portugal
    start_date='2023-08-01',
    end_date='2023-08-31',
    attributes={
        'instrument': 'SLSTR',
        'timeliness': 'NR'
    }
)

print(f"Found {len(results)} fire detection products")

Example 6: Multi-Platform Time Series

from phidown import CopernicusDataSearcher
import pandas as pd

searcher = CopernicusDataSearcher()

# Search for data from both platforms
s3a_results = searcher.search(
    collection_name='SENTINEL-3',
    product_type='OL_2_LFR___',
    aoi_wkt='POLYGON((10 45, 15 45, 15 50, 10 50, 10 45))',
    start_date='2023-01-01',
    end_date='2023-12-31',
    attributes={
        'platform': 'S3A',
        'cloudCover': '[0,30]'
    }
)

s3b_results = searcher.search(
    collection_name='SENTINEL-3',
    product_type='OL_2_LFR___',
    aoi_wkt='POLYGON((10 45, 15 45, 15 50, 10 50, 10 45))',
    start_date='2023-01-01',
    end_date='2023-12-31',
    attributes={
        'platform': 'S3B',
        'cloudCover': '[0,30]'
    }
)

# Combine results
all_results = pd.concat([s3a_results, s3b_results], ignore_index=True)

print(f"Sentinel-3A: {len(s3a_results)} products")
print(f"Sentinel-3B: {len(s3b_results)} products")
print(f"Total: {len(all_results)} products")

Search Optimization Tips

Choose Appropriate Instrument: Select the right instrument for your application (OLCI for ocean color, SLSTR for temperature, SRAL for altimetry).
Use Processing Level Wisely: Level 1 for raw data processing, Level 2 for ready-to-use geophysical products.
Filter by Cloud Cover: For optical instruments (OLCI, SLSTR), use cloud cover filtering to get clear observations.
Consider Timeliness: Use Near Real-Time (NR) for urgent applications, Non Time-Critical (NT) for better quality.
Optimize Temporal Range: Use appropriate date ranges based on the 27-day repeat cycle.
Use Relative Orbit Numbers: For consistent geometry in time series analysis.
Select Proper Product Type: Choose full resolution (FR) for detailed analysis, reduced resolution (RR) for overview applications.

Common Use Cases

Ocean Applications: - Ocean color monitoring: OL_2_WFR___, clear conditions - Sea surface temperature: SL_2_WST___, thermal channels - Ocean altimetry: SR_2_WAT___, precise measurements - Marine ecosystem monitoring: OL_2_WFR___, regular coverage

Land Applications: - Land surface temperature: SL_2_LST___, thermal infrared - Vegetation monitoring: SY_2_VG1___, SYNERGY products - Land altimetry: SR_2_LAN___, elevation measurements - Fire detection: SL_2_FRP___, active fire monitoring

Atmospheric Applications: - Aerosol monitoring: SY_2_AOD___, atmospheric products - Cloud detection: All optical products with cloud masks - Atmospheric correction: Level 2 products

Ice Applications: - Sea ice monitoring: SR_2_LAN_SI, specialized altimetry - Ice surface temperature: SL_2_LST___, polar regions - Ice extent mapping: OLCI and SLSTR products

Technical Specifications

OLCI (Ocean and Land Colour Instrument): - Spectral range: 400-1020 nm - Spatial resolution: 300 m (FR), 1.2 km (RR) - Swath width: 1270 km - Spectral bands: 21 bands - Applications: Ocean color, vegetation, atmosphere

SLSTR (Sea and Land Surface Temperature Radiometer): - Spectral range: 0.55-12 μm - Spatial resolution: 500 m (optical), 1 km (thermal) - Swath width: 1400 km - Spectral bands: 9 bands (6 optical, 3 thermal) - Applications: Sea/land surface temperature, fire detection

SRAL (SAR Radar Altimeter): - Frequency: 13.575 GHz (Ku-band), 5.41 GHz (C-band) - Footprint: ~300 m (ocean), ~1.65 km (land/ice) - Precision: ~3 cm (ocean), ~10 cm (land/ice) - Applications: Ocean/land topography, ice thickness

Orbital Characteristics: - Altitude: 814.5 km - Inclination: 98.65° - Repeat cycle: 27 days - Revisit time: <1 day (depending on latitude) - Local time: 10:00 AM (descending node)

Coverage: - Global coverage: Daily - Polar coverage: Multiple times per day - Equatorial coverage: Every 2-3 days - Ice-free ocean: Complete coverage every 27 days

For more detailed information about Sentinel-3 specifications and applications, refer to the official ESA Sentinel-3 documentation.