Landsat-8 Reference Guide

This reference guide provides detailed information about Landsat-8 data parameters and options available through the Copernicus Data Space Ecosystem.

Overview

Landsat-8 is an Earth observation satellite launched in 2013, carrying two primary instruments for systematic land monitoring. The mission continues the Landsat program’s legacy of providing consistent, high-quality multispectral imagery for land use, land cover change detection, and environmental monitoring.

Key Features: - Operational Land Imager (OLI) - 9 spectral bands including coastal/aerosol band - Thermal Infrared Sensor (TIRS) - 2 thermal infrared bands - 16-day repeat cycle with global coverage - 30-meter spatial resolution for multispectral bands - 100-meter spatial resolution for thermal bands - 15-meter panchromatic band for image sharpening

Search Parameters

Parameter Types

When searching for Landsat-8 data, parameters are passed in two ways:

Direct Parameters: Passed directly to the search() method - collection_name - Mission/collection identifier - product_type - Product type (L1GT, L1T, L1TP, L2SP) - 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 (LEVEL1, LEVEL2, etc.) - platform - Satellite platform (LANDSAT-8) - instrument - Instrument type (OLI_TIRS) - orbitNumber - Absolute orbit number - sensorMode - Sensor mode (DEFAULT) - cloudCover - Cloud cover percentage - status - Product availability status - organisationName - Data provider (ESA, USGS) - path - WRS-2 path number - row - WRS-2 row number - sunAzimuth - Sun azimuth angle - sunElevation - Sun elevation angle

Basic Parameters

Collection Name

Use 'LANDSAT-8-ESA' as the collection name.

results = searcher.search(collection_name='LANDSAT-8-ESA')

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((-120 35, -110 35, -110 45, -120 45, -120 35))'
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    aoi_wkt=aoi_wkt
)

Product Parameters

Product Types

Landsat-8 offers various product types with different processing levels:

# Search for Level 2 surface reflectance products
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    product_type='L2SP'
)

Processing Level

Available processing levels:

LEVEL1 / LEVEL1GT - Level 1 Systematic Terrain Correction
LEVEL1T - Level 1 Precision Terrain Correction
LEVEL1TP - Level 1 Precision and Terrain Correction
LEVEL2 / LEVEL2SP - Level 2 Surface Reflectance

# Search for Level 2 products
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    attributes={'processingLevel': 'LEVEL2'}
)

Platform

Landsat-8 platform:

LANDSAT-8 - Landsat-8 satellite (launched 2013)

# Search for Landsat-8 data
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    attributes={'platform': 'LANDSAT-8'}
)

Instrument

Landsat-8 instrument:

OLI_TIRS - Combined Operational Land Imager and Thermal Infrared Sensor

# Search for OLI/TIRS data
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    attributes={'instrument': 'OLI_TIRS'}
)

Sensor Mode

Landsat-8 sensor mode:

DEFAULT - Standard Earth observation mode

# Search for default sensor mode
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    attributes={'sensorMode': 'DEFAULT'}
)

Cloud Cover

Cloud Cover Percentage

Filter products by cloud cover percentage (0-100%).

# Search for products with less than 20% cloud cover
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    attributes={'cloudCover': '[0,20]'}
)

WRS-2 Path/Row Parameters

Path and Row

Landsat-8 uses the Worldwide Reference System-2 (WRS-2) for systematic coverage:

path - WRS-2 path number (1-233)
row - WRS-2 row number (1-248)

# Search for specific path/row
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    attributes={
        'path': '44',
        'row': '34'
    }
)

Orbit Parameters

Orbit Number

Absolute orbit number (integer value or range).

# Single orbit
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    attributes={'orbitNumber': '12345'}
)

# Orbit range
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    attributes={'orbitNumber': '[12345,12350]'}
)

Solar Angle Parameters

Sun Azimuth and Elevation

Solar illumination conditions during image acquisition:

sunAzimuth - Sun azimuth angle in degrees
sunElevation - Sun elevation angle in degrees

# Search for optimal solar conditions
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    attributes={
        'sunElevation': '[30,90]',  # High sun elevation
        'sunAzimuth': '[120,240]'   # Southern illumination
    }
)

Data Provider

Organisation Name

Data provider organizations:

ESA - European Space Agency
USGS - United States Geological Survey

# Search for USGS-provided data
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    attributes={'organisationName': 'USGS'}
)

Quality and Status

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='LANDSAT-8-ESA',
    attributes={'status': 'ONLINE'}
)

Practical Examples

Example 1: Agricultural Monitoring

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for surface reflectance products over agricultural area
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    product_type='L2SP',
    aoi_wkt='POLYGON((-100 40, -95 40, -95 45, -100 45, -100 40))',  # Midwest US
    start_date='2023-06-01',
    end_date='2023-08-31',
    attributes={
        'cloudCover': '[0,10]',
        'sunElevation': '[45,90]'
    }
)

print(f'Found {len(results)} agricultural monitoring products')

Example 2: Urban Heat Island Analysis

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for thermal infrared data for urban analysis
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    product_type='L2SP',
    aoi_wkt='POLYGON((-74.25 40.5, -73.7 40.5, -73.7 40.9, -74.25 40.9, -74.25 40.5))',  # NYC
    start_date='2023-07-01',
    end_date='2023-07-31',
    attributes={
        'cloudCover': '[0,15]',
        'instrument': 'OLI_TIRS'
    }
)

print(f'Found {len(results)} urban thermal products')

Example 3: Forest Change Detection

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for specific path/row for consistent geometry
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    product_type='L2SP',
    start_date='2020-01-01',
    end_date='2023-12-31',
    attributes={
        'path': '226',
        'row': '62',  # Amazon region
        'cloudCover': '[0,20]',
        'processingLevel': 'LEVEL2'
    }
)

print(f'Found {len(results)} forest monitoring products')

Example 4: Coastal Water Quality

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for coastal aerosol band data
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    product_type='L2SP',
    aoi_wkt='POLYGON((-81 24, -80 24, -80 26, -81 26, -81 24))',  # Florida Keys
    start_date='2023-01-01',
    end_date='2023-12-31',
    attributes={
        'cloudCover': '[0,5]',
        'bands': '3'  # Coastal/aerosol band
    }
)

print(f'Found {len(results)} coastal water quality products')

Example 5: Seasonal Vegetation Analysis

from phidown import CopernicusDataSearcher
import pandas as pd

searcher = CopernicusDataSearcher()

# Search for multi-seasonal data
seasons = [
    ('Spring', '2023-03-01', '2023-05-31'),
    ('Summer', '2023-06-01', '2023-08-31'),
    ('Fall', '2023-09-01', '2023-11-30')
]

all_results = []
for season_name, start_date, end_date in seasons:
    results = searcher.search(
        collection_name='LANDSAT-8-ESA',
        product_type='L2SP',
        aoi_wkt='POLYGON((-106 39, -105 39, -105 40, -106 40, -106 39))',  # Colorado
        start_date=start_date,
        end_date=end_date,
        attributes={
            'cloudCover': '[0,15]',
            'path': '33',
            'row': '32'
        }
    )
    results['season'] = season_name
    all_results.append(results)

combined_results = pd.concat(all_results, ignore_index=True)
print(f'Found {len(combined_results)} seasonal products')

Example 6: Solar Angle Optimization

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for optimal solar illumination conditions
results = searcher.search(
    collection_name='LANDSAT-8-ESA',
    product_type='L1TP',
    aoi_wkt='POLYGON((-110 35, -105 35, -105 40, -110 40, -110 35))',  # Utah
    start_date='2023-05-01',
    end_date='2023-09-30',
    attributes={
        'cloudCover': '[0,10]',
        'sunElevation': '[50,90]',  # High sun angle
        'sunAzimuth': '[150,210]'   # Southern aspect
    }
)

print(f'Found {len(results)} optimally illuminated products')

Search Optimization Tips

Use Appropriate Processing Level: L1TP for geometric accuracy, L2SP for atmospheric correction.
Filter by Cloud Cover: Essential for optical analysis - use strict thresholds for quantitative work.
Consider Solar Angles: High sun elevation reduces shadows, specific azimuth angles optimize illumination.
Use Path/Row for Consistency: Same path/row ensures consistent viewing geometry for time series.
Optimize Temporal Windows: Account for 16-day repeat cycle and seasonal variations.
Select Proper Bands: Use coastal/aerosol band for atmospheric studies, thermal bands for temperature.
Consider Data Provider: USGS typically provides the most complete archive.

Common Use Cases

Land Cover Mapping: - Surface reflectance products: L2SP with low cloud cover - Multi-seasonal data for phenology - Consistent path/row for change detection

Agricultural Applications: - NDVI/vegetation indices: Bands 4 (red) and 5 (NIR) - Crop stress monitoring: Thermal bands 10 and 11 - Irrigation mapping: SWIR bands 6 and 7

Water Quality Monitoring: - Coastal/aerosol band: Band 1 for atmospheric correction - Turbidity assessment: Visible bands 2, 3, 4 - Algal bloom detection: Red edge simulation

Urban Studies: - Urban heat islands: Thermal bands with high spatial detail - Impervious surface mapping: SWIR and thermal combinations - Air quality: Coastal/aerosol band

Forest Monitoring: - Deforestation detection: Multi-temporal L2SP products - Fire mapping: Thermal anomalies in bands 10 and 11 - Health assessment: Red edge proxy using NIR/red ratio

Geological Applications: - Mineral mapping: SWIR bands 6 and 7 - Rock type discrimination: Band combinations - Structural geology: Panchromatic band for detail

Technical Specifications

OLI (Operational Land Imager): - Spectral range: 0.43-1.38 μm - Spatial resolution: 30 m (multispectral), 15 m (panchromatic) - Swath width: 185 km - Spectral bands: 9 bands including coastal/aerosol - Radiometric resolution: 12-bit

TIRS (Thermal Infrared Sensor): - Spectral range: 10.6-12.51 μm - Spatial resolution: 100 m (resampled to 30 m) - Swath width: 185 km - Spectral bands: 2 thermal infrared bands - Radiometric resolution: 12-bit

Orbital Characteristics: - Altitude: 705 km - Inclination: 98.2° - Repeat cycle: 16 days - Revisit time: 16 days - Local time: 10:00 AM (descending node) - WRS-2 system: 233 paths × 248 rows

Spectral Bands:

Coverage: - Global land coverage: Every 16 days - Scene size: 185 × 185 km - Daily imaging capacity: ~400 scenes - Archive: February 2013 to present

For more detailed information about Landsat-8 specifications and data processing, refer to the official USGS Landsat documentation. - 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.