Satellite and Remote Sensing Terms You Should Know

1. Earth Observation (EO)
The collection of information about Earth’s physical, chemical and biological systems using satellites, aircraft or drones. EO data powers applications like agriculture optimisation, climate monitoring, disaster response and smart-city planning.

2. Remote Sensing
The science of obtaining information about objects or areas from a distance - typically via sensors on satellites or aircraft. These sensors detect reflected or emitted electromagnetic energy (visible, infrared, microwave) from Earth’s surface.
 

3. Landsat Program
The world’s longest-running Earth-observation satellite mission (since 1972, by NASA & US Geological Survey). Landsat satellites provide multispectral imagery at ~30-metre resolution every ~16 days, allowing long-term monitoring of land cover, vegetation, deforestation, etc.

4.    Sentinel Satellites
A series of Earth-observation missions by the European Space Agency under the Copernicus Programme.

• Sentinel-1: Radar (SAR) – weather-independent imaging.
• Sentinel-2: Multispectral optical imagery – vegetation, land use.
• Sentinel-3: Ocean & land monitoring.
• Sentinel-5P: Atmosphere and air-quality monitoring.

Free and open for many users, making them essential in modern geospatial workflows.
 

5. Synthetic Aperture Radar (SAR)
A radar-imaging system that emits microwave pulses and measures the return. Because SAR works through clouds and at night, it’s invaluable for flood mapping, infrastructure change detection, terrain deformation and many disaster-use cases.

6. NDVI (Normalized Difference Vegetation Index)
A widely used vegetation health index derived from the red and near-infrared (NIR) bands of satellite imagery:
Values range from −1 to +1; higher values generally indicate healthier and denser vegetation. Commonly derived from Landsat, Sentinel-2, MODIS, etc.

7. MODIS (Moderate Resolution Imaging Spectroradiometer)
A sensor aboard NASA’s Terra and Aqua satellites capturing 36 spectral bands at ~250 m to 1 km resolution. Used for large-scale environmental monitoring — vegetation, ocean-colour, aerosols and climate trends.

8. LEO (Low Earth Orbit)
Satellites orbiting between ~200 km and ~2,000 km above Earth. They complete orbits in ~90-120 minutes and are widely used for imaging, communications and Earth observation constellations (e.g., PlanetScope, Starlink, OneWeb).

9. GEO (Geostationary Orbit)
Satellites that orbit ~36,000 km above the Earth’s equator and appear fixed relative to the surface. Common for weather satellites, communications and broadcast services due to their continuous view of the same region.

10. Sun-Synchronous Orbit (SSO)
A near-polar orbit that passes over the same area of Earth at the same local solar time each day. Ensures consistent lighting conditions in imagery, which is crucial for time-series analysis and change detection.

11. Drone (UAV) Imagery
Unmanned Aerial Vehicles (drones) capture high-resolution, on-demand imagery at local scale. These imagery sources complement satellite data by offering detailed and flexible coverage for specific sites - in agriculture, construction or inspection use-cases.

12. Areal Imagery
Imagery taken from aircraft, balloons or other airborne platforms (lower altitude than satellites). These provide very high spatial resolution - often used in city planning, mapping or environmental surveys.

13. Resolution
A critical term in remote sensing:

• Spatial resolution: size of one pixel on the ground (e.g., 10 m, 30 m, 0.5 m).
• Spectral resolution: number and width of spectral bands captured by the sensor.
• Temporal resolution: how often a sensor revisits a given area.
• Radiometric resolution: the sensor’s ability to discriminate small differences in energy.

14. Spectral Bands
Specific ranges of the electromagnetic spectrum that sensors capture (e.g., blue, red, NIR, SWIR). Combining bands allows analysts to identify features or conditions invisible to the human eye (e.g., stressed vegetation, water-stress, soil moisture).

15. Image Stacking (Band or “Mock Stacking”)
The process of combining multiple spectral bands or multiple scenes (images) into a single composite image for analysis. For example, stacking Sentinel-2 bands (B4, B3, B2, plus B8) to create a multiband image that enables NDVI calculation. Temporal stacking (combining images over time) also helps generate cloud-free mosaics or track change.

16. Ground Station
A facility on Earth that communicates with satellites - receiving data (downlink) and sending commands or data (uplink). Ground stations are foundational to getting raw satellite data and transforming it into usable imagery or datasets.

17. Temporal Analysis
Analyzing imagery from multiple dates to detect change - such as vegetation growth cycles, urban expansion, deforestation or post-disaster recovery.

18. Open Data Platforms
Platforms that provide free or open access to satellite/remote-sensing data:

• USGS EarthExplorer (for Landsat).
• Copernicus Open Access Hub (for Sentinel).
• NASA Earthdata (for MODIS and many others).
• Google Earth Engine (cloud-based large-scale analysis).

19. Footprint
The ground-area covered by a satellite image (in one pass). A larger footprint means more area covered but potentially less detail; a smaller footprint means higher detail but less area.

20. Calibration and Validation (Cal/Val)
Processes ensuring satellite sensors and imagery products are accurate and reliable. Calibration adjusts for sensor drift or atmospheric influences; validation involves comparing satellite data with field observations or reference datasets.