Table 13.
Comparison between spaceborne and airborne sensors.
| Parameter | Spaceborne | Airborne |
|---|---|---|
| Time of overpass | Mostly fixed | Flexible |
| Spatial resolution | Ground Sampling Distance (GSD) up to 0.5 m for panchromatic images. For multi-band images, it ranges from a few meters (low altitude sensors) up to a few kilometers for high altitude sensors | Ground Sampling Distance (GSD) < 5 m |
| Spectral resolution | Mostly panchromatic (one band) to multispectral, recently developed sensors like HyspIRI, CHRIS, and HICO are hyperspectral | Panchromatic to hyperspectral |
| Temporal resolution (Revisit time) | Days | Minutes |
| Calibration | Precalibration before launch, then on-board characterization (usually yearly) | Before launch + possible on-board |
| Cost | Free (non-commercial), up to about $50 per sq km (commercial). High spatial resolution imagery can be very expensive (~$2–10 k per scene) | Average costs of $350 per square mile (Chipman et al. 2009) |
| Stability | High | Low, due to turbulence |
| Swath width | High (up to 2500 km for low altitude sensors, a full hemisphere for high altitude sensors) | Small (up to 10 km per flight line) |
| Interpretation approaches | Mostly empirical-and semi-empirical-based approaches | Both empirical and analytical approaches |
| Complexity of image processing | Less complex compared to hyperspectral sensors | Processing of hyperspectral images is more complex and requires specific skills |
| Constraints | Limited to the coverage schedule of the satellite, including weather/cloud constraints; this can be challenging when trying to conduct water quality monitoring at a certain time of the year or dealing with project schedules | Coverage schedule is flexible |
| Geographic coverage areas | Local, regional, and global | Local and regional |