Which factors should be considered when choosing remote sensing products?
Factors to consider when selecting remote sensing products include spatial resolution, spectral resolution, radiometric resolution, and temporal resolution.
Spatial resolution refers to the size of the smallest object that can be detected in an image. The basic unit in an image is called a pixel. One-meter spatial resolution means each pixel image represents an area of one square meter. The smaller an area represented by one pixel, the higher the resolution of the image.
Spectral resolution refers to the number of bands and the wavelength width of each band recorded by a sensor. For any given sensor, each band represents a specific portion of the electromagnetic spectrum. Multi-spectral sensors, such as those on Quickbird and SPOT satellites, can measure four bands that include visible blue, green, and red and near infrared (NIR), while other satellite sensors such as Landsat and Aster can measure seven or more bands. Hyper-spectral sensors measure energy in narrower and more numerous bands than multi-spectral sensors. The narrow bands of hyper-spectral sensors are more sensitive to variations in reflected and emitted energy and therefore have a greater potential to detect subtle differences (e.g., crop stress, plant community, etc.) than is the case with multi-spectral sensors. Multi-spectral and hyper-spectral images often are used together to provide a more complete picture of ground conditions.
Radiometric resolution refers to the sensitivity of a remote sensor to variations in the reflectance levels. The higher the radiometric resolution of a sensor, the more sensitive it is to detecting small differences in reflectance values. Higher radiometric resolution allows a sensor to provide a more detailed measurement within a specific portion of the electromagnetic spectrum.
Temporal resolution refers to how often a remote sensing platform collects images of an area. Geo-stationary satellites can provide continuous data, while the more common orbiting satellites can only provide data each time they pass over an area. Cloud cover often interferes with multi- and hyper-spectral data collection from satellite-based sensors since it masks ground features. Remote sensing accomplished by cameras mounted on airplanes is often used to provide data for applications requiring more frequent visits. Remote sensors located in fields or attached to agricultural equipment can provide a very fine temporal resolution.