Ask AI: In remote sensing, what does the term 'spectral resolution' refer to?

In remote sensing, the term 'spectral resolution' refers to the ability of a sensor to distinguish between different wavelengths of the electromagnetic spectrum. It is defined by the number of spectral bands in which the sensor can capture data and the width of each band. Higher spectral resolution means that the sensor can detect finer differences in wavelength, allowing for more detailed and precise measurements of the spectral characteristics of the observed objects or phenomena. For example: - A sensor with low spectral resolution might have broad bands that cover large portions of the spectrum, such as the visible or infrared regions. - A sensor with high spectral resolution, like a hyperspectral sensor, can capture data in hundreds of narrow, contiguous spectral bands, making it possible to identify and analyze materials and surfaces more accurately based on their specific spectral signatures. Higher spectral resolution is particularly useful for applications like vegetation analysis, mineral exploration, and environmental monitoring, where distinguishing subtle differences in spectral properties is crucial.