In a certain way, a color image, in the RGB model, is a multispectral image, since through its channels it describes the reflectance for the R, G and B band. The difference is that an RGB image is intended for its colorimetric values to be read under human perception, while a multispectral image does not have a number of bands under which it can be reflected or described that it is a multispectral image or technique.[6]​ Therefore, to describe it we rely on the following fundamentals:

• - The spectral interval.

The ability to access one or other bands of the spectrum depends on the type of sensor or filters used and the scope of the project. It is possible to access various regions of the electromagnetic spectrum described by their wavelengths.

• - The spectral resolution.

The ability to discretize into a number of bands, described by their maximum peak and width, a given region of the spectrum. That is, the more bands there are, the smaller the width and the greater the resolution that will allow us to better describe the reflectance of an object.

• - The temporal scale.

Referring to the time interval that is represented in an image, therefore, many images represent multiple moments in time that would help us understand or evaluate phenomena that become evident with the simple passage of time.

• - Agriculture.

Thanks to drones or satellites equipped with spectral cameras, the state of development of crop fields, the presence of pests or other pathologies such as lack of nutrients in the soil can be diagnosed. As the EcuRed article explains,[7]​ spectral images in agriculture provide a series of advantages based on data collection, analysis and interpretation. Thanks to these images, agricultural professionals are able to know the exact needs for inputs, both at a global and specific level.

• - Satellite image.

• - Remote sensing.

• - Spy satellite.

• - Harold Hough: Satellite Surveillance, Loompanics Unlimited, 1991, ISBN 1-55950-077-8.

• - Carlos Pérez: Remote Sensing: Notions and Applications, University of Salamanca, 2006, ISBN 978-84-611-1613-3").

• - José Manuel Pereira Uzal: Imaging diagnosis in non-visible bands on cultural heritage: An approach to infrared, ultraviolet imaging, fluorescence and image analysis, 2020, Books on Demand, 2020, ISBN 978-84-1326-121-8.

• - José A. Sobrino: Remote Sensing, Universitat de València, 2021, ISBN 978-84-370-4220-6.

• - Sc.chula.ac.th.

• - Academic.emporia.edu.

• - Multispectral imaging in ColourLex.

In a certain way, a color image, in the RGB model, is a multispectral image, since through its channels it describes the reflectance for the R, G and B band. The difference is that an RGB image is intended for its colorimetric values to be read under human perception, while a multispectral image does not have a number of bands under which it can be reflected or described that it is a multispectral image or technique.[6]​ Therefore, to describe it we rely on the following fundamentals:

• - The spectral interval.

The ability to access one or other bands of the spectrum depends on the type of sensor or filters used and the scope of the project. It is possible to access various regions of the electromagnetic spectrum described by their wavelengths.

• - The spectral resolution.

The ability to discretize into a number of bands, described by their maximum peak and width, a given region of the spectrum. That is, the more bands there are, the smaller the width and the greater the resolution that will allow us to better describe the reflectance of an object.

• - The temporal scale.

Referring to the time interval that is represented in an image, therefore, many images represent multiple moments in time that would help us understand or evaluate phenomena that become evident with the simple passage of time.

• - Agriculture.

Thanks to drones or satellites equipped with spectral cameras, the state of development of crop fields, the presence of pests or other pathologies such as lack of nutrients in the soil can be diagnosed. As the EcuRed article explains,[7]​ spectral images in agriculture provide a series of advantages based on data collection, analysis and interpretation. Thanks to these images, agricultural professionals are able to know the exact needs for inputs, both at a global and specific level.

• - Satellite image.

• - Remote sensing.

• - Spy satellite.

• - Harold Hough: Satellite Surveillance, Loompanics Unlimited, 1991, ISBN 1-55950-077-8.

• - Carlos Pérez: Remote Sensing: Notions and Applications, University of Salamanca, 2006, ISBN 978-84-611-1613-3").

• - José Manuel Pereira Uzal: Imaging diagnosis in non-visible bands on cultural heritage: An approach to infrared, ultraviolet imaging, fluorescence and image analysis, 2020, Books on Demand, 2020, ISBN 978-84-1326-121-8.

• - José A. Sobrino: Remote Sensing, Universitat de València, 2021, ISBN 978-84-370-4220-6.

• - Sc.chula.ac.th.

• - Academic.emporia.edu.

• - Multispectral imaging in ColourLex.