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Human vision is constrained to the visible portion of the electromagnetic spectrum. Remote sensing systems, however, provide the opportunity to measure beyond the visible and into the full spectrum.

Of the total energy radiated by objects around us, we can only see a small part. Our eyes can only perceive the visible range of the electromagnetic spectrum.

Different portions of the spectrum are relevant for providing information about various features on the Earth's surface. But we first have to identify which band or bands to use. Which calls for an understanding of spectral signatures.

In this section, we look at the applications of multispectral imagery and point out where hyperspectral imagery might come in. In a bid to show the importance of different portions of the electromagnetic spectrum, we outline those that are relevant for each application.

When a plant is unhealthy or under stress, it produces less chlorophyll. Less chlorophyll means higher reflectance in the visible red region (chlorophyll absorbs visible radiation, especially in the blue and red wavelengths). At the same time, as the water content in leaves decreases, reflectance in the near-infrared region increases. These changes alter the position and shape of the red edge band.

The above example demonstrates one of the advantages of being able to see beyond the visible spectrum. Luckily, we do not have to depend on our eyes for this.

For example, the graph below shows the spectral response patterns of conifer, deciduous, and grass vegetation. While their reflectance in the visible range is almost alike, they display different reflectance patterns in the near-infrared range enabling their classification.

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Determination of soil organic matter content is important for soil management, estimation of available nutrients for precision agriculture, and climate variability studies.

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The application of fine spectral resolution hyperspectral images, however, enables the identification of a large range of minerals and allows for the determination of their composition and abundance.

The following table contains examples of hyperspectral sensors, along with details such as number of bands, swatch, and resolution.

Aside from mineral exploration, multispectral imagery also facilitates the environmental monitoring of mining areas. Discover how geospatial technology enables mine operators to protect the environment and plan remediation throughout the mine lifecycle.

Spectral reflectance curves of bermudagrass under different levels of water deprivation. (Source: Caturegli, L., Matteoli, S., Gaetani, M. et al. Effects of water stress on spectral reflectance of bermudagrass. Sci Rep 10, 15055 (2020). https://doi.org/10.1038/s41598-020-72006-6) (CC BY 4.0)

Multispectral imagery is imagery containing multiple spectral bands of the electromagnetic spectrum. They are collected by sensors that measure reflected energy in specific portions of the electromagnetic spectrum.

Beyond the visible range, however, vegetation species have different reflectance characteristics making them easier to differentiate. In this example, therefore, using reflectance characteristics of vegetation beyond the visible range allows us to classify different tree species and examine changes in their distribution (as in the maps below).Â

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Aside from water quality, multispectral imagery also supports the monitoring of ocean environments. Discover why satellite imagery is the key to ocean monitoring.Â

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Some satellite sensors can detect visible and nonvisible portions of the electromagnetic spectrum. Such sensors are called multispectral or hyperspectral sensors, and the resulting images are multispectral or hyperspectral imagery.

The spectral response pattern differs among different plant species enabling species recognition using remotely sensed imagery.

For vegetation, the rate of increase in reflectance is the highest in the region between the visible red and near-infrared bands. This region is known as the red edge band.

The major difference between multispectral and hyperspectral images is the number of bands within the same portion of the electromagnetic spectrum.Â

The visible portion–what we perceive with our eyes–is further segmented into blue, green, and red regions. Similarly, the infrared band is segmented into near-infrared, mid-infrared, and far-infrared regions.

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Source: Meng, Y.; Cao, B.; Mao, P.; Dong, C.; Cao, X.; Qi, L.; Wang, M.; Wu, Y. Tree Species Distribution Change Study in Mount Tai Based on Landsat Remote Sensing Image Data. Forests 2020, 11, 130. https://doi.org/10.3390/f11020130 (CC BY 4.0)

The above spectral response characteristics aid in the development of vegetation indices, which are useful for the extraction of vegetation traits from multispectral and hyperspectral data. They also give rise to the following applications of multispectral and hyperspectral imagery for vegetation analyses.

Source: Meng, Y.; Cao, B.; Mao, P.; Dong, C.; Cao, X.; Qi, L.; Wang, M.; Wu, Y. Tree Species Distribution Change Study in Mount Tai Based on Landsat Remote Sensing Image Data. Forests 2020, 11, 130. https://doi.org/10.3390/f11020130 (CC BY 4.0)

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Hyperspectral imagery, therefore, improves the identification and quantitative assessment of the physical and chemical properties of the objects of interest, e.g., vegetation, water, soils, minerals, etc.Â

Additionally, in some cases, the construction of vegetation indices using the narrow bands of hyperspectral imagery yields better estimates of biophysical parameters (e.g., biomass and leaf area index) than vegetation indices derived from multispectral imagery.

When light hits vegetation, reflection, absorption, or transmission occurs. These are influenced by the leaves' chemical, structural, and biological properties. This interaction between plants and light enables the discovery of plant properties. In summary:

Soil reflectance in the visible wavelengths decreases with increasing soil moisture up to the point of saturation---where there is no more change. Similarly, soil reflectance in the near-infrared region decreases with increasing moisture. These reflectance characteristics allow for the determination of moisture content using multispectral bands.

For example, the video below shows how Landsat 8 multispectral imagery helped scientists identify algal bloom in Lake Utah:

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Studies have shown that soil reflectance properties in different multispectral bands can be used to estimate soil organic matter. For example, an increase in organic matter (even amounts only exceeding 2%) has a significant effect on soil reflectance. Specifically, organic matter reduces overall soil reflectivity up to an organic matter content of 5% beyond which there is no more change.

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Source: Vangi, E.; D'Amico, G.; Francini, S.; Giannetti, F.; Lasserre, B.; Marchetti, M.; Chirici, G. The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination. Sensors 2021, 21, 1182. https://doi.org/10.3390/s21041182 (CC BY 4.0)

For example, the image below illustrates changes in the shape and position of the red-edge band for plants at three different levels of water stress.

There are lots of rocks and minerals on the surface of the earth. Each has unique reflectance properties determined by color, texture, chemical composition, etc.

That said, depending on water characteristics like depth, suspended sediments, and aquatic vegetation, water bodies have unique reflectance properties. Multispectral imagery is thus useful in examining these variable water environments.

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Wavelength ranges of hyperspectral imaging systems. (Source: Xie, Y.; Plett, D.; Liu, H. The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat. AgriEngineering 2021, 3, 924-941. https://doi.org/10.3390/agriengineering3040058) (CC BY 4.0)

Multispectral and hyperspectral images reveal insights about our changing world beyond our human experience. They tell us about the mineral composition of the soil, biomass and plant health, surface roughness, vegetation types, ocean temperatures, and so much more.

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Variation of soil reflectance with water content. (Source: Li, T.; Mu, T.; Liu, G.; Yang, X.; Zhu, G.; Shang, C. A Method of Soil Moisture Content Estimation at Various Soil Organic Matter Conditions Based on Soil Reflectance. Remote Sens. 2022, 14, 2411. https://doi.org/10.3390/rs14102411) (CC BY 4.0)

Vegetation appears green because it reflects the visible green portion of the electromagnetic spectrum but absorbs the visible red and blue portions

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Most multispectral imagery contains 4–12 bands in the visible to the infrared portions of the electromagnetic spectrum. According to a USGS report, multispectral images may contain up to 36 wavelength bands.

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Note: The spectral signature of an object is not constant. It varies based on, among other things, the physical properties of the material, atmospheric effects, and the sensor's view angle. Therefore, spectral response curves are sometimes drawn as shadings–as opposed to a thin line–to show variability.

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Spectral signature is the amount of incident energy reflected by a material over different portions of the electromagnetic spectrum. We usually represent spectral signatures as a graph (a spectral response curve) showing the variation in reflected energy across different wavelengths.

Multispectral imagery is useful for the differentiation of vegetation types, soils, water, and human-made structures. Owing to their higher spectral resolution, hyperspectral imagery enables a finer scale distinction of features. Hyperspectral imagery is especially useful for uncovering spectral details which cannot be detected by multispectral sensors.

The advantage of hyperspectral remote sensing lies in the acquisition of an almost continuous reflectance spectrum for each pixel in an image. The narrow bands enable the detection of slight differences in features, which would otherwise be undetected using the relatively broad wavelength bands of multispectral imagery.

From a visual inspection, we can infer that the site has vegetation cover because of the green color. However, we cannot distinguish the vegetation species–let alone detect changes in their distribution between the two years.  This is because different vegetation species have almost similar reflectance within the visible range of the electromagnetic spectrum: they reflect green light.

Multispectral imagery plays a vital role in the initial stages of mineral exploration: enabling discrimination and mapping of different rocks and minerals based on their reflectance in the visible and infrared portions of the spectrum.

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Further, examining spectral response characteristics aids in the development of indices that are used to quantify various feature characteristics. An example is the Normalized Difference Vegetation Index (NDVI) used for determining areas with vegetation cover and evaluating their condition.

Plant water content and biochemistry (e.g., protein and cellulose content) influences reflectance in the shortwave infrared region  (1000--2500 nm)

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Spectral signatures of various materials. (Yang, Q.; Liu, X.; Wu, W. A Hyperspectral Bidirectional Reflectance Model for Land Surface. Sensors 2020, 20, 4456. https://doi.org/10.3390/s20164456) (CC BY 4.0)

The number of multispectral bands (top) compared to the number of hyperspectral bands (bottom) in the same portion of the electromagnetic spectrum.

Because of their broad spectral bands, multispectral imagery is limited in the detailed classification of rocks and minerals---not to mention estimating the distribution of mineral constituents within a pixel.

Water quality is assessed using several indicators including dissolved minerals, suspended material, bacteria levels, oxygen levels, salinity, etc. These indicators affect the optical properties of water in the visible and near-infrared portions, and their identification can therefore benefit from remote sensing techniques.

Hyperspectral imagery contains many more wavelength bands collected in narrow, adjacent sections across the visible and infrared regions of the electromagnetic spectrum.

The electromagnetic spectrum is the entire range of wavelengths of electromagnetic radiation. For analytical purposes, the electromagnetic spectrum is divided into bands (portions), each representing a range of wavelengths. We refer to the bands by name: gamma rays, x-rays, ultraviolet rays, visible light (0.4µm--0.7µm), infrared (0.7µm--100µm), microwaves (1mm--1m), and radio waves.

The spectral reflectance of soils varies by their physical and chemical properties. These interactions give rise to multispectral data applications for studying soil properties like moisture content, organic matter content, iron content, texture, roughness, and mineral composition.Â

Water only reflects up to approximately 10% of incident energy. Of these, most are in the visible portion of the electromagnetic spectrum, with little or no reflectance in the near-infrared region.

Typical spectral signature of a green leaf and factors influencing the curve. (Source: Moroni, M.; Porti, M.; Piro, P. Design of a Remote-Controlled Platform for Green Roof Plants Monitoring via Hyperspectral Sensors. Water 2019, 11, 1368. https://doi.org/10.3390/w11071368) (CC BY 4.0)

Hyperspectral images contain more information on subtle spectral features of vegetation than multispectral images. They are thus useful for discriminating between plant varieties, detecting vegetation stress, and even estimating biomass in higher detail.