More spectral bands, more insight, more action

Spectral content, together with spatial and radiometric resolution, is one of the most important parameters when analysing remote-sensing information. Where spatial resolution allows you to detect and identify objects or assets, this blog will explore the use of multiple spectral bands to classify, quantify and predict a specific state of an asset or object.
Commercial remote sensing is rapidly evolving and has become an integral tool in making socio-economic decisions. Today it is used in a wide range of applications such as food security, global conflicts, environmental issues, land sustainability and more.

This evolution is partly due to the availability of new remote-sensing technologies and an increase in access to space and partly due to new computing technologies, availability of software services, advanced algorithms solving specific challenges, machine learning and more.

New entrants into this market are usually overwhelmed by the amount of data and options available. One of the first decisions they are confronted with is what spectral bands should I use for my challenge or, in most cases, what problems can I solve with the spectral content available to me?

The most common spectral bands used are red, green and blue in the visible part of the spectrum and the near infrared band. Remote-sensing platforms launched over the last decade doubled the spectral resolution, opening up the market for a plethora of possible applications in nearly all industrial verticals from agriculture, forestry and mining to asset management, insurance and the financial sector.

Compared to RGB remote sensing, the number of possible applications is considerably greater. This increase in the quality and amount of remote-sensing data will ultimately enable better-informed and more effective decisions across a wide variety of industries.

The main advantage of an increased number of spectral bands is that it will significantly enhance the accuracy with which assets and resources are detected and change over time, identify specific reasons for the changes and classify the differences and measure the severity of the change, not only in the physical environment but also socio-economically.

Various satellites are providing 7 or more spectral bands in the VNIR region. Both Sentinel-2 and Worldview-2 have 8 spectral bands in the 450 nm to 900 nm region. Landsat-8 only provides 4 bands in this spectrum. Figure 1 provides a comparison between the MultiScape100’s “Off-the-Shelf” Bands and the Worldview-2, Sentinel-2 and Landsat-8 spectral bands.

Figure 1: A comparison between the spectral bands of Worldview-2, Sentinel-2 Landsat-8 and MultiScape100’s “Off-the-Shelf” bands with the reflectance curve of vegetation in the background.

It is important to monitor vegetation in multiple spectral bands to obtain an overall “picture” of the landscape’s health. The near-infrared bands are great for Leaf Area Index (LAI) calculations, while the green band is sensitive to the total chlorophyll (reflected), ideal for the monitoring of plant health and vigour. Vegetation, or rather chlorophyll within the plant, absorbs the red part of the spectrum. A change in reflectance in this band is a good indicator or overall plant health. Shifts in the red-edge bands are also a good indicator of changes in plant health. The next table provides a detail overview of the applications and value of each spectral band.

Blue [450 – 510 nm]

Readily absorbed by chlorophyll in plants.

Provides good penetration of water.

Less affected by atmospheric scattering and absorption compared to the Coastal Blue Band.

Sensitive to vegetation senescing, carotenoid, browning and soil background.

Can be used for atmospheric corrections.

Yellow [585 – 625 nm]

Very important for feature classification.

Detects the “yellowness” of particular vegetation, both on land and in the water.

Green [510 – 580 nm]

Able to focus more precisely on the peak reflectance of healthy vegetation.

Ideal for calculating plant vigor.

Very helpful in discriminating between types of plant material when used in conjunction with the Yellow band.

Sensitive to total chlorophyll in vegetation.

Red Edge [690 – 730 nm] [700 – 715 nm]

Centered strategically at the onset of the high reflectivity portion of vegetation response.

Very valuable in measuring plant health and aiding in the classification of vegetation.

Position of red edge; consolidation of atmospheric corrections / fluorescence baseline.

Red [655 – 690 nm]

Better focused on the absorption of red light by chlorophyll in healthy plant materials.

One of the most important bands for vegetation discrimination.

Very useful in classifying bare soils, roads, and geological features.

Maximum chlorophyll absorption.

Near Infrared [780 – 900 nm]

Very effective for the estimation of moisture content and plant biomass.

Effectively separates water bodies from vegetation, identifies types of vegetation and also discriminates between soil types.

Used to calculate Leaf Area Index (LAI).

Each of the three xScape100 solutions features its own strengths and limitations when it applies to spectral resolution:


High-definition blue, green and red images provide adequate information for asset and infrastructure detection and identification. This is ideal for coastline monitoring, the tracking of vessels, monitoring the patterns of life and uncovering activities in inaccessible areas. Figure 2 shows the relative spectral response of the TriScape100. A NIR cut-off filter is applied on the sensor to prevent NIR “leakage” above 700nm.

Figure 2: The TriScape100’s relative filter response.


This multispectral imager brings 7 bands across the visible and near-infrared spectrum to the xScape100 family. This sensor is ideal for a wide range of applications such as understanding changes within the ecosystem and socio-economic environment, the uncovering and validation of potential investments, monitoring strategic assets and identifying market opportunities and business risks before anyone else. Figure 3 shows the relative spectral response of the Sentinel-2 bands. A selection of these bands are an option for the MultiScape100.

Figure 3: Sentinel-2’s relative spectral bands. This is a possible option for the MultiScape100.


Hyperspectral sensors extend the number of spectral bands into the hundreds. This feature allows for the identification of specific plant and tree species. The NDVI information content is ten times higher, being able to classify changes in plant health much more accurately than a multispectral system. The relative response of the 154 bands of the HyperScape100 are shown in Figure 4.

Figure 4: The relative spectral response of the 154 bands of the HyperScape100.

When designing a remote-sensing mission, the selection of spectral bands must match the primary objective of the mission. This can be a daunting task. The xScape100 series of optical payloads bring a wide range of option that will allow the mission’s planner to customize the spectral resolution.

Please see the xScape100 series of payloads’ datasheets for more information on the technology features.