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NV5 Geospatial Blog

Each month, NV5 Geospatial posts new blog content across a variety of categories. Browse our latest posts below to learn about important geospatial information or use the search bar to find a specific topic or author. Stay informed of the latest blog posts, events, and technologies by joining our email list!



Deploy, Share, Repeat: AI Meets the Analytics Repository

Deploy, Share, Repeat: AI Meets the Analytics Repository

10/13/2025

The upcoming release of ENVI® Deep Learning 4.0 makes it easier than ever to import, deploy, and share AI models, including industry-standard ONNX models, using the integrated Analytics Repository. Whether you're building deep learning models in PyTorch, TensorFlow, or using ENVI’s native model creation tools, ENVI... Read More >

Blazing a trail: SaraniaSat-led Team Shapes the Future of Space-Based Analytics

Blazing a trail: SaraniaSat-led Team Shapes the Future of Space-Based Analytics

10/13/2025

On July 24, 2025, a unique international partnership of SaraniaSat, NV5 Geospatial Software, BruhnBruhn Innovation (BBI), Netnod, and Hewlett Packard Enterprise (HPE) achieved something unprecedented: a true demonstration of cloud-native computing onboard the International Space Station (ISS) (Fig. 1). Figure 1. Hewlett... Read More >

NV5 at ESA’s Living Planet Symposium 2025

NV5 at ESA’s Living Planet Symposium 2025

9/16/2025

We recently presented three cutting-edge research posters at the ESA Living Planet Symposium 2025 in Vienna, showcasing how NV5 technology and the ENVI® Ecosystem support innovation across ocean monitoring, mineral exploration, and disaster management. Explore each topic below and access the full posters to learn... Read More >

Monitor, Measure & Mitigate: Integrated Solutions for Geohazard Risk

Monitor, Measure & Mitigate: Integrated Solutions for Geohazard Risk

9/8/2025

Geohazards such as slope instability, erosion, settlement, or seepage pose ongoing risks to critical infrastructure. Roads, railways, pipelines, and utility corridors are especially vulnerable to these natural and human-influenced processes, which can evolve silently until sudden failure occurs. Traditional ground surveys provide only periodic... Read More >

Geo Sessions 2025: Geospatial Vision Beyond the Map

Geo Sessions 2025: Geospatial Vision Beyond the Map

8/5/2025

Lidar, SAR, and Spectral: Geospatial Innovation on the Horizon Last year, Geo Sessions brought together over 5,300 registrants from 159 countries, with attendees representing education, government agencies, consulting, and top geospatial companies like Esri, NOAA, Airbus, Planet, and USGS. At this year's Geo Sessions, NV5 is... Read More >

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Digital Number, Radiance, and Reflectance

Anonym

Here at NV5 Geospatial Tech Support, we often hear from folks who are confused about exactly what information is stored in the pixel values for their imagery, and how to get from the values they have to the values they need. I thought I'd take a few minutes to clear up some of that confusion.

 

Digital Number (DN)

The generic term for pixel values is Digital Number or DN. It is commonly used to describe pixel values that have not yet been calibrated into physically meaningful units.

If you just want to look at an image, and don't intend to interpret the pixel values in terms of some physically meaningful, quantitative value like radiance or reflectance (or any value derived from radiance or reflectance values, such as abundance), then it may be just fine to keep your image in its original DN values.

 

Radiance

Radiance is the amount of radiation coming from an area. To derive a radiance image from an uncalibrated image, a gain and offset must be applied to the pixel values. These gain and offset values are typically retrieved from the image's metadata or received from the data provider. ENVI provides a tool called Radiometric Calibration which will correct DN values to radiance for many data products that are distributed with calibration gain and offset values in the metadata.

There are a number of units that might be used for radiance pixel values, and it is important to understand which units are used for the values in your radiance image. For example, radiance images that are used as input to FLAASH (in ENVI's Atmospheric Correction Module) are expected to have units of µW/(cm2*sr*nm). ENVI's calibration tool allows you to control the units with which your radiance image is output.

Radiance includes radiation reflected from the surface, bounced in from neighboring pixels, and reflected from clouds above the area of the pixel. Radiance is also affected by the source of the radiation, which for optical imagery is the sun. If you look at the spectrum of a radiance pixel, it will have the overall shape of the solar spectrum, which peaks at green wavelengths (about 500 nm).

Radiance (left) and surface reflectance (right) spectra for dry grass (black), green grass (red), water(green) and pavement (blue). Notice that all of the radiance spectra trend toward higher values at about 500 nm, because the spectrum of the sun peaks at about 500 nm . The gaps in the reflectance spectra indicate wavelengths at which radiation is strongly absorbed by the atmosphere and so no signal from the surface can be detected. Image courtesy of Spectral Sciences Inc.

 

Typically, for quantitative analysis of multispectral or hyperspectral image data, radiance images are corrected to reflectance images.

 

Reflectance

Reflectance is the proportion of the radiation striking a surface to the radiation reflected off of it. Some materials can be identified by their reflectance spectra, so it is common to correct an image to reflectance as a first step toward locating or identifying features in an image.

Top of Atmosphere Reflectance

Top-of-atmosphere reflectance (or TOA reflectance) is the reflectance measured by a space-based sensor flying higher than the earth's atmosphere. These reflectance values will include contributions from clouds and atmospheric aerosols and gases.

Surface Reflectance

Surface Reflectance is the reflectance of the surface of the Earth. Clouds and other atmospheric components do not affect surface reflectance spectra. Typically images of surface reflectance are derived from calibrated radiance images. There are many ways to derive reflectance images from radiance images, including model-based atmospheric corrections such as those included in ENVI's Atmospheric Correction Module.

 

In summary, it is common to receive an uncalibrated image directly from the data provider, then calibrate it to a radiance image. The radiance image is then atmospherically corrected, resulting in a surface reflectance image. At this point, the reflectance image is ready to be used to extract quantitative information about features on the surface.

3 comments on article "Digital Number, Radiance, and Reflectance"

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a

http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6737293


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Srinivas Kolluru

"Reflectance is the proportion of the radiation striking a surface to the radiation reflected off of it". This is wrong. The correct definition is "Reflectance is the proportion of the radiation reflected off a surface to the radiation striking it." (mentioned in http://www.harrisgeospatial.com/portals/0/pdfs/envi/PreprocessAVIRIS.pdf, please correct it.


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assignment writers

Sensors measure the brilliance, which relates to the shine in a provided guidance toward the sensor; it valuable to characterize likewise the reflectance as the proportion of reflected versus add up to power vitality.

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