Imaging spectrometry from aerial or spaceborne platforms, also known as hyperspectral remote sensing, provides dense sampled and fine structured spectral information for each image pixel, allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The ground sample distance is 30 m with 1024 pixels across track. In this article, a detailed review is given on the applicability of DESIS data based on the specifics of the instrument, the characteristics of the ISS orbit, and the methods applied to generate products. The various DESIS data products available for users are described with the focus on specific processing steps. The results of the data quality and product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements. The limitations of the DESIS data products are also subject to a critical examination.

A comprehensive radiometric characterization of raw-data format imagery acquired with the Raspberry Pi 3 and V2.1 camera module is presented. The Raspberry Pi is a high-performance single-board computer designed to educate and solve real-world problems. This small computer supports a camera module that uses a Sony IMX219 8 megapixel CMOS sensor. This paper shows that scientific and engineering-grade imagery can be produced with the Raspberry Pi 3 and its V2.1 camera module. Raw imagery is shown to be linear with exposure and gain (ISO), which is essential for scientific and engineering applications. Dark frame, noise, and exposure stability assessments along with flat fielding results, spectral response measurements, and absolute radiometric calibration results are described. This low-cost imaging sensor, when calibrated to produce scientific quality data, can be used in computer vision, biophotonics, remote sensing, astronomy, high dynamic range imaging, and security applications, to name a few.

A fully automated, National Institute of Standards and Technology (NIST)-traceable artificial light source called Terra Vega has been developed to radiometrically calibrate the Visible Infrared Imaging Radiometer (VIIRS) Day Night Band (DNB) working in high gain stage (HGS) mode. The Terra Vega active point source is a calibrated integrating sphere that is only a fraction in size of a VIIRS DNB pixel. As such, it can be considered analogous to a ground-based photometric reference star. Vicarious calibrations that employ active point sources are different than those that make use of traditional extended sources and can be applyed to quantify the brightness of artificial light sources. The active source is successfully fielded, and early results indicate that it can be used to augment and validate the radiometric calibration of the VIIRS DNB HGS sensor on both the Suomi National Polar-orbiting Partnership (NPP) and NOAA-20 satellites. The VIIRS DNB HGS sensor can benefit from this technology as on-board calibration is challenging and hinges on transferring low gain stage (LGS) calibration using a solar diffuser to the medium gain stage (MGS) and HGS via regions of overlap. Current vicarious calibration methods that use a lunar-illuminated extended source estimate the HGS radiometric accuracy to within 8-15%. By comparison, early results and analysis showed that Terra Vega is stable to about 1%. Under clear dark night conditions, predicted top-of-atmosphere radiance from Terra Vega ranged between 1–11% of VIIRS measured values. Terra Vega’s excellent stability opens up new opportunities to validate and develop nighttime imaging applications based on point sources.

One of the biggest changes in the world of optical remote sensing over the last several years is the sheer increase in the number of sensors that are imaging the Earth in moderate to high spatial resolution. With respect to the calibration of these sensors, they are broadly classified into two types, namely government systems and commercial systems. Because of the differences in the design and mission of these sensor types, calibration approaches are often substantially different. Thus, an opportunity exists to foster discussion between calibration teams for these sensors with the goal of improving overall sensor calibration and data interoperability. The approach used to accomplish this task was a one-day workshop where team members from both government and commercial sensors could share best practices, discuss methods for collaboration and improvement, and make recommendations for continuing activities. Five major recommendations were developed from the event that focused on coordinated activities using pseudo invariant calibration sites (PICS), broader and more consistent communication, collaboration on specific cross-calibration opportunities, developing a reference sensor for all optical systems, and encouraging the coordinated development of surface reflectance products. Workshop participants concluded that regular interactions between these teams could foster a better calibration of all sensor systems and accelerate the improved interoperability of surface products.

The satellite cross-calibration radiometer (SCR) is a conceptual on-orbit hyperspectral imaging radiometer that transfers the radiometric calibration from a “gold-standard” reference instrument such as the Landsat 8/9 Operational Land Imager (OLI) to other civil, international, or commercial “client” multispectral satellite systems via near-simultaneous cross-calibration acquisitions. The spectral resolution, spectral range, spatial resolution, and signal-to-noise ratio (SNR) all significantly impact the complexity and cost of hyperspectral SCRs, so it is important to understand their effect on cross-calibration quality. This paper discusses the results of a trade study to quantify the effects of varying ground sample distance (GSD), number of independent samples, and instrument/scene noise on cross-calibration gain uncertainties. The trade study used a simulated SCR cross-calibration with near-simultaneous nadir overpasses (SNOs) of the Landsat 8 OLI acting as the reference instrument and the DLR Earth Sensing Imaging Spectrometer (DESIS) acting as a surrogate SCR hyperspectral instrument. Results demonstrate that cross-calibration uncertainty is only minimally affected by spatial resolution and SNR, which may allow SCR instruments to be developed at a lower cost.

The satellite imagery of nighttime lights (NTLs) has been studied to understand human activities, economic development, and more recently, the ecological impact of brighter night skies. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) offers perhaps the most advanced nighttime imaging capabilities to date, but its large pixel size and single band capture large-scale changes in NTL while missing granular but important details, such as lighting type and brightness. To better understand individual NTL sources in a region, the spectra of nighttime lights captured by the DLR Earth Sensing Imaging Spectrometer (DESIS) were extracted and compared against near-coincident VIIRS DNB imagery. The analysis shows that DESIS’s finer spatial and spectral resolutions can detect individual NTL locations and types beyond what is possible with the DNB. Extracted night light spectra, validated against ground truth measurements, demonstrate DESIS’s ability to accurately detect and identify narrow-band atomic emission lines that characterize the spectra of high-intensity discharge (HID) light sources and the broader spectral features associated with different light-emitting diode (LED) lights. These results suggest the possible application of using hyperspectral data from moderate-resolution sensors to identify lamp construction details, such as illumination source type and light quality in low-light contexts. NTL data from DESIS and other hyperspectral sensors may improve the scientific understanding of light pollution, lighting quality, and energy efficiency by identifying, evaluating, and mapping individual and small groups of light sources.