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NASA NTRS1982

Imaging spectrometer technologies for advanced earth remote sensing

A major requirement of multispectral imaging systems for advanced earth remote sensing is the provision for greater spectral resolution and more versatile spectral band selection. The imaging spectrometer instrument concept provides this versatility by the combination of pushbroom imaging and spectrally dispersing optics using area array detectors in the focal plane. The shuttle imaging spectrometer concept achieves 10- and 20-meter ground instantaneous fields of view with 20-nanometer spectral resolution from earth orbit. Onboard processing allows the selection of spectral bands during flight; this, in turn, permits the sensor parameters to be tailored to the experiment objectives. Advances in optical design, infrared detector arrays, and focal plane cooling indicate the feasibility of the instrument concept and support the practicability of a validation flight experiment for the shuttle in the late 1980s. Previously announced in STAR as N83-28542

Wellman, J. B.↗

NASA NTRS1994

Development of an Aerosol Opacity Retrieval Algorithm for Use with Multi-Angle Land Surface Images

In 1998, the Multi-angle Imaging SpectroRadiometer (MISR) will fly aboard the EOS-AM1 spacecraft. MISR will enable unique methods for retrieving the properties of atmospheric aerosols, by providing global imagery of the Earth at nine viewing angles in four visible and near-IR spectral bands. As part of the MISR algorithm development, theoretical methods of analyzing multi-angle, multi-spectral data are being tested using images acquired by the airborne Advanced Solid-State Array Spectroradiometer (ASAS). In this paper we derive a method to be used over land surfaces for retrieving the change in opacity between spectral bands, which can then be used in conjunction with an aerosol model to derive a bound on absolute opacity.

Advanced Solid-State Array Spectroradiometer ASAS ↗

NASA NTRS2010

An Overview of the Landsat Data Continuity Mission

The advent of the Landsat Data Continuity Mission (LDCM), currently with a launch readiness date of December, 2012, will see evolutionary changes in the Landsat data products available from the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The USGS initiated a revolution in 2009 when EROS began distributing Landsat data products at no cost to requestors in contrast to the past practice of charging the cost of fulfilling a request; that is, charging $600 per Landsat scene. To implement this drastic change, EROS terminated data processing options for requestors and began to produce all data products using a consistent processing recipe. EROS plans to continue this practice for the LDCM and will required new algorithms to process data from the LDCM sensors. All previous Landsat satellites flew multispectral scanners to collect image data of the global land surface. Additionally, Landsats 4, 5, and 7 flew sensors that acquired imagery for both reflective spectral bands and a single thermal band. In contrast, the LDCM will carry two pushbroom sensors; the Operational Land Imager (OLI) for reflective spectral bands and the Thermal InfraRed Sensor (TIRS) for two thermal bands. EROS is developing the ground data processing system that will both calibrate and correct the data from the thousands of detectors employed by the pushbroom sensors and that will also combine the data from the two sensors to create a single data product with registered data for all of the OLI and TIRS bands.

Irons, James R.↗

NASA NTRS2019

Atmospheric Correction of Satellite Ocean-Color Imagery During the PACE Era

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will carry into space the Ocean Color Instrument (OCI), a spectrometer measuring at 5 nm spectral resolution in the ultraviolet (UV) to near infrared (NIR) with additional spectral bands in the shortwave infrared (SWIR), and two multi-angle polarimeters that will overlap the OCI spectral range and spatial coverage, i. e., the Spectrometer for Planetary Exploration (SPEXone) and the Hyper-Angular Rainbow Polarimeter (HARP2). These instruments, especially when used in synergy, have great potential for improving estimates of water reflectance in the post Earth Observing System (EOS) era. Extending the top-of-atmosphere (TOA) observations to the UV, where aerosol absorption is effective, adding spectral bands in the SWIR, where even the most turbid waters are black and sensitivity to the aerosol coarse mode is higher than at shorter wavelengths, and measuring in the oxygen A-band to estimate aerosol altitude will enable greater accuracy in atmospheric correction for ocean color science. The multi-angular and polarized measurements, sensitive to aerosol properties (e.g., size distribution, index of refraction), can further help to identify or constrain the aerosol model, or to retrieve directly water reflectance. Algorithms that exploit the new capabilities are presented, and their ability to improve accuracy is discussed. They embrace a modern, adapted heritage two-step algorithm and alternative schemes (deterministic, statistical) that aim at inverting the TOA signal in a single step. These schemes, by the nature of their construction, their robustness, their generalization properties, and their ability to associate uncertainties, are expected to become the new standard in the future. A strategy for atmospheric correction is presented that ensures continuity and consistency with past and present ocean-color missions while enabling full exploitation of the new dimensions and possibilities. Despite the major improvements anticipated with the PACE instruments, gaps/issues remain to be filled/tackled. They include dealing properly with whitecaps, taking into account Earth-curvature effects, correcting for adjacency effects, accounting for the coupling between scattering and absorption, modeling accurately water reflectance, and acquiring a sufficiently representative dataset of water reflectance in the UV to SWIR. Dedicated efforts, experimental and theoretical, are in order to gather the necessary information and rectify inadequacies. Ideas and solutions are put forward to address the unresolved issues. Thanks to its design and characteristics, the PACE mission will mark the beginning of a new era of unprecedented accuracy in ocean-color radiometry from space.

Robert J Frouin↗

NASA NTRS2016

Monochromatic Measurements of the JPSS-1 VIIRS Polarization Sensitivity

Polarization sensitivity is a critical property that must be characterized for spaceborne remote sensing instruments designed to measure reflected solar radiation. Broadband testing of the first Joint Polar-orbiting Satellite System (JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS) showed unexpectedly large polarization sensitivities for the bluest bands on VIIRS (centered between 400 and 600 nm). Subsequent ray trace modeling indicated that large diattenuation on the edges of the bandpass for these spectral bands was the driver behind these large sensitivities. Additional testing using the National Institute of Standards and Technologies Traveling Spectral Irradiance and Radiance Responsivity Calibrations Using Uniform Sources was added to the test program to verify and enhance the model. The testing was limited in scope to two spectral bands at two scan angles; nonetheless, this additional testing provided valuable insight into the polarization sensitivity. Analysis has shown that the derived diattenuation agreed with the broadband measurements to within an absolute difference of about0.4 and that the ray trace model reproduced the general features of the measured data. Additionally, by deriving the spectral responsivity, the linear diattenuation is shown to be explicitly dependent on the changes in bandwidth with polarization state.

Monochromatic↗

DOE OSTI2023

Snapshot multispectral imaging using a diffractive optical network

Abstract Multispectral imaging has been used for numerous applications in e.g., environmental monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical network-based multispectral imaging system trained using deep learning to create a virtual spectral filter array at the output image field-of-view. This diffractive multispectral imager performs spatially-coherent imaging over a large spectrum, and at the same time, routes a pre-determined set of spectral channels onto an array of pixels at the output plane, converting a monochrome focal-plane array or image sensor into a multispectral imaging device without any spectral filters or image recovery algorithms. Furthermore, the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization states. Through numerical simulations, we present different diffractive network designs that achieve snapshot multispectral imaging with 4, 9 and 16 unique spectral bands within the visible spectrum, based on passive spatially-structured diffractive surfaces, with a compact design that axially spans ~72 λ m , where λ m is the mean wavelength of the spectral band of interest. Moreover, we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially repeating virtual spectral filter array with 2 × 2 = 4 unique bands at terahertz spectrum. Due to their compact form factor and computation-free, power-efficient and polarization-insensitive forward operation, diffractive multispectral imagers can be transformative for various imaging and sensing applications and be used at different parts of the electromagnetic spectrum where high-density and wide-area multispectral pixel arrays are not widely available.

36 MATERIALS SCIENCE↗

NASA NTRS1998

High Performance Parallel Architectures

Traditional remote sensing instruments are multispectral, where observations are collected at a few different spectral bands. Recently, many hyperspectral instruments, that can collect observations at hundreds of bands, have been operational. Furthermore, there have been ongoing research efforts on ultraspectral instruments that can produce observations at thousands of spectral bands. While these remote sensing technology developments hold great promise for new findings in the area of Earth and space science, they present many challenges. These include the need for faster processing of such increased data volumes, and methods for data reduction. Dimension Reduction is a spectral transformation, aimed at concentrating the vital information and discarding redundant data. One such transformation, which is widely used in remote sensing, is the Principal Components Analysis (PCA). This report summarizes our progress on the development of a parallel PCA and its implementation on two Beowulf cluster configuration; one with fast Ethernet switch and the other with a Myrinet interconnection. Details of the implementation and performance results, for typical sets of multispectral and hyperspectral NASA remote sensing data, are presented and analyzed based on the algorithm requirements and the underlying machine configuration. It will be shown that the PCA application is quite challenging and hard to scale on Ethernet-based clusters. However, the measurements also show that a high- performance interconnection network, such as Myrinet, better matches the high communication demand of PCA and can lead to a more efficient PCA execution.

El-Ghazawi, Tarek↗

NASA NTRS1981

Technologies for the multispectral mapping of earth resources

The application of solid-state detector array imaging to the remote sensing of the earth as a follow-on to currently planned Landsat missions provides the opportunity to achieve significantly increased performance. First-order advantages to be anticipated are higher sensitivity resulting in greater radiometric accuracies and improved geometric fidelity. The Multispectral Mapper design concept is responsive to a broad range of user needs by incorporating in-flight selection of spectral bands, instantaneous fields of view, and swath width. This versatility is achieved by the use of an imaging spectrometer which permits both spatial and spectral sampling in the image plane using area array detectors. Other improvements over the current Landsat multispectral scanner and Thematic Mapper include higher spatial resolution and inherently precise registration of the spectral bands. The key technologies required in order to realize these improvements include short-wavelength infrared detectors, wide field of view, broad spectral coverage optics, focal plane cooling, and high-speed onboard signal processing. Significant development activities will be required if an advanced remote sensing capability is to be implemented.

Wellman, J. B.↗

NASA NTRS2000

Dimension Reduction of Hyperspectral Data on Beowulf Clusters

El-Ghazawi, Tarek↗

NASA NTRS1992

Submillimeter-wavelength heterodyne spectroscopy and remote sensing of the upper atmosphere

Remote sensing by means of heterodyne spectroscopy at sub-mm wavelengths, which are rich in the spectral lines of atmospheric molecules, can furnish measurements for monitoring changes and studying processes in the earth's upper atmosphere. An experiment for this purpose, covering spectral bands near 63, 183, and 205 GHz, has become operational on the NASA Upper Atmosphere Research Satellite; an experiment whose spectral bands reach to 2.5 THz is under study for future earth observations.

Waters, Joe W.↗

NASA NTRS2019

Early Calibration and Performance Assessments of NOAA-20 VIIRS Thermal Emissive Bands

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the NOAA-20 (previously JPSS-1) spacecraft has successfully operated since its launch in November, 2017. Similar to the first VIIRS instrument on the Suomi-NPP spacecraft, data is collected in 22 spectral bands that are calibrated by a set of onboard calibrators. This paper provides an overview of the NOAA-20 VIIRS on-orbit operation and calibration, with a particular focus on the thermal emissive bands (TEBs). Results presented in this paper include the on-orbit changes in the TEB spectral band responses, detector noise characterization, and key calibration parameters, such as the non-linear coefficients derived from the blackbody warm-up cool-down cycle. Other issues, such as the early mission long-wave infrared (LWIR) response degradation due to icing on the dewar window, and their impact on sensor calibration are also discussed. Since launch, the VIIRS instrument temperature has been stable to within Â± 0.8K and the cold focal plane temperatures are well controlled with variations less than 40 mK. With the exception of the early degradation observed in the LWIR bands, the TEB gains have been stable to within 0.04% (except I5 at 0.07%). Based on the current performance, VIIRS is expected to meet its calibration requirements throughout its design lifetime.

NEdT↗

NASA NTRSDate not supplied

Satellite-Derived Imagery and Transfer Learning: A Novel Technique for Land Cover Classification

Land cover classification is a continuing research topic due to its relevance to land use and land cover changes from impacts such as climate change, agriculture, urbanization, and hazardous weather. Simple access to frequently changing land cover classifications could provide knowledge and decision support to various researchers and agencies across the globe for each of above-mentioned and related influences. This research aims to provide a novel technique for land cover classification of remote sensing imagery; harnessing Artificial Neural Networks and transfer learning (TL). Knowledge sharing techniques within machine learning are typically utilized when training datasets are sparse or transitions between data modalities is required. In this case, two data modalities, multi and hyperspectral data, are considered for knowledge transfer. The large number of continuous spectral bands available from hyperspectral sensors typically provide increased sophisticated land classification capability compared to more traditional multispectral imagery with limited discrete spectral bands. However, large-scale, frequent access to hyperspectral imagery is relatively limited. The proposed classification technique would therefore prove useful in regions in which hyperspectral data are not readily available for classification but multispectral data are. Image segmentation models are trained on each multi and hyperspectral datasets. Knowledge sharing techniques are then applied to each model to understand what knowledge, if any, is gained when moving between the data modalities. The datasets utilized for training and testing a U-Net model include the European Space Agency’s multispectral imager Sentinel-2 and the hyperspectral German Aerospace Center’s Earth Sensing Imaging Spectrometer (DESIS). Initially, only two classes, land and water, are classified for simplicity. However, more complex classes can be added if knowledge sharing is successful. The workflow for image classification through supervised image segmentation with a U-Net model will be discussed along with metrics calculated before and after TL for both Sentinel-2 and DESIS data are applied. Additionally, future steps to advance the sophistication of this technique as well as other applicable methodologies will be explored.

Emily Foshee↗

NASA NTRS1975

Soil salinity detection

The author has identified the following significant results. Growth forms and herbage biomass production varied considerably among saline and nonsaline soil range sites in Starr County. Grasses on saline soil sites were shallow-rooted and short whereas on nonsaline sites there was an intermixture of short and midgrass species. Differentiation between primarily undisturbed saline and nonsaline rangelands, in Starr County, is partially possible using film optical density readings from Skylab imagery. Differentiation among eight saline and nonsaline soil sites in Cameron County, using black and white and color film was not possible according to statistical results from both DMRT and correlation analysis. Linear analysis showed that Bendix 24-band MSS data (aircraft) collected at 1700 m and 4800 m, as well as Skylab and LANDSAT-1 MSS data, were significantly correlated to electrical conductivity readings. In Starr County, the best spectral band for detection of saline soil levels, using black and white SO-022 film, was in the 0.6 to 0.7 micron spectral region. In Cameron County, the best spectral bands for detection of saline soil levels were the 2.3 to 2.43 micron, 0.72 to 0.76 micron, 0.69 to 1.75 micron, and 0.7 to 1.1 micron spectral regions.

Wiegand, C. L.↗

NASA NTRS2008

On-Orbit Noise Characterization for MODIS Reflective Solar Bands

Since launch, the Moderate Resolution Imaging Spectroradiometer (MODIS) has operated successfully on-board the NASA Earth Observing System (EOS) Terra and EOS Aqua spacecraft. MODIS is a passive cross-track scanning radiometer that makes observations in 36 spectral bands with spectral wavelengths from visible (VIS) to long-wave infrared. MODIS bands 1-19 and 26 are the reflective solar bands (RSB) with wavelengths from 0.41 to 2.2 micrometers. They are calibrated on-orbit using an on-board solar diffuser (SD) and a SD stability monitor (SDSM) system. For MODIS RSB, the level 1B calibration algorithm produces top of the atmosphere reflectance factors and radiances for every pixel of the Earth view. The sensor radiometric calibration accuracy, specified at each spectral band's typical scene radiance, is 2% for the RSB reflectance factors and 5% for the RSB radiances. Also specified at the typical scene radiance is the detector signal-to-noise ratio (SNR), a key sensor performance parameter that directly impacts its radiometric calibration accuracy and stability, as well as the image quality. This paper describes an on-orbit SNR characterization approach developed to evaluate and track MODIS RSB detector performance. In order to perform on-orbit SNR characterization, MODIS RSB detector responses to the solar illumination reflected from the SD panel must be corrected for factors due to variations of the solar angles and the SD bi-directional reflectance factor. This approach enables RSB SNR characterization to be performed at different response levels for each detector. On-orbit results show that both Terra and Aqua MODIS RSB detectors have performed well since launch. Except for a few noisy or inoperable detectors which were identified pre-launch, most RSB detectors continue to meet the SNR design requirements and are able to maintain satisfactory short-term stability. A comparison of on-orbit noise characterization results with results derived from pre-launch calibration and characterization are also provided.

Xiong, X.↗

NASA NTRS1982

The imaging spectrometer approach

Two important sensor design drivers are the requirement for spatial registration of the spectral components and the implementation of the advanced multispectral capability, including spectral band width, number of bands and programmability. The dispersive approach, fundamental to the imaging spectrometer concept, achieves these capabilities by utilizing a spectrometer to disperse the spectral content while preserving the spatial identity of the information in the cross-track direction. Area array detectors in the spectrometer focal plane detect and store the spatial and multispectral content for each line of the image. The choice of spectral bands, image IFOV and swath width is implemented by programmed readout of the focal plane. These choices in conjunction with data compression are used to match the output data rate with the telemetry link capability. Progress in the key technologies of optics, focal plane detector arrays, onboard processing, and focal plane cooling supports the viability of the imaging spectrometer approach.

Wellman, J. B.↗

NASA NTRS2019

Early Calibration and Performance Assessments of NOAA-20 VIIRS Thermal Emissive Bands

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the NOAA-20 (previously JPSS-1) spacecraft has successfully operated since its launch in November, 2017. Similar to the rst VIIRS instrument on the Suomi-National Polar-orbiting Partnership (SNPP) spacecraft, the data are collected in 22 spectral bands that are calibrated by a set of onboard calibrators. This paper provides an overview of the NOAA-20 VIIRS on-orbit operation and calibration, with a particular focus on the thermal emissive bands (TEBs). The results presented in this paper include the on-orbit changes in the TEB spectral band responses, detector noise characterization, and key calibration parameters, such as the nonlinear coefcients derived from the blackbody warm-up cool-down cycles. Other issues, such as the early mission long-wave infrared (LWIR) response degradation due to icing on the dewar window, and their impact on sensor calibration are also discussed. Since launch, the VIIRS instrument temperature has been stable to within Â±0.8 K and the cold focal plane temperatures are well controlled with variations less than 40 mK. With the exception of the early degradation observed in the LWIR bands, the TEB gains have been stable to within 0.04% (except I5 at 0.07%). Based on the current performance, VIIRS is expected to meet its calibration requirements throughout its design lifetime.

noise equivalent 25 differential temperature (NEdT↗

NASA NTRS1986

Verification of small-scale water vapor features in VAS imagery using high resolution MAMS imagery

The Multispectral Atmospheric Mapping Sensor (MAMS), a modification of NASA's Airborne Thematic Mapper, is described, and radiances from the MAMS and the VISSR Atmospheric Sounder (VAS) are compared which were collected simultaneously on May 18, 1985. Thermal emission from the earth atmosphere system in eight visible and three infrared spectral bands (12.3, 11.2 and 6.5 microns) are measured by the MAMS at up to 50 m horizontal resolution, and the infrared bands are similar to three of the VAS infrared bands. Similar radiometric performance was found for the two systems, though the MAMS showed somewhat less attenuation from water vapor than VAS because its spectral bands are shifted to shorter wavelengths away from the absorption band center.

Menzel, Paul W.↗

NASA NTRS2005

Status of the MODIS Level 1B Algorithms and Calibration Tables

The Moderate Resolution Imaging Spectroradiometer (MODIS) makes observations using 36 spectral bands with wavelengths from 0.41 to 14.4 m and nadir spatial resolutions of 0.25km, 0.5km, and 1km. It is currently operating onboard the NASA Earth Observing System (EOS) Terra and Aqua satellites, launched in December 1999 and May 2002, respectively. The MODIS Level 1B (L1B) program converts the sensor's on-orbit responses in digital numbers to radiometrically calibrated and geo-located data products for the duration of each mission. Its primary data products are top of the atmosphere (TOA) reflectance factors for the sensor's reflective solar bands (RSB) and TOA spectral radiances for the thermal emissive bands (TEB). The L1B algorithms perform the TEB calibration on a scan-by-scan basis using the sensor's response to the on-board blackbody (BB) and other parameters which are stored in Lookup Tables (LUTs). The RSB calibration coefficients are processed offline and regularly updated through LUTs. In this paper we provide a brief description of the MODIS L1B calibration algorithms and associated LUTs with emphasis on their recent improvements and updates developed for the MODIS collection 5 processing. We will also discuss sensor on-orbit calibration and performance issues that are critical to maintaining L1B data product quality, such as changes in the sensor's response versus scan-angle.

MODIS LEVEL 1B ALGORITHMS↗