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None given. From the paper: It appears that the best possible AVIRIS calibration would be a combination of a radiative transfer correction followed by a ground calibration.
The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission has collected surface elevation measurements for over 5 years. ICESat-2 carries an instrument that emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat-2 acquisitions during the melting season. We performed two case studies over the Greenland Ice Sheet to quantify bias in ICESat-2 signals over snow: one to validate Airborne Topographic Mapper (ATM) data against Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) grain sizes, and a second to estimate ICESat-2 bias relative to ATM. We used snow optical grain sizes derived from ATM and AVIRIS-NG to attribute altimetry bias to snowpack properties. For the first case study, the mean and standard deviation of optical grain sizes were 340 ± 65 µm (AVIRIS-NG) and 670 ± 420 µm (ATM). A mean altimetry bias of 4.81 ± 1.76 cm was found for ATM, with larger biases linked to increases in grain size. In the second case study, we found a mean grain size of 910 ± 381 µm and biases of 6.42 ± 1.77 cm (ICESat-2) and 9.82 ± 0.97 cm (ATM). The grain sizes and densities needed to recreate biases with a model are uncommon in nature, so we propose that additional surface attributes must be considered to characterize ICESat-2 bias over snow. The altimetry biases are within the accuracy requirements of the ICESat-2 mission, but we cannot rule out more significant errors over coarse-grained snow.
Imaging spectrometers, such as the Airborne Visible/Infrared Imaging (AVIRIS), measure spectra in the region from 400 to 2500 nm at nominally 10-nrn sampling. This gives approximately 200 continuous spectral channels of data for every spatial element measured. These spectroscopic measurements are used to determine the composition and infer processes of the Earth system through spectroscopy analysis. Spectroscopic analysis is based on the physics, chemistry, and biology revealed through the interaction of energy with matter recorded in each measured spectrum
We introduce and evaluate an approach for the simultaneous retrieval of aerosol and surface properties from Airborne Visible/Infrared Imaging Spectrometer Classic (AVIRIS-C) data collected during wildfires. The joint National Aeronautics and Space Administration (NASA) National Oceanic and Atmospheric Administration Fire Influence on Regional to Global Environments and Air Quality field campaign took place in August 2019, and involved two aircraft and coordinated ground-based observations. The AVIRIS-C instrument acquired data from onboard NASA's high altitude ER-2 research aircraft, coincident in space and time with aerosol observations obtained from the Aerosol Robotic Network (AERONET) DRAGON mobile platform in the smoke plume downwind of the Williams Flats Fire in northern Washington in August 2019. Observations in this smoke plume were used to assess the capacity of optimal-estimation based retrievals to simultaneously estimate aerosol optical depth (AOD) and surface reflectance from Visible Shortwave Infrared (VSWIR) imaging spectroscopy. Radiative transfer modeling of the sensitivities in spectral information collected over smoke reveal the potential capacity of high spectral resolution retrievals to distinguish between sulfate and smoke aerosol models, as well as sensitivity to the aerosol size distribution. Comparison with ground-based AERONET observations demonstrates that AVIRIS-C retrievals of AOD compare favorably with direct sun AOD measurements. Our analyses suggest that spectral information collected from the full VSWIR spectral interval, not just the shortest wavelengths, enables accurate retrievals. We use this approach to continuously map both aerosols and surface reflectance at high spatial resolution across heterogeneous terrain, even under relatively high AOD conditions associated with wildfire smoke.
A radiative transfer model was used to calculate the spectral reflectance of thin snow overlying dark soil. Model results show that directional hemispherical reflectance depends on density, grain size, and solar and viewing geometries. Measurements of thin snow spectral Bidirectional Reflectance Distribution Function (BRDF) show that the reflectance in the visible wavelengths is reduced when the sensor is near nadir yet, at a viewing zenith of 75 degrees the same snowpack will appear to be optically thick. If the snow is sufficiently thin and snow grains are large, the spectral signature of the substrate may influence snow reflectance. This phenomenon was also detected in reflectance data collected using the ground based PIDAS (Portable Instantaneous Display and Analysis Spectrometer). Concurrent airborne data from the AVIRIS instrument (Advanced Visible/Infrared Imaging Spectrometer) show effects of thin snow and mixed pixels for areas of thin and patchy spring snow.
The laboratory spectral and radiometric calibration of the AVIRIS science data collected since 1987 is described. The instrumentation and procedures used in the calibration are discussed and the accuracy achieved in the laboratory as determined by measurement and calculation is compared with the requirements. Instrument performance factors affecting radiometry are described. The paper concludes with a discussion of future plans.
The laboratory spectral and radiometric calibration of the AVIRIS science data collected since 1987 is described. The instrumentation and procedures used in the calibration are discussed and the accuracy achieved in the laboratory as determined by measurement and calculation is compared with the requirements. Instrument performance factors affecting radiometry are described. The paper concludes with a discussion of future plans.
The objective of this project is to calibrate and validate the on orbit radiometric characteristics of SeaWIFS with underflights of NASA's calibrated Airborne Visible/Infrared Imaging Spectrometer.
The AVIRIS onboard calibrator became operational in 1992. This subsystem on the AVIRIS sensor has operated nominally throughout the 1992 and 1993 flight season. This paper describes the data measured by the onboard calibration and the two primary uses of these data.
Imaging spectrometry data must be spectrally, radiometrically and geometrically calibrated in order to: 1) derive physical parameters from measured spectral radiance, 2) compare data acquired from different regions and at different times, 3) compare and analyze the imaging spectrometry data with data acquired from other calibrated sensors, and 4) compare and analyze data with results from computer models. The calibration of AVIRIS data is the process by which laboratory characterization data are applied to raw instrument data (digitized number versus spectral channels) to produce quantitative spectra (radiance versus wavelength) for each image pixel in units of spectral radiance. The AVIRIS sensor and calibration process are described by Vane and the application of the calibration data to the raw digital data is described by Green. This calibration process is validated for in-flight performance of the sensor using a rigorous ground-truth campaign. This workshop paper reviews the laboratory characterization data set that is used in the AVIRIS calibration process. The laboratory measurements used to acquire the calibration data are divided into three classes: 1) spectral calibration, 2) radiometric calibration, and 3) spatial calibration.
Imaging spectrometry data must be spectrally, radiometrically and geometrically calibrated. This calibration process is validated for in-flight performance of the sensor using a rigorous ground truth campaign.
Topics presented include the evolution of the Airborne Visible/Infrared Imaging Spectrometer flight and ground data processing system, direct mineral identification with Geoscan Mk II Advanced Multispectral Scanner, a new approach to imaging spectroscopy, and a linear-wedge spectrometer. Also presented are the effects of moisture content and chemical composition on the near-infrared spectra of forest foliage, change detection in vegetation using 1989 AVIRIS data, and the inversion of high-spectral-resolution data.