Simulated radiance cubes are used to gain a quantitative understanding of FLAASH reflectance retrieval errors due to viewing geometry errors. For a particular zenith view angle error, the retrieval error increases as the viewing becomes more off-nadir.

Spectral radiance measurements in the solar reflective regime, which are obtained from downward looking space-based or airborne imaging spectrometer systems must be compensated for the influence of the atmosphere in order to retrieve earth surface reflectance spectra. The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) atmospheric correction code was updated with a new option to handle measurements from a new pushbroom sensor system having a continuously varying viewing geometry over an image scene. However, the new technique employed in FLAASH to handle this atmospheric compensation scenario does not explicitly account for the different viewing geometry values for each line in an image scene, which causes some error in the FLAASH retrievals. Simulated radiance cubes are used to gain a quantitative understanding of FLAASH retrieval errors due to viewing geometry errors.

Testing MODTRATM5 (MOD5) capabilities against NASA satellite state-of-the-art radiance and irradiance measurements has recently been undertaken. New solar data have been acquired from the SORCE satellite team, providing measurements of variability over solar rotation cycles, plus an ultra-narrow calculation for a new solar source irradiance, extending over the full MOD5 spectral range. Additionally, a MOD5-AIRS analysis has been undertaken with appropriate channel response functions. Thus, MOD5 can serve as a surrogate for a variety of perturbation studies, including two different modes for including variations in the solar source function, Io: (1) ultra-high spectral resolution and (2) with and without solar rotation. The comparison of AIRS-related MOD5 calculations, against a suite of 'surrogate' data generated by other radiative transfer algorithms, all based upon simulations supplied by the AIRS community, provide validation in the Long Wave Infrared (LWIR). All ~2400 AIRS instrument spectral response functions (ISRFs) are expected to be supplied with MODTRANTM5. These validation studies show MOD5 replicates line-by-line (LBL) brightness temperatures (BT) for 30 sets of atmospheric profiles to approximately -0.02°K average offset and <1.0°K RMS.

We describe a new visible-near infrared short-wavelength infrared (VNIR-SWIR) atmospheric correction method for multi- and hyperspectral imagery, dubbed QUAC (Quick Atmospheric Correction) that also enables retrieval of the wavelength-dependent optical depth of the aerosol or haze and molecular absorbers. It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The approach is based on the empirical finding that the spectral standard deviation of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially spectrally flat. It allows the retrieval of reasonably accurate reflectance spectra even when the sensor does not have a proper radiometric or wavelength calibration, or when the solar illumination intensity is unknown. The computational speed of the atmospheric correction method is significantly faster than for the first-principles methods, making it potentially suitable for real-time applications. The aerosol optical depth retrieval method, unlike most prior methods, does not require the presence of dark pixels. QUAC is applied to atmospherically correction several AVIRIS data sets and a Landsat-7 data set, as well as to simulated HyMap data for a wide variety of atmospheric conditions. Comparisons to the physics-based Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) code are also presented.

Atmospheric Correction Algorithms (ACAs) are used in applications of remotely sensed Hyperspectral and Multispectral Imagery (HSI/MSI) to correct for atmospheric effects on measurements acquired by air and space-borne systems. The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm is a forward-model based ACA created for HSI and MSI instruments which operate in the visible through shortwave infrared (Vis-SWIR) spectral regime. Designed as a general-purpose, physics-based code for inverting at-sensor radiance measurements into surface reflectance, FLAASH provides a collection of spectral analysis and atmospheric retrieval methods including: a per-pixel vertical water vapor column estimate, determination of aerosol optical depth, estimation of scattering for compensation of adjacency effects, detection/characterization of clouds, and smoothing of spectral structure resulting from an imperfect atmospheric correction. To further improve the accuracy of the atmospheric correction process, FLAASH will also detect and compensate for sensor-introduced artifacts such as optical smile and wavelength mis-calibration. FLAASH relies on the MODTRAN TM radiative transfer (RT) code as the physical basis behind its mathematical formulation, and has been developed in parallel with upgrades to MODTRAN in order to take advantage of the latest improvements in speed and accuracy. For example, the rapid, high fidelity multiple scattering (MS) option available in MODTRAN4 can greatly improve the accuracy of atmospheric retrievals over the 2-stream approximation. In this paper, advanced features available in FLAASH are described, including the principles and methods used to derive atmospheric parameters from HSI and MSI data. Results are presented from processing of Hyperion, AVIRIS, and LANDSAT data.

Shadow-insensitive detection or classification of surface materials in atmospherically corrected hyperspectral imagery can be achieved by expressing the reflectance spectrum as a linear combination of spectra that correspond to illumination by the direct sum and by the sky. Some specific algorithms and applications are illustrated using HYperspectral Digital Imagery Collection Experiment (HYDICE) data.

Atmospheric emission, scattering and photon absorption degrade spectral imagery data and reduce its utility. The Air Force Research Laboratory and Spectral Sciences, Inc, are developing a MODTRAN4-based 'atmospheric mitigation' algorithm to support current and planned IR-visible-UV sensor spectral radiance imagery measurements. The intent is to provide surface reflectance and emissivity imagery data of sufficient accuracy for input into subsequent analyses of sur;face properties, effectively removing the atmospheric component. This report is the result of the application of the atmospheric mitigation algorithm to a NASA/JPL AVIRIS spectral image cube as a pre-processing step towards improving the performance of image categorization routines.

Surface emissivities at 91 and 150 GHz are retrieved using a microwave radiative transfer model and the atmospheric profiles and brightness temperatures (TBs) for the cloud-free cases from a global data set of collocated SSM/T-2 and radiosonde measurements. The retrieved emissivity values for various Earth surface types are consistent with those from other studies. The retrieval scheme displays limited sensitivity to uncertainty in the input data and the retrieved values are found to have good reproducibility for a given region. The possible use of special sensor microwave/imager (SSM/I) TB measurements for estimation of 91- and 150-GHz emissivities is explored using a subset of the retrieved emissivity cases which was augmented with SSM/I data. It is demonstrated that SSM/I TBs are potentially useful for making accurate estimates of surface emissivities at 91 and 150 GHz. This should lead to improved SSM/T-2 lower tropospheric water vapor profile retrievals.

: A new operational sensor will be on board the next polar-orbiting defense Meteorological Satellite Program spacecraft, which is scheduled to be launched in June 1987. It is called the Special Sensor Microwave/Imager (SSM/I). The SSM/I is a seven channel, four frequency linearly polarized, passive microwave radiometer. The SSM/I will provide estimates of several surface and atmospheric parameters. One of the parameters is cloud amount (percent cloud coverage), which is the topic of this report. SSM/I cloud amount estimates will include some of the situations in which there are difficulties with the Air Force Global Weather Central's Real-Time Nephanalysis automated global cloud analysis using visible and infrared satellite data. Hughes Aircraft Company developed two algorithms for estimating cloud amounts from SSM/I brightness temperatures. One is applicable over snow backgrounds; the other, over land backgrounds. However, it is not possible to obtain cloud amount estimates for land covered with vegetation. No cloud amount estimation algorithms for ocean or oceanic ice backgrounds were required to be developed; even though the potential over both of these backgrounds is good. (RH)