Detection performance of LWIR passive standoff chemical agent sensors is strongly influenced by various scene parameters, such as atmospheric conditions, temperature contrast, concentration-path length product (CL), agent absorption coefficient, and scene spectral variability. Although temperature contrast, CL, and agent absorption coefficient affect the detected signal in a predictable manner, fluctuations in background scene spectral radiance have less intuitive consequences. The spectral nature of the scene is not problematic in and of itself; instead it is spatial and temporal fluctuations in the scene spectral radiance that cannot be entirely corrected for with data processing. In addition, the consequence of such variability is a function of the spectral signature of the agent that is being detected and is thus different for each agent. To bracket the performance of background-limited (low sensor NEDN), passive standoff chemical sensors in the range of relevant conditions, assessment of real scene data is necessary1. Currently, such data is not widely available2. To begin to span the range of relevant scene conditions, we have acquired high fidelity scene spectral radiance measurements with a Telops FTIR imaging spectrometer3. We have acquired data in a variety of indoor and outdoor locations at different times of day and year. Some locations include indoor office environments, airports, urban and suburban scenes, waterways, and forest. We report agent-dependent clutter measurements for three of these backgrounds.

Longwave Infrared (LWIR) data sets collected from airborne platforms provide opportunities for study of atmospheric and surface features in the emissive spectral regime. The transfer of radiation for LWIR scenes can be formulated in a manner that allows recovery of the surface-leaving radiance (a result of atmospheric compensation). Using a forward radiative transfer model, a number of modifications to the atmospheric component of the scene can be made and applied to the surface-leaving radiance to predict sensor radiance that reflects a desired scenario. One such modification is the inclusion of a layer of effluent, the structure of which can be simulated by a plume model. Additionally, a different set of atmospheric conditions can be modeled and used to replace the conditions present in the scene. The resultant scene radiance field can be used to test algorithms for effluent characterization since the composition of the effluent layer and the intervening atmosphere is known. This approach allows for the embedding of a plume layer containing any combination of effluents from a set of over 400 gas spectra, the dispersion of which can be simulated using various plume models. Examples of simulated plume scenes are given, one of which contains an existing plume which is replicated using known emission information. Comparison of the real and simulated plume brightness temperatures yielded differences on the order of 0.2 K.

: The EO-1 satellite is part of NASA's New Millennium Program (NMP). It consists of three imaging sensors: the multispectral Advanced Land Imager (ALI), Hyperion, and Atmospheric Corrector. Hyperion provides a high-resolution hyperspectral imager capable of resolving 220 spectral bands (from 0.4 to 2.5 micron) with a 30-m resolution. The instrument images a 7.5 km by 100 km land area per image. Hyperion is currently the only spaceborne HSI data source since the launch of EO-1 in late 2000. A cloud-cover detection algorithm was developed for application to EO-1 Hyperion hyperspectral data. The algorithm uses only bands in the reflected solar spectral regions to discriminate clouds from surface features and was designed to be used on board the EO-1 satellite as part of the EO-1 Extended Mission Phase of the EO-1 Science Program. The cloud-cover algorithm uses only 6 bands to discriminate clouds from other bright surface features such as snow, ice, and desert sand. The technique was developed using 20 Hyperion scenes with varying cloud amount, cloud type, underlying surface characteristics, and seasonal conditions. Results from the application of the algorithm to these test scenes are given with a discussion on the accuracy of the procedure used in the cloud cover discrimination. Compared to subjective estimates of the scene cloud cover, the algorithm was typically within a few percent of the estimated total cloud cover.

A systems analysis framework for assessing performance of long wave infra-red (LWIR) hyperspectral chemical imaging sensors (HCIS) is presented. The trade space study includes assessment of HCIS detection sensitivity and deployment impact on meeting specified mission requirements.

The EO-1 satellite is part of NASA's New Millennium Program (NMP). It consists of three imaging sensors: the multispectral Advanced Land Imager (ALI), Hyperion and Atmospheric Corrector. Hyperion provides a high-resolution hyperspectral imager capable of resolving 220 spectral bands (from 0.4 to 2.5 micron) with a 30 m resolution. Three examples of EO-1 Hyperion data analysis efforts are illustrated. The discussion begins with a cloud cover algorithm that utilizes only solar reflective channels to discriminate cloud types and cloud/surface features. The algorithm is applied to a variety of Hyperion scenes that depict various cloud types, surface features and seasonal conditions. The second example illustrates hyperspectral application to coastal characterization. Hyperion data from Chesapeake Bay from 19 February 2002 are analyzed. Chlorophyll retrieval results are shown. The results compare favorably with data from other sources. The third example deals with terrain analysis for background classification applications. Abundance levels of lush vegetation and bare soil are estimated for image pixels located between different fields of crops over Coleambally Irrigation Area, Australia on 7 March 2000.

The EO-1 satellite consist of three imaging sensors including Hyperion, which is currently the only space-borne HSI data source. The unique capability of hyperspectral sensing for coastal characterization provides the possibility for exploring the coupled effects of coastal features and a more accurate atmospheric compensation. It is illustrated in this paper that hyperspectral data inherently provide more information for feature extraction than multispectral data despite Hyperion having a relatively low SNR. Chlorophyll retrievals are obtained from Hyperion data and the results compare favorably with data from other sources. The analysis illustrates the potential value of Hyperion (and HSI in general) data to coastal characterization.

VNIR-SWIR data from DOE MTI satellite are used to demonstrate the retrieval of aerosol and cloud properties. MTI data offer high spatial resolution and high SNR data. Furthermore, collection from both nadir and off-nadir views offer a unique opportunity to assess atmospheric path length effects both through clear and cloud conditions. Data sets were acquired to investigate cloud and aerosol properties: 29 July and 22 August 2000 over the coastal region of Massachusetts near Plymouth.Two topics are investigated: (1) retrieval of aerosol optical properties, and (2) characterization of water and ice clouds at nadir and off-nadir views.Data collection on 22 August 2000 represents a relatively clear atmospheric condition in the vicinity of Pilgrim Power Plant, Plymouth. Data over both vegetated land and ocean are analyzed. Two algorithms for aerosol retrieval over land are compared: the conventional dense-dark vegetation (DDV) algorithm and a generalized VIS-SWIR reflectance correlation and scatter-plot analysis (VSP) algorithm. Optical depths at multiple wavelengths and aerosol type were derived and compared with ground based AERONET data. It is demonstrated that the VSP algorithm captures the spectral variability in aerosol extinction, and thus performs better.Data collection from 29 July 2000 over the same area was investigated for cloud characteristics at different viewing geometries. Top-of-the-Atmosphere (TOA) reflectance statistics is computed for a common cloudy region. It is observed that in cloud free regions, nadir TOA reflectance is lower than that from off-nadir observations. This is due to the increased atmospheric scattering effect from the longer paths. On the other hand, TOA reflectance over cloud area depends on the scattering phase function and the look angle. Here we use simple expressions to illustrate that the effects for water and ice particles can be quite different resulting in very different viewing geometry effects between cumulus and cirrus clouds.

A conventional approach to HSI processing and exploitation has been to first perform atmospheric compensation so that surface features can be properly characterized. In this paper, the application of visible and IR spectral information to atmospheric characterization is discussed and illustrated with hyperspectral data in the VNIR, SWIR and MWIR data.AVIRIS and ARES data are utilized. The Airborne Visible-InfraRed Imaging Spectrometer (AVIRIS) sensor contains 224 bands, each with a spectral bandwidth of approximately 10 mn, allowing it to cover the entire range between 4 and 2.5 mum. For a NASA ER-2 flight altitude of 20 km, each pixel is 20 m in size, yielding a ground swath width of approximately 10 km. The Airborne Remote Earth Sensing (ARES) sensor was flown on a NASA WB-57 aircraft operated from approximately 15 km altitude. Spectral radiance data from 2.0 to 6.0 mum in 75 contiguous bands were collected. Pixel resolution is approximately 17 by 4.5 m(2) with a swath width of 800 m.Examples of data applications include atmospheric water vapor retrieval, aerosol characterization, delineation of natural and manmade clouds/plumes, and cloud depiction. It is illustrated that though each application may only require a few spectral bands, the ultimate strength of HSI exploitation lies in the simultaneous and adaptive retrievals of atmospheric and surface features. Inter-relationships among different bands are also demonstrated and these are the physical basis for the optimal exploitation of spectral information.

Abstract : Modern high power Doppler VHF radar wind profilers are a valuable source of upper tropospheric turbulence intensity information. These radars can provide more comprehensive characterization of turbulence statistics than any in-situ measurement database, or even models developed from such databases. Sample data sets from two high power radar profilers, located at WSMR, New Mexico, and KSC, Florida, were obtained. The physical processes involved in turbulence production, maintenance, and dissipation were reviewed along with the phenomenology of its detection using radar. Subsequently, methodologies were developed for the retrieval of turbulence intensity statistics, or the so-called turbulence exceedance probabilities (TEP), from such data. The WSMR data analysis shows that, in the 8 to 20 km altitude region, the median turbulence intensity was less than or equal 0.5 m/s. In more turbulent conditions, the 90% and 95% TEP, turbulence intensities were observed to be close to, or slightly in excess of 1.0 m/s, and 1.5 m/s, respectively. The KSC turbulence data analysis resulted in the median close to 1.0 m/s, the 90% TEP near 1.5 m/s, and the maximum 95% TEP of 1.7 m/s. Also considered are the capabilities of a recently developed Clear Air Turbulence (CAT) forecast product. The conclusion is that available TEP guidance could be validated and extended by analysis of data from high power radars.