In this paper, samples of AIRS data in the 1215 to 1615 cm-1 spectral region are analyzed to better understand the effects of water vapor in the mid to upper tropospheric region. Two days representing mid-latitude (20°-40° N) summer (warm and moist) and winter (cold and dry) maritime conditions are selected with cloud-free and 100% cloudy FOVs. The data, both in trend and differences, are well explained by the respective changes in atmospheric temperature and water vapor. These data are then compared with model simulation using MODTRAN. The results also compare favorably. Model simulation further illustrates the value of high spectral resolution for monitoring change in water vapor particularly in the upper troposphere. With the future GOES-R and NPOESS hyperspectral sensors expected to provide much improved atmospheric profile information, better monitoring of atmospheric water vapor will lead to improvements both in weather and climate applications.
This paper examines the use of bi-static lidar to remotely detect the release of aerosolized biological agent. The detection scheme exploits bio-aerosol induced changes in the Stokes parameters of scattered radiation in comparison to scattered radiation from ambient background aerosols alone. A polarization distance metric is introduced to discriminate between changes caused by the two types of aerosols. Scattering code computations are the information source. Three application scenarios are considered: outdoor arena, indoor auditorium, and building heating-ventilation-air-conditioning (HVAC) system. Numerical simulations are employed to determine sensitivity of detection to laser wavelength and to particle physical properties. Results of the study are described and details are given for the specific example of a 1.50 μm lidar system operating outdoors over a 1000-m range.
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.
As we enter a new era of using satellite hyperspectral sensors for weather and other environmental applications, this paper discusses the applicability of using IR hyperspectral data for climate change monitoring; in particular, for quantifying the greenhouse effects. While broadband 1st order statistics quantify radiative forcings, the IR hyperspectral data provides a means of monitoring feedback processes. Radiative transfer modeling of the greenhouse effect is illustrated with examples: varying surface temperature, atmospheric temperature and water vapor. Three spectral greenhouse metrics are discussed: the difference between the surface emission and the outgoing longwave radiation (G), the surface-temperature normalized greenhouse effect (g) and vertical profile of cooling rate (C). Effects of changes in water vapor, clouds, carbon dioxide and methane are modeled and their potential observables identified.
In earlier work, NOAA’s Coastal Service Center in Charleston, S.C. reviewed current remote sensing and took a broader look at that technology applied to coastal resource management. They found 25 application areas and grouped them into 5 broader categories. In this paper we will explain some background and complexity of remote sensing when imaging in shallow water. This region is more complex than the deep ocean but there is synergy or opportunity to
combine remote sensing measurements. Then we will summarize the 25 coastal areas of application with regard to spatial, spectral and temporal remote sensing needs including use of potential hyper-spectral sensors. Finally, we use the example of coral reef observations to explain the difficulty in trying to set remote sensing operational rules.
The compensation for atmospheric effects in the VNIR/SWIR has reached a mature stage of development with many algorithms available for application (ATREM, FLAASH, ACORN, etc.). Compensation of LWIR data is the focus of a number of promising algorithms. A gap in development exists in the MWIR where little or no atmospheric compensation work has been done yet an increased interest in MWIR applications is emerging. To obtain atmospheric compensation over the full spectrum (visible through LWIR), a better understanding of the radiative effects in the MWIR is needed. The MWIR is characterized by a unique combination of reduced solar irradiance and low thermal emission (for typical emitting surfaces), both providing relatively equal contributions to the daytime MWIR radiance. In the MWIR and LWIR, the compensation problem can be viewed as two interdependent processes: compensation for the effects of the atmosphere and the uncoupling of the surface temperature and emissivity. The former requires calculations of the atmospheric transmittance due to gases, aerosols, and thin clouds and the path radiance directed towards the sensor (both solar scattered and thermal emissions in the MWIR). A framework for a combined MWIR/LWIR compensation approach is presented where both scattering and absorption by atmospheric particles and gases are considered.
To better understand the capabilities of hyperspectral imaging spectrometers, a number of organizations planned and carried out a data collection exercise at a desert site in the southwestern United States. As part of this collection, eight soil 'panels' were constructed; four filled with a coarse gravel/sand mixture and four flled with fine soil. Each set of four panels was prepared to represent two moisture and density conditions: wet versus dry and compacted versus loose. Unlike laboratory soil specimens, which use 'purified' samples, these soil flats contained more variability. They therefore better represented the 'natural' environment that would be viewed by an airborne hyperspectral imaging sensor, while still allowing an experimental study under more controlled conditions. This paper examines how well the eight soil types and conditions can be distinguished based on their VNIR/SWIR reflectance spectra derived from field measurements and from airborne hyperspectral measurements made at nearly the same time. A brief review of the phenomenology of soil reflectance spectra will be given. Based on physical attributes of the soils, some new classification approaches have been developed and were applied to the soil panels. These phenomenological methods include examining contrast in certain broadband features and, based on these, calculating various broadband spectral ratios over subsets of the VNIR/SWIR spectral region. The separability of the reflectance spectra from the eight soil panels were also analyzed by applying the Spectral Angle Mapper (SAM) hyperspectral distance metric to quantify the separations between all pairs of soil types and conditions. Finally, a neural network approach was applied to determine distinguishing features of the spectra. The phenomenological approaches, SAM analyses, and the neural network results will be compared.
Hyperspectral imaging (HSI) sensors collect spatially resolved data in hundreds of spectral channels. While the technology matures and finds broad applications, data downlink from the collection platform and near real-time processing remain key challenges, especially for near-term spaceborne sensors. It is desirable to process the data on-board for near real-time analysis and downlink compressed data allowing near full spectral recovery for post-mission analysis. Principal component analysis (PCA) can be used to determine the reduced dimensionality and separate noise components in the data. While PCA is useful for image feature analysis such as smoke/cloud discrimination (Griffin, et al., 2000), it can also be used as a data compression tool. With PCA, the majority of information in an HSI data cube is effectively compressed to a small number of principal components. The data volume is significantly reduced while the feature contrast is enhanced. Spectral information can be recovered from the compressed data with minimal loss. In this paper, the reconstructed data are compared to the original "truth" data with difference analysis using sample AVIRIS imagery. This methodology also allows for the HSI data to be used adaptively for various multispectral band simulations without the constraint of data volume and processing burden. Based on AVIRIS data, emulation of MODIS sensor bands are carried out and compared with the PCA-reconstructed data. Two products are also derived and compared: Normalized Difference Vegetation Index (NDVI) and the integrated column water vapor (CWV) using the full set of AVIRIS data and the reconstructed spectral information.
The EO-1 satellite is part of NASA's New Millennium Program (NMP). It consists of three imaging sensors: the multi-spectral 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 space-borne HSI data source since the launch of EO-1 in late 2000. The discussion begins with the unique capability of hyperspectral sensing to coastal characterization: (1) most ocean feature algorithms are semi-empirical retrievals and HSI has all spectral bands to provide legacy with previous sensors and to explore new information, (2) coastal features are more complex than those of deep ocean that coupled effects are best resolved with HSI, and (3) with contiguous spectral coverage, atmospheric compensation can be done with more accuracy and confidence, especially since atmospheric aerosol effects are the most pronounced in the visible region where coastal feature lie. EO-1 data from Chesapeake Bay from 19 February 2002 are analyzed. In this presentation, it is first illustrated that hyperspectral data inherently provide more information for feature extraction than multispectral data despite Hyperion has lower SNR than ALI. Chlorophyll retrievals are also shown. 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. Future measurement requirements (air borne and space borne) are also discussed.
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 code 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 is 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.
To demonstrate the utility of EO-1 data, combined analysis of panchromatic, multispectral (ALI, Advanced Land Imager) and hyperspectral (Hyperion) data was conducted. In particular, the value added by HSI with additional spectral information will be illustrated. Data sets from Coleambally Irrigation Area, Australia on 7 March 2000 and San Francisco Bay area on 17 January 2000 are employed for the analysis. Analysis examples are shown for surface characterization, anomaly detection, spectral unmixing and image sharpening.
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.
Longwave Infrared (LWIR) radiation comprising atmospheric and surface emissions provides information for a number of applications including atmospheric profiling, surface temperature and emissivity estimation, and cloud depiction and characterization. The LWIR spectrum also contains absorption lines for numerous molecular species which can be utilized in quantifying species amounts. Modeling the absorption and emission from gaseous species using various radiative transfer codes such as MODTRAN-4 and FASE (a follow-on to the line-by-line radiative transfer code FASCODE) provides insight into the radiative signature of these elements as viewed from an airborne or space-borne platform and provides a basis for analysis of LWIR hyperspectral measurements. In this study, a model platform was developed for the investigation of the passive outgoing radiance from a scene containing an effluent plume layer. The effects of various scene and model parameters including ambient and plume temperatures, plume concentration, as well as the surface temperature and emissivity on the outgoing radiance were estimated. A simple equation relating the various components of the outgoing radiance was used to study the scale of the component contributions. A number of examples were given depicting the spectral radiance from plumes composed of single or multiple effluent gases as would be observed by typical airborne sensors. The issue of detectability and spectral identification was also discussed.
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 nm, allowing it to cover the entire range between 4 and 2.5 mm. 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 micrometers in 75 contiguous bands were collected. Pixel resolution is approximately 17 by 4.5 m2 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.
Hyperspectral imaging has emerged as a useful technology for target recognition and anomaly detection.
However, passive hyperspectral sensors in the VNIR/SWIR are limited to daytime and fair weather operations.
Furthermore, for applications such as material identification, the need for reflectance spectra requires either inscene calibration panels or detailed atmospheric information. Active hyperspectral sensing has the potential to
increase the utility of hyperspectral imaging by enabling nighttime operation and non-cooperative conversion to reflectance. At MIT Lincoln Laboratory we have developed an active hyperspectral sensor system to investigate
combining active illumination with hyperspectral imaging. Our primary illumination source is a novel broadband ‘white light’ laser, developed at MIT Lincoln Laboratory. Initial phenomenology measurements have revealed an
additional benefit of active illumination - enhanced scene contrast due to shadow reduction. We have demonstrated two orders of magnitude decrease in false alarm rates with active illumination versus passive.
Hyperspectral imagers have the unique capability of doing both material identification and anomaly detection. However, hyperspectral imagers with hundreds of co-registered contiguous bands are difficult to field particularly if real-time processing is required. With judicious choice of bands, the anomaly detection performance of a multispectral sensor can rival that of hyperspectral sensors. In order to achieve this performance, the choice of multispectral bands relies on the presence of exploitable target or background spectral features. The universality of these features will determine the overall utility of a multispectral system. We have discovered that water vapor features in the SWIR (Short Wave InfraRed) can be used to distinguish manmade objects from natural backgrounds. As an example, we will show that two broad bands chosen to exploit these features make most manmade objects detectable in the presence of natural clutter with few false alarms.
This paper presents an overview of the latest version of a MODTRAN4-based atmospheric correction (or "compensation") algorithm developed by Spectral Sciences, Inc. and the Air Force Research Laboratory for spectral imaging sensors. New upgrades to the algorithm include automated aerosol retrieval, cloud masking, and speed improvements. In addition, MODTRAN4 has been updated to correct recently discovered errors in the HITRAN-96 water line parameters. Reflectance spectra retrieved from AVIRIS data are compared with "ground truth" measurements, and good agreement is found.
Two approaches, one for discriminating features in a set of AVIRIS scenes dominated by areas of smoke, plumes, clouds and burning grassland as well as scarred (burned) areas and another for identifying those features are presented here. A semiautomated feature extraction approach using principal components analysis was used to separate the scenes into feature classes. Typically, only 3 component images were used to classify the image. A physics-based approach which utilized the spectral diversity of the features in the image was used to identify the nature of the classes produced in the component analysis. The results from this study show how the two approaches can be used in unison to fully characterize a smoke or cloud-filled scene.
For hyperspectral data analysis, the general objective for atmospheric compensation algorithms is to remove solar illumination and atmospheric effects from the measured spectral data so that surface reflectance can be retrieved. This then allows for comparison with library data for target identification. Recent advances in spectral sensing capability have led to the development of a number of atmospheric compensation algorithms for hyperspectral data analysis. In this paper, three topics will be discussed: (1) algorithm evaluation of two physics-based approaches: ATREM and the AFRL model, (2) sensitivity analysis of the effects of various input parameters to surface reflectance retrieval, and (3) algorithm enhancements of how water vapor and aerosol retrievals can be better conducted than current algorithms. Examples using existing hyperspectral data, including those from HYDICE, AVIRIS will be discussed. Results will also be compared with truth information derived from ground and satellite based meteorological data.
A new, state-of-the-art atmospheric correction algorithm for the solar spectral range has been developed based on the MODTRAN4 code. The primary data products are surface reflectance spectra, column water vapor maps and relative surface elevation maps. In addition, a radiance simulation tool, an automated visibility retrieval algorithm and a spectral 'polishing' algorithm are included. Validations of retrievals have been carried out by analyzing data that encompass a variety of atmospheric and surface conditions. Some results and their implications for atmospheric correction and spectroscopy are discussed.
Background phenomenology databases and models are essential for the design and assessment of electro-optical sensing systems. The MWIR band has been proposed to satisfy a number of specific requirements in the DoD space based mission areas. However, the phenomenology database in the MWIR to support the design and performance evaluation is limited. Currently the high resolution infrared radiation sounder (HIRS/2) onboard NOAA 12, an operational polar orbiting environmental and weather satellite, offers continual global coverage of several bands in the MWIR. In particular, Channel 17 operates in the heart of the 4.23 micrometer carbon-dioxide band. Though with coarse resolution (approximately 20 km), the vast database offers a good baseline understanding of the MWIR phenomenology related to space based MWIR systems on (1) amplitude variation as function of latitude, season, and solar angle, (2) correlation to relevant MWIR features such as high-altitude clouds, stratospheric warming, aurora and other geomagnetic activities, (3) identification of potential low spatial frequency atmospheric features, and (4) comparison with future dedicated measurements. Statistical analysis on selected multiple orbits over all seasons and geographical regions was conducted. Global magnitude and variation in these bands were established. The overall spatial gradient on the 50 km scale was shown to be within sensor noise; this established the upper bound of spatial frequency in the heart-of-the-carbon-dioxide-band. Results also compared favorably with predictions from atmospheric background models such as the Synthetic High Altitude Radiance Code (SHARC-3).