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.
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.
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.
Based on simulated atmospheric and sensor effects, we identify spectral resolution and per-channel signal-to-noise ratio (SNR) requirements for thermal infrared spectrometers that allow effluent quantification to any desired precision. This work is based on the use of MODTRAN-4 to explore the effects of temperature contrast and effluent concentration on the spectral slopes of particular absorption features. These slopes can be estimated from remotely sensed spectral data by use of least-squares techniques. The precision of these estimates is based on two factors related to spectral quality: the number of spectral samples that lie along an absorption feature and the radiometric accuracy of the samples themselves. The least-squares process also calculates the slope estimation error variance, which is related to the effluent quantification uncertainty by the same function that maps the slope itself to effluent quantity. The effluent quantification precision is thus shown to be a function of the spacing between spectral channels and the per-channel SNR. The relationship between SNR, channel spacing and effluent quantification precision is expressed as an equation defining a surface of constant "difficulty." This surface can be used to evaluate parameter sensitivities of sensors in design, to appropriately task sensors, or to evaluate effluent quantification tasks in terms of feasibility.
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.
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.
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.
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.
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.
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.
In support of hyperspectral sensor system design and parameter tradeoff investigations, an analytical end-to-end remote sensing system performance forecasting model has been extended to the longwave infrared (LWIR). The model uses statistical descriptions of surface emissivities and temperature variations in a scene and propagates them through the effects of the atmosphere, the sensor, and processing transformations. A resultant system performance metric is then calculated based on these propagated statistics. This paper presents the theory and operation of extensions made to the model to cover the LWIR. Theory is presented on combining both surface spectral emissivity variation with surface temperature variation on the upwelling radiance measured by a downward-looking LWIR hyperspectral sensor. Comparisons of the model predictions with measurements from an airborne LWIR hyperspectral sensor at the DoE ARM site are presented. Also discussed is the implementation of a plume model and radiative transfer equations used to incorporate a thin man-made effluent plume in the upwelling radiance. Example parameter trades are included to show the utility of the model for sensor design and operation applications.
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.
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.
Atmospheric scattering of ultraviolet light is examined as a mechanism for short-range, non-line-of-sight (NLOS) communication between nodes in energy-constrained distributed sensor networks. The physics of scattering is discussed and modeled, and progress in the development of solid state sources and detectors is briefly summarized. The performance of a representative NLOS UV communication system is analyzed by means of a simulation model and compared to conventional RF systems in terms of covertness and transceiver power. A test bed for evaluating NLOS UV communication hardware and modulation schemes is described.
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.
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.
The results of a series of microwave measurements were made to confirm the relationship between measured brightness temperatures (Tb) and the Fresnel equations as a function of local incidence angle, polarization and frequency. The Phillips Laboratory's microwave transmission code RADTRAN allows the input of surface emissivity or reflectivity and the thermometric temperature of the surface to account for surface radiation properties. The experimental verification was carried out using the Millimeter Wave Analysis of Passive Signatures (MAPS) system. MAPS consists of a spectral camera mounted on an scissors lift tower with elevation/azimuth positioning and operating at frequencies of 35, 60 and 95 GHz, with both vertical and horizontal polarization.
Since August of 1994, the Defense Meteorological Satellite Program (DMSP) has had on-orbit two satellite platforms (F-11 and F-12) which carry the special sensor microwave water vapor sounder (SSM/T-2). The sensor consists of 5 channels: three located symmetrically about the 183 GHz water vapor absorption line, one at 150 GHz and a 91.65 GHz window channel. Calibration of the SSM/T-2 on both platforms was undertaken to verify the absolute accuracy of the microwave measurements. Initial findings show a discrepancy between measurements from the SSM/T-2 and an independent aircraft-mounted instrument of only 1-1.6 K at 183 GHz. Basic channel information obtained from radiative transfer modeling provides an insight into the surface and atmospheric contributions to the channel observations and the sensitivity of the channels to various atmospheric phenomena. From the modeling and calibration studies a number of interesting channel signatures were observed in the SSM/T-2 measurements including the effects of the underlying surface, fractional cloud coverage and type, and precipitation occurrence. The signatures of clouds and precipitation (over water and land) have been identified and efforts to derive detection algorithms have been made.
From studies of the special sensor microwave water vapor sounder (SSM/T-2) brightness temperature (Tb) measurements, channel signatures were identified for various surface and atmospheric conditions. The sensor consists of 5 channels: three located about the 183 GHz water vapor absorption line, one at 150 GHz and a 91.65 GHz window channel. Additional sensor information was used (specifically SSM/I, OLS and GOES visible and infrared imagery) to determine the presence of clouds and precipitation in the SSM/T-2 field-of-view (FOV). Non-precipitating clouds over water generally display Tb signatures similar to clear FOVs although some differences do occur, especially for the 91 GHz channel. For data collected in the western equatorial Pacific, the presence of light rain over water caused the warmest Tb to shift to 150 GHz. As the rain rate and scattering in the FOV increased, the 183 plus or minus 1 GHz Tb became the warmest of the three atmospheric channels. For the study of the effect of precipitation over land, SSM/I and manually digitized radar (MDR) data were collocated with SSM/T-2 observations. Techniques that examined the distribution of the Tb differences between neighboring pixels appear to provide a robust technique to identify precipitation. This technique also worked over water surfaces.
Cirrus clouds were the focus of an intensive field study in Kansas in November of 1991. During this period, measurements of the downwelling radiation from cirrus clouds in the water vapor rotational band (18 - 28 micrometers ) were made from the NCAR King Air Research Aircraft. The instrument, the SSH-2, is a 17 channel passive radiometer with seven channels spanning this spectral region. Measurements were made under a variety of atmospheric conditions ranging from clear to opaque multi-layered cloud systems. In this paper, two King Air flights were examined: one under clear conditions and one during a cirrus event. Calibration of the instrument was completed for each flight and the relative difference between measurements during the two cases are presented. The latter case included flight tracks below and through the cirrus clouds. It is clear from the measurements that a significant difference exists between the measurements during the cirrus event and under clear skies. Based upon model estimations these differences may be used to determine certain microphysical properties of the cirrus cloud.
A study was undertaken to determine the accuracy of DMSP SSM/T-2 water vapor sounder brightness temperature measurements by independent comparison with co-located aircraft measurements. Five underflights of the SSM/T-2 were made by NASA ER-2 research aircraft which carried the MIR, an instrument with similar channels and scan characteristics to the SSM/T-2 and a stated accuracy of 1 K. The flights occurred on both coasts of the U.S. with both water and land surfaces targeted for measurements. Comparisons of the SSM/T-2 and MIR 183 GHz measurements over water fields of view (FOVs), which provide the most accurate estimate of the true instrument bias, display RMS differences of 0.9 to 1.6 K, roughly within the accuracy limits of the calibrating MIR instrument. Larger differences occur for regions where surface emissivity variations are significant (up to 11 K for coast and land FOVs). The overall conclusion is that the SSM/T-2 suffers no significant bias in its calibration.
Total Precipitable Water (TPW) calculations using the Special Sensor Microwave Water Vapor Sounder (SSM/T-2) launched November 1991 on the Defense Meteorological Satellite Program (DMSP) F-11 satellite are compared with TPW values obtained from analysis of the collocated Special Sensor Microwave Imager (SSM/I). The four data sets used were collected over the ocean. The different characteristics of these instruments are described. Their response to the ambient conditions indicate that the two independent measures of TPW generally agree within 20 percent over the range of TPW observed. Some direct measurements of TPW obtained from radiosondes at the time and in proximity to the satellite overpasses are presented for independent comparison.