Hyperspectral (HS) images are highly accurate for mineral discrimination, but available areas are limited. For this reason, several methods have been proposed to extend the mineral map of the overlap region between HS and multispectral (MS) images to the surrounding area with no HS image. One such method, proposed by Hirai and Tonooka, discriminates minerals using MS images by obtaining the endmembers of MS images from the positions of the endmember pixels of HS images in the overlap region. While this method (referred to as HT method) has the advantage of being less susceptible to the spectral distortions of HS and MS images, it also has the problem of reduced accuracy due to misalignment between HS and MS images. We proposed an improved HT method that reduces the effects of the above problems by incorporating a process that improves the robustness against misalignment by searching for the best MS endmember pixel around the position of the HS endmember pixel and a process that determines more optimum threshold value of each mineral in the spectral angle mapper method used in the HT method. As a result of evaluation using an AVIRIS image as an HS image and a World View-3 image as an MS image at Cuprite, Nevada, the improved method improved the overall accuracy by 2.6% compared with the original HT method, and in the case that the HS and the MS images were misaligned, the overall accuracy of the original method decreased by 7.0%, while the improved method decreased by only 1.5%. These results indicate that the improved method can perform as expected.
A hyperspectral (HS) imager is more effective than a multispectral (MS) imager in mineral discrimination, but spatial coverage of HS images is limited in comparison to that of MS images. Thus Kruse and Perry have proposed a method that uses coincident HS imaging and MS imaging data to extend mineral mapping to larger areas. We propose a method modified from the Kruse and Perry’s (K&P) method. Though the K&P method derives the MS-based endmember spectra by weighting the HS-based endmember spectra with the response functions of the MS sensor bands, the proposed method obtains the MS-based endmember spectra from surface reflectance spectra of the MS pixels at the same positions with the HS pixels selected as the HS-based endmembers in the overlapping area. The validation study using airborne visible/infrared imaging spectrometer and advanced spaceborne thermal emission and reflection radiometer images over Cuprite and Goldfield areas, Nevada, USA, demonstrates that the proposed method is more robust against spectral inconsistency between the HS- and the MS-images caused by calibration and/or atmospheric correction errors than the K&P method, though the proposed method is more sensitive to co-registration errors between the HS- and the MS-images than the K&P method.
This study focused on determining the past changes and predicting the future trends in eutrophication of the Bolgoda North lake, Sri Lanka using in situ Chlorophyll-a (Chl-a) measurements and Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) satellite data. This Lake is located in a mixed land use area with industries, some agricultural lands, middle income and high income housing, tourist hotels and low income housing. From March to October 2013, water samples from five sampling sites were collected once a month parallel to ASTER overpass and Chl-a, nitrate and phosphate contents of each sample were measured using standard laboratory methods. Cloud-free ASTER scenes over the lake during the 2000-2013 periods were acquired for Chl-a estimation and trend analysis. All ASTER images were atmospherically corrected using FLAASH software and in-situ Chl-a data were regressed with atmospherically corrected three ASTER VNIR band ratios of the same date. The regression equation of the band ratio and Chl-a content with the highest correlation, which was the green/red band ratio was used to develop algorithm for generation of 15-m resolution Chl-a distribution maps. According to the ASTER based Chl-a distribution maps it was evident that eutrophication of this lake has gradually increased from 2008-2011. Results also indicated that there had been significantly high eutrophic conditions throughout the year 2013 in several regions, especially in water stagnant areas and adjacent to freshwater outlets. Field observations showed that this lake is receiving various discharges from factories. Unplanned urbanization and inadequacy of proper facilities in the nearby industries for waste management have resulted in the eutrophication of the water body. If the present trends of waste disposal and unplanned urbanization continue, enormous environmental problems would be resulted in future. Results of the present study showed that information from satellite remote sensing can play a useful role in the development of time series Chl-a distribution maps. Such information is important for the future predictions, development and management of this area as well as in the conservation of this water body.
The ASTER instrument onboard the NASA’s Terra satellite launched in December 1999 has three subsystems divided by the spectral regions. ASTER thermal infrared (TIR) subsystem has five TIR bands with a spatial resolution of 90 m. Since March 2000 after the initial checkout period, many vicarious calibration (VC) experiments have been conducted for ASTER/TIR in lakes such as Lake Tahoe (NV/CA), Salton Sea (CA), and Lake Kasumigaura (Japan), and in dry lakes such as Railroad Valley (NV), Alkali Lake (NV), and Coyote Lake (CA). In the present paper, 307 VC matchup data obtained by three organizations were analyzed. Overall results show that a typical difference between the at-sensor radiance acquired by onboard calibration (OBC) and that predicted by VC is about 0.5 to 1 K in the water sites and about 1 to 2 K in the land sites. The results of the responsivity analysis indicate that VC is well tracking the responsivity changes measured by OBC, though the recent discrepancy at band 10 should be investigated with more VC results. The results of the offset analysis indicate that the short term calibration (STC) which is performed at a blackbody temperature of 270 K before every Earth observation has worked normally. It is therefore concluded that the ASTER/TIR instrument has been keeping the designed accuracy (1 K for the temperature range of 270 to 340 K) since the launch.
Water temperature monitoring for inland water bodies like lakes and reservoirs is important in the aspects of biodiversity
conservation, and global warming monitoring. However, most of inland water bodies except for a few large water bodies
have not fully or never been monitored on water temperature, partly because in-situ temperature measurements are not
easy for small water bodies which are widely scattered and variously managed by individuals, companies, governments
etc. Thus, the satellite-based lake and reservoir temperature database in Japan (SatLARTD-J) has been developed since
2009. At present, the database contains surface temperature data for 934 water bodies which were retrieved from thermal
infrared (TIR) images of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument
onboard NASA’s Terra satellite, but its temporal resolution is only four times per year in average. In order to improve
this, the author demonstrates regression imputation for SatLARTD-J using ground air temperature data provided from
the Automated Meteorological Data Acquisition System (AMeDAS) operated by Japan Meteorological Agency. The
validation study using in-situ data from two Japanese lakes indicates that an expected imputation error will be about 2 K.
The suitability of a handheld spectrometer and ASTER satellite data for monitoring water quality in coastal waters of Sri
Lanka and inland waters of Japan was tested in November 2010 to March 2012. In-situ Chlorophyll-a (Chl-a), turbidity,
total suspended solid, secchi depth and reflectance data were measured at ASTER overpass times in Negombo estuary,
Trincomalee bay, Puttalam and Chilaw lagoons, Sri Lanka, and in Lake Senba and Lake Kasumigaura, Japan. ASTER
based Chl-a retrieval algorithms were developed support with in-situ Chl-a and MODIS OC3 Chl-a. The original
MODIS Chl-a and the in-situ Chl-a were regressively analyzed for determination of a MODIS Chl-a correction equation
because it may overestimate in tropical coastal waters. Then, three ASTER VNIR band ratios were compared for
correlation with the corrected MODIS Chl-a and in-situ Chl-a. Finally, the regression equation of the ASTER band ratio,
B1/B2, with highest correlation was used for generation of high-resolution Chl-a distribution maps. Significant
correlation between the ratio of the reflectance peak at 705 nm and the Chl-a absorption at 678 nm and the in-situ Chl-a
content was observed and these reflectance ratios were used to establish spectrometric Chl-a estimation algorithms. The
proposed algorithms successfully determined localized environmental effects in the study areas. ASTER-based high
resolution Chl-a distribution maps will be derived more precisely by further correction of these algorithms, which will be
useful in mitigate impacts of the environment change in those coastal and inland water environments.
For many lives that inhabit inland water bodies such as lakes and reservoirs, water temperature is an important
environmental factor-the shift of water temperature may cause to replace some species by others in an ecosystem. On
the other hand, some reports indicate that the surface temperatures of some lakes or reservoirs have been increasing with
time due to global warming. Thus, water temperature monitoring for inland waters like lakes and reservoirs is important
as aspects of biodiversity conservation and global warming monitoring. However, many water bodies except for some
large lakes have not fully or never been monitored on water temperature. We therefore have been developing a satellitebased
lake and reservoir temperature database (SatLARTD) since 2009. As of August 2011, SatLARTD in Japan
(SatLARTD-J) has been nearly completed using thermal infrared (TIR) imagery observed by the ASTER instrument
onboard the Terra satellite, providing water surface temperatures for 899 Japanese water bodies greater than a size of 270
m by 270 m (3 by 3 pixels in ASTER/TIR), with daily max/min air temperatures. In the future version, other satellite
data like Terra/MODIS will be added for improvement of the temporal resolution. We also wish to extend the target area
from only Japan to Asia or the world.
In the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) Project, two kinds of algorithms are
used for cloud assessment in Level-1 processing. The first algorithm based on the LANDSAT-5 TM Automatic Cloud
Cover Assessment (ACCA) algorithm is used for a part of daytime scenes observed with only VNIR bands and all
nighttime scenes, and the second algorithm based on the LANDSAT-7 ETM+ ACCA algorithm is used for most of
daytime scenes observed with all spectral bands. However, the first algorithm does not work well for lack of some
spectral bands sensitive to cloud detection, and the two algorithms have been less accurate over snow/ice covered areas
since April 2008 when the SWIR subsystem developed troubles. In addition, they perform less well for some
combinations of surface type and sun elevation angle. We, therefore, have developed the ASTER cloud coverage
reassessment system using MODIS cloud mask (MOD35) products, and have reassessed cloud coverage for all ASTER
archived scenes (>1.7 million scenes). All of the new cloud coverage data are included in Image Management System
(IMS) databases of the ASTER Ground Data System (GDS) and NASA's Land Process Data Active Archive Center (LP
DAAC) and used for ASTER product search by users, and cloud mask images are distributed to users through Internet.
Daily upcoming scenes (about 400 scenes per day) are reassessed and inserted into the IMS databases in 5 to 7 days after
each scene observation date. Some validation studies for the new cloud coverage data and some mission-related analyses
using those data are also demonstrated in the present paper.
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a high-spatial-resolution
multispectral imager on the Terra satellite launched in December 1999. The ASTER thermal infrared (TIR) subsystem
has five spectral bands with a spatial resolution of 90 m in the TIR spectral region, which are used for generation of the
standard products of surface temperature and surface spectral emissivity. High-resolution surface emissivity at five
spectral bands is unique, and is particularly useful for geological mapping. However, the emissivity product is not always
easy to use, because (1) its image size is about 60 km square which is not large enough for regional-scale studies, (2) its
imaged area is not fixed to the world reference system (WRS) due to a flexible pointing system, and (3) standard
atmospheric correction often fails under humid conditions. Thus, in order to improve the usability of the ASTER
emissivity product, we are generating land surface emissivity maps in a regional scale by applying improved retrieval
algorithms and stack/mosaic processing to an ASTER orthogonal projection dataset which have been produced from the
ASTER data archives by the Advanced Industrial Science and Technology (AIST), Japan. In the present paper, we
introduce East-Asia land surface emissivity maps as the first result of this project. A comparison study with MODIS
monthly emissivity products (MOD11C3) indicates that the generated maps give more reasonable emissivity spectra with
higher spatial resolution than the MODIS emissivity products, though the maps have missing pixels in high latitude areas
and humid areas.
Cloud assessment for ASTER nighttime scenes is not accurate because the ASTER Cloud Coverage Assessment
Algorithm (ACCAA) thresholds with only one thermal infrared (TIR) band for nighttime scenes. First in the present
paper, it is shown that the original ACCAA cloud masks differ considerably from the masks interpolated from MODIS
Cloud Mask Products (MOD35), and this discrepancy is caused from errors in the ACCAA masks by visual check for
543 scenes. In addition, uncertain pixels included in MOD35 masks, which are classified to neither cloud nor clear, are
visually checked for 76 scenes. Then, the ASTER nighttime cloud mask database using MOD35 products is introduced.
It provides the interpolated MOD35 cloud masks for almost all ASTER nighttime scenes (143,242 scenes as of July
2008) through Internet. The database also shows that clear scenes with cloud coverage of 20% or less are about 34% of
the total nighttime scenes. In the final part of the paper, an algorithm for reclassifying an interpolated MOD35 mask
using ASTER measurements is proposed and applied to 42 test scenes. The algorithm will work well for some scenes,
but less well for snow/ice surfaces, and thin, cirrus, and high clouds, due to the band limitation of ASTER/TIR. If a
spatial uniformity test is added, the algorithm performance may be improved.
The MODIS/ASTER (MASTER) airborne simulator which has fifty bands in the visible to the thermal-infrared spectral
regions was developed mainly to support the Advanced Spaceborne Thermal Emission and Reflection radiometer
(ASTER) and the Moderate resolution Imaging Spectroradiometer (MODIS) instrument teams in the areas of algorithm
development, calibration and validation, but its wide spectral capability is also useful for other studies such as geology,
environmental monitoring, and land management. Currently, only MASTER product distributed to users is a level-1B at-sensor
radiance product, so that if a user needs surface reflectance and/or emissivity/temperature, the user should apply
atmospheric correction to a level-1B product. Thus in the present study, we derived surface reflectance and emissivity
spectra from MASTER data acquired over Railroad Valley Playa, NV/USA, by atmospheric correction with various
atmospheric sources like Aerosol Robotic Network (AERONET) products, and then compared with in-situ measured
spectra for both reflective and emissive regions. Calibration errors in the reflective region which caused discrepancy
from the in-situ spectra were reduced by adjusting the MASTER radiance to ASTER and MODIS radiances at the top of
the atmosphere. We also compared the spectral similarity in the reflective region versus that in the emissive region, for
MASTER spectra, and the spectra of ASTER spectral library and in-situ spectra, as an example of discrimination
analysis using both reflective and emissive bands.
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the Terra satellite has five spectral bands (bands 10 to 14) in the thermal infrared (TIR) spectral region. In February-March 2005, we conducted a field campaign for ASTER/TIR on frozen Lake Kussharo in Japan using a multi-band radiometer (CIMEL CE312) which has five spectral bands compatible with the ASTER/TIR bands. The first purpose is to make vicarious calibration (VC) of the ASTER/TIR bands using a low-temperature target below 270 K, and the second purpose is to investigate the spectral behavior of snow/ice emissivity in the ASTER/TIR bands. The VC experiment was successfully conducted on 4 March, using the coldest target (about 262 K at the sensor) among the past VCs conducted for ASTER/TIR. The results show that the at-sensor radiances predicted by VC match the ASTER image radiances within the designed calibration accuracy (2 K for 240 to 270 K), indicating that the ASTER/TIR bands are well calibrated for the temperature range around 262 K. On the other hand, band emissivity measurements of snow and ice surfaces show that spectral emissivity changes with snow/ice conditions and with a viewing angle, particularly in bands 13 and 14 (10.2 to 11.7 μm). Finally, the surface emissivity ratio (SER) between bands 13 and 14 is shown to be useful for snow/ice monitoring, using ASTER imagery obtained in February-March 2005.
The Moderate Resolution Imaging Spectroradiometer (MODIS) project has operationally provided land surface temperature (LST) and emissivity imagery produced from mid-infrared bands by either of two atmospheric correction algorithms. One is the generalized split-window algorithm. This algorithm can be applied to each observed scene, and the spatial resolution of generated products is 1 km, but the emissivity data in the products are empirically estimated by a classification-based method. Another is the physics-based day/night algorithm. In this algorithm, both LST and emissivity are physically determined using mid-infrared measurements, but a pair of day/night scenes is necessary for each processing, and the spectral resolution of generated products is degraded to 5 km. In the present paper, the water vapor scaling (WVS) method (Tonooka, 2001 and 2005) is applied to three MODIS thermal infrared (TIR) bands (29, 31, and 32) as an alternative approach. This method is an atmospheric correction algorithm for TIR multi-spectral data including land surfaces, designed mainly for the five TIR spectral bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the Terra satellite. The WVS method is on the basis of a traditional approach using a radiative transfer code, such as MODTRAN, combined with external atmospheric profiles, but the errors included in profiles are reduced on a pixel-by-pixel basis using an extended multi-channel approach. In the present paper, the WVS method for the
three MODIS TIR bands is proposed, and applied to actual imagery for preliminary validation.
KEYWORDS: Calibration, Sensors, Black bodies, Radiometry, Temperature metrology, Satellites, Spatial resolution, Data processing, Short wave infrared radiation, Mirror pointing
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), on the NASA Terra satellite, has three radiometers, the VNIR, SWIR and TIR. The TIR radiometer has five bands (10 to 14) in the thermal infrared region with a spatial resolution of 90 m. These TIR bands are radiometrically calibrated by a single onboard blackbody whose temperature can be changed between 270 K and 340 K. In the normal operation mode the blackbody is kept at 270 K, and a constant coefficient in a quadratic radiometric calibration equation for each detector is adjusted at that temperature before each Earth observation. Once in 33 days the gain term can be updated by a long term calibration in which the blackbody is measured at 270, 300, 320, and 340 K. The sensor response of all bands (particularly band 12) has been degrading since the launch, and periodical updating of the gain coefficient does not fully follow the degradation, so that the calibration error on level-1 products is sometimes unacceptable. We therefore have developed approximation equations for the coefficients to predict the most reasonable radiometric calibration coefficients (RCC) at the time of the observation. This will be implemented soon in the Level-1 data processing.
In snow/ice remote sensing with the thermal infrared (TIR) spectral region, the most important target parameter is surface temperature which is a key parameter in climate process studies. Surface emissivity is usually an uncertain factor in temperature determination, but laboratory measurements indicate that snow/ice emissivity spectra include some information on conditions such as grain size and cementation. We therefore investigate the applicability of a spectral emissivity change to snow/ice condition monitoring. First, snow/ice emissivity spectra extracted from a spectral library are evaluated, and the surface emissivity ratio (SER) of two bands located between 10.5 and 12.5 micro is proposed as a snow/ice condition index. Next, snow/ice emissivity spectra measured on site with a multi-band TIR radiometer are shown to have a spectral behavior almost consistent with the library spectra. Then, two-band temperature/emissivity separation (TES) equations for snow/ice surfaces for AVHRR, ASTER, and MODIS, which can be used for retrieving surface emissivities at two bands, are derived from the spectral library. Finally, the SER images acquired by ASTER and MODIS over snow/ice fields around Abashiri, Japan, and Dry Valley, Antarctica, are evaluated. The results show that the SER has a spatial variation of 1 or 2% over snow/ice surfaces, and also the thermal log residual (TLR) ratio as well as the SER is useful for snow/ice condition monitoring. Consequently, the SER and the TLR ratio will be useful for detecting some difference of snow/ice conditions under clear sky conditions in either daytime or nighttime.
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) consists of three subsystems divided by the wavelength region: Visible and Near-Infrared (VNIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR) subsystems. The VNIR, SWIR and TIR subsystems have 3, 6, and 5 spectral bands with the spatial resolution of 15, 30, and 90m, respectively. The purpose of this study is to propose an algorithm for generating SWIR and TIR imagery with a 15m resolution based on spectral similarity. In the algorithm, SWIR images are first super-resolved using VNIR images, and TIR images are then super-resolved using VNIR and super-resolved SWIR images. The first step is as follows: 1) degrade the resolution of the VNIR images to 30m by pixel aggregation with the point spread function (PSF) of SWIR, 2) generate a homogeneous pixel map with a 30m resolution from the original VNIR images, 3) generate a multi-way tree for VNIR and SWIR spectra by stepwise clustering for the 30m-resolution VNIR and SWIR images, 4) generate super-resolved SWIR images by allocating the most likely SWIR spectrum to each 15m-resolution pixel based on spectral similarity in VNIR using the 30m-resolution VNIR and SWIR images, and the multi-way tree, and 5) modify the super-resolved SWIR images using the PSF as to be fully restorable to the original images. The second step is similar, except that super-resolved TIR images are derived from both the VNIR and the super-resolved SWIR images. In the latter part of the study, the algorithm is validated using ASTER data.
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), one of five sensors on Terra, has five bands (10 to 14) in the thermal infrared (TIR) region. These TIR bands are radiometrically calibrated by one onboard blackbody with the function of changing temperature between 270 and 340 K. In normal operation the blackbody is set up at 270 K, and a constant coefficient in a quadratic radiometric calibration equation for each detector is adjusted at that temperature before each Earth observation, but the gain coefficient cannot be adjusted at this time, while it can periodically be updated by long term calibration in which the blackbody is measured at 270, 300, 320, and 340 K. On the other hand the sensor response of all bands (particularly band 12) has been degrading since the launch, and periodical updating of the gain coefficient does not fully follow the degradation, so that the calibration error on level-1 (L1) products is often unacceptable. We therefore have developed a recalibration method which is easily applied to L1 products by a general user. By using this method, the calibration error will mostly be reduced below the level of NEDT.
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), one of five sensors on Terra, has bands 4 to 9 in the short-wave infrared (SWIR) region. These bands, particularly bands 5 and 9, are affected by band-to-band crosstalk. A crosstalk correction algorithm already developed is practically used for reducing a leaked ghost image, but does not satisfactorily work for all scenes. We therefore analyze crosstalk effects in more detail for improving this algorithm. As the results, it is shown that crosstalk includes several band-to-band/intra-band components, and the cause of each component is estimated to be reflection, scattering, and/or refraction in a CCD chip and/or interference filters. Based on these facts, a new crosstalk correction algorithm is developed by improving the original algorithm. In the new algorithm, all known crosstalk components are included, kernel functions for convolution with a source image are updated, and sensitivity correction applied for keeping consistency with radiometric calibration is improved. Comparison results indicate that the new algorithm reduces ghost images more correctly than the original algorithm.
Linear unmixing of spectra from daytime Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images can be used to improve the spatial resolution of temperatures calculated for streams that are not fully resolved in the 90-m thermal infrared (TIR) data. We first examine ASTER 15-m Visible-Near Infrared (VNIR) data to select three endmembers using a simple automated technique. These endmembers correspond to vegetation, shade/water, and other scene components (e.g. urban/soil/non-photosynthetic vegetation). Then the 15-m VNIR data are unmixed into the three corresponding fraction images. Threshold and adjacency tests are used to separate the shade and water fractions creating a total of four fraction images that together are used to specify the amount of the scene components in each 90-m TIR pixel. The emitted thermal radiance (ETR) from each of the scene components can be estimated if we assume that it is the same as for a
The standard atmospheric correction algorithm for five thermal infrared (TIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is currently based on radiative transfer computations with global assimilation data on a pixel-by-pixel basis. In the present paper, we verify this algorithm using 100 ASTER scenes globally acquired during the early mission period. In this verification, the max-min difference (MMD) of the water surface emissivity retrieved from each scene is used as an atmospheric correction error index, since the water surface emissivity is well known; if the MMD retrieved is large, an atmospheric correction error also will be possibly large. As the results, the error of the MMD retrieved by the standard atmospheric correction algorithm and a typical temperature/emissivity separation algorithm is shown to be remarkably related with precipitable water vapor, latitude, elevation, and surface temperature. It is also mentioned that the expected error on the MMD retrieved is 0.05 for the precipitable water vapor of 3 cm.
Land Surface Emissivity product is one of standard products generated from Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) on NASA's Terra satellite launched in December 1999. This product is important for detailed lithologic mapping and precise land surface temperature determination. The accuracy of ASTER-derived emissivity is a function of various factors such as radiometric calibration of the instrument, assumptions used in a temperature-emissivity separation algorithm, and spatial temperature/material mixture in a pixel. In this study, the effects of spatial material mixture on ASTER-derived emissivity are investigated as one of the validation activities of this product. First, the mixture effects on ASTER-derived emissivity are evaluated through numerical simulations under various land surface material and temperature conditions. Also, at several sites including Cuprite, Nevada, ASTER-derived emissivity and airborne sensor-derived emissivity are compared. Applications of ASTER emissivity products to environmental and geologic studies will be also presented.
Calibration of the five EOS ASTER instrument emission bands (90 m pixels at surface) is being checked during the operational life of the mission using field measurements simultaneous with the image acquisition. For water targets, radiometers, temperature measuring buoys and local radiosonde atmospheric profiles are used to determine the average water surface kinetic temperature over areas roughly 3 X 3 pixels in size. The in-band surface leaving radiance is then projected through the atmosphere using the MODTRAN radiation transfer code allowing an at sensor radiance comparison. The instrument at sensor radiance is also projected to the water surface allowing a comparison in terms of water surface kinetic temperature. Over the first year of operation, the field measurement derived at sensor radiance agrees with the image derived radiance to better than plus/minus 1% for all five bands indicating both stable and accurate operation.
In order to simulate the ASTER's thermal infrared sensor that is one of the unique features of the ASTER, a new airborne thermal infrared imaging spectrometer -- airborne ASTER simulator (AAS) -- was planned and manufactured by a Japanese science group. The AAS having unique twenty bands in the TIR region was used for the field experiment in June 1996. Test site was Cuprite, Nevada, U.S.A. The basaltic zone, silicified mountain and playa are typical targets of the flights. The purpose of this experiment was to obtain the high spectral resolution TIR (thermal infrared) data. These data were used for the development and validation of temperature and emissivity separation algorithm. Along the trajectory on the ground, the radiance temperature measurement was synchronized with the AAS flight. ASTER simulation data sets were synthesized from these airborne data, and the performance of temperature and emissivity separation was evaluated by these data.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.