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Geoscience and Remote Sensing, IEEE Transactions on

Issue 3  Part 1 • Date March 2013

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Displaying Results 1 - 25 of 47
  • [Front cover]

    Page(s): C1
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  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Page(s): C2
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  • Table of contents

    Page(s): 1049 - 1051
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  • Foreword to the Special Issue on Intercalibration of Satellite Instruments

    Page(s): 1052 - 1055
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  • Overview of Intercalibration of Satellite Instruments

    Page(s): 1056 - 1080
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1971 KB) |  | HTML iconHTML  

    Intercalibration of satellite instruments is critical for detection and quantification of changes in the Earth's environment, weather forecasting, understanding climate processes, and monitoring climate and land cover change. These applications use data from many satellites; for the data to be interoperable, the instruments must be cross-calibrated. To meet the stringent needs of such applications, instruments must provide reliable, accurate, and consistent measurements over time. Robust techniques are required to ensure that observations from different instruments can be normalized to a common scale that the community agrees on. The long-term reliability of this process needs to be sustained in accordance with established reference standards and best practices. Furthermore, establishing physical meaning to the information through robust Système International d'unités traceable calibration and validation (Cal/Val) is essential to fully understand the parameters under observation. The processes of calibration, correction, stability monitoring, and quality assurance need to be underpinned and evidenced by comparison with “peer instruments” and, ideally, highly calibrated in-orbit reference instruments. Intercalibration between instruments is a central pillar of the Cal/Val strategies of many national and international satellite remote sensing organizations. Intercalibration techniques as outlined in this paper not only provide a practical means of identifying and correcting relative biases in radiometric calibration between instruments but also enable potential data gaps between measurement records in a critical time series to be bridged. Use of a robust set of internationally agreed upon and coordinated intercalibration techniques will lead to significant improvement in the consistency between satellite instruments and facilitate accurate monitoring of the Earth's climate at uncertainty levels needed to detect and attribute the m- chanisms of change. This paper summarizes the state-of-the-art of postlaunch radiometric calibration of remote sensing satellite instruments through intercalibration. View full abstract»

    Open Access
  • CEOS Visualization Environment (COVE) Tool for Intercalibration of Satellite Instruments

    Page(s): 1081 - 1087
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1195 KB) |  | HTML iconHTML  

    Increasingly, data from multiple instruments are used to gain a more complete understanding of land surface processes at a variety of scales. Intercalibration, comparison, and coordination of satellite instrument coverage areas is a critical effort of international and domestic space agencies and organizations. The Committee on Earth Observation Satellites Visualization Environment (COVE) is a suite of browser-based applications that leverage Google Earth to display past, present, and future satellite instrument coverage areas and coincident calibration opportunities. This forecasting and ground coverage analysis and visualization capability greatly benefits the remote sensing calibration community in preparation for multisatellite ground calibration campaigns or individual satellite calibration studies. COVE has been developed for use by a broad international community to improve the efficiency and efficacy of such calibration planning efforts, whether those efforts require past, present, or future predictions. This paper provides a brief overview of the COVE tool, its validation, accuracies, and limitations with emphasis on the applicability of this visualization tool for supporting ground field campaigns and intercalibration of satellite instruments. View full abstract»

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  • In-Situ Transfer Standard and Coincident-View Intercomparisons for Sensor Cross-Calibration

    Page(s): 1088 - 1097
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    There exist numerous methods for accomplishing on-orbit calibration. Methods include the reflectance-based approach relying on measurements of surface and atmospheric properties at the time of a sensor overpass as well as invariant scene approaches relying on knowledge of the temporal characteristics of the site. The current work examines typical cross-calibration methods and discusses the expected uncertainties of the methods. Data from the Advanced Land Imager (ALI), Advanced Spaceborne Thermal Emission and Reflection and Radiometer (ASTER), Enhanced Thematic Mapper Plus (ETM+), Moderate Resolution Imaging Spectroradiometer (MODIS), and Thematic Mapper (TM) are used to demonstrate the limits of relative sensor-to-sensor calibration as applied to current sensors while Landsat-5 TM and Landsat-7 ETM+ are used to evaluate the limits of in situ site characterizations for SI-traceable cross calibration. The current work examines the difficulties in trending of results from cross-calibration approaches taking into account sampling issues, site-to-site variability, and accuracy of the method. Special attention is given to the differences caused in the cross-comparison of sensors in radiance space as opposed to reflectance space. The results show that cross calibrations with absolute uncertainties <; 1.5% (1σ) are currently achievable even for sensors without coincident views. View full abstract»

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  • Cross Calibration Over Desert Sites: Description, Methodology, and Operational Implementation

    Page(s): 1098 - 1113
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    Radiometric cross calibration of Earth observation sensors is a crucial need to guarantee or quantify the consistency of measurements from different sensors. Twenty desert sites, historically selected, are revisited, and their radiometric profiles are described for the visible to the near-infrared spectral domain. Therefore, acquisitions by various sensors over these desert sites are collected into a dedicated database, Structure d'Accueil des Données d'Etalonnage, defined to manage operational calibrations and the required SI traceability. The cross-calibration method over desert sites is detailed. Surface reflectances are derived from measurements by a reference sensor and spectrally interpolated to derive the surface and then top-of-atmosphere reflectances for spectral bands of the sensor to calibrate. The comparison with reflectances really measured provides an estimation of the cross calibration between the two sensors. Results illustrate the efficiency of the method for various pairs of sensors among AQUA-Moderate Resolution Imaging Spectroradiometer (MODIS), Environmental Satellite-Medium Resolution Imaging Spectrometer (MERIS), Polarization and Anisotropy of Reflectance for Atmospheric Sciences Couples With Observations From a Lidar (PARASOL)-Polarization and Directionality of the Earth Reflectances (POLDER), and Satellite pour l'Observation de la Terre 5 (SPOT5)-VEGETATION. MERIS and MODIS calibrations are found to be very consistent, with a discrepancy of 1%, which is close to the accuracy of the method. A larger bias of 3% was identified between VEGETATION-PARASOL on one hand and MERIS-MODIS on the other hand. A good consistency was found between sites, with a standard deviation of 2% for red to near-infrared bands, increasing to 4% and 6% for green and blue bands, respectively. The accuracy of the method, which is close to 1%, may also depend on the spectral bands of both sensor to calibrate and reference sensor (up to 5% in the worst case) - nd their corresponding geometrical matching. View full abstract»

    Open Access
  • Support Vector Regression-Based Downscaling for Intercalibration of Multiresolution Satellite Images

    Page(s): 1114 - 1123
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    This paper introduces a nonlinear super-resolution method for converting low spatial resolution data into high spatial resolution data to calibrate multiple sensors with a moderate spatial resolution difference, e.g., the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (30 m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible and near infrared (NIR) sensors (15 m). A preliminary linear calibration was first applied to reduce the radiometric difference. The remaining nonlinear part of the radiometric and spatial resolution differences were then calibrated by downscaling the ETM+ data to ASTER data using a support vector regression (SVR)-based super-resolution method. Experiments were conducted on two subsets (representing rural and urban areas) of the ETM+ and ASTER scenes located in the central United States on top of atmospheric reflectance observed on August 13, 2001. It was found that the radiometric difference between the two sensors caused by their spectral band difference could be largely reduced by a linear transfer equation, and the reduction could be more than 60% for the green and NIR bands. The SVR-calibrated data showed improvement over the linearly calibrated data in terms of quantitative measures and visual analysis. Furthermore, SVR calibration improved the spatial resolution of the ETM+ data toward resembling the 15-m cell size of the ASTER pixel. Consequently, the proposed method has the potential to extend an ASTER scene's swath width to match that of an ETM+ scene. View full abstract»

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  • Monitoring Satellite Radiance Biases Using NWP Models

    Page(s): 1124 - 1138
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1584 KB) |  | HTML iconHTML  

    Radiances measured by satellite radiometers are often subject to biases due to limitations in their radiometric calibration. In support of the Global Space-based Inter-Calibration System project, to improve the quality of calibrated radiances from atmospheric sounders and imaging radiometers, an activity is underway to compare routinely measured radiances with those simulated from operational global numerical weather prediction (NWP) fields. This paper describes the results obtained from the first three years of these comparisons. Data from the High-resolution Infrared Radiation Sounder, Spinning Enhanced Visible and Infrared Imager, Advanced Along-Track Scanning Radiometer, Advanced Microwave Sounding Unit, and Microwave Humidity Sounder radiometers, together with the Atmospheric Infrared Sounder, a spectrometer, and the Infrared Atmospheric Sounding Interferometer, an interferometer, were included in the analysis. Changes in mean biases and their standard deviations were used to investigate the temporal stability of the bias and radiometric noise of the instruments. A double difference technique can be employed to remove the effect of changes or deficiencies in the NWP model which can contribute to the biases. The variation of the biases with other variables is also investigated, such as scene temperature, scan angle, location, and time of day. Many of the instruments were shown to be stable in time, with a few exceptions, but measurements from the same instrument on different platforms are often biased with respect to each other. The limitations of the polar simultaneous nadir overpasses often used to monitor biases between polar-orbiting sensors are shown with these results due to the apparent strong dependence of some radiance biases on scene temperature. View full abstract»

    Open Access
  • Sensor Intercalibration Over Dome C for the ESA GlobAlbedo Project

    Page(s): 1139 - 1146
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    The response of a variety of Earth observation satellite sensors from multiple platforms, as well as different sensors on the same platform, were cross-compared over Dome C in Antarctica. A single unified methodology was employed to remove geometric, temporal, and atmospheric biases between different sensors with a range of spatial resolutions, bandwidths, and overpass times. The result assumes no “correct” baseline value, only relative responses. The resulting responses of the sensors, namely, AATSR, MERIS, and VEGETATION 2, are here presented and, where overlap occurs, compared with the results from other groups. In addition to applying the Quality Assurance for Earth Observation protocols for the quality assurance of environmental monitoring satellites, this paper discusses the intercalibration of several sensors used to establish the technical basis of the ESA GlobAlbedo project. View full abstract»

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  • The Characterization of Deep Convective Clouds as an Invariant Calibration Target and as a Visible Calibration Technique

    Page(s): 1147 - 1159
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    Deep convective clouds (DCCs) are ideal visible calibration targets because they are bright nearly isotropic solar reflectors located over the tropics and they can be easily identified using a simple infrared threshold. Because all satellites view DCCs, DCCs provide the opportunity to uniformly monitor the stability of all operational sensors, both historical and present. A collective DCC anisotropically corrected radiance calibration approach is used to construct monthly probability distribution functions (PDFs) to monitor sensor stability. The DCC calibration targets were stable to within 0.5% and 0.3 % per decade when the selection criteria were optimized based on Aqua MODerate Resolution Imaging Spectroradiometer 0.65-μm -band radiances. The Tropical Western Pacific (TWP), African, and South American regions were identified as the dominant DCC domains. For the 0.65-μm band, the PDF mode statistic is preferable, providing 0.3% regional consistency and 1% temporal uncertainty over land regions. It was found that the DCC within the TWP had the lowest radiometric response and DCC over land did not necessarily have the highest radiometric response. For wavelengths greater than 1 μm, the mean statistic is preferred, and land regions provided a regional variability of 0.7 % with a temporal uncertainty of 1.1 % where the DCC land response was higher than the response over ocean. Unlike stratus and cirrus clouds, the DCC spectra were not affected by water vapor absorption. View full abstract»

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  • GSICS Inter-Calibration of Infrared Channels of Geostationary Imagers Using Metop/IASI

    Page(s): 1160 - 1170
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    The first products of the Global Space-based Inter-Calibration System (GSICS) include bias monitoring and calibration corrections for the thermal infrared (IR) channels of current meteorological sensors on geostationary satellites. These use the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) on the low Earth orbit (LEO) Metop satellite as a common cross-calibration reference. This paper describes the algorithm, which uses a weighted linear regression, to compare collocated radiances observed from each pair of geostationary-LEO instruments. The regression coefficients define the GSICS Correction, and their uncertainties provide quality indicators, ensuring traceability to the selected community reference, IASI. Examples are given for the Meteosat, GOES, MTSAT, Fengyun-2, and COMS imagers. Some channels of these instruments show biases that vary with time due to variations in the thermal environment, stray light, and optical contamination. These results demonstrate how inter-calibration can be a powerful tool to monitor and correct biases, and help diagnose their root causes. View full abstract»

    Open Access
  • An Evaluation of the Uncertainty of the GSICS SEVIRI-IASI Intercalibration Products

    Page(s): 1171 - 1181
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    Global Space-based Inter-Calibration System (GSICS) products to correct the calibration of the infrared channels of the Meteosat/SEVIRI (Spinning Enhanced Visible and Infrared Imager) geostationary imagers are based on comparisons of collocated observations with Metop/IASI (Infrared Atmospheric Sounding Interferometer) as a reference instrument. Each step of the cross-calibration algorithm is analyzed to produce a comprehensive error budget, following the Guide to the Expression of Uncertainty in Measurement. This paper aims to validate the quality indicators provided as uncertainty estimates with the GSICS correction. The methodology presented provides a framework to allow quantitative tradeoffs between the collocation criteria and the number of collocations generated to recommend further algorithm improvements. It is shown that random errors dominate systematic ones and that combined standard uncertainties (with coverage factor k = 1) in the corrected brightness temperatures are ~ 0.01 K for typical clear sky conditions but increase rapidly for low radiances - by more than one order of magnitude for 210 K scenes, corresponding to cold cloud tops. View full abstract»

    Open Access
  • Ice Contamination of Meteosat/SEVIRI Implied by Intercalibration Against Metop/IASI

    Page(s): 1182 - 1186
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    The intercalibration of the infrared channels of the geostationary Meteosat/Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellite instruments shows that most channels are radiometrically consistent with those of Metop/IASI (Infrared Atmospheric Sounding Interferometer), which is used as a reference instrument. However, the 13.4-μm channel shows a cold bias of ~1 K in warm scenes, which changes with time. This is shown to be consistent with the contamination of SEVIRI by a layer of ice ~1 μm thick building up on the optics, which is believed to have condensed from water outgassed from the spacecraft. This ice modifies the spectral response functions and, hence, the weighting functions of the channels in stronger atmospheric absorption bands, thus introducing an apparent calibration error. Analysis of the radiometer's gain using an onboard black body source and a view of cold space confirms a loss consistent with transmission through a layer of comparable thickness, which also increases the radiometric noise-particularly for channels near the 12-μm libration band of water ice. Intercalibration, such as the Global Space-based Inter-Calibration System Correction, offers an empirical method to correct this bias. View full abstract»

    Open Access
  • Radiometric Calibration Accuracy of GOES Sounder Infrared Channels

    Page(s): 1187 - 1199
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    Quality infrared (IR) radiances and their derived products from the Geostationary Environmental Operational Satellite (GOES) Sounder are very important data sources to weather prediction and nowcasting applications for the continental United States and adjacent ocean regions. With demanding requirements for more accurate weather nowcasting models and climate change studies, it is necessary to assess and improve the radiometric calibration accuracy of the GOES Sounder data. The objective of this paper is to examine the GOES Sounder IR radiometric calibration accuracy, and the diurnal calibration variation using intercalibration with two well-calibrated hyperspectral radiometers onboard low earth orbit satellites, the Atmospheric Infrared Sounder (AIRS) on the Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) on the Metop-A satellite. The results show that most sounder IR channels of GOES-11 through GOES-15 are well calibrated outside of the satellite midnight effect time period, with a less than 0.5 K of mean bias of brightness temperature with respect to IASI. Yet, the impact of the satellite midnight effect on the radiance quality varies greatly at different IR channels among different satellites. Further research is needed to understand the changes of instrument environmental flux on the GOES IR radiance around satellite midnight. View full abstract»

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  • Correction for GOES Imager Spectral Response Function Using GSICS. Part II: Applications

    Page(s): 1200 - 1214
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1922 KB) |  | HTML iconHTML  

    During the Geostationary Operational Environmental Satellite (GOES)-14 and -15 post-launch test (PLT) for science periods, an up to ~ 2 K mean brightness temperature (Tb) bias with respect to collocated Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) observations was observed in the absorptive IR channels of the GOES-14/15 Imagers. These large scene-dependent biases were believed to be caused mainly by spectral characterization errors. In this paper, we refined the spectral response function (SRF) shift algorithm which was developed during the GOES-13 PLT period to improve the GOES-14/15 Imager IR radiometric calibration accuracy by accurately calculating the impact of blackbody on the calibrated scene radiance. The uncertainty of the SRF shift algorithm was estimated and used to guide the final selection of the total amount of central wave-number shift. This refined algorithm was first verified with GOES-13 Imager Ch6 data and then used to evaluate and further revise the audited GOES-14/15 SRFs provided by the instrument vendor. Based on this algorithm, the optimal SRF shifts were -1.98 cm-1 for GOES-13 Ch6, -8.25 cm-1 for GOES-14 Ch3, -0.25 cm-1 for GOES-14 Ch6, -6.25 cm-1 for GOES-15 Ch3 and +0.50 cm-1 for GOES-15 Ch6. The newly shifted SRFs were operationally implemented into the GOES-14/15 Imager IR calibrations in the August of 2011 and successfully reduced the mean all-sky Tb bias with respect to the reference instrument to less than 0.15 K. The scene-dependent bias, which can be nonlinear at large erroneous SRF, was also greatly reduced. The same method was applied to correct the GOES-12 Imager Ch6 SRF which has a changing SRF error during its mission life. A strong linear relationship between the optimal SRF shifts and the mean Tb bias with respect to the AIRS data was observed at this channel. This strong linear relationship can be used to revise the GOES-12- Ch6 SRF for a better radiance simulation. The method described in this paper is particularly important to evaluate and revise the erroneous SRF, if it exists, after satellite launch yet before it becomes fully operational. View full abstract»

    Open Access
  • Correction for GOES Imager Spectral Response Function Using GSICS. Part I: Theory

    Page(s): 1215 - 1223
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (846 KB) |  | HTML iconHTML  

    A cold bias of ~-2 K was found for Channel 6 (13.3 μm) of the Imager instrument on the 13th of Geostationary Operational Environmental Satellite (GOES-13) during its postlaunch tests. Similar bias was found previously for GOES-12 and for other instruments (the High Resolution Infrared Radiation Sounder, the Moderate Resolution Imaging Spectroradiometer, and the Spinning Enhanced Visible and Infrared Imager) in the similar spectral region. It was often suspected that the spectral response function (SRF) of these instruments may be in error; in some cases, it had been demonstrated that an altered SRF can eliminate most of the differences between the measured and the expected values. Using products recently developed for the Global Space-based Inter-Calibration System, this paper concluded that an SRF error is the root cause for the GOES Imager Channel 6 bias. Based on this theory, an algorithm was developed to correct for the bias. Application of this correction to GOES-13 Imager Channel 6 resulted in an SRF shift of -2.1 cm-1. The remaining biases have mean of nearly zero and much reduced standard deviation and are independent of the thermal structure of the interlaying atmosphere. This correction has also been successfully applied of other channels and of other GOES, which was described in a companion paper. View full abstract»

    Open Access
  • Effects of Ice Decontamination on GOES-12 Imager Calibration

    Page(s): 1224 - 1230
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (918 KB) |  | HTML iconHTML  

    More precise and accurate geostationary measurements are highly needed for satellite applications. It was well known that the Geostationary Operational Environmental Satellite (GOES)-12 imager was susceptible to water-ice contamination, and thus, several decontamination efforts were carried out to remove built-up ice on the instrument during operation. The intercalibration results of GOES-12 with the Atmospheric Infrared (IR) Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI) indicate that the calibration accuracy of GOES-12 was impacted by the decontamination procedures. Relative to the AIRS and the IASI, the GOES-12 imager radiances or brightness temperatures increased in the CO2 sounding channel (channel 6, 13.3 μm) and decreased in the water-vapor absorption channel (channel 3, 6.5 μm) but was less changed in the window channel (channel 4, 10.7 μm). A simple conceptual model is then proposed to give a physical explanation on the different behaviors of three IR channels in response to the ice-removal procedures. View full abstract»

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  • Intercalibration of FY-2C/D/E Infrared Channels Using AIRS

    Page(s): 1231 - 1244
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    The Fengyun (FY)-2 series satellites are the first-generation geosynchronous (GEO) Earth observation satellites operated by the National Satellite Meteorological Center (NSMC), China Meteorological Administration (CMA). The FY-2 satellites' main payload is a multispectral imager. The radiances from the FY-2C/D/E imagers were compared to the Atmospheric Infrared Sounder (AIRS), which is in low Earth orbit (LEO) on Aqua, a National Aeronautics and Space Administration satellite. The intercalibration of FY-2C/D/E infrared (IR) channels using AIRS was carried out based on the Global Space-based Inter-Calibration System (GSICS) GEO-LEO intercalibration algorithm. All the FY-2C/D/E data archived at the Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI, USA, were processed and compared with their operational calibrations. Select comparisons and longer term analyses between the new intercalibrated results and the operational calibrations of FY-2C/D/E's IR channels were demonstrated. The results show that the current operational calibration for FY-2C/D/E does not compare favorably based on the FY-AIRS intercalibration. The future operational calibration of FY-2D and FY-2E could be revised using GSICS corrections from the intercalibration with AIRS. The historical FY-2C/D/E data could be recalibrated with the GSICS GEO-LEO intercalibration algorithm at NSMC/CMA. View full abstract»

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  • The Intercalibration of Geostationary Visible Imagers Using Operational Hyperspectral SCIAMACHY Radiances

    Page(s): 1245 - 1254
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    Spectral band differences between sensors can complicate the process of intercalibration of a visible sensor against a reference sensor. This can be best addressed by using a hyperspectral reference sensor whenever possible because they can be used to accurately mitigate the band differences. This paper demonstrates the feasibility of using operational Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) large-footprint hyperspectral radiances to calibrate geostationary Earth-observing (GEO) sensors. Near simultaneous nadir overpass measurements were used to compare the temporal calibration of SCIAMACHY with Aqua Moderate Resolution Imaging Spectroradiometer band radiances, which were found to be consistent to within 0.44% over seven years. An operational SCIAMACHY/GEO ray-matching technique was presented, along with enhancements to improve radiance pair sampling. These enhancements did not bias the underlying intercalibration and provided enough sampling to allow up to monthly monitoring of the GEO sensor degradation. The results of the SCIAMACHY/GEO intercalibration were compared with other operational four-year Meteosat-9 0.65-μm calibration coefficients and were found to be within 1% of the gain, and more importantly, it had one of the lowest temporal standard errors of all the methods. This is more than likely that the GEO spectral response function could be directly applied to the SCIAMACHY radiances, whereas the other operational methods inferred a spectral correction factor. This method allows the validation of the spectral corrections required by other methods. View full abstract»

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  • Evaluation of ISCCP Multisatellite Radiance Calibration for Geostationary Imager Visible Channels Using the Moon

    Page(s): 1255 - 1266
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    Since 1983, the International Satellite Cloud Climatology Project (ISCCP) has collected Earth radiance data from the succession of geostationary and polar-orbiting meteorological satellites operated by weather agencies worldwide. Meeting the ISCCP goals of global coverage and decade-length time scales requires consistent and stable calibration of the participating satellites. For the geostationary imager visible channels, ISCCP calibration provides regular periodic updates from regressions of radiances measured from coincident and collocated observations taken by Advanced Very High Resolution Radiometer instruments. As an independent check of the temporal stability and intersatellite consistency of ISCCP calibrations, we have applied lunar calibration techniques to geostationary imager visible channels using images of the Moon found in the ISCCP data archive. Lunar calibration enables using the reflected light from the Moon as a stable and consistent radiometric reference. Although the technique has general applicability, limitations of the archived image data have restricted the current study to Geostationary Operational Environmental Satellite and Geostationary Meteorological Satellite series. The results of this lunar analysis confirm that ISCCP calibration exhibits negligible temporal trends in sensor response but have revealed apparent relative biases between the satellites at various levels. However, these biases amount to differences of only a few percent in measured absolute reflectances. Since the lunar analysis examines only the lower end of the radiance range, the results suggest that the ISCCP calibration regression approach does not precisely determine the intercept or the zero-radiance response level. We discuss the impact of these findings on the development of consistent calibration for multisatellite global data sets. View full abstract»

    Open Access
  • Applications of Spectral Band Adjustment Factors (SBAF) for Cross-Calibration

    Page(s): 1267 - 1281
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    To monitor land surface processes over a wide range of temporal and spatial scales, it is critical to have coordinated observations of the Earth's surface acquired from multiple spaceborne imaging sensors. However, an integrated global observation framework requires an understanding of how land surface processes are seen differently by various sensors. This is particularly true for sensors acquiring data in spectral bands whose relative spectral responses (RSRs) are not similar and thus may produce different results while observing the same target. The intrinsic offsets between two sensors caused by RSR mismatches can be compensated by using a spectral band adjustment factor (SBAF), which takes into account the spectral profile of the target and the RSR of the two sensors. The motivation of this work comes from the need to compensate the spectral response differences of multispectral sensors in order to provide a more accurate cross-calibration between the sensors. In this paper, radiometric cross-calibration of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors was performed using near-simultaneous observations over the Libya 4 pseudoinvariant calibration site in the visible and near-infrared spectral range. The RSR differences of the analogous ETM+ and MODIS spectral bands provide the opportunity to explore, understand, quantify, and compensate for the measurement differences between these two sensors. The cross-calibration was initially performed by comparing the top-of-atmosphere (TOA) reflectances between the two sensors over their lifetimes. The average percent differences in the long-term trends ranged from -5% to +6%. The RSR compensated ETM+ TOA reflectance (ETM+*) measurements were then found to agree with MODIS TOA reflectance to within 5% for all bands when Earth Observing-1 Hyperion hyperspectral data were used to produce the SBAFs. These differences were later reduced to with- n 1% for all bands (except band 2) by using Environmental Satellite Scanning Imaging Absorption Spectrometer for Atmospheric Cartography hyperspectral data to produce the SBAFs. View full abstract»

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  • Assessment of Spectral, Misregistration, and Spatial Uncertainties Inherent in the Cross-Calibration Study

    Page(s): 1282 - 1296
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    Cross-calibration of satellite sensors permits the quantitative comparison of measurements obtained from different Earth Observing (EO) systems. Cross-calibration studies usually use simultaneous or near-simultaneous observations from several spaceborne sensors to develop band-by-band relationships through regression analysis. The investigation described in this paper focuses on evaluation of the uncertainties inherent in the cross-calibration process, including contributions due to different spectral responses, spectral resolution, spectral filter shift, geometric misregistrations, and spatial resolutions. The hyperspectral data from the Environmental Satellite SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY and the EO-1 Hyperion, along with the relative spectral responses (RSRs) from the Landsat 7 Enhanced Thematic Mapper (TM) Plus and the Terra Moderate Resolution Imaging Spectroradiometer sensors, were used for the spectral uncertainty study. The data from Landsat 5 TM over five representative land cover types (desert, rangeland, grassland, deciduous forest, and coniferous forest) were used for the geometric misregistrations and spatial-resolution study. The spectral resolution uncertainty was found to be within 0.25%, spectral filter shift within 2.5%, geometric misregistrations within 0.35%, and spatial-resolution effects within 0.1% for the Libya 4 site. The one-sigma uncertainties presented in this paper are uncorrelated, and therefore, the uncertainties can be summed orthogonally. Furthermore, an overall total uncertainty was developed. In general, the results suggested that the spectral uncertainty is more dominant compared to other uncertainties presented in this paper. Therefore, the effect of the sensor RSR differences needs to be quantified and compensated to avoid large uncertainties in cross-calibration results. View full abstract»

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  • Assessment of Spectral Band Impact on Intercalibration Over Desert Sites Using Simulation Based on EO-1 Hyperion Data

    Page(s): 1297 - 1308
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    Since the beginning of the 1990s, stable desert sites have been used for the calibration monitoring of many different sensors. Many attempts at sensor intercalibration have been also conducted using these stable desert sites. As a result, site characterization techniques and the quality of intercalibration techniques have gradually improved over the years. More recently, the Committee on Earth Observation Satellites has recommended a list of reference pseudo-invariant calibration sites for frequent image acquisition by multiple agencies. In general, intercalibration should use well-known or spectrally flat reference. The reflectance profile of desert sites, however, might not be flat or well characterized (from a fine spectral point of view). The aim of this paper is to assess the expected accuracy that can be reached when using desert sites for intercalibration. In order to have a well-mastered estimation of different errors or error sources, this study is performed with simulated data from a hyperspectral sensor. Earth Observing-1 Hyperion images are chosen to provide the simulation input data. Two different cases of intercalibration are considered, namely, Landsat 7 Enhanced Thematic Mapper Plus with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Environmental Satellite MEdium Resolution Imaging Spectrometer (MERIS) with Aqua MODIS. The simulation results have confirmed that intercalibration accuracy of 1% to 2% can be achieved between sensors, provided there are a sufficient number of available measurements. The simulated intercalibrations allow explaining results obtained during real intercalibration exercises and to establish some recommendations for the use of desert sites for intercalibration. View full abstract»

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.

 

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Editor-in-Chief
Antonio J. Plaza
University of Extremadura