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Inter-calibration of the Moderate-Resolution Imaging Spectroradiometer and the AlongTrack Scanning Radiometer-2
International Journal of Remote Sensing
Abstract
The performance of the on board calibrator for bands 1 ( 0.66 µm), 2 ( 0.86 µm) and 4 ( 0.55 µm) of the Moderate-Resolution Imaging Spectroradiometer (MODIS Terra) has been evaluated by comparison of the top-of-atmosphere (TOA) albedos measured in the three bands over a six-day period with a three-year record of TOA albedos measured in the 0.56, 0.66 and 0.86 µm channels of the Along-Track Scanning Radiometer-2 (ATSR-2). The albedo measurements were made over two radiometrically stable sites located in the Libyan desert (22°0N, 28°30E), Sudan, and in the Sonoran desert (32°0N, 114°6W), Mexico. MODIS Terra albedos are within ±2.5 per cent of those measured in the corresponding channels of ATSR-2. Analysis of the measurements, and of model-derived albedos in the three channels (bands) of the two instruments, indicates that the MODIS Terra on board calibrator for bands 1, 2 and 4 is functioning as expected, and that either of the two instruments can be used to monitor the in-orbit performance of the other.
... In addition to the aforementioned factors, spectral response function (SRF) variations of different sensors have been recognized as one of the most important factors affecting the continuity of multisensor monitoring of global vegetation [15], [16], [21]- [26]. SRF describes the relative sensitivity of the sensor to monochromatic radiation of different wavelengths and is normally determined in the laboratory using a tunable laser or a scanning monochromator [27]. ...
... Steven et al. [25] provided additional background on the problem of cross calibrating vegetation indices and reported on a simulation study involving red and NIR spectral bands and vegetation indices for 15 satellite sensors. Rao et al. [26] presented results on the cross-sensor correction of MODIS and the European Remote Sensing satellite-2 Along-Track Scanning Radiometer-2 based on desert sites as common targets. They emphasized how crucial it is to take into consideration the spectral characteristics of the sensors and the scene to avoid compromising the efficacy of SRF cross-sensor correction. ...
... Additionally, once the satellite data sets are corrected for atmospheric effects, the TOC SRF crosssensor correction coefficients can be used instead of the use of TOA coefficients priori to atmospheric corrections. Although previous studies have addressed the problem of SRF cross-sensor corrections (e.g., [15], [16], [21]- [26]), one can argue that the use of the previous results has been limited to 6. Variations of NDVI between SPOT VGETATION (VGT1 and VGT2) plotted against the corresponding value of MODIS Terra (MD) from mean monthly growing season measured satellite data before spectral response function (SRF) cross-sensor correction (Row 1 left), and after SRF cross-sensor correction (Row 1 right). Row 2 left: before SRF cross-sensor correction from measured top-of-canopy validation data. ...
Global and regional vegetation assessment strategies
often rely on the combined use of multisensor satellite data. Variations
in spectral response function (SRF) which characterizes the
sensitivity of each spectral band have been recognized as one of the
most important sources of uncertainty for the use of multisensor
data. This paper presents the SRF differences among 21 Earth
observation satellite sensors and their cross-sensor corrections
for red, near infrared (NIR), and shortwave infrared (SWIR)
reflectances, and normalized difference vegetation index (NDVI)
aimed at global vegetation monitoring. The training data set to
derive the SRF cross-sensor correction coefficients were generated
from the state-of-the-art radiative transfer models. The results
indicate that reflectances and NDVI fromdifferent satellite sensors
cannot be regarded as directly equivalent. Our approach includes
a polynomial regression and spectral curve information generated
from a training data set representing a wide dynamics of vegetation
distributions to minimize land cover specific SRF cross-sensor
correction coefficient variations. The absolute mean SRF caused
differences were reduced from 33.9% (20.1%) to 9.4% (6%) for
red, from 3.2% (8.9%) to 1% (1.1%) for NIR, from 2.9% (3.6%)
to 1.9%(1.6%) for SWIR, and from 7.1%(9%) to 1.8%(1.7%) for
NDVI, after applying the SRF cross-sensor correction coefficients
on independent top of canopy (top of atmosphere) data for all-embraced-
sensor comparisons. Variations in processing strategies,
non spectral differences, and algorithm preferences among sensor
systems and data streams hinder cross-sensor spectra and NDVI
comparability and continuity. The SRF cross-sensor correction
approach provided here, however, can be used for studies aiming
at large-scale vegetation monitoring with acceptable accuracy.
... Scientists have predicted responses and biases between satellite sensors for other instruments by applying spectral response functions to simulated at-sensor spectral radiances or top-of-atmosphere reflectances using both radiative transfer simulations and also hyperspectral aircraft/satellite sensor measurements [12][13][14][15]. These simulations and aircraft campaigns assume or view well-characterized Earth targets with spatially and temporally uniform optical properties, such as deserts [16][17][18][19]. Bremer et al. have also discussed the importance of matching spectral bands between VIIRS and ABI and on-orbit opportunities for their cross-calibration [3]. ...
... We now have the opportunity to study the recently released ABI spectral response functions and compare them with VIIRS to maintain consistency between polar and geostationary instrument data for the numerical weather prediction community, using similar techniques to those mentioned above. The radiometric biases between ABI and VIIRS are predicted by computing their responses to earth scenes at well-characterized desert sites: the Sonoran Desert and White Sands National Monument [17,18], Fig. 1. Basic optical layout of ABI (N/S, north/south; E/W, east/ west; MWIR, mid-wave infrared; LWIR, long-wave infrared). ...
The Advanced Baseline Imager (ABI), which will be launched in late 2015 on the National Oceanic and Atmospheric Administration’s Geostationary Operational Environmental Satellite R-series satellite, will be evaluated in terms of its data quality postlaunch through comparisons with other satellite sensors such as the recently launched Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership satellite. The ABI has completed much of its prelaunch characterization and its developers have generated and released its channel spectral response functions (response versus wavelength). Using these responses and constraining a radiative transfer model with ground reflectance, aerosol, and water vapor measurements, we simulate observed top of atmosphere (TOA) reflectances for analogous visible and near infrared channels of the VIIRS and ABI sensors at the Sonoran Desert and White Sands National Monument sites and calculate the radiometric biases and their uncertainties. We also calculate sensor TOA reflectances using aircraft hyperspectral data from the Airborne Visible/Infrared Imaging Spectrometer to validate the uncertainties in several of the ABI and VIIRS channels and discuss the potential for validating the others. Once on-orbit, calibration scientists can use these biases to ensure ABI data quality and consistency to support the numerical weather prediction community and other data users. They can also use the results for ABI or VIIRS anomaly detection and resolution.
... Earth Target Type Reference Dry lake beds or playas (Biggar et al., 2003; Gu et al., 1992; Rondeaux et al., 1998; Santer et al., 1992; Slater et al., 1987; Teillet et al., 1990; Thome, 2001; Thome et al., 1997 Thome et al., , 1998 Thome et al., , 2003a Wheeler et al., 1994; Wu et al., 1997) Deserts (Cabot et al., 1999Cabot et al., , 2000 Cosnefroy et al., 1996; Rao & Chen, 1995 Rao et al., 2003) Ice or snow fields (Loeb, 1997; Nieke et al., 2003; Six et al., 2004; Tahnk & Coakley, 2001) Atmospheric scattering (Iwabuchi, 2003; Kaufman & Holben, 1993; Martiny et al., 2005; Santer & Martiny, 2003) Uniform cloud cover (Iwabuchi, 2003; Kaufman & Holben, 1993; Vermote & Kaufman, 1998) Ocean glint (Kaufman & Holben, 1993; Vermote & Kaufman, 1998) Semi-arid rangeland (Teillet et al., 1998Teillet et al., , 1999) Grassland targets (Black et al., 2003; Schiller, 2003) Multiple target types (Hill & Aifadopoulou, 1990; Koepke, 1982; Teillet et al., 1997a Teillet et al., , 2001a The list does not include examples of the many articles that describe the use of pseudo-invariant features to normalize satellite imagery for multi-temporal analysis without yielding absolute radiometric calibration coefficients.Table 2 lists the sensors and their acronyms). The Landsat solar-reflective spectral domain was adopted as the spectral framework for these comparisons (Table 3). ...
... 3 P.M. Teillet et al. / Remote Sensing of Environment xx (2007) xxx–xxx ARTICLE IN PRESS Rao et al. (2003) presented results on the inter-calibration of Terra MODIS and the European Remote Sensing satellite-2 (ERS-2) Along-Track Scanning Radiometer-2 (ATSR-2) based on desert sites as common targets. They emphasized how crucial it is to take into consideration the spectral character of the sensors and the scene to avoid compromising the efficacy of inter-calibration. ...
In order for quantitative applications to make full use of the ever-increasing number of Earth observation satellite systems, data from the various imaging sensors involved must be on a consistent radiometric scale. This paper reports on an investigation of radiometric calibration errors due to differences in spectral response functions between satellite sensors when attempting cross-calibration based on near-simultaneous imaging of common ground targets in analogous spectral bands, a commonly used post-launch calibration methodology. Twenty Earth observation imaging sensors (including coarser and higher spatial resolution sensors) were considered, using the Landsat solar reflective spectral domain as a framework. Scene content was simulated using spectra for four ground target types (Railroad Valley Playa, snow, sand and rangeland), together with various combinations of atmospheric states and illumination geometries. Results were obtained as a function of ground target type, satellite sensor comparison, spectral region, and scene content. Overall, if spectral band difference effects (SBDEs) are not taken into account, the Railroad Valley Playa site is a “good” ground target for cross calibration between most but not all satellite sensors in most but not all spectral regions investigated. “Good” is defined as SBDEs within ± 3%. The other three ground target types considered (snow, sand and rangeland) proved to be more sensitive to uncorrected SBDEs than the RVPN site overall. The spectral characteristics of the scene content (solar irradiance, surface reflectance and atmosphere) are examined in detail to clarify why spectral difference effects arise and why they can be significant when comparing different imaging sensor systems. Atmospheric gas absorption features are identified as being the main source of spectral variability in most spectral regions. The paper concludes with recommendations on spectral data and tools that would facilitate cross-calibration between multiple satellite sensors.
... To date, the cross-comparison analysis covers most of the different optical-and radar-based satellite systems currently in use for earth observation. These studies involve both the intercalibration among different satellites [3][4][5][6][7][8][9] and different sensors built within the same satellite systems. For example, regarding the Landsat system, different intercalibration algorithms have been identified both with different satellite systems [7,[10][11][12][13][14][15][16][17][18] and with different sensors such as Multi Spectral Scanner (MSS), Enhanced Thematic Mapper plus (ETM+) and Thematic Mapper (TM) [19][20][21][22][23][24][25]. ...
Landsat 8 is the most recent generation of Landsat satellite missions that provides remote sensing imagery for earth observation. The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, together with Landsat-8 Operational Land Imager (OLI) and Thermal Infrared sensor (TIRS) represent fundamental tools for earth observation due to the optimal combination of the radiometric and geometric images resolution provided by these sensors. However, there are substantial differences between the information provided by Landsat 7 and Landsat 8. In order to perform a multi-temporal analysis, a cross-comparison between image from different Landsat satellites is required. The present study is based on the evaluation of specific intercalibration functions for the standardization of main vegetation indices calculated from the two Landsat generation images, with respect to main land use types. The NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), LSWI (Land Surface Water Index), NBR (Normalized Burn Ratio), VIgreen (Green Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and EVI (Enhanced Vegetation Index) have been derived from August 2017 ETM+ and OLI images (path: 188; row: 32) for the study area (Basilicata Region, located in the southern part of Italy) selected as a highly representative of Mediterranean environment. Main results show slight differences in the values of average reflectance for each band: OLI shows higher values in the near-infrared (NIR) wavelength for all the land use types, while in the short-wave infrared (SWIR) the ETM+ shows higher reflectance values. High correlation coefficients between different indices (in particular NDVI and NDWI) show that ETM+ and OLI can be used as complementary data. The best correlation in terms of cross-comparison was found for NDVI, NDWI, SAVI, and EVI indices; while according to land use classes, statistically significant differences were found for almost all the considered indices calculated with the two sensors.
... The calibration of spaceborne sensors is critical to ensure continuity and accuracy in long-term studies of geophysical parameters. 1 Over the past 40 years, there have been several Earth observing systems launched to measure changes in Earth's surface and atmosphere. 2 Spaceborne sensors are continuously in development to ensure long-term studies of geophysical parameters, but inherent temporal gaps reduce the ability to monitor changes in the Earth's environment. Landsat-8 represents the latest spaceborne satellite from the Landsat Data Continuity Mission. 3 Landsat-8 has a spatial resolution of 30 m, which was designed to support most environmental studies. ...
The ability of sensors to detect changes in the Earth's environment is dependent on retrieving radiometrically consistent and calibrated measurements from its surface. Intercalibration provides consistency among satellite instruments and ensures fidelity of scientific information. Intercalibration is especially important for spaceborne satellites without any on-board calibration, as accuracy of instruments is significantly affected by changes that occur postlaunch. To better understand the key parameters that impact the intercalibration process, this paper describes a simulation environment that was developed to support the primary mission of the Algodones Dunes campaign. Specifically, measurements obtained from the campaign were utilized to create a synthetic landscape to assess the feasibility of using the Algodones Dunes system as an intercalibration site for spaceborne instruments. The impact of two key parameters (differing view-angles and temporal offsets between instruments) on the intercalibration process was assessed. Results of these studies indicate that although the accuracy of intercalibration is sensitive to these parameters, proper knowledge of their impact leads to situations that minimize their effect. This paper concludes with a case study that addresses the feasibility of performing intercalibration on the International Space Station's platform to support NASA's CLARREO, the climate absolute radiance and refractivity observatory, mission. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
... Traditional method to estimate the impact of spectral differences relies on plenty of simulation experiments using a radiative transfer model. Simulation experiments should cover most atmospheric and surface situations by changing plenty of input parameters [8][9][10][11][12][13][14] . In this study, the top of atmospheric (TOA) hyperspectral reflectance spectra from the GOME-2 are used to simulate observations differences between the three sensors, which can easily achieve various actual conditions. ...
Intercalibration against a well-calibrated instrument at Low Earth Orbit (LEO) is a common method which has been widely used to assess the in-flight calibration of a new instrument. Different instruments on LEO spacecraft with similar spectral channels can be compared with each other using their simultaneous nadir observations (SNO). The postlaunch calibrations of Medium Resolution Spectral Imager (MERSI) and the Visible Infrared Radiometer (VIRR) in visible channels which are two major multi-spectral imaging radiometers onboard FY-3C are addressed based on SNO intercalibration method. Collection 6 reflectance products of AQUA MODIS are used as reference. The spectral difference impacts of matching channels are simulated and adjusted using GOME-2 hyperspectral measurements. As monitoring the stability of monthly forcing fits, it is found the linear fitting slopes of MERSI VIS channel 1∼12 are scene reflectance dependence with relative differences greater than 20%, while the monthly forcing fits of VIRR show well agreement in VIS channels. This is proved to attribute to the nonlinear response of MERSI as the monthly measurements cover different dynamic ranges. A new radiometric calibration equation considering nonlinear correction is proposed based on an on orbit linear adjustment to prelaunch quadratic calibration. The new calibrations are more consistent with SNO samples, and greatly improve the performance over high reflective scene comparing with linear results verified by statistical measurements over Deep Convective Clouds targets. It is demonstrated that other reference is necessary in ocean color channels as MODIS reflectance is within 10% where the nonlinear feature is likely much serious. It is an invaluable lesson that the temporal variation of calibration slope not always indicates the detector's degradation, but maybe is the valuable information that helps to expose undiscovered characters of instrument.
... With radiative transfer (RT) codes, Rao et al. [8] proposed linear regression equations for intercalibration of GOES-8 Imager and National Oceanic and Atmospheric Administration (NOAA)-14 Advanced Very High Resolution Radiometer (AVHRR) under varying atmospheric conditions and Sun-target-sensor geometries. Subsequently, Rao et al. [9] used similar regressions for intercalibration between Moderate Resolution Imaging Spectroradiometer (MODIS) and Along Track Scanning Radiometer (ATSR)-2. The regression was found to be more significant for MODIS versus ATSR-2 than for Imager versus AVHRR, yet no quantitative analysis was addressed, and the associated uncertainties remained difficult to trace or quantify. ...
Satellite instruments have acquired large volume images at different spatial, spectral, radiometric, and temporal resolutions. Reliable detection of long-term environmental change requires critical sensor intercalibration destined to minimize inconsistency in these multisensor images. However, uncertainty in intercalibration has not yet been comprehensively quantified in most existing studies. This paper developed a quantitative relationship between multisensor images in solar reflective bands by accounting for sensor difference, atmospheric condition, and Sun–target–sensor geometry. The relationship was validated with collocated and concurrent TERRA MODIS/NOAA-17 AVHRR images over the Dunhuang calibration site. Then, it was used to investigate sensitivity of intercalibration to intersensor scale factor, total ozone concentration (TOC), total precipitable water vapor content (TPW), and aerosol optical thickness (AOT). The main conclusions include: 1) error in intersensor scale factor may induce a maximum uncertainty of 2.35% for both visible (VIS) and near-infrared (NIR) bands; 2) error in TOC can produce a maximum uncertainty of 0.30% for VIS band but very minor impact on NIR band; 3) error in TPW may generate a maximum uncertainty of 8.81% for NIR band, particularly for a dry atmosphere; 4) error in AOT can result in a maximum uncertainty of 0.25% for VIS band and 0.96% for NIR band at near-nadir, and 10.16% and 8.18% for heavy aerosol loadings at a very high solar angle. The following study quantifies uncertainties in intercalibration for solar reflective bands and thus offers guidance for intercalibration.
... Similarly, rangeland and grasslands have been shown to be suitable for vicarious absolute calibration but less desirable for long-term trending due to the phenological and bidirectional reflectance distribution function (BRDF) effects [45][46][47][48]. Deserts are potentially the best sites for long-term trending and cross-calibration but may be subject to BRDF effects if dunes are present [49][50][51][52][53][54]. ...
... In addition to the aforementioned factors, one of the most important senor characteristics, the relative spectral response function (SRF), varied among different sensors. This variation has a significant effect on the continuity of multi-sensor monitoring of global vegetation [30,[47][48][49][50][51][52][53][54][55]. Therefore, many studies have focus on this "spectral issue". ...
Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from -12.67% to 36.30% for the red reflectance, -8.52% to -0.23% for the NIR reflectance, and -9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed higher differences than did the other sensors with respect to MODIS. A series of optimum models were presented for remote sensing data assimilation between MODIS and other sensors.
... In addition, time series cross-calibrations are often performed to monitor the sensor's attenuation. Many satellite sensors have carried out cross-calibration, such as NOAA/AVHRR [1,2], SPOT/ VEGETATION [3], SPOT/HRVIR [3], Landsat-5/TM [4,5], Terra/MODIS [6][7][8], MIR-Station/MOMS [9], OrbView-2/ SeaWiFS [6,10], and ADEOS/POLDER [7,11]. In China, there are also many scientists engaged in the cross-calibration research, and realized cross-calibration for many domestic remote sensors, such as CBERS/CCD [12][13][14], CBERS/WFI [13], BJ-1/CCD [15], SZ-3/CMODIS [16,17], FY-1D/VIRR [16], HJ-1/CCD [18][19][20], and HY-1A/COCTS [21]. ...
Radiometric calibration of sensor is the basis of quantitative remote sensing, and uncertainty analysis is critical to ensure the accuracy of cross-calibration. Therefore, firstly, cross-calibration formulas were improved by redefining calibration coefficient and spectral band matching factor. In these formulas, c
ci
was redefined as the calibration coefficient of normalized apparent reflectance, and spectral band matching factor as the ratio of normalized apparent reflectance. Secondly, based on the contrast of ideal and actual conditions in cross-calibration, 8 sources of cross-calibration uncertainty were proposed: calibration uncertainty of standard sensor; pixel matching uncertainty; spectral band matching factor uncertainty caused by site altitude setting error, atmospheric parameters setting error, surface spectrum source, surface bidirectional reflectance characteristic, and error of atmospheric radiative transfer model; and uncertainty caused by other factors which were not considered. Finally, the contribution of each uncertainty was further analyzed and discussed for the HJ-1 CCD camera. The results provide many valuable references for evaluating the feasibility of alternative cross-calibration measurements.
... Dry lake beds or playas Best calibration targets, but may be subject to soil moisture effects and snow cover [1][2][3][4][5][6][7][8][9][10][11][12][13] Deserts Potentially good calibration targets, but subject to BRDF effects if there are dunes [14][15][16][17][18][19] Ice or snow fields May work well in the VNIR, but solar zenith angles tend to be large [20][21][22][23] Semi-arid rangeland May work well if limited phenological activity and terrain flat [24][25] Grassland targets Requires surface measurements to work well; subject to phenological and BRDF effects [26][27] Atmospheric scattering Works well for specialists, but less practical to use operationally [28][29][30][31] Uniform cloud cover Works well for specialists, but less practical to use operationally [28][29]32] Ocean glint Works well for specialists, but less practical to use operationally [28,32] Multiple target types Provide a range of intensities that help improve accuracies [33][34][35][36][37][38][39] 2. The site should have a surface reflectance greater than 0.3 in order to provide higher signal-to-noise ratio (SNR) and reduce uncertainties due to the atmospheric path radiance. 3 The surface of the site should be horizontal and have nearly Lambertian reflectance to minimize uncertainties due to differences in solar illumination and observation geometries. ...
This paper provides a comprehensive list of prime candidate terrestrial targets for consideration as benchmark sites for the post-launch radiometric calibration of space-based instruments. The key characteristics of suitable sites are outlined primarily with respect to selection criteria, spatial uniformity, and temporal stability. The establishment and utilization of such benchmark sites is considered to be an important element of the radiometric traceability of satellite image data products to SI standards for use in the accurate monitoring of environmental change.
... However, the inability to determine all relevant conditions for the remote sensing system at the time of data acquisition limits the effectiveness of these methods, resulting in the development of alternative more empirically-based approaches. A number of cross-sensor normalization techniques have been utilized including: a) comparison with other well-calibrated sensors (Vermote & Saleous, 2006;Heidinger et al., 2002;Rao et al., 2003); b) radiometric normalization techniques based on invariant targets and statistical properties of the data (Schmidt et al., 2008); and c) cross-sensor calibration based on direct comparison of measurements acquired over the same target at the same time with the same acquisition geometry. The last method, which is referred to as Simultaneous Nadir Overpass (SNO), is often used and is elaborated in Cao et al. (2004) and Cao et al. (2005). ...
Systematic error in long-term satellite data records resulting from inter-sensor differences or other persistent influences such as satellite orbital drift can greatly affect the use of these data to monitor land surface dynamics and trends. In this research an identification and correction procedure for systematic error is developed and used to evaluate the NOAA AVHRR long-term satellite data record produced by the Canada Center for Remote Sensing (CCRS). The record is composed of observations acquired by seven AVHRR sensors during the period 1985–2011. It includes two types of AVHRR sensor: AVHHR-2 flown onboard NOAA-9, -11, and ‐14, and AVHRR-3 onboard NOAA-16, -17, -18 and ‐19. Systematic error between sensors was identified through evaluation of synchronized nadir overpass (SNO) observations. The first order systematic error correction was derived from SNO comparison and then further optimized using a reference calibration target. Examination showed considerable difference between AVHRR-2 and AVHRR-3 measurements, which are largely attributed to differences in sensor design characteristics, uncertainty in sensor radiometric calibration, and imperfections in data processing. The results also show overall higher consistency between data from missions with AVHRR-3 than with AVHRR-2 sensors. The developed approach for correction of systematic error in time series was validated based on statistical analysis of eight independent pseudo-invariant targets not used for the initial correction development. Trends in these targets largely caused by the difference between AVHRR-2 and ‐3 sensors are shown to be removed or reduced after the correction was applied.
... Work has shown that portions of the Saharan desert are suitable invariant sites and also meet many of the characteristics listed for an ideal site, especially that of minimal cloud cover and precipitation 12 . Previous work that utilizes the Saharan desert includes temporal trending of sensor calibration 13,14 , sensor intercomparisons 15,16,17 , flat fielding wide field of view sensors by characterizing several sites 18 , and comparison of aerosol optical depth products over desert sites 19 . For this work, a Saharan test site offers the opportunity to utilize a site much larger than Railroad Valley and evaluate the accuracy of using a site for which groundbased data are not available. ...
The Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne and satellite based sensors. Often, ground-truth measurements at these tests sites are not always successful due to weather and funding availability. Therefore, RSG has also employed automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor. This work studies the feasibility of determining the radiometric calibration of a hyperspectral imager using multispectral imagery. The work relies on the Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. Hyperion bands are compared to MODIS by band averaging Hyperion's high spectral resolution data with the relative spectral response of MODIS. The results compare cross-calibration scenarios that differ in image acquisition coincidence, test site used for the calibration, and reference sensor. Cross-calibration results are presented that show agreement between the use of coincident and non-coincident image pairs within 2% in most bands as well as similar agreement between results that employ the different MODIS sensors as a reference.
... Rao et al. 8 presented results on the inter-calibration of Terra MODIS and the European Remote Sensing satellite-2 (ERS2) Along-Track Scanning Radiometer-2 (ATSR-2) based on desert sites as common targets. They emphasized how crucial it is to take into consideration the spectral character of the sensors and the scene to avoid compromising the efficacy of inter-calibration. ...
This paper reports on an investigation of radiometric calibration errors due to differences in spectral response functions between satellite sensors when attempting cross-calibration based on near-simultaneous imaging of common ground targets in analogous spectral bands. Five Earth observation sensors on three satellite platforms were included on the basis of their overpass times being within 45 minutes of each other on the same day (Landsat-7 ETM+; EO-1 ALI; Terra MODIS; Terra ASTER; Terra MISR). The simulation study encompassed spectral band difference effects (SBDE) on cross-calibration between all combinations of the sensors considered, using the Landsat solar reflective spectral domain as a framework. Scene content was simulated using ground target spectra for the calibration test sites at Railroad Valley Playa, Nevada and Niobrara Grassland, Nebraska. Results were obtained as a function of calibration test site, satellite sensor, and spectral region. Overall, in the absence of corrections for SBDE, the Railroad Valley Playa site is a "good" to "very good" ground target for cross-calibration between most but not all satellite sensors considered in most but not all spectral regions investigated. "Good" and "very good" are defined as SBDEs within +/-3 % and +/-1 %, respectively. Without SBDE corrections, the Niobrara test site is only "good" for cross-calibration between certain sensor combinations in some spectral regions. The paper includes recommendations for spectral data and tools that would facilitate cross-calibration between multiple satellite sensors.
... Trishchenko et al. (2002) focused on moderate resolution satellite sensors, including the AVHRRs onboard the NOAA-6, -7, -8, -10, -11, -12, -14, -15, -16 spacecraft, Terra Moderate-resolution Imaging Spectroradiometer (MODIS), VGT, and Global Imager (GLI) on the second Advanced Earth Observing Satellite (ADEOS-2), all with respect to NOAA-9 AVHRR. Rao et al. (2003) presented results on the inter-calibration of Terra MODIS and the ERS-2 ATSR-2 based on desert sites as common targets. Thome et al. (2003) used RVPN to cross-calibrate Earth Observing-1 (EO-1) Advanced Land Imager (ALI), EO-1 Hyperion, MODIS, and Ikonos with respect to ETM+. ...
The paper presents the results of an extended analysis of image data sets acquired during the tandem-orbit configuration in 1999 for the purposes of radiometric cross-calibration of the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-5 Thematic Mapper (TM) sensors. Earlier work focused on the tandem pair for the Railroad Valley Playa, Nevada (RVPN) site to tie down the Landsat-5 TM calibration based on the more accurate Landsat-7 ETM+ calibration. This paper describes new results based on as many as eight tandem image pairs. The additional tandem images are of primarily vegetated areas for which little or no ground reference data were available. Increasing the number of tandem pairs yielded results for the Landsat 5 TM gain coefficients within approximately ± 1% of the RVPN-based results in spectral bands 1, 2, 3 and 7, and within − 2% and − 4% of the RVPN-based results for spectral bands 4 and 5, respectively.
... Remote Sensing of Environment 112 (2008) 1117 -1129 www.elsevier.com/locate/rse the sensor behavior of the latter compared to the former (Heidinger et al., 2002;Minnis et al., 2002;Rao et al., 2003Rao et al., , 2001. Vermote and Saleous (2006) used calibrated MODIS data to derive an absolute calibration for NOAA-16 AVHRR data and reached a 1% consistency between the two sensors with their coefficients. ...
We have developed an automatic method to monitor the AVHRR instrument sensitivity over time in the short-wave reflective channels to ensure that trends in the data series obtained by this instrument are real and not sensor artefacts.Our radiometric calibration method uses the Multivariate Alteration Detection (MAD) algorithm to statistically select invariant features over land areas from multiple image-pairs that are compared to assess changes in the instrument's calibration. This method requires no a priori regional knowledge and is globally applicable. A calibrated time series from Pseudo-Invariant Features located in central Australia are shown to have long-term trends removed. The resulting MAD-based calibration has a root mean squared error of ∼ 5–6% for both channels 1 and 2 and is in alignment with other approaches.
The vegetation index (VI) and vegetation water index (VIw) have long been used for plant water stress detection indiscriminately, without considering the effects of differences in their band selection. To address this, this study quantitatively compared the difference of sensor dependence for the two indices based on canopy/atmospheric radiative transfer model. Five different bandwidths at canopy and top-of-atmosphere scale were simulated separately for 23 classic indices. The results show that VIws exhibited better correlation with vegetation water content (VWC) at both scale (R2 : 0.835; 0.812) in comparison with VIs (R2 : 0.474; 0.475). To quantitatively describe the uncertainty caused by bandwidth, a new index variability was established. VIws and VIs performed entirely differently: at canopy scale, the uncertainty caused by bandwidths for VIws and VIs is 13.703% and 43.451%, respectively. However, at top-of-atmosphere scale, the uncertainty for VIws and VIs is 32.021% and 41.265%. VIws exhibited less dependence on bandwidth and were more affected by atmospheric effect than VIs. We attribute these differences to differences in band selection: VIws based on water absorption features are more sensitive to not only variation of VWC but also atmospheric conditions. Conversely, as chlorophyll absorption features which VIs are calculated on effectively avoid atmospheric absorption features and are located in red edge region, VIs are found less affected by the atmosphere condition and extremely sensitive to bandwidth. Results figure out the differences we should focus on when we choose VI or VIw from different sensors for VWC retrieval.
The International Satellite Cloud Climatology Project (ISCCP) B1 data, which were recently rescued at the National Oceanic and Atmospheric Administration's National Climatic Data Center (NOAA/NCDC), are a resource for the study of the earth's climate. The ISCCP B1 data represent geostationary satellite imagery for all channels, including the infrared (IR), visible, and IR water vapor sensors. These are global 3-hourly snapshots from satellites around the world, covering the time period from 1979 to present at approximately 10-km spatial resolution. ISCCP B1 data will be used in the reprocessing of the cloud products, resulting in a higher-resolution ISCCP cloud climatology, surface radiation budget (SRB), etc. To realize the promise of a higher-resolution cloud climatology from the B1 data, an independent assessment of the calibration of the visible band was performed. The present study aims to accomplish this by cross-calibrating with the intercalibrated Advanced Very High Resolution Radiometer (AVHRR) reflectance data from the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) dataset. Since the reflectance calibration approach followed in the PATMOS-x dataset is radiometrically tied to the absolute calibration of the National Aeronautics and Space Administration's (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) imager instrument, the present intercalibration scheme yields calibration coefficients consistent with MODIS. Results from this study show that the two independent sets (this study and the ISCCP) of results agree to within their mutual uncertainties. An independent approach to calibration based on multiyear observations over spatially and temporally invariant desert sites has also been used for validation. Results reveal that for most of the geostationary satellites, the mean differencewith ISCCP calibration is less than 3%with the randomerrors under2%.Another result is that this extends the intercalibrated record to beyond what ISCCP provides (prior to 1983 and beyond 2009).
Global climate change studies require long-term, radiometrically accurate, and stable observations from a number of satellites. Spatially uniform and radiometrically/spectrally stable vicarious calibration sites can be used to quantify the sensor gain change over time, monitor the instrument performance, and compare measurements from multiple instruments to maintain consistent radiometric calibration. This study uses AQUA moderate resolution imaging spectroradiometer (MODIS), MetOp-A advanced very high resolution radiometer (AVHRR), and Earth Observing-1 Hyperion to analyze the radiometric and spectral characteristics of the Dome C and Sonoran Desert sites and perform intercomparisons. The radiometric stability of both sites over a period of eight years as evaluated using AQUA MODIS is found to be better than 2% in the visible (0.64 μm) and near-infrared regions (0.86 μm), assuming the MODIS calibration is stable. The bidirectional reflectance distribution (BRDF) effect over Dome C is large, with greater than 5% seasonal variation, compared to the Sonoran Desert with less than 2% variation. However, the BRDF impact can be reduced to less than 2% for Dome C after normalization by an appropriate BRDF model. For water vapor absorption channels, such as AVHRR channel 2 (0.86 μm), this study suggests that the Sonoran Desert is largely affected with greater than 6% absorption variability compared to that of Dome C with less than 2%. The study also reveals that the operationally calibrated AVHRR top-of-atmosphere reflectance is lower than that of MODIS by about 8%.
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.
In an era when the number of Earth observation satellites is rapidly growing and measurements from satellite sensors are used to address increasingly urgent global issues, often through synergistic and operational combinations of data from multiple sources, it is imperative that scientists and decision-makers are able to rely on the accuracy of Earth observation data products. The characterization and calibration of these sensors, particularly their relative biases, are vital to the success of the developing integrated Global Earth Observation System of Systems (GEOSS) for coordinated and sustained observationsof the Earth. This can only reliably be achieved in the postlaunch environment through the careful use of observations by multiple sensor systems over common, well-characterized terrestrial targets (i.e., on or near the Earth's surface). Through greater access to andunderstanding of these vital reference standard sites and their use, the validity and utility of information gained from Earth remote sensing will continue to improve. This paperprovides a brief overview of the use of reference standard sites for postlaunch sensor radiometric calibration from historical, current, and future perspectives. Emphasis is placed on optical sensors operating in the visible, near-infrared, and shortwave infrared spectral regions.
Analyses of a 4.5 year SNO (Simultaneous Nadir Overpass) time series between AVHRR on NOAA-16 and -17 suggest that the AVHRR observations based on operational vicarious calibration have become very consistent since mid 2004. This study also suggests that the SNO method has reached a high level of relative accuracy (~1.5%, 1 sigma) for both the 0.63 and 0.84 mum bands, which outperforms many other vicarious methods for satellite radiometer calibration. Meanwhile, for AVHRR and MODIS, a 3.5 year SNO time series suggests that the SNO method has achieved a 0.9% relative accuracy (1 sigma) for the 0.63 mum band, while the relative accuracy for the 0.84 um band is on the order of +/- 5% and significantly affected by the spectral response differences between AVHRR and MODIS. Although the AVHRR observations from NOAA-16 and -17 agree well, they significantly disagree with MODIS observations according to the SNO time series. A 9% difference was found for the 0.63 mum band (estimated uncertainty of 0.9%, 1 sigma), and the difference is even larger if the spectral response differences are taken into account. Similar bias for the 0.84 mum band is also found with a larger uncertainty due to major differences in the spectral response functions between MODIS and AVHRR. It is expected that further studies with Hyperion observations at the SNOs would help us estimate the biases and uncertainty due to spectral differences between AVHRR and MODIS. It is expected that in the near future, the calibration of the AVHRR type of instruments can be made consistent through rigorous cross-calibration using the SNO method. These efforts will contribute to the generation of fundamental climate data records (FCDRs) from the nearly 30 years of AVHRR data for a variety of geophysical products including aerosol, vegetation, and surface albedo, in support of global climate change detection studies.
A series of 10 advanced very high resolution radiometers (AVHRRs) flown on National Oceanic and Atmospheric Administration (NOAA)'s polar-orbiting satellites for over 20 years has provided data suitable for many quantitative remote sensing applications. To be useful for geophysical research, each radiometer must be accurately calibrated, which poses problems in the AVHRR reflectance channels because they have no onboard calibration. Previous studies have shown that values of the reflectance channel calibrations, accurately measured during preflight, change abruptly immediately after launch and then change slowly during the satellite's lifetime. The presence of the dual-gain reflectance channels on the current series of AVHRRs also complicates the application of previous calibration techniques. A technique is presented here for calibrating the AVHRR dual-gain reflectance channels using Moderate Resolution Imaging Spectrometer (MODIS) data. This method employs selective criteria to reproduce a laboratory type calibration where instrument counts observed by AVHRR are matched to reflectances measured by MODIS on a pixel by pixel basis for coincident and co-located scenes. Unlike AVHRR, MODIS employs onboard calibration of its reflectance channels. The goal here was to explore the utility of using MODIS to calibrate the new dual-gain reflectance channels of the AVHRR. The AVHRRs in the NOAA-KLM series of spacecraft employ a dual-gain approach to increase the sensitivity to dark scenes. Traditional methods using radiometrically stable targets to calibrate the reflectance channels of AVHRR typically do not provide data for both gain settings. The data from two scenes that met the over-pass criteria are analyzed. The regression of the MODIS reflectances versus the AVHRR counts for these scenes were able to produce calibration slopes and intercepts in both the low and high gain regions. The reflectance differences using the MODIS-derived calibration compared to preflight calibration are well within the expected behavior of the AVHRR during its first year in orbit. Comparison with reference ch1 and ch2 reflectance values from NOAA 9 for a Libyan Desert Target were within 5% of those using the MODIS-derived calibration. While the determination of the absolute accuracy this approach needs further study, it clearly offers the potential for calibration of the AVHRR dual-gain reflectance channels.
Satellite detection of the global climate change signals as small as a few percent per decade in albedo critically depends on consistent and accurately calibrated Level 1B (L1B) data or Fundamental Climate Data Records (FCDRs). Detecting small changes in signal over decades is a major challenge not only to the retrieval of geophysical parameters from satellite observations, but more importantly to the current state-of-the-art calibration, since such small changes can easily be obscured by erroneous variations in the calibration, especially for instruments with no onboard calibration, such as the Advanced Very High Resolution Radiometer (AVHRR). Without dependable FCDRs, its derivative Thematic Climate Data Records (TCDRs) are bound to produce false trends with questionable scientific value. This has been increasingly recognized by more and more remote sensing scientists. In this study we analyzed the consistency of calibrated reflectance from the operational L1B data between AVHRR on NOAA-16 and -17 and between NOAA-16/AVHRR and Aqua/MODIS, based on Simultaneous Nadir Overpass (SNO) observation time series. Analyses suggest that the NOAA-16 and -17/AVHRR operationally calibrated reflectance became consistent two years after the launch of NOAA-17, although they still differ by 9% from the MODIS reflectance for the 0.63 mum band. This study also suggests that the SNO method has reached a high level of relative accuracy (~1.5%) for estimating the consistency for both the 0.63 and 0.84 mum bands between AVHRRs, and a 0.9% relative accuracy between AVHRR and MODIS for the 0.63 mum band. It is believed that the methodology is applicable to all historical AVHRR data for improving the calibration consistency, and work is in progress generating FCDRs from the nearly 30 years of AVHRR data using the SNO and other complimentary methods. A more consistent historical AVHRR L1B data set will be produced for a variety of geophysical products including aerosol, vegetation, cloud, and surface albedo to support global climate change detection studies.
Stable earth sites are essential for comparing the measurements from different satellite instruments in the visible and near-infrared in order to maintain the consistency of radiometric calibration, and for quantifying the sensor degradation over time. This study focuses on the radiometric and spectral characterization and comparison between two potential calibration sites, Dome C and Sonoran Desert. The long-term TOA reflectance time series analysis using MODIS observation shows that the radiometric stability of Dome C and Sonoran Desert is better than 2% over the period of 8 years. The study also shows that Dome C is much affected by seasonal variation due to bi-directional reflection, compared to the Sonoran Desert, although the BRDF normalization reduced the uncertainty of Dome C observations to less than 2% for both the visible and NIR band. For AVHRR band 2, at Sonoran Desert, a large variability (>6%) is observed compared to that of MODIS (
In this letter, we propose an algorithm to detect the presence of forest fires using data from both geostationary and polar-orbiting satellites. The very frequent acquisitions of the Spinning Enhanced Visible and Infrared Imager radiometer placed onboard the Meteosat Second Generation-9 satellite are used as main source for the algorithm, while the MEdium Resolution Imaging Spectrometer global vegetation index and the Advanced Along-Track Scanning Radiometer measurements are used to enhance the reliability of the detection. The problem is approached in a ??global?? way, providing the basis for an automated system that is not dependent on the local area properties. In cooperation with the Centre de Suivi E??cologique (Dakar, Senegal), the proposed algorithm was implemented in a ??Multisource Fire Risk Management System?? for the Senegal area, as briefly described in this letter. A field campaign of one week was carried out in order to perform a validation of the system's detections, showing a good agreement with the fire coordinates measured on the ground. Furthermore, a consistency check was performed using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Rapid Response System, showing that more than 76% of high-confidence MODIS events are detected by the algorithm.
The use of remote sensing instruments on orbiting satellite platforms in the study of Earth Science and environmental monitoring was officially inaugurated with the April 1, 1960 launch of the Television Infrared Observation Satellite (TIROS) [1]. The first TIROS accommodated two television cameras and operated for only 78 days. However, the TIROS program, in providing in excess of 22,000 pictures of the Earth, achieved its primary goal of providing Earth images from a satellite platform to aid in identifying and monitoring meteorological processes. This marked the beginning of what is now over four decades of Earth observations from satellite platforms. reflected and emitted radiation from the Earth using instruments on satellite platforms. These measurements are input to climate models, and the model results are analyzed in an effort to detect short and long-term changes and trends in the Earth's climate and environment, to identify the cause of those changes, and to predict or influence future changes. Examples of short-term climate change events include the periodic appearance of the El Nino-Southern Oscillation (ENSO) in the tropical Pacific Ocean [2] and the spectacular eruption of Mount Pinatubo on the Philippine island of Luzon in 1991. Examples of long term climate change events, which are more subtle to detect, include the destruction of coral reefs, the disappearance of glaciers, and global warming. Climatic variability can be both large and small scale and can be caused by natural or anthropogenic processes. The periodic El Nino event is an example of a natural process which induces significant climatic variability over a wide range of the Earth. A classic example of a large scale anthropogenic influence on climate is the well-documented rapid increase of atmospheric carbon dioxide occurring since the beginning of the Industrial Revolution [3]. An example of the study of a small-scale anthropogenic influence in climate variability is the Atlanta Land-use Analysis Temperature and Air-quality (ATLANTA) project [4]. This project has found that the replacement of trees and vegetation with concrete and asphalt in Atlanta, Georgia, and its environs has created a microclimate capable of producing wind and thunderstorms. A key objective of climate research is to be able to distinguish the natural versus human roles in climate change and to clearly communicate those findings to those who shape and direct environmental policy.
The Moderate-Resolution Imaging Spectroradiometer (MODIS) is a key instrument on the NASA Earth Observing System. It is a multispectral sensor that will be used to track long-term global change in the land, atmosphere, and ocean components of the earth. Major advances are being made with MODIS over previous sensors in the form of improved on-orbit sensor characterization and calibration using a system of onboard calibrators. This article describes those calibrators and provides an early estimate of the expected accuracy for the MODIS calibrated datasets resulting from the use of these calibrators. The focus of the paper is the calibration approach that is being implemented at-launch for the top-of-the-atmosphere data products.
Four methods for the in-flight radiometric calibration and cross calibration of multispectral imaging sensors are described. Three make use of ground-based reflectance, irradiance, and radiance measurements in conjunction with atmospheric measurements and one compares calibrations between sensors. Error budgets for these methods are presented and their validation is discussed by reference to SPOT and TM results and shown to meet the EOS requirements in the solar-reflective range.
The signature analysis of reflectance spectra of autumn Aesculus hippocastanum L. and Acer platanoides L. leaves revealed spectral bands maximally (near 550 and 705 nm) and minimally (at more than 750 nm) sensitive to variation in chlorophyll content, which can serve as sensitive indicators of early stages of leaf senescence. Several functions of reflectance directly proportional to chlorophyll-a have been found. These make it possible to determine chlorophyll accurately with a background of high pigment concentration.
The Along-Track Scanning Radiometer-2 (ATSR-2) is equipped with visible and near-infrared channels at 1.6, 0.87, 0.66, and 0.56 microm. An in-flight visible calibration (VISCAL) system used to convert the raw signal to top-of-the-atmosphere reflectances is described. To monitor the long-term stability of the VISCAL, a number of large-area stable terrestrial sites have been employed. We describe the methods used to determine the long-term drifts in the ATSR-2 onboard calibration device and evaluate the suitability of the sites for calibration monitoring.
The solar channel of the geostationary meteorological satellite METEOSAT-5 is calibrated with radiance simulations of a radiative transfer model. The method compares counts from the radiometer with calculated radiances for cloud free ocean and desert, respectively. The calibration based on the model compares within 10% with the absolute calibration obtained through an aircraft campaign (Kriebel et al., 1996). A theoretical analysis gives an estimated calibration error of about 10%. The method has also been applied to calibrate the VIS channel of Meteosat-6, for which no aircraft calibration campaign has been conducted yet. In future the operational application of the method is planned during the commissioning of new satellites.
The basic physical principles underlying three common techniques for the vicarious calibration of the post-launch performance of meteorological satellite sensor are briefly reviewed. The techniques considered are: (a) using 'radiometrically stable' desert calibration targets which yield relative degradation rates; (b) congruent path aircraft/satellite radiance measurements which yield absolute calibrations; and radiative transfer model simulation methods which yield absolute calibrations. The applications of the three techniques will be illustrated, using the visible and near-IR channels of the Advanced Very High Resolution Radiometer flown on the NOAA polar-orbiting operational environmental satellites as an example. The establishment of inter-satellite calibration linkages, and cross-satellite sensor calibration will be briefly mentioned.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
A simple technique for the inter-calibration of meteorological satellite sensors in the visible and near-infrared is described. The technique is based on using inter-relationships established using model simulations of the radiation measured at the top of the atmosphere by the reference and candidate sensors to transfer the calibration of the former to the latter, using a radiometrically stable desert calibration site as a calibration transfer platform. The application of the technique is illustrated with examples of the inter-calibration of the visible and near-infrared channels of the Advanced Very High Resolution Radiometer (AVHRR), the GOES Imager, the Along-Track Scanning Radiometer-2 (ATSR-2), and the Moderate-resolution Imaging Spectroradiometer (MODIS).
Three techniques for the absolute calibration of two reflective channels of the NOAA AVHRR sensors are described and compared. The technique 1 is based on field measurements and refers to another calibrated satellite sensor; technique 2 utilizes the reflectance-based calibration method; and technique 3 estimates the calibration by reference to another satellite sensor. NOAA-9 and NOAA-10 AVHRR images are analyzed. It is observed that the responsivity of the images degrade compared to prelaunch calibration. The effect of degradation in responsivity of the AVHRR sensors on vegetation indices are studied. It is noted that the uncertainties in the calibration methods is about 7-10 percent making them useful for many applications.
The post-launch degradation of the visible (channel 1: 0.58- 068 microns) and near-infrared (channel 2: approx. 0.72 - l.l microns) channels of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-7, -9, and -11 Polar-orbiting Operational Environmental Satellites (POES) was estimated using the south-eastern part of the Libyan Desert as a radiometrically stable calibration target. The relative annual degradation rates, in per cent, for the two channels are, respectively: 3.6 and 4.3 (NOAA-7); 5.9 and 3.5 (NOAA-9); and 1.2 and 2.0 (NOAA-11). Using the relative degradation rates thus determined, in conjunction with absolute calibrations based on congruent path aircraft/satellite radiance measurements over White Sands, New Mexico (USA), the variation in time of the absolute gain or slope of the AVHRR on NOAA-9 was evaluated. Inter-satellite calibration linkages were established, using the AVHRR on NOAA-9 as a normalization standard. Formulae for the calculation of calibrated radiances and albedos (AVHRR usage), based on these interlinkages, are given for the three AVHRRs.
The stability of the AVHRR scanners on board the NOAA 6, 7, and 9 satellites was assessed using the Libian Desert as a target site and a directional reflectance model for determinations of the degradation rates for the channel 1 (0.57-0.69 micron) of the scanners. It was found that the degradation rates determined for 68 months of observations (May 1980-October 1987) were 0, 3.5, and 6.0 percent per year for NOAA 6, 7, and 9, respectively. An analysis based on zonal measurements covering half of the earth's surface showed that these rates are applicable to all surface types and other spacecraft-based earth-viewing scanners subject to shortwave degradation.
A method for calibrating satellite sensors (such as the AVHRR visible and NIR bands) is proposed by which the sensors are calibrated using well-known physical characteristics of the atmosphere, ocean, and deserts, as well as the digital satellite imagery. The approach, independent of ground support, used the following three phenomena: molecular scattering over the ocean for absolute visible band calibration; ocean glint, to transfer the calibration from the visible band to the NIR band; and desert reflectance to monitor, independently, the stability of the visible and NIR bands. The method was applied to NOAA-7, -9, and -11 sensors. The results of the ocean and the desert calibration methods were found to differ in the brightness range and the spectral response of the radiance source (molecular scattering over the ocean versus the desert reflectance).
Over the past two decades, a key indicator of climate change has
been the long time series of global maps of the normalized difference
vegetation index (NDVI), derived from remotely sensed data acquired with
a series of NOAA advanced very high resolution radiometer (AVHRR)
instruments from space. These NDVI values are calculated from relatively
broad AVHRR channels in the red and near-infrared regions. Continuation
of this long term data set is extremely valuable for climate-related
research, However, sometime in the coming decade, the AVHRR time series
measurements will no longer be continued. Instead, the measurements will
be made using newer generation satellite instruments having narrower
channels and improved spatial resolution. For example, the moderate
resolution imaging spectroradiometer (MODIS) onboard the Terra
spacecraft has several narrow channels in the 0.4-1.0 spectral range.
The NDVI values derived from the MODIS red channel and near-IR channel
will be biased compared to those derived from the broader AVHRR channels
because of differences in channel positions and widths for the two
instruments. The narrow MODIS near-IR channel is only slightly affected
by atmospheric water vapor absorption, while the broad AVHRR near-IR
channel is strongly affected by water vapor absorption. As a result, the
largest bias comes from the near-IR channels on the two instruments. To
a lesser extent, the bias also comes from the differences between the
red channel positions and the widths of MODIS and AVHRR instruments. In
this paper, the authors describe a practical method for simulating AVHRR
NDVI values using several narrower MODIS channels in the 0.4-1.0 μm
spectral range, including the MODIS green channel and the water vapor
absorption channel
Vicarious calibration of EOS sensors
- P N S
- S F B
- K J T
- D L G
- P R S
S, P. N., B, S. F., T, K. J., G, D. L., and S, P. R., 1996, Vicarious calibration of EOS sensors, Journal of Oceanic and Atmospheric T echnology, 13, 349–359.
Calibration of the Advanced Very High Resolution Radiometer. In Remote Sensing and Climate Change: Role of Earth observation
- C R N S
R, C. R. N., and S, J. T., 2001, Calibration of the Advanced Very High Resolution Radiometer. In Remote Sensing and Climate Change: Role of Earth observation, edited by A. P. Cracknell (London: Springer-Praxis), pp. 117–157.