Reflectance Retrieval Research Articles

Aerosol optical depth and land surface reflectance from multiangle AATSR measurements: global validation and intersensor comparisons

This paper presents the results and satellite intercomparisons for the retrieval of aerosol optical depth (AOD) and land surface bidirectional reflectance using the Multiangle Advanced Along-Track Scanning Radiometer (AATSR). The algorithm developed is based on inversion of a physical model of light scattering that requires no a priori knowledge of the land surface. The model is evaluated for a number of sites around the world to test its operation over a range of aerosol types and land covers including dark and bright surfaces. Validation is performed using Aerosol Robotic Network ground-based sun-photometer measurements and by intercomparison with independent estimates of AOD derived from spaceborne instruments including Multiangle Imaging Spectroradiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Total Ozone Mapping Spectrometer (TOMS) aerosol products. Results show good agreement (Pearson's correlation coefficient r2=0.70 for all sites combined) between the AATSR-derived estimates of AOD and the sun-photometer measurements. There is also a high correlation (r2=0.84) between the AATSR- and MISR-derived AOD estimates, but the correlations of the AATSR-derived AOD with MODIS-derived AOD and TOMS aerosol index are lower. In addition, the ability of the sensor to discriminate between different aerosol types is evaluated. Moreover, the estimates of the aerosol properties are used for atmospheric correction of the top-of-atmosphere reflectance. The AATSR surface reflectances are compared with the MODIS bidirectional reflectance distribution function/Albedo and MISR surface products and are shown to correspond with root-mean-square errors of 0.03 and 0.06 or better, respectively. The retrieval method is applied on an image basis resulting in an image of surface reflectance and a separate map of AOD. A map of AOD at 550 nm covering the Sahel and southern Sahara region is presented to demonstrate operation at regional and potentially global scales

A method for the surface reflectance retrieval from PROBA/CHRIS data over land: application to ESA SPARC campaigns

The Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-Board Autonomy (PROBA) platform system provides the first high spatial resolution hyperspectral/multiangular remote sensing data from a satellite system, what represents a new source of information for Earth Observation purposes. A fully consistent radiative transfer approach is always preferred when dealing with the retrieval of surface reflectance from hyperspectral/multiangular data. However, due to the reported calibration anomalies for CHRIS data, a direct atmospheric correction based on physical radiative transfer modeling is not possible, and the method must somehow compensate for such calibration problems in specific wavelength ranges. A dedicated atmospheric correction algorithm for PROBA/CHRIS data over land is presented in this work. It consists in the combination of radiative transfer and empirical line approaches to atmospheric correction, in order to retrieve surface reflectance images free from both the atmospheric distortion and artifacts due to miscalibration. The atmospheric optical parameters and the updated set of calibration coefficients are obtained jointly in an autonomous process, without the need for any ancillary data. Results from the application of the algorithm to PROBA/CHRIS data from the two European Space Agency SPectra bARrax Campaign (SPARC) held at the Barrax study site (La Mancha, Spain) in 2003 and 2004 are presented in this work, focusing on the validation of the final surface reflectance using in situ measurements acquired simultaneously to PROBA overpasses.

Remote sensing of the terrestrial environment using middle infrared radiation (3.0–5.0 µm)

The middle infrared (MIR) spectral region, between 3.0 and 5.0 µm in the electromagnetic spectrum, features a myriad of atmospheric windows. The favourable atmospheric penetration of electromagnetic radiation at MIR wavelengths has been readily noted. To exploit this atmospheric window, there have been several remote sensing instruments acquiring radiation from the terrestrial environment. However, these data remain under-utilized for terrestrial environmental studies. The principal reason for this is the hybrid nature of this spectral region, as it comprises a combination of both reflected and emitted radiation during daytime measurement. Despite this, there are many attributes of the MIR spectral region that should encourage its use. Studies focusing on a wide range of terrestrial environment characteristics using MIR radiation have advocated that these data be used. Moreover, there has been a progressive maturation of the methods for processing the radiation acquired by instruments in the MIR spectral region, allowing the informed use of the unique information provided by this part of the spectrum. In particular, these methods have allowed the retrieval of MIR reflectance from the full MIR radiation acquired by a sensor and this information has been demonstrated to be particularly useful for the study of the terrestrial environment and its change. It would appear that the full potential of MIR radiation is unrealized. This paper is a review that aims to motivate and advise of future research using MIR radiation, and in particular MIR reflectance. The paper outlines the physical principles determining MIR radiation interactions with the terrestrial environment; reviews current status of processing methods of MIR radiation to retrieve MIR reflectance; and how the retrieved data have been used. In conclusion, future research priorities are suggested.

A consistency analysis of surface reflectance and leaf area index retrieval from overlapping clear-sky Landsat ETM+ imagery

In this study, the consistency of systematic retrievals of surface reflectance and leaf area index was assessed using overlap regions in adjacent Landsat Enhanced Thematic Mapper-Plus (ETM+) scenes. Adjacent scenes were acquired within 7–25 days apart to minimize variations in the land surface reflectance between acquisition dates. Each Landsat ETM+ scene was independently geo-referenced and atmospherically corrected using a variety of standard approaches. Leaf area index (LAI) models were then applied to the surface reflectance data and the difference in LAI between overlapping scenes was evaluated. The results from this analysis show that systematic LAI retrieval from Landsat ETM+ imagery using a baseline atmospheric correction approach that assumes a constant aerosol optical depth equal to 0.06 is consistent to within ±0.61 LAI units. The average absolute difference in LAI retrieval over all 10 image pairs was 26% for a mean LAI of 2.05 and the maximum absolute difference over any one pair was 61% for a mean LAI of 1.13. When no atmospheric correction was performed on the data, the consistency in LAI retrieval was improved by 1%. When a scene-based dense, dark vegetation atmospheric correction algorithm was used, the LAI retrieval differences increased to 28% for a mean LAI of 2.32. This implies that a scene-based atmospheric correction procedure may improve the absolute accuracy of LAI retrieval without having a major impact on retrieval consistency. Such consistency trials provide insight into the current limits concerning surface reflectance and LAI retrieval from fine spatial resolution remote sensing imagery with respect to the variability in clear-sky atmospheric conditions.

HATCH: results from simulated radiances, AVIRIS and hyperion

The atmospheric correction program High-accuracy Atmospheric Correction for Hyperspectral data (HATCH) has been developed specifically to convert radiance from imaging spectrometer sensors to reflectance on a pixel-by-pixel basis. HATCH was developed to update the previously available model, the Atmosphere Removal model (ATREM). In this paper, we test the HATCH model against model data using MODTRAN 4 as well as with Airborne Visible/Infrared Imaging Spectroradiomter and Hyperion data for which simultaneous ground reflectances were acquired. We also compare HATCH to the commercially available ACORN model. Results show that HATCH produces smoother-looking spectra than its predecessor ATREM and is less influenced by liquid water in vegetation. Comparisons with MODTRAN were made by calculating above-atmosphere radiances from a hypothetical target with a 0.5 reflectance at all wavelengths between 400 and 2500 nm and retrieving them with HATCH. The results show a maximum deviation of 10% at several wavelengths, highlighting the differences between the models. Hyperion images contain striping artifacts. We show that optimum removal is obtained by normalizing the means and standard deviations of each column after converting radiance to reflectance with an atmospheric model. HATCH produces water vapor corrections virtually unaffected by vegetation liquid water if the water vapor band at 940 nm is used in the calculation. Retrievals using the 1140-nm band are subject to errors associated with liquid water in vegetation. Retrievals of reflectance from Hyperion radiances require use of the 1140-nm band because the 940-nm band falls in the detector crossover region.

Evaluation of the potential of hyperion for fire danger assessment by comparison to the airborne visible/infrared imaging spectrometer

Parameters derived from remote sensing that can be used to assess fire danger include surface reflectance, live and dead biomass, canopy water content, species composition, and fuel state. Spectral bands and wavelength locations of traditional multispectral data make assessment of fire danger in Mediterranean shrublands difficult, although fire danger parameters have been derived from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. We compare nearly simultaneous acquisition of Hyperion and AVIRIS to evaluate spaceborne monitoring potential of fire danger in Southern California chaparral. Field spectra were acquired to support reflectance retrieval and construct a spectral library for vegetation mapping. Reflectance spectra retrieved from Hyperion and AVIRIS had similar shape and albedo, but SNR was five times higher in AVIRIS. Fuel condition was assessed using the endmember fractions from spectral mixture analysis, with both Hyperion and AVIRIS imaging spectrometer data providing similar fractions and spatial distributions. Hyperion demonstrated good capability for separating spectral signals from bare soil and dry plant litter. Canopy water content was compared using the 980- and 1200-nm liquid water bands, the water index, and the normalized difference water index. Results showed that Hyperion is capable of retrieving canopy water at 1200 nm, but demonstrates poor performance at 980 nm. Sensor noise and instrumental artifacts account for poor performance in this spectral region. Overall, full-spectrum measures outperformed band ratios because of a lower sensitivity to sensor noise in individual bands. Species and community mapping showed similar patterns with better accuracy for AVIRIS relative to Hyperion, but with both instruments achieving only 79% and 50% overall accuracy, respectively.

A practical algorithm to infer soil and foliage component temperatures from bi-angular ATSR-2 data

An operational algorithm is proposed to retrieve soil and foliage component temperatures over heterogeneous land surface based on the analysis of bi-angular multi-spectral observations made by ATSR-2. Firstly, on the basis of the radiative transfer theory in a canopy, a model is developed to infer the two component temperatures using six channels of ATSR-2. Four visible, near-infrared and short wave infrared channels are used to estimate the fractional vegetation cover within a pixel. A split-window method is developed to eliminate the atmospheric effects on the two thermal channels. An advanced method using all four visible, near-infrared and short wave channel measurements at two view angles is developed to perform atmospheric corrections in those channels allowing simultaneous retrieval of aerosol opacity and land surface bi-directional reflectance. Secondly, several case studies are undertaken with ATSR-2 data. The results indicate that both foliage and soil temperatures can be retrieved from bi-angular surface temperatures measurements. Finally, limitations and uncertainties in retrieving component temperatures using the present algorithm are discussed.

Radiative transfer codes applied to hyperspectral data for the retrieval of surface reflectance

The present investigation evaluates surface reflectance retrieved from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Compact Airborne Spectrographic Imager ( casi™) data using the atmospheric radiative transfer (RT) codes: atmospheric removal program (ATREM), Canadian advanced modified 5S (CAM5S) and moderate atmospheric radiance and transmittance model (MODTRAN4). The retrieved surface reflectances were compared with ground-based reflectances acquired with a GER3700™ spectroradiometer for a playa and canola target. The results showed that the best overall performance was achieved with MODTRAN4 (average relative error of 2.3%), followed by ATREM (3.6%) and CAM5S (4.2%). Major differences occur in the stronger gas absorption regions. At wavelengths unaffected by strong gaseous absorption, the performance was similar for the three RT codes even though ATREM and CAM5S make extensive use of analytical expressions and, therefore, have faster execution times.

An algorithm using visible and 1.38-μm channels to retrieve cirrus cloud reflectances from aircraft and satellite data

The Moderate Resolution Imaging Spectro-Radiometer (MODIS) on the Terra spacecraft has a channel near 1.38 /spl mu/m for remote sensing of high clouds from space. The implementation of this channel on MODIS was primarily based on previous analysis of hyperspectral imaging data collected with the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). We describe an algorithm to retrieve cirrus bidirectional reflectance using channels near 0.66 and 1.38 /spl mu/m. It is shown that the apparent reflectance of the 1.38-/spl mu/m channel is essentially the bidirectional reflectance of cirrus clouds attenuated by the absorption of water vapor above cirrus clouds. A practical algorithm based on the scatterplot of 1.38-/spl mu/m channel apparent reflectance versus 0.66-/spl mu/m channel apparent reflectance has been developed to scale the effect of water vapor absorption so that the true cirrus reflectance in the visible spectral region can be obtained. To illustrate the applicability of the present algorithm, results for cirrus reflectance retrievals from AVIRIS and MODIS data are shown. The derived cirrus reflectance in the spectral region of 0.4-1 /spl mu/m can be used to remove cirrus contamination in a satellite image obtained at a visible channel. An example of such an application is shown. The spatially averaged cirrus reflectances derived from MODIS data can be used to establish global cirrus climatology, as is demonstrated by a sample global cirrus reflectance image.

Improved semi-arid community type differentiation with the NOAA AVHRR via exploitation of the directional signal

Mapping semi-arid vegetation types at the community level is extremely difficult for optical sensors with large ground footprints such as the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). Attempts to use solar wavelength AVHRR data in community type differentiation have often resulted in unacceptable classification errors which are usually attributed to noise from topographic and soil background variations, inaccurate reflectance retrieval and poor registration. One source of variation which is rarely accounted for adequately is the directional signal resulting from the combined effects of the surface bidirectional reflectance distribution function (BRDF) and the variation of viewing and illumination geometry as a function of scan angle, season, latitude and orbital overpass time. In this study, a linear semiempirical kernel-driven BRDF model is used to examine the utility:of the directional signal in community and cover type differentiation over discontinuous but statistically homogeneous semi-arid canopies in Inner Mongolia Autonomous Region, China, and New Mexico, USA. This research shows that the directional signal resulting from the physical structure of the canopy-soil complex can be retrieved to provide information which is highly complementary to that obtained in the spectral domain.

Effects of aerosol vertical inhomogeneity on the upwelling radiance and satellite remote sensing of surface reflectance

There are two widely used radiative models without consideration of aerosol inhomogeneity for satellite remote sensing application, the Homogeneous Model and the Two-layer Model with aerosol in the lower layer. In this paper, effects of the aerosol vertical inhomogeneity on upwelling radiance and satellite remote sensing of surface reflectance are analyzed through numerical simulations by using two models. As shown in the simulations by using 24 representative aerosol models, there is often a considerably large error in upwel-ling radiance calculated by two models (Homogeneous and Two-layer) for the short wavelength channel with strong molecular scattering, owing to the difference between molecular and aerosol scattering proper-ties. For the long wavelength channel, the error is small if aerosol optical parameters are less variable with height, but it could also be significant if there are aerosol layers with different scattering phase functions and single scattering albedo. The radiance errors by the Homogeneous Model and the Two-layer Model can be up to 31.4% and 31.5% for the clean atmosphere, and in case of turbid atmosphere 67.8% and 59.2%, respectively. The radiance error could result in a large uncertainty of surface reflectance retrievals, especially for the short wavelength channel and the strongly absorbing aerosol. For the turbid atmosphere with strong-ly absorbing aerosol, the Homogeneous Model and the Two-layer Model are not suitable for atmospheric correction application.

A priori knowledge accumulation and its application to linear BRDF model inversion

A priori knowledge can significantly improve the retrieval of surface bidirectional reflectance and spectral albedo from satellite observations. Here a priori knowledge takes the form of field measurements of bidirectional reflectance factors for various surface cover types in red and near‐infrared bands. Bidirectional reflectance and albedo retrieval refers to inversion of a kernel‐driven bidirectional reflectance distribution function (BRDF) model using surface reflectance observations derived from orbiting spacecraft. A priori knowledge is applied when noise and poor angular sampling reduce the accuracy of model inversion given a limited number of observations. In such cases, a priori knowledge can indicate when retrieved kernel weights or albedos are outside expected bounds, leading to a closer examination of data. If data are noisy, a priori knowledge can be used to smooth the data. If the data exhibit poor angular sampling, a priori knowledge can be used according to Bayesian inference theory to yield a posteriori estimates of unknown kernel weights. In the latter application, Bayes theory is applied in data space rather than in parameter space. Extensive study and simulation using 73 sets of field observations and 395 spaceborne observation sets from the POLDER instrument validates the importance of a priori information in improving inversions and BRDF retrievals.

A simplified method for retrieval of ground level reflectance of targets from airborne video imagery

This paper describes an efficient method for retrieval of ground reflectance characteristics of targets from calibrated multispectral airborne video data for routine operational airborne missions. The method uses a simplified atmospheric scattering model in combination with a dark-object subtraction procedure to estimate the effect of the atmosphere in the path between the target and the sensor, as well as the adjacent environmental effect, on the radiation signal received by an airborne sensor. The simplicity of the atmospheric scattering model is maintained by the assumption that the air density within the targetsensor path in the lower atmosphere is sufficiently uniform for operations of the Charles Sturt University's (CSU) Multispectral Airborne Video System (MAVS). The MAVS acquires imagery in blue, green, red and near-infrared (NIR) narrow spectral bands. The MAVS is radiometrically calibrated and has a consistent radiometric response in-flight. An important feature of the new method is the coupling of the image based brightness data (DN) of a dark-object and the system radiometric calibration coefficients to determine the path reflectance and the environmental reflectance of the target. The sum of the path reflectance and the environment reflectance is known as haze reflectance. The haze reflectance indicates the amount of atmospheric haze in the airborne imagery. The simplified atmospheric model is then employed to determine the actual ground reflectance of the targets using the haze subtracted apparent (total) reflectance of the target at the altitude of the airborne sensor. The apparent reflectance of the target at the sensor altitude is obtained directly from the image based DN data and the system radiometric calibration coefficients. An interesting aspect of this simplified method is that an estimate of the environmental reflectance can be obtained as a by-product of the atmospheric haze calculation using a dark-object subtraction technique. The retrieved ground reflectance characteristics from calibrated MAVS imagery are now being used routinely for remote quantitative monitoring of agricultural and environmental targets.

A simplified method for retrieval of ground level reflectance of targets from airborne video imagery

This paper describes an efficient method for retrieval of ground reflectance characteristics of targets from calibrated multispectral airborne video data for routine operational airborne missions. The method uses a simplified atmospheric scattering model in combination with a dark-object subtraction procedure to estimate the effect of the atmosphere in the path between the target and the sensor, as well as the adjacent environmental effect, on the radiation signal received by an airborne sensor. The simplicity of the atmospheric scattering model is maintained by the assumption that the air density within the targetsensor path in the lower atmosphere is sufficiently uniform for operations of the Charles Sturt University's (CSU) Multispectral Airborne Video System (MAVS). The MAVS acquires imagery in blue, green, red and near-infrared (NIR) narrow spectral bands. The MAVS is radiometrically calibrated and has a consistent radiometric response in-flight. An important feature of the new method is the coupling of the image based brightness data (DN) of a dark-object and the system radiometric calibration coefficients to determine the path reflectance and the environmental reflectance of the target. The sum of the path reflectance and the environment reflectance is known as haze reflectance. The haze reflectance indicates the amount of atmospheric haze in the airborne imagery. The simplified atmospheric model is then employed to determine the actual ground reflectance of the targets using the haze subtracted apparent (total) reflectance of the target at the altitude of the airborne sensor. The apparent reflectance of the target at the sensor altitude is obtained directly from the image based DN data and the system radiometric calibration coefficients. An interesting aspect of this simplified method is that an estimate of the environmental reflectance can be obtained as a by-product of the atmospheric haze calculation using a dark-object subtraction technique. The retrieved ground reflectance characteristics from calibrated MAVS imagery are now being used routinely for remote quantitative monitoring of agricultural and environmental targets.

Effects of Incorporating a Brightband Model in a Downward-Looking Radar Rainfall Retrieval Algorithm

Abstract A range-profiling method proposed for nadir-looking rain radar has been investigated using aircraft measurements of a typhoon event at 10 and 35 GHz. In order to take into account the effects of change in the hydrometeor phase along the radar beam, it was necessary to modify the original method. Instead of using an analytic expression for the retrieval of radar reflectivity, a numerical solution is described that enables one to include the melting layer contribution. Results show a marked improvement in the estimated rainfall rates even when a fairly simple brightband model is used, indicating the usefulness and necessity of incorporating a brightband model in a retrieval of rain rate with a downward-looking radar. The improvement occurs mainly in the retrieved Ka-band results and highlights the importance of the melting layer in retrieval algorithms, particularly for high frequencies.

Bandpass-resampling effects on the retrieval of radiance and surface reflectance

Radiative transfer models are often employed to derive the surface reflectance for Earth-looking multispectral scanners or imaging spectrometers. For this purpose the calculated radiances have to be resampled with the spectral channel response functions of the instrument. Three methods of bandpass resampling the product terms of the radiative transfer equation are compared: the exact method and two commonly used approximations. Error budgets for the two approximate methods are given for typical multispectral and hyperspectral sensors. The error depends on the wavelength, bandwidth, atmospheric parameters, and atmospheric path length.

Testing a LiSK BRDF Model with in Situ Bidirectional Reflectance Factor Measurements over Semiarid Grasslands

The non-Lambertian nature of the terrestrial surface is a major source of unexplained variability in wide-swath satellite sensor data acquired in the solar reflective wavelengths, hindering quantitative analysis in the spectral, temporal, and locational domains. The interactions of light with the surface are governed by the bidirectional reflectance distribution function (BRDF), and modeling this is one of the most promising methods for describing and explaining this variability. Here the Roujean linear semiempirical kernel-driven (LiSK) model was tested against two independent bidirectional reflectance factor datasets that were acquired close to ground level over seminatural semiarid grasslands in Xilingol, Inner Mongolia (People's Republic of China) and in Arizona (United States). The objectives were to determine how well the model is able to describe and explain observed bidirectional reflectance factor distributions in the red and near-infrared wavelengths, to explore its utility in correcting such data for angular variations, and the likely impact of such corrections on cover-type discrimination. The sensitivity of the model to reductions in the number and angular distribution of the bidirectional reflectance observations with which it is inverted was also evaluated. The results show that the model is able to describe the observed multiangular BRFs with good accuracy and with low sensitivity to the number of angular inputs, with observations in the forward-scattering direction shown to be important in constraining inversions. The behavior of retrieved parameters indicates that one or more of the simplifying assumptions made in the model derivation is likely to be too severe for explaining BRDF in the near-infrared region; non-negligible anisotropic multiple scattering and the assumption of an optically thick medium mean that a physical interpretation of parameters is unlikely to be valid. However, the model is shown to provide an effective means of correcting for BRDF effects, allowing greater precision and consistency than hitherto possible in the retrieval of surface spectral reflectance over semiarid grasslands and concrete improvements in cover-type discrimination.

Atmospheric correction of satellite ocean color imagery: the black pixel assumption

The assumption that values of water-leaving radiance in the near-infrared (NIR) are negligible enable aerosol radiative properties to be easily determined in the correction of satellite ocean color imagery. This is referred to as the black pixel assumption. We examine the implications of the black pixel assumption using a simple bio-optical model for the NIR water-leaving reflectance [rho(w)(lambda(NIR))](N). In productive waters [chlorophyll (Chl) concentration >2 mg m(-3)], estimates of [rho(w)(lambda(NIR))](N) are several orders of magnitude larger than those expected for pure seawater. These large values of [rho(w)(lambda(NIR))](N) result in an overcorrection of atmospheric effects for retrievals of water-leaving reflectance that are most pronounced in the violet and blue spectral region. The overcorrection increases dramatically with Chl, reducing the true water-leaving radiance by roughly 75% when Chl is equal to 5 mg m(-3). Relaxing the black pixel assumption in the correction of Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) satellite ocean color imagery provides significant improvements in Chl and water-leaving reflectance retrievals when Chl values are greater than 2 mg m(-3). Improvements in the present modeling of [rho(w)(lambda(NIR))](N) are considered, particularly for turbid coastal waters. However, this research shows that the effects of nonzero NIR reflectance must be included in the correction of satellite ocean color imagery.

Retrieval of red spectral albedo and bidirectional reflectance using AVHRR HRPT and GOES satellite observations of the New England region

As a prototyping exercise for the moderate‐resolution imaging spectroradiometer (MODIS) albedo/BRDF product, we demonstrate the retrieval of bidirectional reflectance distribution functions (BRDFs) and red spectral albedo measures for the New England region, United States, from merged AVHRR and GOES radiances at a 1 km2 (nominal) spatial scale. These data were acquired during a 25‐day period in early fall 1995. The spatial pattern of BRDF retrievals shows that urban, suburban, and interurban regions exhibit directional scattering that is well modeled by the geometric optics of shadow casting. The directional reflectance of more continuous forest areas is better described by volume‐scattering mechanisms. Spectral albedos are larger in urban and suburban areas than in forested regions, as might be expected from the strong absorption of leaves in the red wave band. The red spectral albedo generally increases with solar zenith angle, as has been noted in ground measurements of broadband albedo. A number of technical limitations discussed constrain the absolute accuracy of retrieved albedos presented here, although the spatial patterns of albedo and the consistency of the BRDF shapes inspire confidence. These limitations will largely be overcome with application of our algorithm to data from the MODIS and MISR instruments on the EOS AM‐1 platform.

Retrieval of land surface bidirectional reflectance and aerosol opacity from ATSR-2 multiangle imagery

New satellite instruments that sample top-of-atmosphere radiance at a number of view angles offer the potential for improved retrieval of atmospheric aerosol opacity, land surface bidirectional reflectance, and biophysical parameters. This paper presents a method for simultaneous retrieval of aerosol opacity and land surface bidirectional reflectance, which utilizes the dual view capability of the second Along-Track Scanning Radiometer (ATSR-2). Analysis of a physically based model of light scattering results in two simple equations defining possible spectral variation of land surface bidirectional reflectance distribution function (BRDF). These are used as constraints to anew inversion of a model of atmospheric scattering to simultaneously retrieve atmospheric aerosol opacity and bidirectional reflectance from top-of-atmosphere radiance. The inversion assumes no a priori knowledge of the land surface cover. Sensitivity is evaluated using both simulated and field-measured data to reproduce expected ATSR-2 observations. Where an atmosphere of known aerosol scattering properties, but of unknown optical depth, is available, results show mean absolute error in retrieval of aerosol opacity of the greater of 0.02 or 15% relative error and bidirectional reflectance retrieval at 55 nm to an accuracy of <0.01. Where a number of candidate aerosol models are available, results show discrimination of dominant aerosol type is possible in 95% of cases considered. The methods perform best over dark surfaces, such as vegetation, but show accurate retrieval over soil and pixels containing a number of cover types.