Narrowband to broadband conversions of land surface albedo: II. Validation

In the first paper of this series, we developed narrowband to broadband albedo conversion formulae for a series of sensors. These formulae were determined based on extensive radiative transfer simulations under different surface and atmospheric conditions. However, it is important to validate the simulation results using independent measurement data. In this paper, the validation results for three broadband albedos (total-shortwave, -visible and -near-IR albedos) using ground measurement of several cover types on five different days at Beltsville, MD are presented. Results show that the conversion formulae in the previous paper are very accurate and the average residual standard errors of the resulting broadband albedos for most sensors are around 0.02, which meets the required accuracy for land surface modeling.     

NOAA AVHRR land surface albedo algorithm development

The primary objective of this research is to develop a surface albedo model for the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). The primary test site is the Konza prairie, Kansas (U.S.A.), used by the International Satellite Land Surface Climatology Project (ISLSCP) in the First ISLSCP Field Experiment (FIFE). In this research, high spectral resolution field spectrometer data was analyzed to simulate AVHRR wavebands and to derive surface albedos. Development of a surface albedo algorithm was completed by analysing a combination of satellite, field spectrometer, and ancillary data. Estimated albedos from the field spectrometer data were compared to reference albedos derived using pyranometer data. Variations from surface anisotropy of reflected solar radiation were found to be the most significant albedo-related error. Additional error or sensitivity came from estimation of a shortwave mid-IR reflectance (1.3-4.0 mu m) using the AVHRR red and near-IR bands. Errors caused by the use of AVHRR spectral reflectance to estimate both a total visible (0.4-0.7 mu m) and near-IR (0.7-1.3 mu m) reflectance were small. The solar spectral integration, using the derived ultraviolet, visible, near-IR and SW mid-IR reflectivities, was not sensitive to many clear-sky changes in atmospheric properties and illumination conditions.     

Validation of the Climate-SAF surface broadband albedo product: Comparisons with in situ observations over Greenland and the ice-covered Arctic Ocean

This paper describes a validation study performed by comparing the Climate-SAF Surface Albedo Product (SAL) to ground truth observations over Greenland and the ice-covered Arctic Ocean. We compare Advanced Very High Resolution Radiometer (AVHRR)-based albedo retrievals to data from the Greenland Climate Network (GCN) weather stations and the floating ice station Tara for polar summer 2007. The AVHRR dataset consists of 2755 overpasses. The overpasses are matched to in situ observations spatially and temporally. The SAL algorithm presented here derives the surface broadband albedo from AVHRR channels 1 and 2 using an atmospheric correction, temporal sampling of an empirical Bidirectional Reflectance Distribution Function (BRDF), and a narrow-to-broadband conversion algorithm. The satellite product contains algorithms for snow, sea ice, vegetation, bare soil, and water albedo. At the Summit and DYE-2 stations on the Greenland ice sheet, instantaneous SAL RMSE is 0.073. The heterogeneous surface conditions at satellite pixel scale over the stations near the Greenland west coast increase RMSE to > 0.12. Over Tara, the instantaneous SAL RMSE is 0.069. The BRDF sampling approach reduces RMSE over the ice sheet to 0.053, and to 0.045 over Tara. Taking into account various sources of uncertainty for both satellite retrievals and in situ observations, we conclude that SAL agrees with in situ observations within their limits of accuracy and spatial representativeness.     

Semi-empirical approach for estimating broadband albedo of snow

Snow is a medium that exhibits highly anisotropic reflectance throughout the solar spectrum. The anisotropic nature of snow shows more variability in snow metamorphic processes for wavelengths beyond 1.0 μm than in the visible spectrum. This behavior poses challenges for the development of a model that can retrieve broadband albedo from reflectance measurements throughout the snow season. In this paper, a semi-empirical model is presented to estimate near infrared (0.8-2.5 μm) albedo of snow. This model estimates spectral albedo at a wavelength of 1.240 μm using only three variables: solar zenith angle, scattering angle and measured reflectance, which is used to retrieve near infrared albedo. To form a base for such a model, quantification of reflectance patterns and variability in varying snow condition, i.e. snow grain size, and sun-sensor geometry are prerequisite. In this study the DIScrete Ordinate Radiative Transfer (DISORT) model is used to simulate bi-directional reflectance. The performance of the developed model is evaluated by using DISORT simulated spectral albedo for various snow grain sizes and solar zenith angles, as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ measurements. The developed model is shown to be capable of estimating spectral albedo at 1.240 μm with acceptable accuracy. The mean error (ME), mean absolute error (MAE), and root mean squared error (RMSE) in the estimates are found to be 0.053, 0.055 and 0.064, respectively, for a wide range of sun-sensor geometries and snow grain sizes. The model shows better accuracy for spectral albedo estimates than for those computed using the Lambertian reflectance assumption for snow, reducing the error in the range and standard deviation by 75% and 65%, respectively. Applying the model to MODIS, the retrieved albedo is found to be in good quantitative agreement (r = 0.82) with in-situ measurements. These improvements in albedo estimation should allow more accurate use of remote sensing measurements in climate and hydrological models.     

Using angular and spectral shape similarity constraints to improve MISR aerosol and surface retrievals over land

The Multi-angle Imaging SpectroRadiometer (MISR) instrument on the Terra satellite has demonstrated the capability to retrieve aerosol optical depths, surface bidirectional reflectance factors, and hemispherical reflectances over a wide variety of land surface types. In particular, its multiangular imaging design has enabled the application of algorithms that minimize sensitivity of the aerosol retrievals to the brightness of the underlying surface. The novel aerosol algorithm that was developed prior to launch has had notable quantitative success. Over certain scene types, however, the approach contained obvious spatial artifacts, so a postlaunch refinement to the algorithm was implemented. It constrains the retrieved aerosol models and optical depths such that the implied angular shape of the surface hemispherical-directional reflectance factor (HDRF) is similar among all of the MISR wavelengths. This upgrade has resulted in three tangible benefits: (1) the occurrence of outliers has been dramatically reduced, (2) correlations with AERosol RObotic NETwork (AERONET) aerosol sunphotometer data are quantitatively improved, and (3) the quality of surface products is markedly enhanced. MISR Level 2 aerosol and surface products are archived at the NASA Langley Atmospheric Sciences Data Center. Those having version numbers v0012 and higher incorporate this upgrade in the data processing software.     

An empirical anisotropy correction model for estimating land surface albedo for radiation budget studies

Land surface albedo is one of the key parameters in the radiation budget, the hydrological cycle and climate modeling studies. It is now widely understood that large errors may occur in the estimation of surface albedo without taking into consideration the anisotropy reflectance effect, which is a general feature of the earth surface. Two major anisotropic correction methods exist for the retrieval of land surface albedo under clear sky conditions. One method involves linearly converting from top of the atmosphere (TOA) albedo to surface albedo, and another is based on the inversion of the Bidirectional Reflectance Distribution Function (BRDF) model of the surface. In the present study, a new approach that utilizes an empirical model for estimating surface albedo has been proposed for snow free land surfaces under clear sky conditions. We analyzed the bidirectional reflectance data set with numerous samples representing various land cover types, which derived from POLDER/ADEOS-1 multi-angle imagery data and distributed by MEDIAS-France. Through the analysis, an empirical relation between bidirectional reflectance and albedo was established and has been discussed in detail. The proposed model can be used for direct estimation of surface albedo from a single BRF observation when the sun-target-sensor geometry is known. No BRDF model inversion scheme is necessary. The present model has no or weak dependence on the existing land surface classifications, and is insensitive to wavelength. The theoretical absolute accuracy of the estimated albedo is approximately 0.010 for visible (0.4-0.7 μm), 0.023 for near infrared (0.7-3.0 μm) and 0.016 for shortwave (0.2-3.0 μm), respectively. Albedo consistency with viewing geometry has been verified, resulting in good agreement for albedo estimated from various viewing directions. Validation of the satellite estimated albedo derived by the proposed method, using field observations were also presented and results show it can give reasonably accurate estimation. The proposed method is expected to be a suitable candidate for practical applications of albedo estimation for sensors that do not perform multi-angle observations.     

Theoretical noise sensitivity of BRDF and albedo retrieval from the EOS-MODIS and MISR sensors with respect to angular sampling

The sensitivity of the semiempirical RossThick-LiSparse Ambrals BRDF model to random noise in observed multiangular reflectances was investigated through a study of the impact of angular sampling. The mathematical properties of (linear, additive) kernel-driven BRDF models allow the analytical derivation of so-called weights of determination or noise amplification factors which quantify the uncertainty in retrieved parameters such as nadir-view reflectance or albedo at various solar zenith angles, or in the BRDF model parameters themselves. The study was carried out using simulated angular sampling for the MODIS and MISR instruments to be flown on NASA's Earth Observing System AM-1 platform, as a function of latitude, day of year and sampling period. A similar study was carried out for comparison using the modified RPV BRDF model, a multiplicative model. Results show that the retrieved parameters, reflectance and albedo can be expected to have noise amplification factors that are less than unity, indicating that the retrievals are stable with respect to random noise under the angular sampling schemes occurring. The BRDF model parameters themselves were found to be more susceptible to noise than many of the derived products, especially for the modified RPV model. The effect of different angular sampling regimes on the uncertainty of derived information was further explored. This study provides an indication of the reliability to be expected from the operational BRDF/albedo products from the MODIS and MISR instruments. The findings may qualitatively also apply to AVHRR, SPOT VEGETATION and similar satellite angular sampling regimes.     

Snow surface albedo estimation using terrestrial photography

A flexible and inexpensive remote sensing tool for albedo estimation using conventional terrestrial photography and its validation on an Alpine glacier are described. The proposed technique involves georeferencing oblique photographs to a digital elevation model (DEM), defining a mapping function between the information contained on a given pixel of the image and the corresponding cell of the DEM. This is attained by performing a perspective projection of the DEM after a viewing transformation into the camera coordinate system. Once the image is georeferenced, the reflectance values recorded by the film or digital camera are corrected for topographic and atmospheric influences and for the effect of the photographic process (lens-film-developing-scanning). Atmospheric transmittance is evaluated using the MODTRAN radiative transfer model. Diffuse and direct irradiation are estimated using a parametric solar irradiation model. The solar-ground geometry, anisotropy of reflected radiation, the effect of surrounding topography and the portion of visible sky are evaluated using terrain algorithms applied to the DEM. The response of the camera-film-scanner system is evaluated using an empirical approach. The result is a geographically correct map of normalized reflectance values. By comparing these to a surface of known albedo, the spatial distribution of albedos is calculated. Comparisons to in situ measurements on the Mer de Glace glacier, French Alps, show good agreement. Sources of error are identified and ways of improvement addressed. The georeferencing algorithm, implemented into the Interactive Data Language (IDL) is available from the author and at the user contributed IDL library at www.rsinc.com.     

Identification of land surface temperature and albedo trends in AVHRR Pathfinder data from 1982 to 2005 for northern Siberia

The arctic regions are highly vulnerable to climate change. Climate models predict an increase in global mean temperatures for the upcoming century. The arctic environment is subject to significant changes of the land surface. Especially the changes of vegetation pattern and the phenological cycle in the taiga–tundra transition area are of high importance in climate change research. This study focuses on time series and trend analysis of land surface temperature, albedo, snow water equivalent, and normalized difference vegetation index information in the time period of 1982–2005 for northern Siberia. The findings show strong dependencies between these parameters and their inter-annual dynamics, which indicate changes in vegetation growing period. We found a strong negative correlation between land surface temperature and albedo conditions for the beginning (60–90%) of the growing season for selected hot spot trend regions in northern Siberia.     

Deriving surface albedo measurements from narrow band satellite data

Abstract

A target calibration procedure for obtaining surface albedo from satellite data is presented. The methodology addresses two key issues, the calibration of remotely-sensed, discrete wavelength, digital data and the derivation of an albedo measurement (defined over the solar short wave spectrum) from spectrally limited observations. Twenty-seven LANDSAT observations, calibrated with urban targets (building roof-tops and parking lots), are used to derive spatial and seasonal patterns of surface reflectance and albedo for four land cover types, city, suburb, farm and forest.     

Generating global surface albedo products from multiple geostationary satellites

Operational weather geostationary satellites have acquired data for more than two decades and offer thus the possibility to generate long time series of Essential Climate Variables like surface albedo as suggested by GCOS. This paper investigates the possibility to generate consistent global, to the exception of the polar regions, surface broadband albedo product from these satellites. In this context, the paper addresses two specific issues. Firstly, the spatial consistency of surface albedo derived from five different geostationary satellites is examined in detail. Secondly, this product is compared with the equivalent MODIS one to define the temporal consistency between surface albedo derived with old geostationary instruments and technologically advanced radiometers like MODIS. The analysis of the surface albedo products has revealed a good agreement between the products derived from the various geostationary satellites. Comparison of this global product with the one routinely derived from MODIS shows that on the average, the mean relative difference between these two data sets agree within 10%. These first encouraging results open therefore a new avenue for the exploitation of the archived data for the generation of long time series, covering the last 25 years or so, of global surface albedo from geostationary weather satellites.     

三维重建中曲面反照率值的估计与应用

从单幅图像获得物体的表面高度是计算机视觉中的一个重要研究领域,其中一种重要的方法就是从明暗恢复形状(ShapefromShading,简称SFS)。在SFS的各种不同算法中都需要曲面的反照率值,反照率值的估算是否准确直接影响了三维重建的效果。针对反照率值的估算,已经产生了很多有效的算法。文中讨论了三种反照率值的估计算法及其优缺点,并将局部反照率估计算法引入到三维重建中,解决了由全局反照率值重建的弊端。     

Evaluation of the consistency of the three MRPV model parameters provided by the MISR level 2 land surface products: a case study in Mainland Spain

The multiangular Rahman–Pinty–Verstraete modified (MRPV) semi-empirical model uses three parameters (ρ₀, Θ, and k) for describing the anisotropy of an arbitrary target. They have been usefully proved to characterize some forest attributes and land covers. However, there is no enough evaluation of the consistency of this product, and the possible affection from different factors in the reliability of them. Here, we explored the consistency of the MRPV parameters provided in the MISR L2 Land Surface (MIL2ASLS) product, with data from Mainland Spain, grouping MISR images into close time pairs. Thus, it was studied the three MRPV parameters through retrieving Spearman’s rank correlation coefficient (ρ) and mean relative differences related to every pair of images. The results showed the ρ₀ parameter presented higher consistency than the others, with ρ over 0.85 and meant relative differences around 15%. The k parameter showed ρ over 0.65 and average relative disagreements over 8%. Finally, the Θ parameter reached ρ around 0.60. The Θ mean differences were over 25% unless the combination of the blue band which was especially bad and its values were up to 50%. So, it is crucial having into account when the parameters of this product are used to look into the band and the own parameter.     

Retrieval of land surface albedo and temperature using data from the Indian geostationary satellite: a case study for the winter months

The shortwave and longwave radiation budget at land surfaces is largely dependent on two fundamental quantities, the albedo and the land surface temperature (LST). A time series (November 2005 to March 2006) of daily data from the Indian geostationary satellite Kalpana‐1 Very High Resolution Radiometer (K1VHRR) sensor in the visible (VIS), water vapour (WV) and thermal infrared (TIR) bands from noontime (0900 GMT) observations were processed to retrieve these quantities in clear skies for five winter months. Cloud detection was carried out using bispectral threshold tests (in both VIS and TIR bands) in a dekadal time series. Surface albedo was retrieved using a simple atmospheric transmission model. K1VHRR albedo was compared with Moderate Resolution Imaging Spectroradiometer (MODIS) AQUA noontime albedo over different land targets (agriculture, forest, desert, scrub and snow) that showed minimum differences over agriculture and forest. The comparison of spatial albedo over different landscapes yielded a root mean square deviation (RMSD) of 0.021 in VHRR albedo (9% of MODIS albedo). A mono‐window algorithm was implemented with a single TIR band to retrieve the LST. Its accuracy was also verified over different land targets by comparison with aggregated MODIS AQUA LST. The maximum RMSD was obtained over agriculture. Spatial comparison of VHRR and AQUA LSTs over homogeneous and heterogeneous landscape cutouts revealed an overall RMSD of 2.3 K. An improvement in the retrieval accuracy is expected to be achieved with atmospheric products from the sounder and split thermal bands in the imager of future INSAT 3D missions.     

Analysis of broadband surface BRDFs derived from TOA SW CERES measurements for surfaces classified by the IGBP land cover

Most studies on the reflectance properties of the Earth's surface are addressed estimating the bidirectional reflectance distribution function (BRDF) of high spatial resolution and high spectral resolution satellite measurements. This article assesses the development of broadband (BB) BRDFs from radiances corresponding to large footprints classified according to the International Geosphere-Biosphere Programme (IGBP) land-cover classification. Top-of-atmosphere (TOA) shortwave (SW) CERES (Clouds and the Earth's Radiant Energy System) measurements are employed to invert the bidirectional reflectance factor (BRF) Rahman–Pinty–Verstraete (RPV) model for regions identified with the same IGBP type. The inversion of this non-linear parametric model is optimized to improve the computation efficiency and merged into a radiative transfer model to correct the surface radiances for the atmospheric effect. Analysis of the nature of the reflectance field simulated for several regions selected for every IGBP type determines whether the creation of general BRF models for surfaces defined by the same IGBP land cover is feasible. According to the results gathered in this study, the BB BRDFs for regions classified by the IGBP classification show values for the coefficient of variation (CV) between 3.5% and 44.1%. Consequently, the high differences achieved in the reflectance fields discourage the creation of BRDFs based on the IGBP land types.     

Correcting atmospheric masking to retrieve the spectral albedo of land surfaces from satellite measurements

A radiative transfer approach to the problem of atmospheric correction of satellite images in the solar spectral range is presented which includes all multiple scattering processes without any approximation. The numerical solution is accepted as satisfying, if the numerical accuracy is better than I per cent. This means that the accuracy of the atmospheric correction depends almost exclusively on the quality of the auxiliary data on the atmospheric state and the surface reflection indicatrix. Byextensivemodel calculations these parameter driven error bounds have been quantified. Thus the calculation results in a corrected albedo image with specified error bounds. This seems to be the first algorithm available for atmospheric correction of real imagery data which relies on a numerical exact treatment of multiple scattering. The program EXACT (EXact Atmospheric Correction Technique) has so far been used with Landsat Thematic Mapper (TM), NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer) and also with airborne Daedalus ATM images. The algorithm has been validated by comparison of satellite data to ground measurements and between different sensors. Errors of the derived albedos were found to remain below 0·01 for visible and near-infrared sensor channels of this set of radiometers.     

Validation of land surface temperature derived from MSG/SEVIRI with in situ measurements at Gobabeb,Namibia

Land surface temperature (LST) derived from Meteosat Second Generation/?Spinning-Enhanced Visible and Infrared Imager MSG/SEVIRI data is an operational product of the Land Surface Analysis Satellite Applications Facility (LSA SAF). The LST has a temporal resolution of 15 minutes, a sampling distance of 3 km at nadir, and a targeted accuracy of better than 2 K. Gobabeb (Namibia) is one of Karlsruhe Institute of Technology's (KIT's) four dedicated stations for LST validation. In March 2010, a field survey was performed to characterize the Gobabeb site more closely. SAF LST and in situ LST obtained over a period of 3 days from additional measurements with a telescopic mast on the Namib gravel plains were in good agreement with each other (bias 1.0 K). For the same period, the bias between SAF LST and Gobabeb main station LST was even smaller (0.4 K). A mobile measurement system was set up by fixing the telescopic mast to a four-wheel drive. Around solar noon, LST from in situ measurements along a 40 km track and LST from Gobabeb main station had a bias of 0.4 K and a standard deviation of 1.2 K, which means that in situ LSTs at Gobabeb main station are representative for large parts of the gravel plains. Exploiting this relationship, 2 years of LST from MSG/SEVIRI were compared with in situ LST from Gobabeb main station. The magnitude of the monthly biases between the two data sets was generally less than 1.0 K and root mean square errors were below 1.5 K. Furthermore, the bias appears to exhibit a seasonality, which could be accounted for in future validation work.     

Validation and analysis of aerosol optical thickness retrieval over land

Aerosol optical thickness (AOT) retrieval from Moderate Resolution Imaging Spectroradiometer (MODIS) data has been well established over oceans, but this is not the case over land. In this article, the AOT data sets retrieved by exploiting the synergy of TERRA and AQUA MODIS data (SYNTAM) over land are validated with ground-based measurements from Aerosol Robotic Network (AERONET) data, as well as from the National Aeronautics and Space Administration (NASA) AOT products, amended with a DeepBlue algorithm in Asian (15–60° N and 35–150° E) and American areas (30–40° N and 100–120° W). Overall, AOT retrieval errors of around 10–20% against AERONET data are found at both 1 and 10 km resolutions. The spectral and spatial sensitivities of the AOT correlation are explicitly addressed at both 1 and 10 km resolutions. Three window sizes, 1?×?1, 3?×?3 and 5?×?5, are tested for SYNTAM to evaluate the effect of window size on parameter statistics, and it is found that the accuracy of the SYNTAM method decreases with increasing window size. The validations at three spectral bands of 0.47, 0.55 and 0.66 μm show that the accuracies of different bands are 80–90% similar, and that the band at 0.47 μm has the highest accuracy most of the time. Comparisons between AOT data sets derived from the SYNTAM and AOT products from the NASA Dark Dense Vegetation (DDV) and the DeepBlue algorithms are also conducted using data from the USA. More pixels with AOT values for the area could be retrieved using the SYNTAM method with the NASA DeepBlue algorithm. The AOT values of more than 90% of pixels derived by both methods are very close. This clearly shows that AOT data from SYNTAM are very close to the AOT data set from the NASA DeepBlue algorithm in cloud-free areas. The synergic use of both the SYNTAM and DeepBlue algorithms could produce AOT values over much greater land areas.     

Approximating the average daily surface albedo with respect to soil roughness and latitude

The present study explores the diurnal variations in blue-sky albedo (α) of soils under clear sky conditions with respect to surface roughness. Three roughness levels of ploughed and unploughed soil surfaces, developed from the same loessial material, were examined. The relation between α of the surfaces and the solar zenith angle, determined during the experiment, enabled us to predict the diurnal α variation of the surfaces throughout the year at a given latitude, between 75° S and 75° N. The optimal time (T _O) for measuring the soil albedo by an instantaneous observation was considered as the best represented time for the daily averaged value within an error lower than ±2%. It was found that the T _O, falling at different times depending on the soil surface roughness, limits the possibilities of data achievement by remote-sensing satellites along one of their sun-synchronous orbits.     

The ratio of spectral albedo to spectral directed reflection coefficient for a smooth water surface

This communication describes the relation between spectral albedo Aλ and bidirectional spectral reflection coefficient ?λ for a smooth water surface for vertical view directions.