Broadband Albedo Research Articles

Simultaneous Estimation of Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation, and Surface Albedo From Multiple-Satellite Data

Leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and surface broadband albedo are three routinely generated land-surface parameters from satellite observations, which have been widely used in land-surface modeling and environmental monitoring. Currently, most global land products are retrieved separately from individual satellite data. Many issues, such as data gaps, spatial and temporal inconsistencies, and insufficient accuracy under certain conditions resulting from the inadequacies of single-sensor observations, have made the incorporation of multiple sensors a reasonable solution. In this paper, an approach to simultaneous estimation of LAI, broadband albedo, and FAPAR from multiple-satellite sensors is further refined. The method, improved from that proposed in an earlier study using Moderate Resolution Imaging Spectroradiometer (MODIS) data, consists of several steps. First, a coupled dynamic and radiative-transfer model based on MODIS, SPOT/VEGETATION, and Multiangle Imaging SpectroRadiometer data was developed to retrieve LAI values and use them to construct a time-evolving dynamic model. Second, an iteration process with predefined exit criteria was developed to obtain consistent gap-filled LAI estimates. Third, a spectral albedo based on the retrieved LAI values was simulated using a radiative-transfer model and then converted to a broadband albedo using empirical methods. Snow-covered pixels identified by normalized difference snow index thresholds were adjusted to the weighted average of the underlying albedo and the maximum snow albedo. Finally, the FAPAR of green vegetation was calculated as a combination of the albedo at the top of the canopy, the soil albedo, and the transmittance of the PAR down to the background. Validation of retrieved LAI, albedo, and FAPAR values obtained from multiple-satellite data over ten study sites has demonstrated that the proposed method can produce more accurate products than presently distributed global products.

Evaluation of satellite remote sensing albedo retrievals over the ablation area of the southwestern Greenland ice sheet

MODerate resolution Imaging Spectroradiometer (MODIS) albedo products have been validated over spatially uniform, snow-covered areas of the Greenland ice sheet (GrIS) using the so-called single ‘point-to-pixel’ method. This study expands on this methodology by applying a ‘multiple-point-to-pixel’ method and examination of spatial autocorrelation (here using semivariogram analysis) by using in situ observations, high-resolution WorldView-2 (WV-2) surface reflectances, and MODIS Collection V006 daily blue-sky albedo over a spatially heterogeneous surfaces in the lower ablation zone in southwest Greenland. Our results using 232 ground-based samples within two MODIS pixels, one being more spatial heterogeneous than the other, show little difference in accuracy among narrow and broad band albedos (except for Band 2). Within the more homogenous pixel area, in situ and MODIS albedos were very close (error varied from −4% to +7%) and within the range of ASD standard errors. The semivariogram analysis revealed that the minimum observational footprint needed for a spatially representative sample is 30m. In contrast, over the more spatially heterogeneous surface pixel, a minimum footprint size was not quantifiable due to spatial autocorrelation, and far exceeds the effective resolution of the MODIS retrievals. Over the high spatial heterogeneity surface pixel, MODIS is lower than ground measurements by 4–7%, partly due to a known in situ undersampling of darker surfaces that often are impassable by foot (e.g., meltwater features and shadowing effects over crevasses). Despite the sampling issue, our analysis errors are very close to the stated general accuracy of the MODIS product of 5%. Thus, our study suggests that the MODIS albedo product performs well in a very heterogeneous, low-albedo, area of the ice sheet ablation zone.Furthermore, we demonstrate that single ‘point-to-pixel’ methods alone are insufficient in characterizing and validating the variation of surface albedo displayed in the lower ablation area. This is true because the distribution of in situ data deviations from MODIS albedo show a substantial range, with the average values for the 10th and 90th percentiles being −0.30 and 0.43 across all bands. Thus, if only single point is taken for ground validation, and is randomly selected from either distribution tails, the error would appear to be considerable. Given the need for multiple in-situ points, concurrent albedo measurements derived from existing AWSs, (low-flying vehicles (airborne or unmanned) and high-resolution imagery (WV-2)) are needed to resolve high sub-pixel variability in the ablation zone, and thus, further improve our characterization of Greenland's surface albedo.

Simultaneous inversion of multiple land surface parameters from MODIS optical–thermal observations

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface variables usually focus on individual parameters separately even from the same satellite observations, resulting in inconsistent products. Moreover, no efforts have been made to generate global products from integrated observations from the optical to Thermal InfraRed (TIR) spectrum. Particularly, Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal, which contains both reflected and emitted radiation.In this paper, we propose a unified algorithm for simultaneously retrieving six land surface parameters – Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Emissivity (LSE), Land Surface Temperature (LST), and Upwelling Longwave radiation (LWUP) by exploiting MODIS visible-to-TIR observations. We incorporate a unified physical radiative transfer model into a data assimilation framework. The MODIS visible-to-TIR time series datasets include the daily surface reflectance product and MIR-to-TIR surface radiance, which are atmospherically corrected from the MODIS data using the Moderate Resolution Transmittance program (MODTRAN, ver. 5.0). LAI was first estimated using a data assimilation method that combines MODIS daily reflectance data and a LAI phenology model, and then the LAI was input to the unified radiative transfer model to simulate spectral surface reflectance and surface emissivity for calculating surface broadband albedo and emissivity, and FAPAR. LST was estimated from the MIR–TIR surface radiance data and the simulated emissivity, using an iterative optimization procedure. Lastly, LWUP was estimated using the LST and surface emissivity. The retrieved six parameters were extensively validated across six representative sites with different biome types, and compared with MODIS, GLASS, and GlobAlbedo land surface products. The results demonstrate that the unified inversion algorithm can retrieve temporally complete and physically consistent land surface parameters, and provides more accurate estimates of surface albedo, LST, and LWUP than existing products, with R2 values of 0.93 and 0.62, RMSE of 0.029 and 0.037, and BIAS values of 0.016 and 0.012 for the retrieved and MODIS albedo products, respectively, compared with field albedo measurements; R2 values of 0.95 and 0.93, RMSE of 2.7 and 4.2K, and BIAS values of −0.6 and −2.7K for the retrieved and MODIS LST products, respectively, compared with field LST measurements; and R2 values of 0.93 and 0.94, RMSE of 18.2 and 22.8W/m2, and BIAS values of −2.7 and −14.6W/m2 for the retrieved and MODIS LWUP products, respectively, compared with field LWUP measurements.

Cross-Comparison of Albedo Products for Glacier Surfaces Derived from Airborne and Satellite (Sentinel-2 and Landsat 8) Optical Data

Surface albedo partitions the amount of energy received by glacier surfaces from shortwave fluxes and modulates the energy available for melt processes. The ice-albedo feedback, influenced by the contamination of bare-ice surfaces with light-absorbing impurities, plays a major role in the melting of mountain glaciers in a warming climate. However, little is known about the spatial and temporal distribution and variability of bare-ice glacier surface albedo under changing conditions. In this study, we focus on two mountain glaciers located in the western Swiss Alps and perform a cross-comparison of different albedo products. We take advantage of high spectral and spatial resolution (284 bands, 2 m) imaging spectrometer data from the Airborne Prism Experiment (APEX) and investigate the applicability and potential of Sentinel-2 and Landsat 8 data to derive broadband albedo products. The performance of shortwave broadband albedo retrievals is tested and we assess the reliability of published narrow-to-broadband conversion algorithms. The resulting albedo products from the three sensors and different algorithms are further cross-compared. Moreover, the impact of the anisotropy correction is analysed depending on different surface types. While degradation of the spectral resolution impacted glacier-wide mean albedo by about 5%, reducing the spatial resolution resulted in changes of less than 1%. However, in any case, coarser spatial resolution was no longer able to represent small-scale variability of albedo on glacier surfaces. We discuss the implications when using Sentinel-2 and Landsat 8 to map dynamic glaciological processes and to monitor glacier surface albedo on larger spatial and more frequent temporal scales.

Multi-Staged NDVI Dependent Snow-Free Land-Surface Shortwave Albedo Narrowband-to-Broadband (NTB) Coefficients and Their Sensitivity Analysis

Narrowband-to-broadband conversion is a critical procedure for mapping land-surface broadband albedo using multi-spectral narrowband remote-sensing observations. Due to the significant difference in optical characteristics between soil and vegetation, NTB conversion is influenced by the variation in vegetation coverage on different surface types. To reduce this influence, this paper applies an approach that couples NTB coefficient with the NDVI. Multi-staged NDVI dependent NTB coefficient look-up tables (LUT) for Moderate Resolution Imaging Spectroradiometer (MODIS), Polarization and Directionality of Earth’s Reflectance (POLDER) and Advanced Very High Resolution Radiometer (AVHRR) were calculated using 6000 spectra samples collected from two typical spectral databases. Sensitivity analysis shows that NTB conversion is affected more by the NDVI for sensors with fewer band numbers, such as POLDER and AVHRR. Analysis of the validation results based on simulations, in situ measurements and global albedo products indicates that by using the multi-staged NDVI dependent NTB method, the conversion accuracies of these two sensors could be improved by 2%–13% on different NDVI classes compared with the general method. This improvement could be as high as 15%, on average, and 35% on dense vegetative surface compared with the global broadband albedo product of POLDER. This paper shows that it is necessary to consider surface reflectance characteristics associated with the NDVI on albedo-NTB conversion for remote sensors with fewer than five bands.

A predictive model for the spectral “bioalbedo” of snow

AbstractWe present the first physical model for the spectral “bioalbedo” of snow, which predicts the spectral reflectance of snowpacks contaminated with variable concentrations of red snow algae with varying diameters and pigment concentrations and then estimates the effect of the algae on snowmelt. The biooptical model estimates the absorption coefficient of individual cells; a radiative transfer scheme calculates the spectral reflectance of snow contaminated with algal cells, which is then convolved with incoming spectral irradiance to provide albedo. Albedo is then used to drive a point‐surface energy balance model to calculate snowpack melt rate. The model is used to investigate the sensitivity of snow to algal biomass and pigmentation, including subsurface algal blooms. The model is then used to recreate real spectral albedo data from the High Sierra (CA, USA) and broadband albedo data from Mittivakkat Gletscher (SE Greenland). Finally, spectral “signatures” are identified that could be used to identify biology in snow and ice from remotely sensed spectral reflectance data. Our simulations not only indicate that algal blooms can influence snowpack albedo and melt rate but also highlight that “indirect” feedback related to their presence are a key uncertainty that must be investigated.

An improved carbon dioxide snow spectral albedo model: Application to Martian conditions

AbstractCarbon dioxide ice is abundant on the Martian surface and plays an important role in the planet's energy budget due to its high reflectivity and seasonal variation. Here we adapt the terrestrial Snow, Ice, and Aerosol Radiation (SNICAR) model to simulate CO2 snow albedo across the ultraviolet, visible, and near‐IR spectra (0.2–5.0 µm). We apply recent laboratory‐derived refractive indices of CO2 ice, which produce higher broadband CO2 snow albedo (0.93–0.98) than previously estimated. Compared with H2O snow, we find that CO2 snow albedo is much higher in the near‐IR spectrum, less dependent on ice grain size, less dependent on solar zenith angle, and more susceptible to darkening from dust. A mass concentration of 0.01% Martian dust reduces visible and near‐IR CO2 snow albedos by about 60% and 35%, respectively. The presence of small amounts of H2O snow on top of CO2 snow can substantially decrease the surface albedo. Whereas 2.5 cm of H2O snow can completely mask the impact of underlying CO2 ice or the surface, roughly twice as much overlying CO2 snow is required to mask underlying H2O snow. Similarly, a 10% mixing ratio of H2O ice embedded in CO2 snow decreases broadband albedo by 0.18, while 10% CO2 ice elevates H2O snow broadband albedo by 0.10. We also present comparisons between hemispherical albedo produced by SNICAR and observations of directional reflectance of Martian polar ice caps. While imperfect, this best fit analysis provides general ranges of physical parameters in different Martian environments that produce reasonable model‐observation agreement.

Attribution of Greenland's ablating ice surfaces on ice sheet albedo using unmanned aerial systems

Surface albedo, a primary control on the amount of energy available for melt, has considerable spatial heterogeneity across the Greenland ice sheet ablation area. However, the relative importance of distinct surface types on albedo remains unclear. In this study, the causes of mesoscale (10<sup>2</sup> to 10<sup>3</sup> m) albedo variability are assessed using high resolution (decimetre-scale) digital imagery and broadband albedo data acquired by a fixed-wing unmanned aerial system. We characterize the reflectance properties and terrain roughness associated with six distinct surface types identified from a 25 km longitudinal transect across the ablating dark region of the Kangerlussuaq sector. Principal component analysis of the fractional area of each surface type versus coincident Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data reveals the relative importance of each surface type. The highest correlation with mesoscale albedo was the fractional area of distributed impurities. Although not the darkest surface type, their extensive coverage meant that they could explain 65 % of the albedo variability across the survey transect including the presence of the dark region. In contrast, the 2 % mean surface water coverage across our survey transect could only explain 12 % of albedo variation and crevasses, only 17 %. Localised cryoconite patches have the lowest albedo signature but comprise less than 1 % of the survey area and do not appear to reduce mesoscale albedo. We anticipate further reduction in ablation area albedo under future warming as localized areas of distributed impurities, supraglacial water and crevassing increase in extent and conclude that current bare ice area albedo models may advance significantly by representing the evolution of the surface types identified in this study.

A method for improving hotspot directional signatures in BRDF models used for MODIS

The semi-empirical, kernel-driven, linear RossThick-LiSparseReciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model is used to generate the routine MODIS BRDF/Albedo product due to its global applicability and the underlying physics. A challenge of this model in regard to surface reflectance anisotropy effects comes from its underestimation of the directional reflectance signatures near the Sun illumination direction; also known as the hotspot effect. In this study, a method has been developed for improving the ability of the RTLSR model to simulate the magnitude and width of the hotspot effect. The method corrects the volumetric scattering component of the RTLSR model using an exponential approximation of a physical hotspot kernel, which recreates the hotspot magnitude and width using two free parameters (C1 and C2, respectively). The approach allows one to reconstruct, with reasonable accuracy, the hotspot effect by adjusting or using the prior values of these two hotspot variables. Our results demonstrate that: (1) significant improvements in capturing hotspot effect can be made to this method by using the inverted hotspot parameters; (2) the reciprocal nature allow this method to be more adaptive for simulating the hotspot height and width with high accuracy, especially in cases where hotspot signatures are available; and (3) while the new approach is consistent with the heritage RTLSR model inversion used to estimate intrinsic narrowband and broadband albedos, it presents some differences for vegetation clumping index (CI) retrievals. With the hotspot-related model parameters determined a priori, this method offers improved performance for various ecological remote sensing applications; including the estimation of canopy structure parameters.

Estimating Arctic sea-ice shortwave albedo from MODIS data

Abstract Shortwave surface albedo of the Arctic sea-ice zone is one of the most important parameters in the surface energy budget over the northern hemispheric cryosphere. Although many global and regional broadband surface albedo products have been generated from satellite observations, most of them contain albedo over land-surfaces only, and the albedo over ocean and sea-ice surfaces is usually left blank or estimated based on the lambertian-assumption. In this paper, we extend the BRDF-based direct-estimation algorithm for mapping the shortwave surface albedo of the Arctic sea-ice zone with Moderate Resolution Imaging Spectroradiometer (MODIS) data. Firstly, a bidirectional reflectance distribution function (BRDF) database for sea ice was generated based on simulations of the asymptotic radiative transfer (ART) and the three components ocean water albedo (TCOWA) model. Secondly, a linear regression relationship between top of atmosphere (TOA) reflectance and surface broadband albedo was developed for different angular bins. Finally, the direct-estimation coefficients derived by the angular bin regression were stored in a pre-calculated look up table (LUT). When the MODIS L1B swath data are available, the corresponding coefficients are searched based on the solar/view geometry. Therefore, the shortwave surface albedo can be estimated from single-angular observations. Compared with the in situ measurements from the Tara polar ocean expedition in 2007, the results indicate that the albedo estimated by the BRDF-based direct-estimation algorithm has an R2 value of 0.67 and root mean square error (RMSE) of 0.068. The validation results show that the BRDF-based direct-estimation algorithm is suitable for mapping shortwave surface albedo of the Arctic sea-ice zone, which corrects the reflectance anisotropic effect with prior physical BRDF database and makes it possible to generate long-term Arctic surface albedo products with higher spatial and temporal resolutions.

A Study of Land Surface Albedo Conversion Formulas Using Three-Dimensional Canopy Radiative Transfer Modeling

Land surface albedo defines the fraction of short-wave radiation reflected by the Earth's surface and controls the surface energy balance; thus, it is important for environmental and climate scientific communities. Remote sensing is the only means to globally map land surface albedo, however for it to be of use to the aforementioned communities, it must be accurate with respect to Global Climate Observing System (GCOS) requirements. Sources of error are introduced in each step of the provision of land surface albedo products, whereby this letter intends to investigate sources of error introduced by the narrow-band-to-broad-band conversion formula step. The radiative transfer modeling of vegetation is used to simulate spectral albedo over complex 3-D vegetation canopies; then narrow-band-to-broad-band conversion formulas for numerous sensors are applied on the spectral albedo to compute the broad-band albedo (BBA), and the accuracy of formulas is investigated. Results indicate that the effectiveness of conversion formulas is determined by the sensor, depending on the placement and number of the sensor wavebands, the ecosystem complexity, and the broad-band range of the BBA.

Albedo climatology for European land surfaces retrieved from AVHRR data (1990–2014) and its spatial and temporal analysis from green‐up to vegetation senescence

AbstractSatellite‐based, long‐term records of surface albedo characterization that accurately capture spatial and temporal patterns are essential to develop climate models and to monitor the impact of land use changes on the terrestrial energy and water balance. This study presents the first Bidirectional Reflectance Distribution Function (BRDF) and albedo data set derived from the Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage reflectance data acquired on board National Oceanic and Atmospheric Administration and Meteorological Operational platforms from 1990 to 2014 over Europe. The objectives of this paper are to describe the data set's surface albedo climatology and anomalies in the visible, near‐infrared, and shortwave broadbands for the growing season months of May to September in order to facilitate utilization of the data by the climate modeling communities. The results demonstrate that the AVHRR BRDF and albedo data have temporal and spatial patterns that are appropriate for the underlying predominant land cover type and accurately reflect the associated climate variation. Visible and near‐infrared broadband albedo anomalies are found to be contrasting in most years, and their spatial distributions depict responses of vegetation to climate events (e.g., heat waves). Visible albedo of crops and near‐infrared albedo of pastures show a higher interannual variation than respective albedos of other snow‐free land covers, while the interannual standard deviations are found to be lower than 0.015. Our findings indicate the importance of taking into account the spectrally distinct variability of surface albedo when analyzing its complex spatiotemporal dynamics in climate‐related research.

The spectral albedo of sea ice and salt crusts on the tropical ocean of Snowball Earth: II. Optical modeling

AbstractDuring the Snowball Earth events of the Neoproterozoic, tropical regions of the ocean could have developed a precipitated salt lag deposit left behind by sublimating sea ice. The major salt would have been hydrohalite, NaCl•2H2O. The crystals in such a deposit can be small and highly scattering, resulting in an allwave albedo similar to that of snow. The snow‐free sea ice from which such a crust could develop has a lower albedo, around 0.5, so the development of a crust would substantially increase the albedo of tropical regions on Snowball Earth. Hydrohalite crystals are much less absorptive than ice in the near‐infrared part of the solar spectrum, so their presence at the surface would increase the overall albedo as well as altering its spectral distribution. In this paper, we use laboratory measurements of the spectral albedo of a hydrohalite lag deposit, in combination with a radiative transfer model, to infer the inherent optical properties of hydrohalite as functions of wavelength. Using this result, we model mixtures of hydrohalite and ice representing both artificially created surfaces in the laboratory and surfaces relevant to Snowball Earth. The model is tested against sequences of laboratory measurements taken during the formation and the dissolution of a lag deposit of hydrohalite. We present a parameterization for the broadband albedo of cold, sublimating sea ice as it forms and evolves a hydrohalite crust, for use in climate models of Snowball Earth.

Sea ice thickness estimation in the Bohai Sea using geostationary ocean color imager data

A method to estimate the thickness of the sea ice of the Bohai Sea is proposed using geostationary ocean color imager (GOCI) data and then applied to the dynamic monitoring of the sea ice thickness in the Bohai Sea during the winter of 2014 to 2015. First of all, a model is given between the GOCI shortwave broadband albedo and the reflectance of each band with high temporal resolution GOCI data. Then, the relationship model between the sea ice thickness and the GOCI shortwave broadband albedo is established and applied to the thickness extraction of the sea ice in the Bohai Sea. Finally, the sea ice thickness extraction method is tested by the results based on the MODIS data, thermodynamic empirical models (Lebedev and Zubov), and the in situ ice thickness data. The test results not only indicated that the sea ice thickness retrieval method based on the GOCI data was a good correlation (r 2>0.86) with the sea ice thickness retrieved by the MODIS and thermodynamic empirical models, but also that the RMS is only 6.82 cm different from the thickness of the sea ice based on the GOCI and in situ data.

Improvements in Land-Use Classification for Estimating Daytime Surface Temperatures and Sea-Breeze Flows in Southern California

Abstract Understanding the interactions between large-scale atmospheric and oceanic circulation patterns and changes in land cover and land use (LCLU) due to urbanization is a relevant subject in many coastal climates. Recent studies by Lebassi et al. found that the average maximum air temperatures during the summer in two populated California coastal areas decreased at low-elevation areas open to marine air penetration during the period of 1970–2005. This coastal cooling was attributed to an increase in sea-breeze activity. The aims of this work are to better understand the coastal flow patterns and sea–land thermal gradient by improving the land-cover classification scheme in the region using updated airborne remote sensing data and to assess the suitability of the updated regional atmospheric modeling system for representing maritime flows in this region. This study uses high-resolution airborne data from the NASA Hyperspectral Infrared Imager (HyspIRI) mission preparatory flight campaign over Southern California and surface ground stations to compare observations against model estimations. Five new urban land classes were created using broadband albedo derived from the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) sensor and then assimilated into the Weather Research and Forecasting (WRF) Model. The updated model captures the diurnal spatial and temporal sea-breeze patterns in the region. Results show notable improvements of simulated daytime surface temperature and coastal winds using the HyspIRI-derived products in the model against the default land classification, reaffirming the importance of accounting for heterogeneity of urban surface properties.

Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product.

The surface reflectance, i.e., satellite derived top of atmosphere (TOA) reflectance corrected for the temporally, spatially and spectrally varying scattering and absorbing effects of atmospheric gases and aerosols, is needed to monitor the land surface reliably. For this reason, the surface reflectance, and not TOA reflectance, is used to generate the greater majority of global land products, for example, from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Even if atmospheric effects are minimized by sensor design, atmospheric effects are still challenging to correct. In particular, the strong impact of aerosols in the Visible and Near Infrared spectral range can be difficult to correct, because they can be highly discrete in space and time (e.g., smoke plumes) and because of the complex scattering and absorbing properties of aerosols that vary spectrally and with aerosol size, shape, chemistry and density. This paper presents the Landsat 8 Operational Land Imager (OLI) atmospheric correction algorithm that has been developed using the Second Simulation of the Satellite Signal in the Solar Spectrum Vectorial (6SV) model, refined to take advantage of the narrow OLI spectral bands (compared to Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+)), improved radiometric resolution and signal-to-noise. In addition, the algorithm uses the new OLI Coastal aerosol band (0.433-0.450μm), which is particularly helpful for retrieving aerosol properties, as it covers shorter wavelengths than the conventional Landsat, TM and ETM+ blue bands. A cloud and cloud shadow mask has also been developed using the "cirrus" band (1.360-1.390 μm) available on OLI, and the thermal infrared bands from the Thermal Infrared Sensor (TIRS) instrument. The performance of the surface reflectance product from OLI is analyzed over the Aerosol Robotic Network (AERONET) sites using accurate atmospheric correction (based on in situ measurements of the atmospheric properties), by comparison with the MODIS Bidirectional Reflectance Distribution Function (BRDF) adjusted surface reflectance product and by comparison of OLI derived broadband albedo from United States Surface Radiation Budget Network (US SURFRAD) measurements.

Estimating high-resolution top of atmosphere albedo from Moderate Resolution Imaging Spectroradiometer data

Abstract High spatial resolution top-of-atmosphere (TOA) albedo data is needed to study the radiative forcing of natural or anthropogenic events at regional scales. However, existing products are typically estimated using broadband sensors with coarse spatial resolutions. This paper presents a hybrid method to retrieve TOA albedo over land from multispectral data collected by Moderate Resolution Imaging Spectroradiometer (MODIS) at its native spatial resolution. The approach is based on extensive atmospheric radiative transfer (RT) simulations using representative surface and atmospheric conditions as inputs. The clear-sky algorithm explicitly takes surface reflectance anisotropy into account using the POLDER3/PARASOL bidirectional reflectance distribution function database as the boundary condition of RT simulations to first generate TOA spectral albedos and then convert them to broadband albedo. In the cloudy-sky method, surfaces are assumed to be Lambertian and surface spectra over the shortwave spectrum are used to directly obtain TOA broadband albedo. The TOA albedo retrieved from MODIS was compared with the Clouds and the Earth's Radiant Energy System (CERES) TOA flux products, using twelve days of global data (one day each month) in 2007. The two data sets are in good agreement, with a root mean square difference (RMSD) of 0.036 (8.6%) for all Terra instantaneous data and 0.039 (9.1%) for all Aqua instantaneous data. Further analysis revealed that larger discrepancies mainly occurred at pixels of large solar or view zenith angles. RMSD between the two data sets was reduced to ~ 0.02 when the solar zenith angles were limited to 60° and the view zenith angles were limited to 30°.

A New Method for the Estimation of Broadband Apparent Albedo Using Hyperspectral Airborne Hemispherical Directional Reflectance Factor Values

The broadband albedo values retrieved from satellite sensors are usually compared directly to ground measurements. Some authors have noted the necessity of high spatial resolution albedo estimates to fill the gap between ground measurements and satellite retrievals. In this respect, hyperspectral airborne data with high spatial resolution is a powerful tool. Here, a new operational method for the calculation of airborne broadband apparent albedo over the spectral range of 350–2500 nm is presented. This new method uses the Hemispherical Directional Reflectance Factor (HDRF) as a proxy for the narrowband albedo, assuming a Lambertian approximation. The broadband apparent albedo obtained is compared to that estimated using theapparent albedo equation devised for the Moderate Resolution Imaging Spectroradiometer (MODIS). Airborne data were collected using the Airborne Hyperspectral Scanner (AHS). Field data were acquired at three sites: a camelina field, a green grass field, and a vineyard. The HDRF can be used to approximate the narrowband albedo for all View Zenith Angle (VZA) values for flights parallel to the solar principal plane (SPP); for flights orthogonal to the SPP, discrepancies are observed when the VZA approaches −45°. Root Mean Square Error (RMSE) values in the range 0.009–0.018 were obtained using the new method, improving upon previous results over the same area (RMSEs of 0.01–0.03). The relative error in the albedo estimation using the new method is 12% for −36.2° < VZA < 40.8° in the case of flights parallel to the SPP and less than 15% for −13° < VZA < 45° and 45% for VZA = −45° for flights orthogonal to the SPP. The good performance of the new method lies in the use of the at-surface solar irradiance and the proposed integration method.

Estimating net surface shortwave radiation from Chinese geostationary meteorological satellite FengYun-2D (FY-2D) data under clear sky.

Net surface shortwave radiation (NSSR) significantly affects regional and global climate change, and is an important aspect of research on surface radiation budget balance. Many previous studies have proposed methods for estimating NSSR. This study proposes a method to calculate NSSR using FY-2D short-wave channel data. Firstly, a linear regression model is established between the top-of-atmosphere (TOA) broadband albedo (r) and the narrowband reflectivity (ρ1), based on data simulated with MODTRAN 4.2. Secondly, the relationship between surface absorption coefficient (as) and broadband albedo (r) is determined by dividing the surface type into land, sea, or snow&ice, and NSSR can then be calculated. Thirdly, sensitivity analysis is performed for errors associated with sensor noise, vertically integrated atmospheric water content, view zenith angle and solar zenith angle. Finally, validation using ground measurements is performed. Results show that the root mean square error (RMSE) between the estimated and actual r is less than 0.011 for all conditions, and the RMSEs between estimated and real NSSR are 26.60 W/m2, 9.99 W/m2, and 23.40 W/m2, using simulated data for land, sea, and snow&ice surfaces, respectively. This indicates that the proposed method can be used to adequately estimate NSSR. Additionally, we compare field measurements from TaiYuan and ChangWu ecological stations with estimates using corresponding FY-2D data acquired from January to April 2012, on cloud-free days. Results show that the RMSE between the estimated and actual NSSR is 48.56W/m2, with a mean error of -2.23W/m2. Causes of errors also include measurement accuracy and estimations of atmospheric water vertical contents. This method is only suitable for cloudless conditions.

Contrasting snow and ice albedos derived from MODIS, Landsat ETM+ and airborne data from Langjökull, Iceland

Surface albedo is a key parameter in the energy balance of glaciers and ice sheets because it controls the shortwave radiation budget, which is often the dominant term of a glacier's surface energy balance. Monitoring surface albedo is a key application of remote sensing and achieving consistency between instruments is crucial to accurate assessment of changing albedo. Here we take advantage of a high resolution (5 m) airborne multispectral dataset that was collected over Langjokull, Iceland in 2007, and compare it with near contemporaneous ETM+ and MODIS imagery. All three radiance datasets are converted to reflectance by applying commonly used atmospheric correction schemes: 6S and FLAASH. These are used to derive broadband albedos. We first assess the similarity of albedo values produced by different atmospheric correction schemes for the same instrument, then contrast results from different instruments. In this way we are able to evaluate the consistency of the available atmospheric correction algorithms and to consider the impacts of different spatial resolutions. We observe that FLAASH leads to the derivation of surface albedos greater than when 6S is used. Albedo is shown to be highly variable at small spatial scales. This leads to consistent differences associated with specific facies types between different resolution instruments, in part attributable to different surface bi-directional reflectance distribution functions. Uncertainties, however, still exist in this analysis as no correction for variable bi-directional reflectance distribution functions could be implemented for the ETM+ and airborne datasets.