Atmospheric Correction Procedure Research Articles

Remote sensing of tropical ecosystems: Atmospheric correction and cloud masking matter

Tropical rainforests are significant contributors to the global cycles of energy, water and carbon. As a result, monitoring of the vegetation status over regions such as Amazônia has been a long standing interest of Earth scientists trying to determine the effect of climate change and anthropogenic disturbance on the tropical ecosystems and its feedback on the Earth's climate. Satellite-based remote sensing is the only practical approach for observing the vegetation dynamics of regions like the Amazon over useful spatial and temporal scales, but recent years have seen much controversy over satellite-derived vegetation states in Amazônia, with studies predicting opposite feedbacks depending on data processing technique and interpretation. Recent results suggest that some of this uncertainty could stem from a lack of quality in atmospheric correction and cloud screening. In this paper, we assess these uncertainties by comparing the current standard surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4 and MYD13A2 — Vegetation Index) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to results obtained from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MAIAC uses a new cloud screening technique, and novel aerosol retrieval and atmospheric correction procedures which are based on time-series and spatial analyses. Our results show considerable improvements of MAIAC processed surface reflectance compared to MYD09/MYD13 with noise levels reduced by a factor of up to 10. Uncertainties in the current MODIS surface reflectance product were mainly due to residual cloud and aerosol contamination which affected the Normalized Difference Vegetation Index (NDVI): During the wet season, with cloud cover ranging between 90% and 99%, conventionally processed NDVI was significantly depressed due to undetected clouds. A smaller reduction in NDVI due to increased aerosol levels was observed during the dry season, with an inverse dependence of NDVI on aerosol optical thickness (AOT). NDVI observations processed with MAIAC showed highly reproducible and stable inter-annual patterns with little or no dependence on cloud cover, and no significant dependence on AOT (p<0.05). In addition to a better detection of cloudy pixels, MAIAC obtained about 20–80% more cloud free pixels, depending on season, a considerable amount for land analysis given the very high cloud cover (75–99%) observed at any given time in the area. We conclude that a new generation of atmospheric correction algorithms, such as MAIAC, can help to dramatically improve vegetation estimates over tropical rain forest, ultimately leading to reduced uncertainties in satellite-derived vegetation products globally.

Atmospheric Correction and Vicarious Calibration of Oceansat-1 Ocean Color Monitor (OCM) Data in Coastal Case 2 Waters

The Ocean Color Monitor (OCM) provides radiance measurements in eight visible and near-infrared bands, similar to the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) but with higher spatial resolution. For small- to moderate-sized coastal lakes and estuaries, where the 1 × 1 km spatial resolution of SeaWiFS is inadequate, the OCM provides a good alternative because of its higher spatial resolution (240 × 360 m) and an exact repeat coverage of every two days. This paper describes a detailed step-by-step atmospheric correction procedure for OCM data applicable to coastal Case 2 waters. This development was necessary as accurate results could not be obtained for our Case 2 water study area in coastal Louisiana with OCM data by using existing atmospheric correction software packages. In addition, since OCM-retrieved radiances were abnormally low in the blue wavelength region, a vicarious calibration procedure was developed. The results of our combined vicarious calibration and atmospheric correction procedure for OCM data were compared with the results from the SeaWiFS Data Analysis System (SeaDAS) software package outputs for SeaWiFS and OCM data. For Case 1 waters, our results matched closely with SeaDAS results. For Case 2 waters, our results demonstrated closure with in situ radiometric measurements, while SeaDAS produced negative normalized water leaving radiance (nLw) and remote sensing reflectance (Rrs). In summary, our procedure resulted in valid nLw and Rrs values for Case 2 waters using OCM data, providing a reliable method for retrieving useful nLw and Rrs values which can be used to develop ocean color algorithms for in-water substances (e.g., pigments, suspended sediments, chromophoric dissolved organic matter, etc.) at relatively high spatial resolution in regions where other software packages and sensors such as SeaWiFS and Moderate Resolution Imaging Spectrometer (MODIS) have proven unsuccessful. The method described here can be applied to other sensors such as OCM-2 or other Case 2 water areas.

Generating global Leaf Area Index from Landsat: Algorithm formulation and demonstration

This paper summarizes the implementation of a physically based algorithm for the retrieval of vegetation green Leaf Area Index (LAI) from Landsat surface reflectance data. The algorithm is based on the canopy spectral invariants theory and provides a computationally efficient way of parameterizing the Bidirectional Reflectance Factor (BRF) as a function of spatial resolution and wavelength. LAI retrievals from the application of this algorithm to aggregated Landsat surface reflectances are consistent with those of MODIS for homogeneous sites represented by different herbaceous and forest cover types. Example results illustrating the physics and performance of the algorithm suggest three key factors that influence the LAI retrieval process: 1) the atmospheric correction procedures to estimate surface reflectances; 2) the proximity of Landsat-observed surface reflectance and corresponding reflectances as characterized by the model simulation; and 3) the quality of the input land cover type in accurately delineating pure vegetated components as opposed to mixed pixels. Accounting for these factors, a pilot implementation of the LAI retrieval algorithm was demonstrated for the state of California utilizing the Global Land Survey (GLS) 2005 Landsat data archive. In a separate exercise, the performance of the LAI algorithm over California was evaluated by using the short-wave infrared band in addition to the red and near-infrared bands. Results show that the algorithm, while ingesting the short-wave infrared band, has the ability to delineate open canopies with understory effects and may provide useful information compared to a more traditional two-band retrieval. Future research will involve implementation of this algorithm at continental scales and a validation exercise will be performed in evaluating the accuracy of the 30-m LAI products at several field sites.

Estimation of chlorophyll- a concentration in turbid productive waters using airborne hyperspectral data

Algorithms based on red and near infra-red (NIR) reflectances measured using field spectrometers have been previously shown to yield accurate estimates of chlorophyll- a concentration in turbid productive waters, irrespective of variations in the bio-optical characteristics of water. The objective of this study was to investigate the performance of NIR-red models when applied to multi-temporal airborne reflectance data acquired by the hyperspectral sensor, Airborne Imaging Spectrometer for Applications (AISA), with non-uniform atmospheric effects across the dates of data acquisition. The results demonstrated the capability of the NIR-red models to capture the spatial distribution of chlorophyll- a in surface waters without the need for atmospheric correction. However, the variable atmospheric effects did affect the accuracy of chlorophyll- a retrieval. Two atmospheric correction procedures, namely, Fast Line-of-sight Atmospheric Adjustment of Spectral Hypercubes (FLAASH) and QUick Atmospheric Correction (QUAC), were applied to AISA data and their results were compared. QUAC produced a robust atmospheric correction, which led to NIR-red algorithms that were able to accurately estimate chlorophyll- a concentration, with a root mean square error of 5.54 mg m −3 for chlorophyll- a concentrations in the range 2.27–81.17 mg m −3.

Influence of polarimetric satellite data measured in the visible region on aerosol detection and on the performance of atmospheric correction procedure over open ocean waters

An original atmospheric correction algorithm, so-called multi-directionality and POLarization-based Atmospheric Correction (POLAC), is described. This algorithm is based on the characteristics of the multidirectional and polarimetric data of the satellite PARASOL (CNES). POLAC algorithm is used to assess the influence of the polarimetric information in the visible bands on the retrieval of the aerosol properties and the water-leaving radiance over open ocean waters. This study points out that the use of the polarized signal significantly improves the aerosol type determination. The use of the polarized information at one visible wavelength only, namely 490 nm, allows providing estimates of the Angstrom exponent of aerosol optical depth with an uncertainty lower than 4%. Based on PARASOL observations, it is shown that the detection of the fine aerosols is improved when exploiting polarization data. The atmospheric component of the satellite signal is then better modeled, thus improving de facto the water-leaving radiance estimation.

Estimation of cyanobacterial pigments in a freshwater lake using OCM satellite data

Cyanobacteria represent a major harmful algal group in fresh to brackish water environments. Lac des Allemands, a freshwater lake of 49 km 2 southwest of New Orleans, Louisiana on the upper end of the Barataria Estuary, provides a natural laboratory for remote characterization of cyanobacterial blooms because of their seasonal occurrence. The Oceansat-1 satellite Ocean Colour Monitor (OCM) provides measurements similar to SeaWiFS but with higher spatial resolution, and this work is the first attempt to use OCM measurements to quantify cyanobacterial pigments. The satellite signal was first vicariously calibrated using SeaWiFS as a reference, and then corrected to remove the atmospheric effects using a customized atmospheric correction procedure. Then, empirical inversion algorithms were developed to convert the OCM remote sensing reflectance ( R rs ) at bands 4 and 5 (centered at 510.6 and 556.4 nm, respectively) to concentrations of phycocyanin (PC), the primary cyanobacterial pigment. A holistic approach was used to minimize the influence of other optically active constituents on the PC algorithm. Similarly, empirical algorithms to estimate chlorophyll a (Chl a) concentrations were developed using OCM bands 5 and 6 (centered at 556.4 and 669 nm, respectively). The best PC algorithm (R 2 = 0.7450, p < 0.0001, n = 72) yielded a root mean square error (RMSE) of 36.92 μg/L with a relative RMSE of 10.27% (PC from 2.75 to 363.50 μg/L, n = 48). The best algorithm for Chl a (R 2 = 0.7510, p < 0.0001, n = 72) produced an RMSE of 31.19 μg/L with a relative RMSE of 16.56% (Chl a from 9.46 to 212.76 μg/L, n = 48). While more field data are required to further validate the long-term performance of these algorithms, currently they represent the best protocol for establishing a long time-series of cyanobacterial blooms in the Lac des Allemands using OCM data.

Algorithm for retrieval of the effective snow grain size and pollution amount from satellite measurements

We present an algorithm for retrieval of the effective Snow Grain Size and Pollution amount (SGSP) from satellite measurements. As well as our previous version (Zege et al., 2008, 1998), the new algorithm is based on the analytical solution for snow reflectance within the asymptotic radiative transfer theory. The SGSP algorithm does not use any assumptions on snow grain shape and allows for the snow pack bidirectional reflectance distribution function (BRDF). The algorithm includes a new atmospheric correction procedure that allows for snow BRDF. This SGSP algorithm has been thoroughly validated with computer simulations. Its sensitivity to the atmosphere model has been investigated. It is shown that the inaccuracy of the snow characteristic retrieval due to the uncertainty in the aerosol and molecular atmosphere model is negligible, as compared to that due to the measurement errors at least for aerosol loads typical for polar regions. The significant advantage of the SGSP over conventional algorithms, which use a priori assumptions about particle shape and (or) not allow for the BRDF of the individual snow pack, is that the developed retrieval still works at low sun elevations, which are typical for polar regions.

Positive impulse flashover along smooth cylindrical insulating surfaces under variable humidity

Positive impulse flashover along smooth cylindrical insulating surfaces bridging a short rod-plane gap under standard lightning and switching impulse voltages is investigated under variable absolute humidity and by regarding the case of air alone as reference. Flashover is closely related to predischarge phenomena, specifically secondary corona under lightning impulses and corona streamers arrival at the earthed plane under switching impulse voltages. Under lightning impulses the dielectric strength of the gap is highest for air alone and decreases with material permittivity; the opposite is true, although less marked, under switching impulse voltages. Flashover voltage increases with absolute humidity, this being more pronounced for switching than lightning impulse voltages. The inhibitory effect of humidity on flashover, mainly determined by the trajectory of the spark channel with respect to the insulating surface, is similar to that observed in air alone where flashover occurs away from the insulating surface but reduced where the discharge develops close to the insulating surface. For insulating arrangements where at flashover the spark channel adheres to the insulating surface the IEC 60060-1:1989 atmospheric conditions correction procedure should be optimized by considering a reduced humidity correction factor.

Atmospheric correction of thermal-infrared imagery of the 3-D urban environment acquired in oblique viewing geometry

Abstract. This research quantifies and discusses atmospheric effects, which alter the radiance observed by a ground-based thermal-infrared (TIR) camera. The TIR camera is mounted on a boom at a height of 125 m above ground on top of a high-rise building in the city of Berlin, Germany (52.4556° N, 13.3200° E) and observes the Earth's surface. The study shows that atmospheric correction of TIR imagery of the three-dimensional (3-D) urban environment acquired in oblique viewing geometry has to account for spatial variability of line-of-sight (LOS) geometry. We present an atmospheric correction procedure that uses these spatially distributed LOS geometry parameters, the radiative transfer model MODTRANTM5.2 and atmospheric profile data derived from meteorological measurements in the field of view (FOV) of the TIR camera. The magnitude of atmospheric effects varies during the analysed 24-hourly period (6 August 2009) and is particularly noticeable for surfaces showing a strong surface-to-air temperature difference. The differences between uncorrected and corrected TIR imagery reach up to 6.7 K at 12:00. The use of non-spatially distributed LOS parameters leads to errors of up to 3.7 K at 12:00 and up to 0.5 K at 24:00.

Atmospheric Correction Over Coastal Turbid Waters of Bay of Bengal Using OCEANSAT-I Ocean Colour Monitor (OCM) Data

Space-borne ocean-colour remote sensor-detected radiance is heavily contaminated by solar radiation backscattered by the atmospheric air molecules and aerosols. Hence, the first step in ocean-colour data processing is the removal of this atmospheric contribution from the sensor-detected radiance to enable detection of optically active oceanic constituents e.g. chlorophyll-a, suspended sediment etc. In standard atmospheric correction procedure for OCEANSAT-1 Ocean Colour Monitor (OCM) data, NIR bands centered at 765 and 865 nm wavelengths were used for aerosol characterization. Due to high absorption by water molecules, ocean surface in these two wavelengths acts as dark background, therefore, sensor detected radiance can be assumed to have major contribution from atmospheric scattering. For coastal turbid waters this assumption of dark surface fails due to the presence of highly scattering sediments which causes sufficient water-leaving radiance in NIR bands and lead to over-estimation of aerosol radiance resulting in negative water leaving radiance for λ < 700 nm. In the present study, for the turbid coastal waters in the northern Bay of Bengal, the concept of spatial homogeneity of aerosol and water leaving reflectance has been applied to perform atmospheric correction of OCAEANSAT-1 OCM data. The results of the turbid water atmospheric correction have also been validated using in-situ measured water-leaving radiance. Comparison of satellite derived water-leaving radiance for five coastal stations with in-situ measured radiance spectra, indicates an improvement over the standard atmospheric correction algorithm giving physically realistic and positive values. Root Mean Square Error (RMSE) between the in-situ measured and satellite derived water leaving radiance for wavelengths 412 nm, 443 nm, 490 nm, 512 nm and 555 nm was found to be 1.11, 0.718, 0.575, 0.611 and 0.651%, respectively, using standard atmospheric correction procedure. By the use of spatial homogeneity concept, this error was reduced to 0.125, 0.173, 0.176, 0.225, and 0.290 and the correlation coefficient arrived at 0.945, which is an improvement over the standard atmospheric correction procedure.

Negative impulse flashover along cylindrical insulating surfaces bridging a short rod-plane gap under variable humidity

Negative impulse flashover along insulating surfaces bridging a short rod-plane gap is investigated under variable humidity. The specimens, cylindrical in shape and with a smooth surface, were made of PTFE, silicone-rubber, nylon and glazed porcelain and were bridging the gap, which was stressed by standard lightning and switching impulse voltages. Breakdown probability distributions were obtained and the breakdown voltage and time to breakdown were measured. The gap was overstressed by applying voltages higher than that causing 100% breakdown so as to study the effects of the applied peak voltage on breakdown characteristics. When breakdown occurs over an insulating surface the breakdown voltage is significantly lower compared to that obtained for air alone, especially at lower breakdown probabilities and under switching impulse voltages. It is also lower for lightning than switching impulse voltages, this being less marked with increasing material permittivity. Breakdown is closely related to negative corona growth. The effect of absolute humidity on breakdown voltage is minimal. The IEC atmospheric conditions correction procedure yields satisfactory results when breakdown occurs both in air alone and along an insulating surface.

Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS

Eutrophication and cyanobacterial algal blooms present an increasing threat to the health of freshwater ecosystems and to humans who use these resources for drinking and recreation. Remote sensing is being used increasingly as a tool for monitoring these phenomena in inland and near-coastal waters. This study uses the Medium Resolution Imaging Spectrometer (MERIS) to view Zeekoevlei, a small hypertrophic freshwater lake situated on the Cape Flats in Cape Town, South Africa, dominated by Microcystis cyanobacteria. The lake's small size, highly turbid water, and covariant water constituents present a challenging case for both algorithm development and atmospheric correction. The objectives of the study are to assess the optical properties of the lake, to evaluate various atmospheric correction procedures, and to compare the performance of empirical and semi-analytical algorithms in hypertrophic water. In situ water quality parameter and radiometric measurements were made simultaneous to MERIS overpasses. Upwelling radiance measurements at depth 0.66 m were corrected for instrument self-shading and processed to water-leaving reflectance using downwelling irradiance measurements and estimates of the vertical attenuation coefficient for upward radiance, K u, generated from a simple bio-optical model estimating the total absorption, a( λ), and backscattering coefficients, b b( λ). The normalised water-leaving reflectance was used for assessing the accuracy of image-based Dark Object Subtraction and 6S Radiative Transfer Code atmospheric correction procedures applied to MERIS. Empirical algorithms for estimating chlorophyll a (Chl a), Total Suspended Solids (TSS), Secchi Disk depth ( z SD) and absorption by CDOM ( a CDOM) were derived from simultaneously collected in situ and MERIS measurements. The empirical algorithms gave high correlation coefficient values, although they have a limited ability to separate between signals from covariant water constituents. The MERIS Neural Network algorithms utilised in the standard Level 2 Case 2 waters product and Eutrophic Lakes processor were also used to derive water constituent concentrations. However, these failed to produce reasonable comparisons with in situ measurements owing to the failure of atmospheric correction and divergence between the optical properties and ranges used to train the algorithms and those of Zeekoevlei. Maps produced using the empirical algorithms effectively show the spatial and temporal variability of the water quality parameters during April 2008. On the basis of the results it is argued that MERIS is the current optimal sensor for frequent change detection applications in inland waters. This study also demonstrates the considerable potential value for simple TOA algorithms for hypertrophic systems. It is recommended that regional algorithm development be prioritized in southern Africa and that remote sensing be integrated into future operational water quality monitoring systems.

Decoupling error for the atmospheric correction in ocean color remote sensing algorithms

This paper studies the decoupling error associated with the atmospheric correction procedures in the ocean color remote sensing algorithms. The decoupling error is caused by the lack of proper consideration of multiple scattering between the atmospheric and ocean components. In other words, the atmosphere and ocean are not coupled properly. A vector radiative transfer model for the coupled atmosphere and ocean (CAO) system based on the successive order of scattering (SOS) method is used to study the error. The inherent optical properties (IOPs) of the ocean are provided by the most updated bio-optical models. Two wavelengths are used in the study, 412 and 555 nm. For a detector located just above the ocean interface, the decoupling errors range from 0.3% to 7% at 412 nm; and from 0.3% to 3 % at 555 nm for zenith viewing angles smaller than 70°. The decoupling errors are significantly larger for larger zenith viewing angles for this detector. For a detector at the top of the atmosphere (TOA), it is hard to separate the decoupling error from the error introduced by the diffuse transmittance. If we assume the upwelling radiance is uniform just below the ocean surface when estimating the diffuse transmittance, the decoupling errors are from −4% to 8% for zenith viewing angles smaller than 70°; and negative decoupling errors show up at mainly large zenith viewing angles.

Building a consistent medium resolution satellite data set using moderate resolution imaging spectroradiometer products as reference

Medium resolution (10-100m) optical sensor data such as those from the Landsat, SPOT, ASTER, CBERS and IRS-P6 satellites provide detailed spatial information for studies of ecosystems, vegetation biophysics, and land cover. While Landsat remains a cornerstone of medium resolution remote sensing, the ETM+ scan-line corrector failure in 2003 has highlighted the need for methods to integrate radiometry from multiple international sensors in order to create a consistent, long-term observational record. Such an approach needs to compensate for differing acquisition plans, sensor bandwidths, spatial resolution, and orbit coverage. Different processing approaches used in the calibration and atmosphere correction across sensors make integration even harder. In this paper, we propose a generalized reference-based approach to convert medium resolution satellite digital number (DN) to MODIS-like surface reflectance using MODIS products as a reference data set. This approach does not require explicit calibration and atmospheric correction procedures for individual medium resolution sensors, therefore minimizing the potential impact of those procedures due to among-sensor differences. Therefore, data in MODIS era from different sources such as Landsat TM/ETM+, IRS-P6 AWiFS, and TERRA ASTER can be combined for time-series analysis, biophysical parameter retrievals, and other downstream analysis. Our results from Landsat TM/ETM+ show that this approach can produce surface reflectance with a similar accuracy to physical approaches based on radiative transfer modeling with mean absolute differences of 0.0016 and 0.0105 for red and near infra-red bands respectively. The normalized MODIS-like surface reflectances from multiple sensors and acquisition dates are consistent and comparable both spatially and temporally with known trends in phenology.

Satellite Estimation of Chlorophyll-$a$ Concentration Using the Red and NIR Bands of MERIS—The Azov Sea Case Study

We present here the results of calibrating and validating a three-band model and, its special case, a two-band model, which use MEdium Resolution Imaging Spectrometer (MERIS) reflectances in the red and near-infrared spectral regions for estimating chlorophyll-a (chl- a) concentration in inland, estuarine, and coastal turbid productive waters. During four data collection campaigns in 2008 and one campaign in 2009 in the Taganrog Bay and the Azov Sea, Russia, water samples were collected, and concentrations of chl-a and total suspended solids were measured in the laboratory. The data collected in 2008 were used for model calibration, and the data collected in 2009 were used for model validation. The models were applied to MERIS images acquired within two days from the date of in situ data collection. Two different atmospheric correction procedures were considered for processing the MERIS images. The results illustrate the high potential of the models to estimate chl-a concentration in turbid productive (Case II) waters in real time from satellite data, which will be of immense value to scientists, natural resource managers, and decision makers involved in managing the inland and coastal aquatic ecosystems.

Atmospheric Correction Procedures on ASTER Data and Application to Atmospheric Science Use

Atmospheric Correction Procedures on ASTER Data and Application to Atmospheric Science Use

Determination of aerosol optical thickness through the derivation of an atmospheric correction for short-wavelength Landsat TM and ASTER image data: an application to areas located in the vicinity of airports at UK and Cyprus

Aerosol optical thickness is considered to be the most important unknown parameter of every atmospheric correction approach for removing atmospheric effects from satellite remotely sensed images. This study presents a description of the basics of the proposed atmospheric correction procedure, which combines the darkest object subtraction principle and the radiative transfer equations. The method considers the true reflectance values of the selected dark targets acquired in situ and the atmospheric parameters such as the aerosol single scattering phase function, single scattering albedo and water vapour absorption, which are also found from ground measurements. The proposed procedure is applicable to short wavelengths such as Landsat TM band 1, 2 and ASTER band 1 in which water vapour absorption is negligible. The proposed image processing method has been tested successfully to determine the aerosol optical thickness on Landsat-5/TM images of the Lower Thames Valley area located to West London (UK) in the vicinity of Heathrow Airport and to Landsat TM/ETM+ and ASTER images of an area located in the vicinity of Paphos International Airport (Cyprus). The determined aerosol optical thicknesses for the Heathrow Airport area were 0.60, 013 and 0.75 for the Landsat TM images (0.45–0.52 µm) acquired on 17th of May 1985, 2nd of June 1985 and 4th of July 1985. The determined aerosol optical thicknesses for the ASTER (0.52–0.60 µm) images acquired on the 4th of February 2008, 26th of February 2008, 17th of December and 24th of December 2007 were 0.18, 0.39, 0.49 and 0.90, respectively. The accuracy assessment applied using the in situ spectroradiometric and sun-photometer data during the satellite overpass acquired on July–August 2008 for the Paphos area in Cyprus shows satisfactory results both for removing the atmospheric effects and for determining the aerosol optical thickness. Indeed, the high correlation between the determined aerosol optical thickness and those extracted from the visibility values increases the potential of the proposed method.

Investigating the angular and spectral properties of natural targets using CHRIS‐PROBA images of San Rossore test site

This paper addresses the use of the bidirectional reflectance distribution function (BRDF) of natural surfaces for remote sensing applications and environmental monitoring. Multiangle images, acquired by the compact high resolution imaging spectrometer (CHRIS) over the San Rossore test site, have been analysed in order to exploit the potential of using the BRDF for data mining and precise retrieval of biophysical and geophysical parameters. The apparent reflectance and the anisotropy factor (ANIF) of selected soil cover types have been computed, after correcting the at‐sensor radiance for atmospheric effects. The departure of soil reflectance from the standard Lambertian behaviour has been investigated, together with the hypothesis of BRDF spectral‐angular separability. Then, the ANIF dependence on surface optical properties, illumination and viewing geometry has been assessed and demonstrated using an ANIF image. The stability of the atmospheric correction procedure was analysed, studying the retrieved reflectance versus the solar zenith angle.

Assessment of uncertainty in the ocean reflectance determined by three satellite ocean color sensors (MERIS, SeaWiFS and MODIS‐A) at an offshore site in the Mediterranean Sea (BOUSSOLE project)

The match‐up of satellite‐derived reflectances with in situ observations is crucial to evaluate their quality and temporal stability. To contribute to this effort, a project has been set up to collect a data set of in situ radiometric and bio‐optical quantities, in support to satellite ocean color calibration and validation. The project has been named “BOUSSOLE”, and one of its key elements is a deep‐sea optics mooring collecting data on a near‐continuous basis since September 2003. This buoy is deployed in the deep clear waters of the northwestern Mediterranean Sea, and is visited on a monthly basis for servicing and acquisition of complementary data. The characteristics of the work area establish the site as a satisfactory location for validating satellite ocean color observations. A description of the data processing protocols is provided, followed by an analysis of the uncertainty of the buoy radiometry measurements. The results of a match‐up analysis of the marine reflectances, diffuse attenuation coefficients, and chlorophyll concentrations for three major missions, i.e., MERIS, SeaWiFS, and MODIS‐A, are then analyzed. They show poor performances for the bluest band (412 nm) of the three sensors, and performances within requirements at 443 and 490 nm for SeaWiFS and MODIS‐A. These results suggest that a vicarious calibration should be introduced for the MERIS sensor. This analysis also demonstrates that a major effort is still required to improve atmospheric correction procedures whatever the mission.

At-Surface Reflectance and Albedo from Satellite for Operational Calculation of Land Surface Energy Balance

This paper presents a rapid, operational method for estimating at-surface albedo applicable to Landsat and MODIS satellite sensors for typical cloud-free, low-haze conditions and sensor view angles less than 20°. At-surface albedo estimates are required input to various surface energy balance models that are applied operationally. The albedo calculation method was developed using the SMARTS2 radiative transfer model and has been applied in recent versions of the University of Idaho METRIC model as a component of the surface energy balance for determining evapotranspiration. The albedo procedure uses atmospheric correction functions developed to require only general humidity data and a digital elevation model. The atmospheric correction functions have a reduced structure to enhance their operational applicability in routine instantaneous surface energy balances and to estimate evapotranspiration. The method does not require high levels of knowledge in atmospheric physics and radiation transfer processes, common to traditional radiation transfer models, which enhances their use by a broad range of agricultural and hydrologic scientists and engineers. The atmospheric correction and surface albedo estimation procedures are developed primarily for use with Landsat imagery, which does not have an official albedo product. However, the procedure is also applicable to MODIS imagery that has an official albedo product at the 1km scale, for situations where full broadband albedo having 500m resolution is needed, where albedo is needed for select days having small sensor view angles for reduction of pixel blurring, or where image striping or reflectance data fallout has occurred in the standard MODIS albedo product. Method results have been compared to literature values and independent data sets. Test applications against MODIS albedo products in New Mexico, Florida, and Idaho indicate that the expected error for actual albedo from the developed method is within the interval of −0.035 to +0.033 (95% confidence level), equivalent to a standard error of 0.017, over broad ranges in land surface elevation, humidity, and sun angle.