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

North, Peter; Briggs, Stephen; Plummer, S.; Settle, J. J.

doi:10.1109/36.739106

Cited by 116 publications

(77 citation statements)

References 45 publications

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“…(1) -calculates R ang , the surface reflectance by the multi-angle method. This model is developed and justified in North et al (1999) and a summary is provided here. This model has been applied to the (A)ATSR series of dualangle instruments, giving accurate retrieval over a range of surfaces (Bevan et al, 2012).…”

Section: Multi-angle Modelmentioning

confidence: 99%

Synergistic angular and spectral estimation of aerosol properties using CHRIS/PROBA-1 and simulated Sentinel-3 data

Davies

North

2015

Atmos. Meas. Tech.

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Abstract. We develop a method to derive aerosol properties over land surfaces using combined spectral and angular information, such as available from ESA Sentinel-3 mission, to be launched in 2015. A method of estimating aerosol optical depth (AOD) using only angular retrieval has previously been demonstrated on data from the ENVISAT and PROBA-1 satellite instruments, and is extended here to the synergistic spectral and angular sampling of Sentinel-3. The method aims to improve the estimation of AOD, and to explore the estimation of fine mode fraction (FMF) and single scattering albedo (SSA) over land surfaces by inversion of a coupled surface/atmosphere radiative transfer model. The surface model includes a general physical model of angular and spectral surface reflectance. An iterative process is used to determine the optimum value of the aerosol properties providing the best fit of the corrected reflectance values to the physical model. The method is tested using hyperspectral, multi-angle Compact High Resolution Imaging Spectrometer (CHRIS) images. The values obtained from these CHRIS observations are validated using ground-based sun photometer measurements. Results from 22 image sets using the synergistic retrieval and improved aerosol models show an RMSE of 0.06 in AOD, reduced to 0.03 over vegetated targets.

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Section: Multi-angle Modelmentioning

confidence: 99%

Synergistic angular and spectral estimation of aerosol properties using CHRIS/PROBA-1 and simulated Sentinel-3 data

Davies

North

2015

Atmos. Meas. Tech.

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“…The retrieval of aerosol properties is normally made at a coarser grid than the sensor resolution, to allow computational efficiency and to minimize registration error. For the ATSR series, the ratio of surface reflectances at the nadir and forward viewing angles is well correlated across wavebands, and the variation in anisotropy may be modeled simply (Veefkind and de Leeuw, 1998;North et al, 1999). This avoids the need for assumptions on absolute surface brightness or spectral properties.…”

Section: The Swansea University Aatsr Retrieval (Su)mentioning

confidence: 99%

“…This avoids the need for assumptions on absolute surface brightness or spectral properties. The method differs from other approaches by using a more sophisticated physically based surface model to account for spectral variation of the surface anisotropy owing to the variation of the fraction of scattered light with wavelength (North et al, 1999).…”

Section: The Swansea University Aatsr Retrieval (Su)mentioning

confidence: 99%

Aerosol retrieval experiments in the ESA Aerosol_cci project

et al. 2013

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Within the ESA Climate Change Initiative (CCI) project Aerosol cci (2010-2013), algorithms for the production of long-term total column aerosol optical depth (AOD) datasets from European Earth Observation sensors are developed. Starting with eight existing pre-cursor algorithms three analysis steps are conducted to improve and qualify the algorithms: (1) a series of experiments applied to one month of global data to understand several major sensitivities to assumptions needed due to the ill-posed nature of the underlying inversion problem, (2) a round robin exercise of "best" versions of each of these algorithms (defined using the step 1 outcome) applied to four months of global data to identify mature algorithms, and (3) a comprehensive validation exercise applied to one complete year of global data produced by the algorithms selected as mature based on the round robin exercise. The algorithms tested included four using AATSR, three using MERIS and one using PARASOL. This paper summarizes the first step. Three experiments were conducted to assess the potential impact of major assumptions in the various aerosol retrieval algorithms. In the first experiment a common set of four aerosol components was used to provide all algorithms with the same assumptions. The second experiment introduced an aerosol property climatology, derived from a combination of model and sun photometer observations, as a priori information in the retrievals on the occurrence of the common aerosol components. The third experiment assessed the impact of using a common nadir cloud mask for AATSR and MERIS algorithms in order to characterize the sensitivity to remaining cloud contamination in the retrievals against the baseline dataset versions. The impact of the algorithm changes was assessed for one month (September 2008) of data: qualitatively by inspection of monthly mean AOD maps and quantitatively by comparing daily gridded satellite data against daily averaged AERONET sun photometer observations for the different versions of each algorithm globally (land and coastal) and for three regions with different aerosol regimes. The analysis allowed for an assessment of sensitivities of all algorithms, which helped define the best algorithm versions for the subsequent round robin exercise; all algorithms (except for MERIS) showed some, in parts significant, improvement. In particular, using common aerosol components and partly also a priori aerosol-type climatology is beneficial. On the other hand the use of an AATSR-based common cloud mask meant a clear improvement (though with significant reduction of coverage) for the MERIS standard product, but not for the algorithms using AATSR. It is noted that all these observations are mostly consistent for all five analyses (global land, global coastal, three regional), which can be understood well, since the set of aerosol components defined in Sect. 3.1 was explicitly designed to cover different global aerosol regimes (with low and high absorption fine mode, sea salt and dust)

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“…Modelling studies predict that, for certain pairs of wavelengths, the ratios of the Bidirectional Reflectance Factor (BRF) between wavelengths are approximately constant, irrespective of view direction (North et al, 1999). The angular variation of the surface reflectance is due to the macroscopic structure of the underlying surface (Flowerdew and Haigh, 1995), which is of a much larger scale than the wavelength of incident light (Curier et al, 2009).…”

Section: Inverse Problemmentioning

confidence: 99%

“…This approach is used in the dual view algorithm (Veefkind et al, 1999) and the Swansea algorithm (Grey et al, 2006) for ATSR data and the MISR algorithm (Diner et al, 2005). North et al (1999) developed this approach further by considering the variation of the diffuse fraction of light with the wavelength.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of aerosol optical depth over land based on a time series technique using MSG/SEVIRI data

et al. 2012

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Abstract.A novel approach for the joint retrieval of aerosol optical depth (AOD) and aerosol type, using Meteosat Second Generation -Spinning Enhanced Visible and Infrared Imagers (MSG/SEVIRI) observations in two solar channels, is presented. The retrieval is based on a Time Series (TS) technique, which makes use of the two visible bands at 0.6 µm and 0.8 µm in three orderly scan times (15 min interval between two scans) to retrieve the AOD over land. Using the radiative transfer equation for plane-parallel atmosphere, two coupled differential equations for the upward and downward fluxes are derived. The boundary conditions for the upward and downward fluxes at the top and at the bottom of the atmosphere are used in these equations to provide an analytic solution for the AOD. To derive these fluxes, the aerosol single scattering albedo (SSA) and asymmetry factor are required to provide a solution. These are provided from a set of six pre-defined aerosol types with the SSA and asymmetry factor. We assume one aerosol type for a grid of 1 • ×1 • and the surface reflectance changes little between two subsequent observations. A k-ratio approach is used in the inversion to find the best solution of atmospheric properties and surface reflectance. The k-ratio approach assumes that the surface reflectance is little influenced by aerosol scattering at 1.6 µm and therefore the ratio of surface reflectances in the solar band for two subsequent observations can be wellapproximated by the ratio of the reflectances at 1.6 µm. A further assumption is that the surface reflectance varies only slightly over a period of 30 min. The algorithm makes use of numerical minimisation routines to obtain the optimal solution of atmospheric properties and surface reflectance by selection of the most suitable aerosol type from pre-defined sets.A detailed analysis of the retrieval results shows that it is suitable for AOD retrieval over land from SEVIRI data. Six AErosol RObotic NETwork (AERONET) sites with different surface types are used for detailed analysis and 42 other AERONET sites are used for validation. From 445 collocations representing stable and homogeneous aerosol type, we find that >75 % of the MSG-retrieved AOD at 0.6 and 0.8 µm values compare favourably with AERONET observed AOD values, within an error envelope of ± 0.05 ± 0.15τ and a high correlation coefficient (R>0.86). The AOD datasets derived Published by Copernicus Publications on behalf of the European Geosciences Union. L. Mei et al.: Retrieval of aerosol optical depth over landusing the TS method with SEVIRI data is also compared with collocated AOD products derived from NASA TERRA and AQUA MODIS (The Moderate-resolution Imaging Spectroradiometer) data using the Dark Dense Vegetation (DDV) method and the Deep Blue algorithms. Using the TS method, the AOD could be retrieved for more pixels than with the NASA Deep Blue algorithm. This method is potentially also useful for surface reflectance retrieval using SEVIRI observations. The current paper focuses on AOD retrieval ...

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

Cited by 116 publications

References 45 publications

Synergistic angular and spectral estimation of aerosol properties using CHRIS/PROBA-1 and simulated Sentinel-3 data

Synergistic angular and spectral estimation of aerosol properties using CHRIS/PROBA-1 and simulated Sentinel-3 data

Aerosol retrieval experiments in the ESA Aerosol_cci project

Retrieval of aerosol optical depth over land based on a time series technique using MSG/SEVIRI data

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