Development of an operational procedure to estimate surface albedo from the SEVIRI/MSG observing system by using POLDER BRDF measurements

Pokrovsky, Igor; Pokrovsky, O. M.; Roujean, Jean‐Louis

doi:10.1016/s0034-4257(03)00166-4

Cited by 22 publications

(18 citation statements)

References 21 publications

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“…The model parameters f iso , f vol , and f geo , can be adjusted by the number of samples observed during a certain period using multiple linear regression. Several kinds of kernel have been proposed (Pokrovsky et al, 2003). We used two types of volume kernel and three types of geometric kernel (Pokrovsky et al, 2002, Wanner et al, 1995.…”

Section: Kernel-driven Brdf Modelmentioning

confidence: 99%

“…The bidirectional reflectance distribution function (BRDF) model is a mathematical expression of bidirectional reflectance that computes the reflectance observed at certain sun-target-sensor geometries. BRDF is a crucial factor in the accurate derivation of land surface albedo, since it is derived by directional integration of the reflectance (Lucht et al, 2000, Pokrovsky et al, 2002, Pokrovsky et al, 2003. Gao et al (2003) investigated the relations of vegetation structure, which is derived from BRDF model parameters, and land surface type.…”

Section: Introductionmentioning

confidence: 99%

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Bidirectional Reflectance Modeling of the Geostationary Sensor Himawari-8/Ahi Using a Kernel-Driven BRDF Model

Matsuoka¹,

Takagi²,

Akatsuka³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/1 KEY WORDS: Himawari-8/AHI, Bidirectional Reflectance, Kernel-driven BRDF Model, Geostationary Satellite, Intraday Change ABSTRACT:Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.

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Section: Kernel-driven Brdf Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bidirectional Reflectance Modeling of the Geostationary Sensor Himawari-8/Ahi Using a Kernel-Driven BRDF Model

Matsuoka¹,

Takagi²,

Akatsuka³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Fitting parameters of Eqs. (15) and (16) Function The RossThick-LiSparse-R model has been widely validated with its modeling ability [19,20,21,22], and has been used in the MODIS at-launch BRDF/Albedo algorithm (MODIS BRDF/Albedo Product: ATBD V5).…”

Section: Directional Emissivity and Brdf Modelsmentioning

confidence: 99%

“…This illustrates that the BRDF sampling is a warping of the perpendicular plane towards the backscattering area, away from the tropical belt. The lack of the measurements in the principal plane, where the angular effects are most obvious, will lead to biased estimation of BRDF [21].…”

Section: Study Area Related Data and Data Processingmentioning

confidence: 99%

Intercomparison of Two BRDF Models in the Estimation of the Directional Emissivity in MIR Channel From MSG1-SEVIRI Data

Jiang¹,

Li²

2008

Opt. Express

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This work intercompared two Bi-directional Reflectance Distribution Function (BRDF) models, the modified Minnaert's model and the RossThick-LiSparse-R model, in the estimation of the directional emissivity in Middle Infra-Red (MIR) channel from the data acquired by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) onboard the first Meteosat Second Generation (MSG1). The bi-directional reflectances in SEVIRI channel 4 (3.9 μ m) were estimated from the combined MIR and Thermal Infra-Red (TIR) data and then were used to estimate the directional emissivity in this channel with aid of the BRDF models. The results show that: (1) Both models can relatively well describe the non-Lambertian reflective behavior of land surfaces in SEVIRI channel 4; (2) The RossThick-LiSparse-R model is better than the modified Minnaert's model in modeling the bi-directional reflectances, and the directional emissivities modeled by the modified Minnaert's model are always lower than the ones obtained by the RossThick-LiSparse-R model with averaged emissivity differences of ~0.01 and ~0.04 over the vegetated and bare areas, respectively. The use of the RossThick-LiSparse-R model in the estimation of the directional emissivity in MIR channel is recommended.

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“…The primary use of these models has been in normalising remote sensing data to common view-illumination geometries and providing estimates of hemispherical integral quantities such as albedo [5]. More recently, the ability of such models to explain variability in the observed reflectance signal have enabled the development of advanced algorithms for detecting change in the land surface, such as burned area mapping [6], and tracking the evolution surface albedo [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Temporal Constraints on Linear BRDF Model Parameters

Quaife

Lewis

2010

IEEE Trans. Geosci. Remote Sensing

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Linear models of BRDF are useful tools for understanding the angular variability of surface reflectance as observed by medium resolution sensors such as MODIS. These models are operationally used to normalise data to common view and illumination geometries and to calculate integral quantities such as albedo. Currently, to compensate for noise in observed reflectance these models are inverted against data collected during some temporal window for which the model parameters are assumed to be constant. Despite this the retrieved parameters are often noisy for regions where sufficient observations are not available. This paper demonstrates the use of Lagrangian multipliers to allow arbitrarily large windows and at the same time produce individual parameter sets for each day even for regions where only sparse observations are available.

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Development of an operational procedure to estimate surface albedo from the SEVIRI/MSG observing system by using POLDER BRDF measurements

Cited by 22 publications

References 21 publications

Bidirectional Reflectance Modeling of the Geostationary Sensor Himawari-8/Ahi Using a Kernel-Driven BRDF Model

Bidirectional Reflectance Modeling of the Geostationary Sensor Himawari-8/Ahi Using a Kernel-Driven BRDF Model

Intercomparison of Two BRDF Models in the Estimation of the Directional Emissivity in MIR Channel From MSG1-SEVIRI Data

Temporal Constraints on Linear BRDF Model Parameters

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