Temporal Constraints on Linear BRDF Model Parameters

Quaife, Tristan; Lewis, P.

doi:10.1109/tgrs.2009.2038901

Cited by 40 publications

(28 citation statements)

References 23 publications

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“…These so-called "regularisation" methods [47][48][49][50][51][52][53][54] assume temporal and/or spatial correlation as part of the prior distribution, resulting in a much reduced uncertainty [16,21]. In a similar vein, there are DA methods that exploit predictions of the land surface state from a dynamic vegetation model (typically a function of LAI, FAPAR) [55].…”

Section: The Eo-ldas Approachmentioning

confidence: 99%

“…A main disadvantage in the dynamic model approach is the lack of suitable models of the temporal and/or spatial evolution of many of the variables that have a direct control on the observations (e.g., equivalent leaf water or leaf chlorophyll content). Regularisation in this sense is the application of a zero-order model on the evolution of the land surface parameters [16,21,47].…”

Section: The Eo-ldas Approachmentioning

confidence: 99%

“…In order to infer the values of the Price functions, we have taken MODIS observations daily surface reflectance observations between 2009 and 2015 and selected observations between Days of Year 1 and 60, where NDVI was less than 0.25, assuming that these two conditions would result in observed bare soil. We fitted regularised linear kernel models [47] to calculate nadir illumination, nadir viewing reflectances and fitted the first two Price spectral basis functions over the seven MODIS bands using a standard least squares approach. The resulting values (see Table 2) were then used as a description of the soil throughout.…”

Section: Fit To Observationsmentioning

confidence: 99%

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Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

et al. 2017

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Abstract:The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer(MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete Radiative Transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model and resulting in an inference of land surface parameters, such as effective Leaf Area Index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with eight years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three other widely-used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, the MISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements (r 2 > 0.8), as well as the lowest average RMSE (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases (r 2 > 0.85); the RMSE is lower by 4-5%; and the bias is 2% and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground-based LAI (average r 2 = 0.76), but an underestimate of LAI for optically-thick canopies due to saturation (average RMSE = 2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument.

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Section: The Eo-ldas Approachmentioning

confidence: 99%

Section: The Eo-ldas Approachmentioning

confidence: 99%

Section: Fit To Observationsmentioning

confidence: 99%

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Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

et al. 2017

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“…The MCD43A1 Collection 5 parameters showed significant high-frequency variability, beyond what would be expected within any 16-day period [29]. After calibration, MCD43A1 Collection 6 parameters were more continuous than either the uncalibrated time series or the Collection 5 MCD43A1 ( Figure 5).…”

Section: Analysis Of Time-serial Mcd43a1mentioning

confidence: 86%

Assessment of MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) for Directional Reflectance Retrieval

et al. 2017

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Measurements of solar radiation reflected from Earth's surface are the basis for calculating albedo, vegetation indices, and other terrestrial attributes. However, the "bi-directional" geometry of illumination and viewing (i.e., the Bi-directional Reflectance Distribution Function (BRDF)) impacts reflectance and all variables derived or estimated based on these data. The recently released MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) dataset enables retrieval of directional reflectance at arbitrary solar and viewing angles, potentially increasing precision and comparability of data collected under different illumination and observation geometries. We quantified the ability of MCD43A1 Collection 6 for retrieving directional reflectance and compared the daily Collection 6 retrievals to those of MCD43A1 Collection 5, which are retrieved on an eight-day basis. Correcting MODIS-based estimates of surface reflectance from the illumination and viewing geometry of the Terra satellite (MOD09GA) to that of the MODIS Aqua (MYD09GA) overpass, as well as MCD43A4 Collection 6 and Landsat-5 TM images show that the BRDF correction of MCD43A1 Collection 6 results in greater consistency among datasets, with higher R 2 (0.63-0.955), regression slopes closer to unity (0.718-0.955), lower root mean squared difference (RMSD) (0.422-3.142), and lower mean absolute error (MAE) (0.282-1.735) compared to the Collection 5 data. Smaller levels of noise (observed as high-frequency variability within the time series) in MCD43A1 Collection 6 in comparison to Collection 5 corroborates the improvement of BRDF parameters time series. These results corroborates that the daily MCD43A1 Collection 6 product represents the anisotropy of surface features and results in more precise directional reflectance derivation at any solar and viewing geometry than did the previous Collection 5.

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“…Ju et al [119] improved the input data quality with an adapted method based on the MODIS nadir BRDF adjusted reflectance (NBAR) data, and Samain et al [120] fill the gaps in the time-series of BRDF coefficients with a Kalman filter. In the GlobAlbedo product, a regularization method was used to generate daily kernel coefficients [121]. In contrast, the post-processing strategy fills the gaps of the albedo products that have been derived.…”

Section: Gap Fillingmentioning

confidence: 99%

Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products

et al. 2015

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Surface albedo is one of the key controlling geophysical parameters in the surface energy budget studies, and its temporal and spatial variation is closely related to the global climate change and regional weather system due to the albedo feedback mechanism. As an efficient tool for monitoring the surfaces of the Earth, remote sensing is widely used for deriving long-term surface broadband albedo with various geostationary and polar-orbit satellite platforms in recent decades. Moreover, the algorithms for estimating surface broadband albedo from satellite observations, including narrow-to-broadband conversions, bidirectional reflectance distribution function (BRDF) angular modeling, direct-estimation algorithm and the algorithms for estimating albedo from geostationary satellite data, are developed and improved. In this paper, we present a comprehensive literature review on algorithms and products for mapping surface broadband albedo with satellite observations and provide a discussion of different algorithms and products in a historical perspective based on citation analysis of the published literature. This paper shows that the observation technologies and accuracy requirement of applications are important, and long-term, global OPEN ACCESSRemote Sens. 2015, 7 991 fully-covered (including land, ocean, and sea-ice surfaces), gap-free, surface broadband albedo products with higher spatial and temporal resolution are required for climate change, surface energy budget, and hydrological studies.

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Temporal Constraints on Linear BRDF Model Parameters

Cited by 40 publications

References 23 publications

Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

Assessment of MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) for Directional Reflectance Retrieval

Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products

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