A unified model of bidirectional reflectance distribution function for the vegetation canopy

Xu, Xiru; Fan, Wenjie; Li, JuCai; Zhao, Peng; Chen, Gaoxing

doi:10.1007/s11430-016-5082-6

Cited by 14 publications

(10 citation statements)

References 22 publications

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“…Determining the complexity and diversity of the vegetation in the Heihe River Basin is a huge challenge for the accurate retrieval of LAIs. As such, we used UCB model (X. R. Xu et al, ), which is suitable for a variety of vegetation types. In this model, the reflectance of canopy can be approximated as

ρ = ρ^{1} + ρ^{m},

where ρ 1 and ρ m represent the reflectance caused by single scattering and multiple scatterings, respectively.…”

Section: Methodologiesmentioning

confidence: 99%

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Application of an LAI Inversion Algorithm Based on the Unified Model of Canopy Bidirectional Reflectance Distribution Function to the Heihe River Basin

Fan

et al. 2018

JGR Atmospheres

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The leaf area index (LAI) is one of the most important parameters of vegetation canopy structure, which can represent the growth conditions of vegetation effectively. The accuracy, availability, and timeliness of LAI data can be improved greatly, which is of great importance to vegetation‐related research. There are various types of vegetation and terrain conditions in the Heihe River Basin, the second largest inland river basin in northwest China. It is not only helpful to evaluate the accuracy of LAI retrieval algorithms for the complex land surface but also useful to understand the fragile ecological status of the Heihe River Basin. In contrast to previous LAI inversion models, the bidirectional reflectance distribution function unified model can be applied for both continuous and discrete vegetation, and it is appropriate for analyzing heterogeneous vegetation distributions. In this work, we produced 30‐m LAI products once a month in the growing season of 2012. Results show that the algorithm can effectively retrieve LAIs. We verified the LAI product using field measurement data. The mean absolute errors in forest, farmland, and sparse grassland are 0.44, 0.56, and 0.38 respectively, and the R2 is 0.8736. Further analysis shows that main errors come from three parts: errors in the parameters, mistakes in the vegetation classification, and interval of the look‐up table. Mixed pixel is also a problem for this model. Despite this, high resolution and applicability means this algorithm can be a good approach for LAI retrieval.

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ρ = ρ^{1} + ρ^{m},

where ρ 1 and ρ m represent the reflectance caused by single scattering and multiple scatterings, respectively.…”

Section: Methodologiesmentioning

confidence: 99%

“…The calculation of l c depends on the relationship of θ i and θ v ; the detailed process refers to the paper by Xu et al (2017).…”

mentioning

confidence: 99%

Application of an LAI Inversion Algorithm Based on the Unified Model of Canopy Bidirectional Reflectance Distribution Function to the Heihe River Basin

Fan

et al. 2018

JGR Atmospheres

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“…Further research is required to refine this analysis. The analytical directional clumping index models would be applied in the unified model of bidirectional reflectance distribution function for the vegetation canopy [40]. With the Unified BRDF model, the leaf area index can be analytically estimated.…”

Section: Discussionmentioning

confidence: 99%

“…Peng et al [38] rectified the method and applied the directional clumping index in retrieving crop albedo. The average gap fraction of forest was estimated according to the interaction between light regime and the canopy, such as the widely used Geometric-Optical (GO) model that was proposed by Li et al [39], Nilson [7], and Xu [40]. In the GO model, forest gap fraction is the complement of the projected area fraction on the ground.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Directional Clumping Index of Crop and Forest

et al. 2018

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The Clumping Index (Ω) was introduced to quantify the spatial distribution pattern of vegetation elements. It is crucial to improve the estimation accuracy of vital vegetation parameters, such as Leaf Area Index (LAI) and Gross Primary Production (GPP). Meanwhile, the parameterization of Ω is challenging partly due to the varying observations of canopy gaps from different view angles. Many previous studies have shown the increase of Ω with view zenith angle through samples of gap size distribution from in situ measurements. In contrast, remote sensing retrieval algorithms only assign a constant value for each biome type to roughly correct the clumping effect as a compromise between the accuracy and efficiency. In this paper, analytical models are proposed that estimate the directional clumping index (Ω(θ)) of crop and forest at canopy level. The angular variation trend and magnitude of crop Ω(θ) was analyzed within row structure where vegetation elements are randomly spaced along rows. The forest model predicts Ω(θ) with tree density, distribution pattern, crown shape, trunk size, and leaf area and angle distribution function. The models take into account the main directional characteristics of clumping index using easy-to-measure parameters. Test cases showed that Ω(θ) magnitude variation for black spruce forest was 102.3% of the hemispherical average clumping index ( Ω ˜ ), whereas the Larch forest had 48.7% variation, and row crop variation reached 32.4%. This study provided tools to assess Ω(θ) of discontinuous canopies.

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“…where S veg (mm) is vegetation storage capacity, including canopy and stem storage capacity, which is assumed linearly related with vegetation area; S V (mm) is the specific vegetation storage, defined as the depth of water retained by vegetation per unit vegetation area. The values of S V is set to 0.25, 0.15, 0.55, 0.06, and 0.06 for the needleleaf forest, broadleaf forest, shrub, grass and crop, respectively [9]; VAI (m 2 m −2 ) is calculated using a hybrid model of canopy reflectance propose Xu, et al [15]. All the rainfall intercepted by vegetation is assumed to be evaporated into atmosphere, and there are several evaporation components in the whole process that are described by Equation 1: (1) the FVC•P G part is for small storms insufficient to saturate the vegetation; (2) the FVC•P G part is the interception from rainfall beginning to the vegetation saturated, which could be separated into evaporation during vegetation wetting up (including leaves and trunks), and vegetation storage after saturation, which will evaporate after rainfall ceases; and (3) the FVC•E V /R P G − P G part is the evaporation from saturation until rainfall cessation (including from leaves and trunks).…”

Section: Rs-gash Analytical Modelmentioning

confidence: 99%

Developing the Remote Sensing-Gash Analytical Model for Estimating Vegetation Rainfall Interception at Very High Resolution: A Case Study in the Heihe River Basin

et al. 2017

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Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R 2 of the interception ratio are 3.7% and 0.9, respectively, indicating the model's strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study.

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A unified model of bidirectional reflectance distribution function for the vegetation canopy

Cited by 14 publications

References 22 publications

Application of an LAI Inversion Algorithm Based on the Unified Model of Canopy Bidirectional Reflectance Distribution Function to the Heihe River Basin

Application of an LAI Inversion Algorithm Based on the Unified Model of Canopy Bidirectional Reflectance Distribution Function to the Heihe River Basin

Modeling the Directional Clumping Index of Crop and Forest

Developing the Remote Sensing-Gash Analytical Model for Estimating Vegetation Rainfall Interception at Very High Resolution: A Case Study in the Heihe River Basin

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