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

Science China Earth Sciences 2017年第3期60卷 463-477页

作者： XU XiRu FAN WenJie LI JuCai ZHAO Peng CHEN GaoXing Institute of Remote Sensing and Geographic Information System Peking UniversityBeijing 100871China Beijing Key Laboratory of Spatial Information Integration&Its Applications Beijing 100871China

An accurate and operational bidirectional reflectance distribution function （BDRF） canopy model is the basis of quantitative vegetation remote sensing. The canopy reflectance should be approximated as the sum of the single scattering reflectance arising from the sun, pl, and the multiple scattering reflectance arising from the canopy, fin, as their directional characteristics are dramatically different. Based on the existing BRDF model, we obtain a new analytical expression of ρ1 and ρm in this paper, which is suitable for different illumination conditions and different vegetation canopies. According to the geometrical optic model at the leaf scale, the anisotropy of ρ1 can be ascribed to the geometry of the object, sun and the sensor, multiple scale clumping, and the fraction of direct solar radiation and diffuse sky radiation. Then, we parameterize the area ratios of four components： the sunlit foliage, sunlit ground, shadow foliage and shadow ground based on a Poisson distribution, and develop a new approximate analytical single scattering reflectance model. Assuming G=0.5, a recollision probability theory based scattering model is developed which considers the effects of diffuse sky radiation, scattering inside the canopy and rebounds between the canopy and soil. Validation using ground measurements of maize and black spruce forest proves the reliability of the model.

关键词： Vegetation BRDF Unified model clumping index Proportion of direct solar radiation and diffuse sky radiation Recollision probability

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A unified canopy bidirectional reflectance (BRDF) model for row ceops

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Science China Earth Sciences 2012年第5期55卷 824-836页

作者： YAN BinYan XU XiRu FAN WenJie Institute of RS and GIS Peking UniversityBeijing 100871China

Row sowing is a basic crop sowing method in China,and thus an accurate Bidirectional Reflectance Distribution Function (BRDF) model of row crops is the foundation for describing the canopy bidirectional reflectance characteristics and estimating crop ecological *** of the macroscopically geometric difference,the row crop is usually regarded as a transition between continuous and discrete vegetation in previous *** row treated as the unit for calculating the four components in the Geometric Optical model (GO model),the formula would be too complex and difficult to *** study focuses on the microscopic structure of row *** the row crop as a result of leaves clumped at canopy scale,we apply clumping index to link continuous vegetation and row ***,the formula of clumping index is deduced *** taking leaf as the basic unit,we calculate the four components of the GO model and develop a BRDF model for continuous vegetation,which is gradually extended to the unified BRDF model for row *** is of great importance to introduce clumping index into BRDF *** order to evaluate the performance of the unified BRDF model,the canopy BRDF data collected in field experiment,"Watershed Allied Telemetry Experiment Research (WATER)",from May 30th to July 1st,2008 are used as the validation dataset for the simulated *** results show that the unified model proposed in this paper is able to accurately describe the non-isotropic characteristics of canopy reflectance for row *** addition,the model is simple and easy to *** general,there is no irreconcilable conflict between continuous and discrete vegetation,so understanding their common and individual characteristics is advantageous for simulating canopy *** is proven that the four components of the GO model is the basic motivational factor for bidirectional reflectance of all vegetation types.

关键词： row crop BRDF clumping clumping index

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Evaluating the impact of sampling schemes on leaf area index measurements from digital hemispherical photography in Larix principis-rupprechtii forest plots

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Forest Ecosystems 2020年第4期7卷 686-703页

作者： Jie Zou Wei Hou Ling Chen Qianfeng Wang Peihong Zhong Yong Zuo Shezhou Luo Peng Leng The Academy of Digital China(Fujian) Fuzhou UniversityFuzhou 350116China. Key Laboratory of Data Mining and Information Sharing Ministry of EducationFuzhou 350116China. School of Forestry Beijing Forestry UniversityBeijing 100083China. College of Environment and Resources Fuzhou UniversityFuzhou 350116China. College of Resources and Environment Fujian Agriculture and Forestry UniversityFuzhou 350002China.

Background:Digital hemispherical photography(DHP)is widely used to estimate the leaf area index(LAI)of forest plots due to its advantages of high efficiency and low cost.A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling ***,various sampling schemes involving DHP have been used for the LAI estimation of forest *** date,the impact of sampling schemes on LAI estimation from DHP has not been comprehensively ***:In this study,13 commonly used sampling schemes which belong to five sampling types(***,square,cross,transect and circle)were adopted in the LAI estimation of five Larix principis-rupprechtii plots(25m×25 m).An additional sampling scheme(with a sample size of 89)was generated on the basis of all the sample points of the 13 sampling *** typical inversion models and four canopy element clumping index(Ωe)algorithms were involved in the LAI *** impacts of the sampling schemes on four variables,including gap fraction,Ωe,effective plant area index(PAIe)and LAI estimation from DHP were *** LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection ***:Large differences were observed for all four variable estimates(*** fraction,Ωe,PAIe and LAI)under different sampling *** differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models,if the fourΩe algorithms,except for the traditional gap-size analysis algorithm were adopted in the *** accuracy of LAI estimation was not always improved with an increase in sample ***,results indicated that with the appropriate inversion model,Ωe algorithm and sampling scheme,the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%,which is required by the global climate observing system,except in forest plots with extremely large LAI values(~

关键词： Sampling scheme Elementary sampling unit clumping index Leaf area index Digital hemispherical photography Forest Larix

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