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Modelling of fraction of absorbed photosynthetically active radiation in vegetation canopy and its validation

文献类型： 外文期刊

作者： Liu, Rongyuan 1 ; Ren, Huazhong 1 ; Liu, Suhong 1 ; Liu, Qiang 3 ; Li, Xiaowen 1 ;

作者机构： 1.Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China

4.Univ Strasbourg, CNRS, ICube Lab, F-67412 Illkirch Graffenstaden, France

关键词： FPAR;Radiative transfer model;Vertical profile;MSHAW

期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：4.123； 五年影响因子：4.508 ）

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收录情况： SCI

摘要： The Fraction of Absorbed Photosynthetically Active Radiation (FPAR) has been identified as one of the key parameters in calculating ecosystem productivity. The objective of this paper is to model the vertical profile of FPAR in the canopy using a radiative transfer model, the Modified Simultaneous Heat and Water (MSHAW) radiation model. Model analysis indicated that the vertical distribution of the canopy FPAR was dependent on the leaf area index (LAI), average leaf orientation angle (ALA), solar position, and sky conditions. In the validation of the MSHAW model with three varieties of wheat leaf profile at different growth stages, two parabolic functions were developed to approximately reconstruct the shape of the wheat leaf for the first time and, consequently, the vertical profiles of LAI and ALA used to drive the MSHAW model were estimated. The validation results indicated that the estimated FPAR was close to the measurements made with the SunScan canopy analysis system with an RMSE of approximately 0.15 for the continuous canopy. Finally, this paper also discusses a promising method to perform time normalisation on canopy FPAR data using multiple temporal remotely sensed data observations and to retrieve FPAR from remotely sensed data based on the analysis of the MSHAW model. (C) 2015 IAgrE. Published by Elsevier Ltd. All rights reserved.

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[2]Retrieval of LAI and leaf chlorophyll content from remote sensing data by agronomy mechanism knowledge to solve the ill-posed inverse problem. Li, Zhenhai,Nie, Chenwei,Yang, Guijun,Xu, Xingang,Jin, Xiuliang,Gu, Xiaohe. 2014

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