作者:Ni, ZY (Ni, Zhuoya)[ 1,2 ] ; Liu, ZG (Liu, Zhigang)[ 1 ] ; Li, ZL (Li, Zhao-Liang)[ 2,3 ] ; Nerry, F (Nerry, Francoise)[ 2 ] ; Huo, HY (Huo, Hongyuan)[ 4 ] ; Li, XW (Li, Xiaowen)[ 1 ]

INTERNATIONAL JOURNAL OF REMOTE SENSING

卷: 36  期: 19-20  页: 5239-5256  特刊: SI

DOI: 10.1080/01431161.2015.1058987

出版年: OCT 2015

摘要

This article presents a methodology to quantitatively extract the solar-induced fluorescence (SIF) using the canopy reflectance index. The sensitivity analysis was conducted with a spectral vegetation Fluorescence Model (FluorMOD), and the results demonstrate that Sun zenith angle (), fluorescence quantum efficiency (Fi), leaf inclination distribution function (LIDF), leaf temperature (T), leaf area index, and leaf chlorophyll a+b content (chl-a+b) had large effects on the fluorescence radiance at 761nm (LF,761). Based on the results of the sensitivity analysis, the input parameters , Fi, LIDF, T, and chl-a+b varied within a certain range during the generation of the simulated data. Based on the simulated data, R-740/R-630, R-685/R-850, and R-750/R-710 were thought to be the best candidates to extract the fluorescence radiation. The quantitative relationships between the fluorescence retrieved by R-740/R-630, R-685/R-850, and R-750/R-710 and LF,761 were analysed and expressed as functions of , Fi, T, and reflectance index. The correlation coefficients (r) between the fluorescence retrieved using R-685/R-850, R-740/R-630, and R-750/R-710 and LF,761 are 0.94, 0.95, and 0.95, respectively, and the root mean square errors (RMSEs) were 0.32, 0.29, and 0.30Wm(-2)m(-1)sr(-1), respectively. Through comparison with FLD and 3FLD, the method presented in this article yielded better results, and could be used to estimate the fluorescence. This methodology provides new insights into the quantitative retrieval of SIF from the reflectance spectrum.

通讯作者地址: Liu, ZG (通讯作者)

Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing Key Lab Environm Remote Sensing & Digital, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing Key Lab Environm Remote Sensing & Digital, Beijing 100875, Peoples R China

[ 2 ] Univ Strasbourg, CNRS, ICube, Illkirch Graffenstaden, France

[ 3 ] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agri Informat, Minist Agr, Beijing 100193, Peoples R China

[ 4 ] Tianjin Normal Univ, Coll Urban & Environm Sci, Tianjin, Peoples R China