Please wait a minute...
REMOTE SENSING FOR LAND & RESOURCES    2013, Vol. 25 Issue (3) : 30-37     DOI: 10.6046/gtzyyg.2013.03.06
Technology and Methodology |
Simulation analysis of vegetation TOA reflectance based on coupled leaf-canopy-atmosphere radiative transfer model
DIAN Yuanyong1,2, FANG Shenghui2
1. College of Horticulture and Forestry, Huazhong Agricultural University, Wuhan 430079, China;
2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430070, China
Download: PDF(2199 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

Coupled plant leaf spectral model PROSPECT, vegetation canopy spectral model SAIL(scattering by arbitrarily inclined leaves) and atmospheric radiative transfer model 6S(second simulation of the satellite signal in the solar spectrum)were used to simulate the top of atmospheric(TOA) reflectance of vegetation under different conditions. And then the influences on the spectrum of the leaf mesophyll structure parameters, chlorophyll content, leaf dry weight, leaf water content, plant canopy of LAI, solar zenith angle, aerosol optical thickness (AOT), adjacency effect and mix-pixel effect were analyzed. The research results show that the vegetation TOA reflectance error caused by the atmosphere is by far larger than the error caused by the biochemical parameters of plant itself. At the leaf level scale, the main factors causing reflectance change are chlorophyll content and mesophyll structure parameters, the effect of water content is very small on leaf reflectance in 400~900 nm. At the canopy level, the main factors causing spectral change are LAI and leaf angle distribution.

Keywords surface environment      dynamic change      remote sensing      monitoring      interpretation keys     
:  TP 75  
Issue Date: 03 July 2013
E-mail this article
E-mail Alert
Articles by authors
NI Wankui
Cite this article:   
SHANG Hui,NI Wankui. Simulation analysis of vegetation TOA reflectance based on coupled leaf-canopy-atmosphere radiative transfer model[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 30-37.
URL:     OR

[1] 杨曦光,范文义,于颖.基于PROSPECT+SAIL模型的森林冠层叶绿素含量反演[J].光谱学与光谱分析,2010,30(11):3022-3026. Yang X G,Fan W Y,Yu Y.Estimation of forest canopy chlorophyll content based on PROSPECT and SAIL models[J].Spectroscopy and Spectral Analysis,2010,30(11):3022-3026.

[2] Laurent V C E,Verhoef W,Clevers J G P W,et al.Estimating forest variables from top-of-atmosphere radiance satellite measurements using coupled radiative transfer models[J].Remote Sensing of Environment,2011,115(4):1043-1052.

[3] Wang Q,Li P H.Hyperspectral indices for estimating leaf biochemical properties intemperate deciduous forests:Comparison of simulated and measured reflectance data sets[J].Ecological Indicators,2011, 14(1):56-65.

[4] 杨贵军,赵春江,邢著荣,等.基于PROBA/CHRIS遥感数据和PROSAIL模型的春小麦LAI反演[J].农业工程学报,2011,27(10):88-94. Yang G J,Zhao C J,Xing Z R,et al.LAI inversion of spring wheat based on PROBA/CHRIS hyperspectral multi-angular data and PROSAIL model[J].Transactions of the Chinese Society of Agricultural Engineering,2011,27(10):88-94.

[5] Huemmrich K F.The GeoSail model:A simple addition to the SAIL model to describe discontinuous canopy reflectance[J].Remote Sensing of Environment,2001,75(3):423-431.

[6] Bacour C,Jacquemoud S,Tourbier Y,et al.Design and analysis of numerical experiments to compare four canopy reflectance models[J].Remote Sensing of Environment,2002,79(1):72-83.

[7] Jacquemoud S,Baret F.PROSPECT:A model of leaf optical properties spectra[J].Remote Sensing of Environment,1990,34(2):75-91.

[8] Feret J B,Franois C,Asner G P,et al.PROSPECT-4 and 5:Advances in the leaf optical properties model separating photosynthetic pigments[J].Remote Sensing of Environment,2008,112(6):3030-3043.

[9] Dawson T P,Curran P J,Plummer S E.LIBERTY-modeling the effects of leaf biochemical concentration on reflectance spectra[J].Remote Sensing of Environment,1998,65(1):50-60.

[10] Jacquemoud S,Baret F,Andrieu B,et al.Extraction of vegetation biophysical parameters by inversion of the PROSPECT+SAIL models on sugar beet canopy reflectance data:Application to TM and AVIRIS sensors[J].Remote Sensing of Environment,1995,52(3):163-172.

[11] Jacquemoud S,Verhoef W,Baret F,et al.PROSPECT+SAIL models:A review of use for vegetation characterization[J].Remote Sensing of Environment,2009,113(s1):S56-S66.

[12] Verhoef W,Bach H.Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models[J].Remote Sensing of Environment,2003,87(1):23-41.

[13] 吴朝阳,牛铮.基于辐射传输模型的高光谱植被指数与叶绿素浓度及叶面积指数的线性关系改进[J].植物学通报,2008,25(6):714-721. Wu C Y,Niu Z.Improvement in linearity between hyperspectral vegetation indices and chlorophyll content,leaf area index based on radiative transfer models[J].Chinese Bulletin of Botany,2008,25(6):714-721.

[14] 施润和,庄大方,牛铮,等.基于辐射传输模型的叶绿素含量定量反演[J].生态学杂志,2006,25(5):591-595. Shi R H,Zhuang D F,Niu Z,et al.Quantitative inverseion of chlorophyll content based on radiative transfer model[J].Chinese Journal of Ecology,2006,25(5):591-595.

[15] Verhoef W,Bach H.Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data[J].Remote Sensing of Environment,2007,109(2):166-182.

[16] Vermote E F,Tanre D,Deuze J L,et al.Second simulation of the satellite signal in the solar spectrum,6S:An overview[J].IEEE Transaction Geoscience and Remote Sensing,1997,35(3):675-686.

[1] LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[2] DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[3] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[4] QIN Dahui, YANG Ling, CHEN Lunchao, DUAN Yunfei, JIA Hongliang, LI Zhenpei, MA Jianqin. A study on the characteristics and model of drought in Xinjiang based on multi-source data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 151-157.
[5] YANG Wang, HE Yi, ZHANG Lifeng, WANG Wenhui, CHEN Youdong, CHEN Yi. InSAR monitoring of 3D surface deformation in Jinchuan mining area, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 177-188.
[6] LIU Mingxing, LIU Jianhong, MA Minfei, JIANG Ya, ZENG Jingchao. Monitoring of Zanthoxylum bungeanum Maxim planting using GF-2 PMS images and the random forest algorithm: A case study of Linxia, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 218-229.
[7] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[8] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[9] LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[10] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[11] LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[12] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[13] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[14] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[15] LI Mengmeng, FAN Xueting, CHEN Chao, LI Qiannan, YANG Jin. Monitoring and interpretation of land subsidence in mining areas in Xuzhou City during 2016—2018[J]. Remote Sensing for Natural Resources, 2021, 33(4): 43-54.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech