Research on canopy reflectance modeling of Phyllostachys pubescens forest based on the PROSAIL canopy radiative transfer model
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摘要: 冠层光谱反射率直接关系到毛竹（Phyllostachys pubescens Mazel）林冠层参数的反演，对毛竹林地土壤肥力间接估测具有重要意义。以PROSPECT5、PROSAIL模型为基础，从叶片尺度和冠层尺度分析模型参数对叶片和冠层反射率的影响，构建毛竹冠层叶面积指数（LAI）-冠层反射率查找表并通过代价函数选取最优冠层反射率，从而实现毛竹林分冠层反射率的准确模拟。结果表明，在叶片尺度，PROSPECT模型参数敏感性从高到低依次为叶肉结构参数（N） > 叶绿素含量（Cab） > 等效水厚度（EWT） > 干物质含量（Cm） > 类胡萝卜素含量（Car）；在冠层尺度，PROSAIL模型参数敏感性从高到低依次为LAI > Cab > EWT > Cm > N > Car > ALA（平均叶倾角）；叶片尺度反射率整体大于冠层尺度反射率；在400~900 nm波长范围内，PROSAIL模型模拟的冠层光谱反射率与实测光谱反射率拟合效果较好，相对误差为6.71%。Abstract: Canopy spectral reflectance is directly related to inversion of Phyllostachys pubescens Mazel canopy parameters, which is of great significance in the indirect estimation of P. pubescens forest soil fertility. The effects of model parameters on leaf and canopy reflectance at the leaf and canopy scales were analyzed based on PROSPECT and PROSAIL models. PROSAIL canopy radiative transfer was used to establish the leaf area index (LAI) and a canopy reflectance lookup table for P. pubescens forest, with optimum canopy reflectance then determined by the cost function to achieve accurate simulation of P. pubescens forest canopy reflectance. Results showed that at the leaf scale, the sensitivity of the input parameters in the PROSPECT model decreased in the order of leaf structure parameters (N) > chlorophyll content (Cab) > equivalent water thickness (EWT) > dry matter content (Cm) > carotenoid content (Car). At the canopy scale, the sensitivity of the input parameters in the PROSAIL model decreased in the order of LAI > Cab > EWT > Cm > N > Car > ALA (average leaf angle). Leaf reflectance was greater than canopy reflectance. In the 400 to 900 wavelength range, canopy spectral reflectance of the PROSAIL model exhibited better fitting than the measured reflectance, with a relative error of 6.71%.
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Keywords:
- Phyllostachys pubescens /
- PROSAIL model /
- Spectral scale effect /
- Canopy reflectance
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[1] 肖珍珍, 李毅, 冯浩. 西北盐碱土理化性质的高光谱建模及预测[J]. 光谱学与光谱分析, 2016, 36(5):1615-1622. Xiao ZZ, Li Y, Feng H. Hyperspectral models and forcas-ting of physico-chemical properties for salinized soils in northwest China[J]. Spectroscopy & Spectral Analysis, 2016, 36(5):1615-1622.
[2] 王磊, 白由路, 卢艳丽, 王贺. 光谱数据变换对玉米氮素含量反演精度的影响[J]. 遥感技术与应用, 2011, 26(2):220-225. Wang L, Bai YL, Lu YL, Wang H. Effect on retrieval precision for corn N content by spectrum data transformation[J]. Remote Sensing Technology & Application, 2011, 26(2):220-225.
[3] 黄彦, 田庆久, 耿君, 王磊,栾海军. 遥感反演植被理化参数的光谱和空间尺度效应[J]. 生态学报, 2016, 36(3):883-891. Huang Y, Tian QJ, Geng J, Wang L, Luan HJ. Review of spectral and spatial scale effects of remotely sensed biophysical and biochemical vegetation parameters[J]. Acta Ecologica Sinica, 2016, 36(3):883-891.
[4] 肖艳芳. 植被理化参数反演的尺度效应与敏感性分析[D]. 北京:首都师范大学, 2013. [5] 刘良云. 植被定量遥感反演与应用[M]. 北京:科学出版社, 2014. [6] 施润和, 庄大方, 牛铮, 王汶. 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 RH, Zhuang DF, Niu Z, Wang W. Quantitative inversion of chlorophyll content based on radiative transfer model[J]. Chinese Journal of Ecology, 2006, 25(5):591-595.
[7] 程晓娟, 杨贵军, 徐新刚, 陈天恩, 李振海, 冯海宽, 王冬. 新植被水分指数的冬小麦冠层水分遥感估算[J]. 光谱学与光谱分析, 2014, 34(12):3391-3396. Cheng XJ, Yang GJ, Xu XG, Chen TE,Li ZH, Feng HK, Wang D. Estimating canopy water content in wheat based on new vegetation water index[J]. Spectroscopy & Spectral Analysis, 2014, 34(12):3391-3396.
[8] 杨曦光. 高光谱数据提取森林冠层叶绿素及氮含量的研究[D]. 哈尔滨:东北林业大学, 2010. [9] Nouri H, Beecham S, Anderson S, Hassanli AM, Kazemi F. Remote sensing techniques for predicting evapo transpiration from mixed vegetated surfaces[J]. Urban Water J, 2015, 12(5):380-393.
[10] 夏天, 吴文斌, 周清波, 周勇. 冬小麦叶面积指数高光谱遥感反演方法对比[J]. 农业工程学报, 2013, 29(3):139-147. Xia T, Wu WB, Zhou QB, Zhou Y. Comparison of two inversion methods for winter wheat leaf area index based on hyperspectral remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(3):139-147.
[11] 叶舒, 范文义, 孟庆岩. 基于高分一号数据的PROSAIL模型叶面积指数反演[J]. 森林工程, 2016, 32(4):18-21. Ye S, Fan WY, Meng QY. Leaf area index retrieval of GF-1 using PROSAIL[J]. Forest Engineering, 2016, 32(4):18-21.
[12] 陈艳华, 张万昌, 雍斌. 基于TM的辐射传输模型反演叶面积指数可行性研究[J]. 国土资源遥感, 2007,19(2):44-49. Chen YH, Zhang WC, Yong B. A feasibility study of leaf area index inversion using radiative transfer model based on TM data[J]. Remote Sensing for Land & Resources, 2007,19(2):44-49.
[13] 刘轲, 周清波, 吴文斌,陈仲新, 唐华俊. 基于多光谱与高光谱遥感数据的冬小麦叶面积指数反演比较[J]. 农业工程学报, 2016, 32(3):155-162. Liu K, Zhou QB, Wu WB, Chen ZX, Tang HJ. Comparison between multispectral and hyperspectral remote sensing for LAI estimation[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(3):155-162.
[14] 梁亮, 杨敏华, 张连蓬, 林卉, 周兴东. 基于SVR算法的小麦冠层叶绿素含量高光谱反演[J]. 农业工程学报, 2012, 28(20):162-171. Liang L, Yang MH, Zhang LP, Lin H, Zhou XD. Chlorophyll content inversion with hyperspectral technology for wheat canopy based on support vector regression algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(20):162-171.
[15] 方圣辉, 乐源, 梁琦. 基于连续小波分析的混合植被叶绿素反演[J]. 武汉大学学报:信息科学版, 2015, 40(3):296-302. Fang SH, Le Y, Liang Q. Retrieval of chlorophyll content using continuous wavelet analysis across a range of vegetation species[J]. Geomatics and Information Science of Wuhan University, 2015, 40(3):296-302.
[16] 陈瀚阅, 黄文江, 牛铮, 高帅. 基于几何光学模型的人工林叶面积指数遥感反演[J]. 地球信息科学学报, 2012, 14(3):358-365. Chen HY, Huang WJ, Niu Z, Gao S. Estimation of forest leaf area index from remote sensing data using the algorithm based on geometric-optical model[J]. Journal of Geo-Information Science, 2012, 14(3):358-365.
[17] 李合生. 植物生理生化实验原理和技术[M]. 北京:高等教育出版社, 2000:134-135. [18] 高金龙, 侯尧宸, 白彦福,孟宝平, 杨淑霞, 胡远宁, 冯琦胜, 崔霞, 梁天刚. 基于高光谱数据的高寒草甸氮磷钾含量估测方法研究——以青海省贵南县及玛沁县高寒草甸为例[J]. 草业学报, 2016, 25(3):9-21. Gao JL, Hou YC, Bai YF, Meng BP, Yang SX, Hu YN, Feng QS, Cui X, Huo TG. Methods for estimating nitrogen, phosphorus and potassium content based on hyperspectral data from alpine meadows in Guinan and Maqin counties, Qinghai province[J]. Acta Prataculturae Sinica, 2016, 25(3):9-21.
[19] Jacquemoud S, Baret F. PROSPECT:A model of leaf optical properties spectra[J]. Remote Sens Environ, 1990, 34(2):75-91.
[20] Kuusk A. The Hot Spot Effect in Plant Canopy Reflectance[M].Berlin:Springer, 1991, 107(6):139-159.
[21] Jacquemoud S, Verhoef W, Baret F, Bacour C,Zarco-Tejada PJ. PROSPECT + SAIL models:A review of use for vegetation characterization[J]. Remote Sens Environ, 2009, 113(2009):S56-66.
[22] Bacour C, Jacquemoud S, Leroy M, Hautecceur O, Weiss M, et al. Reliability of the estimation of vegetation characteristics by inversion of three canopy reflectance models on airborne POLDER data[J]. Agronomie, 2002, 22(6):555-566.
[23] Crosetto M, Tarantola S. Uncertainty and sensitivity analysis:tools for GIS-based model implementation[J]. Int J Geogr Inf Sci, 2001, 15(5):415-437.
[24] 官凤英, 邓旺华, 范少辉. 毛竹林光谱特征及其与典型植被光谱差异分析[J]. 北京林业大学学报, 2012, 34(3):31-35. Guan FY, Deng WH, Fan SH. Spectral characteristics of Phyllostachys pubescens stand and its differential analysis with typical vegetation[J]. Journal of Beijing Forestry University, 2012, 34(3):31-35.
[25] 赵英时. 遥感应用分析原理与方法[M]. 北京:科学出版社, 2003. [26] 谷成燕, 杜华强, 周国模,韩凝, 徐小军, 赵晓, 孙晓艳. 基于PROSAIL辐射传输模型的毛竹林叶面积指数遥感反演[J]. 应用生态学报, 2013, 24(8):2248-2256. Gu CY, Du HQ, Zhou GM, Han N, Xu XJ, Zhao X, Sun XY. Retrieval of leaf area index of Moso bamboo forest with Landsat Thematic Mapper image based on PROSAIL canopy radiative transfer model[J]. Chinese Journal of Applied Ecology, 2013, 24(8):2248-2256.
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