Estimation Model of Poplar Equivalent Water Thickness Based on Hyperspectral Information

CHENG Zhi-qing; ZHANG Jin-song; MENG Ping; LI Yan-quan; ZHENG Ning

Volume 29 Issue 6

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Estimation Model of Poplar Equivalent Water Thickness Based on Hyperspectral Information

1.
Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation, State Forestry Administration, Beijing 100091, China
2.
Collaborative Innovation Center of Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China

Received Date: 2015-03-30

Abstract

[Objective] To establish a model for the purpose of rapid and effective monitoring of poplar leaf water. [Methods] The equivalent water thickness (EWT) of poplar leaves was used as the token water content, the hyperspectral data of poplar was measured. The range of the equivalent water thickness, measured in poplar leaves, was used as the input parameters of the model. The hyperspectral reflectance data of leaf scale and canopy scale were simulated in different equivalent water thickness. By analyzing common water vegetation index sensitivity of equivalent water thickness, a vegetation index was constructed by the method of vegetation index ratio. The equivalent water thickness estimation accuracy of the leaf scale and canopy scale of poplar was compared with GVMI/MSI, global vegetation moisture index (GVMI) and water stress index (MSI). [Results] The result shows that the accuracies of the equivalent water thickness estimation model of Poplar (R2) with GVMI, MSI and GVMI/MSI as variable are respectively 0.997, 0.995 and 0.998 in leaf scale, and 0.837, 0.836 and 0.973 in canopy scale. GVMI/MSI is the best index for the equivalent water thickness of poplar leaves. [Conclusion] The equivalent water thickness model of poplar leaves modeled by GVMI/MSI has higher prediction accuracy. It is the optimal estimation model of equivalent water thickness of poplar leaves.
- poplar,
- equivalent water thickness,
- spectral model,
- vegetation water index

References

[1]	Nautiyal P C, Rachaputi N R, Joshi Y C. Moisture-deficit-induced changes in leaf-water content, leaf carbon exchange rate and biomass production in groundnut cultivars differing in specific leaf area[J]. Field Crops Research, 2002, 74(1): 67-79.
[2]	Colombo R, Meroni M, Marchesi A, et al. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling[J]. Remote Sensing of Environment, 2008, 112(4): 1820-1834.
[3]	毛罕平,高洪燕,张晓东.生菜叶片含水率光谱特征模型研究[J]. 农业机械学报, 2011,42(5):166-170.
[4]	Hunt G R. Electromagnetic radiation: the communication link in remote sensing[M]//Siegal B S, Gillespie A R. Remote Sensing in Geology, New yourk:willey, 1980: 702.
[5]	梁亮,张连蓬,林卉. 基于导数光谱的小麦冠层叶片含水量反演[J]. 中国农业科学, 2013,46(1):18-29.
[6]	Kim Y, Glenn D M, Park J, et al. Hyperspectral image analysis for water stress detection of apple trees[J]. Computers and Electronics in Agriculture, 2011, 77(2): 155-160.
[7]	Ceccato P, Flasse S, Tarantola S, et al. Detecting vegetation leaf water content using reflectance in the optical domain[J]. Remote Sensing of Environment, 2001, 77(1): 22-33.
[8]	Chuvieco E, Riano D, Aguado I, et al. Estimation of fuel moisture content from multitemporal analysis of landsat thematic mapper reflectance data: applications in fire danger assessment[J]. International Journal of Remote Sensing, 2002, 23(11): 2145-2162.
[9]	Danson F M, Bowyer P. Estimating live fuel moisture content from remotely sensed reflectance[J]. Remote Sensing Environment, 2004, 92: 309-321.
[10]	Ceccato P, Flasse S, Gregoire J. Designing a spectral index to estimate vegetation water content from remote sensing data: part 2. validation and applications[J]. Remote Sensing of Environment, 2002, 82(2): 198-207.
[11]	赵祥,王锦地,刘素红. 耦合辐射传输模型的植被含水量遥感改进监测[J]. 红外与毫米波学报,2010, 29(3): 185-189.
[12]	刘小军, 田永超, 姚霞,等. 基于高光谱的水稻叶片含水量监测研究[J]. 中国农业科学,2012, 45(3): 435-442.
[13]	方美红,居为民基于叶片光学属性的作物叶片水分含量反演模型研究[J]. 光谱学与光谱分析,2015, 30(1): 167-171.
[14]	Hunt E R, Rock B N. Detection of changes in leaf water content using near-and middle-infrared reflectances[J]. Remote sensing of environment, 1989, 30(1): 43-54.
[15]	Gao B. NDWI-a normalized difference water index for remote sensing of vegetation liquid water from space[J]. Remote sensing of environment, 1996, 58(3): 257-266.
[16]	Peñuelas J, Pinol J, Ogaya R, et al. Estimation of plant water concentration by the reflectance water index WI (R900/R970)[J]. International Journal of Remote Sensing, 1997, 18(13): 2869-2875.
[17]	Ceccato P, Gobron N, Flasse S, et al. Designing a spectral index to estimate vegetation water content from remote sensing data: part 1: theoretical approach[J]. Remote Sensing of Environment, 2002, 82(2): 188-197.
[18]	Jackson T J, Chen D, Cosh M, et al. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans[J]. Remote Sensing of Environment, 2004, 92(4): 475-482.
[19]	Chen D, Huang J, Jackson T J. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near-and short-wave infrared bands[J]. Remote Sensing of Environment, 2005, 98(2): 225-236.
[20]	吴见,陈泰生,潘立新. 不同含水量条件下树种叶片光谱差异分析[J]. 光谱学与光谱分析,2015, 30(7):1961-1966.
[21]	Viegas D X, Viegas T P, Ferreira A D. Moisture content of fine forest fuels and fire occurrence in central Portugal [J]. The International Journal of Wildland Fire, 1992, 2: 69-85.
[22]	Inoue Y, Morinaga S, Shibayama M. Nondestructive estimation of water status of intact crop leaves based on spectral reflectance measurements[J]. Japan Journal of Crop Science, 1993, 62 (3): 462-469.
[23]	Danson F M, Steven M D, Malthus T J, et al. High spectral resolution data for determining leaf water content [J]. International Journal of Remote Sensing, 1992, 13 (3): 461-470.
[24]	王洁,徐瑞松,马跃良,等. 植被含水量的遥感反演方法及研究进展[J]. 遥感信息,2008(1):100-105.
[25]	Peñuelas J, Filella I, Biel C, et al. The Reflectance at the 950-970 nm region as an indicator of plant water status[J]. International Journal of Remote Sensing, 1993, 14(10): 1887-1905.
[26]	Jacquemoud S, Baret F. PROSPECT: A model of leaf optical properties spectra[J]. Remote Sensing of Environment, 1990, 34(2): 75-91.
[27]	Verhoef W. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model[J]. Remote sensing of environment, 1984, 16(2): 125-141.
[28]	Carter G A. Primary and secondary effects of water content on the spectral reflectance of leaves[J]. American Journal of Botany, 1991: 916-924.
[29]	杜晓. 植被叶面水遥感监测及其时空特征分析. 北京:中国科学院遥感应用研究所,2006.
[30]	陈小平,王树东,张立福,等. 植被叶片含水量反演的精度及敏感性[J]. 遥感信息,2016, 31 (1):48-57.
[31]	程晓娟,杨贵军,徐新刚,等. 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.

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Estimation Model of Poplar Equivalent Water Thickness Based on Hyperspectral Information

1. Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation, State Forestry Administration, Beijing 100091, China

2. Collaborative Innovation Center of Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China

Received Date: 2015-03-30

Abstract: [Objective] To establish a model for the purpose of rapid and effective monitoring of poplar leaf water. [Methods] The equivalent water thickness (EWT) of poplar leaves was used as the token water content, the hyperspectral data of poplar was measured. The range of the equivalent water thickness, measured in poplar leaves, was used as the input parameters of the model. The hyperspectral reflectance data of leaf scale and canopy scale were simulated in different equivalent water thickness. By analyzing common water vegetation index sensitivity of equivalent water thickness, a vegetation index was constructed by the method of vegetation index ratio. The equivalent water thickness estimation accuracy of the leaf scale and canopy scale of poplar was compared with GVMI/MSI, global vegetation moisture index (GVMI) and water stress index (MSI). [Results] The result shows that the accuracies of the equivalent water thickness estimation model of Poplar (R2) with GVMI, MSI and GVMI/MSI as variable are respectively 0.997, 0.995 and 0.998 in leaf scale, and 0.837, 0.836 and 0.973 in canopy scale. GVMI/MSI is the best index for the equivalent water thickness of poplar leaves. [Conclusion] The equivalent water thickness model of poplar leaves modeled by GVMI/MSI has higher prediction accuracy. It is the optimal estimation model of equivalent water thickness of poplar leaves.

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[1]	Nautiyal P C, Rachaputi N R, Joshi Y C. Moisture-deficit-induced changes in leaf-water content, leaf carbon exchange rate and biomass production in groundnut cultivars differing in specific leaf area[J]. Field Crops Research, 2002, 74(1): 67-79.
[2]	Colombo R, Meroni M, Marchesi A, et al. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling[J]. Remote Sensing of Environment, 2008, 112(4): 1820-1834.
[3]	毛罕平,高洪燕,张晓东.生菜叶片含水率光谱特征模型研究[J]. 农业机械学报, 2011,42(5):166-170.
[4]	Hunt G R. Electromagnetic radiation: the communication link in remote sensing[M]//Siegal B S, Gillespie A R. Remote Sensing in Geology, New yourk:willey, 1980: 702.
[5]	梁亮,张连蓬,林卉. 基于导数光谱的小麦冠层叶片含水量反演[J]. 中国农业科学, 2013,46(1):18-29.
[6]	Kim Y, Glenn D M, Park J, et al. Hyperspectral image analysis for water stress detection of apple trees[J]. Computers and Electronics in Agriculture, 2011, 77(2): 155-160.
[7]	Ceccato P, Flasse S, Tarantola S, et al. Detecting vegetation leaf water content using reflectance in the optical domain[J]. Remote Sensing of Environment, 2001, 77(1): 22-33.
[8]	Chuvieco E, Riano D, Aguado I, et al. Estimation of fuel moisture content from multitemporal analysis of landsat thematic mapper reflectance data: applications in fire danger assessment[J]. International Journal of Remote Sensing, 2002, 23(11): 2145-2162.
[9]	Danson F M, Bowyer P. Estimating live fuel moisture content from remotely sensed reflectance[J]. Remote Sensing Environment, 2004, 92: 309-321.
[10]	Ceccato P, Flasse S, Gregoire J. Designing a spectral index to estimate vegetation water content from remote sensing data: part 2. validation and applications[J]. Remote Sensing of Environment, 2002, 82(2): 198-207.
[11]	赵祥,王锦地,刘素红. 耦合辐射传输模型的植被含水量遥感改进监测[J]. 红外与毫米波学报,2010, 29(3): 185-189.
[12]	刘小军, 田永超, 姚霞,等. 基于高光谱的水稻叶片含水量监测研究[J]. 中国农业科学,2012, 45(3): 435-442.
[13]	方美红,居为民基于叶片光学属性的作物叶片水分含量反演模型研究[J]. 光谱学与光谱分析,2015, 30(1): 167-171.
[14]	Hunt E R, Rock B N. Detection of changes in leaf water content using near-and middle-infrared reflectances[J]. Remote sensing of environment, 1989, 30(1): 43-54.
[15]	Gao B. NDWI-a normalized difference water index for remote sensing of vegetation liquid water from space[J]. Remote sensing of environment, 1996, 58(3): 257-266.
[16]	Peñuelas J, Pinol J, Ogaya R, et al. Estimation of plant water concentration by the reflectance water index WI (R900/R970)[J]. International Journal of Remote Sensing, 1997, 18(13): 2869-2875.
[17]	Ceccato P, Gobron N, Flasse S, et al. Designing a spectral index to estimate vegetation water content from remote sensing data: part 1: theoretical approach[J]. Remote Sensing of Environment, 2002, 82(2): 188-197.
[18]	Jackson T J, Chen D, Cosh M, et al. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans[J]. Remote Sensing of Environment, 2004, 92(4): 475-482.
[19]	Chen D, Huang J, Jackson T J. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near-and short-wave infrared bands[J]. Remote Sensing of Environment, 2005, 98(2): 225-236.
[20]	吴见,陈泰生,潘立新. 不同含水量条件下树种叶片光谱差异分析[J]. 光谱学与光谱分析,2015, 30(7):1961-1966.
[21]	Viegas D X, Viegas T P, Ferreira A D. Moisture content of fine forest fuels and fire occurrence in central Portugal [J]. The International Journal of Wildland Fire, 1992, 2: 69-85.
[22]	Inoue Y, Morinaga S, Shibayama M. Nondestructive estimation of water status of intact crop leaves based on spectral reflectance measurements[J]. Japan Journal of Crop Science, 1993, 62 (3): 462-469.
[23]	Danson F M, Steven M D, Malthus T J, et al. High spectral resolution data for determining leaf water content [J]. International Journal of Remote Sensing, 1992, 13 (3): 461-470.
[24]	王洁,徐瑞松,马跃良,等. 植被含水量的遥感反演方法及研究进展[J]. 遥感信息,2008(1):100-105.
[25]	Peñuelas J, Filella I, Biel C, et al. The Reflectance at the 950-970 nm region as an indicator of plant water status[J]. International Journal of Remote Sensing, 1993, 14(10): 1887-1905.
[26]	Jacquemoud S, Baret F. PROSPECT: A model of leaf optical properties spectra[J]. Remote Sensing of Environment, 1990, 34(2): 75-91.
[27]	Verhoef W. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model[J]. Remote sensing of environment, 1984, 16(2): 125-141.
[28]	Carter G A. Primary and secondary effects of water content on the spectral reflectance of leaves[J]. American Journal of Botany, 1991: 916-924.
[29]	杜晓. 植被叶面水遥感监测及其时空特征分析. 北京:中国科学院遥感应用研究所,2006.
[30]	陈小平,王树东,张立福,等. 植被叶片含水量反演的精度及敏感性[J]. 遥感信息,2016, 31 (1):48-57.
[31]	程晓娟,杨贵军,徐新刚,等. 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.

Estimation Model of Poplar Equivalent Water Thickness Based on Hyperspectral Information

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通讯作者: 陈斌, bchen63@163.com

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Estimation Model of Poplar Equivalent Water Thickness Based on Hyperspectral Information

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