四种常用的太阳诱导植被叶绿素荧光反演方法对比分析研究
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摘要
【目的】太阳诱导叶绿素荧光(SIF)是一种新型的植被参数,可用于监测植物光合作用状态和评估总初级生产力。利用模拟数据对比分析常用SIF反演方法的精度,为野外测量仪器SIF反演方法的选择提供理论基础。【方法】选择SCOPE模型模拟了不同生化理化参数下的模拟数据,并以该数据为基础生成不同光谱分辨率(SR)和不同信噪比(SNR)下的模拟数据集。选择4种常用SIF反演方法进行SIF反演:夫琅禾费暗线法(FLD),3FLD、iFLD和光谱拟合法(SFM)。【结果】基于模拟数据的反演结果表明SFM和i FLD方法能够获得更准确的SIF,其均方根误差(RMSE)分别为0.1142 W/m~2/μm/sr和0.1114 W/m~2/μm/sr;3FLD法亦能取得较准确的SIF结果,其RMSE为0.2014 W/m~2/μm/sr;而FLD法的精度较差,其RMSE大于0.5 W/m~2/μm/sr。在高SR和SNR条件下,SFM和iFLD法明显优于3FLD和FLD法,但随着SR和SNR的降低,4种反演方法的精度也随之降低,其中iFLD法受SNR影响最为明显。【结论】利用SFM和iFLD方法能够得到更准确的SIF反演结果,且随着仪器SR和SNR的提高其反演精度也随着提高,但iFLD方法易受SNR的影响。因此,对于光谱分辨率优于1 nm的测量仪器应优先选择SFM方法来反演SIF。
[Purpose]Solar-induced chlorophyll fluorescence(SIF)is a novel vegetation parameter that can be used to monitor plant photosynthesis status and assess total primary productivity. The accuracy of the four commonly used SIF retrieval methods is compared and analyzed using simulation data and field measured data,which provides a theoretical basis for the selection of SIF retrieval methods for field measurement instruments.[Method]The SCOPE(Soil-Canopy-Observation of Photosynthesis and the Energy Balance)model was selected to simulate the simulated data sets under different vegetation biochemical and physical parameters.Analog dataset under different resolutions(SR)and different signal-to-noise ratio(SNR)were generated based on the data. Four commonly used SIF retrieval methods were selected to retrieve the SIF:Fraunhofer Line Discrimination(FLD),3 FLD(modified FLD),iFLD(improved FLD)and SFM(Spectral Fitting Method)methods.[Result]The retrieval results based on the simulated data show that the SFM and iFLD methods can obtain more accurate SIF results with root mean square error(RMSE)of 0.1142 W/m~2/μm/sr and 0.1114 W/m~2/μm/sr,respectively.The 3 FLD method can also obtain accurate SIF results with an RMSE of 0.2014 W/m~2/μm/sr. The accuracy of the FLD method is poor,and its RSME is greater than 0.5 W/m~2/μm/sr. Under high SR and SNR conditions,SFM and iFLD algorithms are significantly better than 3 FLD and FLD algorithms,but with the decrease of SR and SNR,the accuracy of the four retrieval methods is also reduced,and the iFLD method is most affected by SNR.[Conclusion]The SFM and iFLD methods can obtain more accurate SIF results,and the retrieval accuracy increases with the improvement of SR and SNR,but the iFLD method is susceptible to SNR. Therefore,the SFM method is preferred for spectrometric instruments with spectral resolutions below 1 nm.
[Purpose]Solar-induced chlorophyll fluorescence(SIF)is a novel vegetation parameter that can be used to monitor plant photosynthesis status and assess total primary productivity. The accuracy of the four commonly used SIF retrieval methods is compared and analyzed using simulation data and field measured data,which provides a theoretical basis for the selection of SIF retrieval methods for field measurement instruments.[Method]The SCOPE(Soil-Canopy-Observation of Photosynthesis and the Energy Balance)model was selected to simulate the simulated data sets under different vegetation biochemical and physical parameters.Analog dataset under different resolutions(SR)and different signal-to-noise ratio(SNR)were generated based on the data. Four commonly used SIF retrieval methods were selected to retrieve the SIF:Fraunhofer Line Discrimination(FLD),3 FLD(modified FLD),iFLD(improved FLD)and SFM(Spectral Fitting Method)methods.[Result]The retrieval results based on the simulated data show that the SFM and iFLD methods can obtain more accurate SIF results with root mean square error(RMSE)of 0.1142 W/m~2/μm/sr and 0.1114 W/m~2/μm/sr,respectively.The 3 FLD method can also obtain accurate SIF results with an RMSE of 0.2014 W/m~2/μm/sr. The accuracy of the FLD method is poor,and its RSME is greater than 0.5 W/m~2/μm/sr. Under high SR and SNR conditions,SFM and iFLD algorithms are significantly better than 3 FLD and FLD algorithms,but with the decrease of SR and SNR,the accuracy of the four retrieval methods is also reduced,and the iFLD method is most affected by SNR.[Conclusion]The SFM and iFLD methods can obtain more accurate SIF results,and the retrieval accuracy increases with the improvement of SR and SNR,but the iFLD method is susceptible to SNR. Therefore,the SFM method is preferred for spectrometric instruments with spectral resolutions below 1 nm.
引文
[1]Guanter L, Zhang Y, Jung M, et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences, 2014, 111(14):E1327~E1333.
[2]Sun Y, Frankenberg C, Jung M, et al. Overview of Solar-Induced Chlorophyll Fluorescence(SIF)from the Orbiting Carbon Observatory-2:Retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sensing of Environment, 2018,209:808~823.
[3]Zhang Y, Guanter L, Berry J A, et al. Model-based analysis of the relationship between sun-induced chlorophyll fluorescence and gross primary production for remote sensing applications. Remote Sensing of Environment, 2016, 187:145~155.
[4]Porcar-Castell A, Berry J A. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications:mechanisms and challenges. Journal of Experimental Botany, 2014, 65(15):4065~4095.
[5]Meroni M, Rossini M, Guanter L, et al. Remote sensing of solar-induced chlorophyll fluorescence:Review of methods and applications. Remote Sensing of Environment, 2009, 113:2037~2051.
[6]Frankenberg C, Berry J. Solar induced chlorophyll fluorescence:origins, relation to photosynthesis and retrieval.Amsterdam:Elsevier, 2018, 143~162.
[7]章钊颖,王松寒,邱博,等.日光诱导叶绿素荧光遥感反演及碳循环应用进展.遥感学报, 2019, 23(1):37~52.
[8]Plascyk J A. The MK II Fraunhofer line discriminator(FLD-II)for airborne and orbital remote sensing of solar-stimulated luminescence. Optical Engineering, 1975, 14(4):144339.
[9]Guanter L, Rossini M, Colombo R, et al. Using field spectroscopy to assess the potential of statistical approaches for the retrieval of sun-induced chlorophyll fluorescence from ground and space. Remote Sensing of Environment, 2013, 133(7253):52~61.
[10]Damm A, Erler A, Hillen W, et al. Modeling the impact of spectral sensor configurations on the FLD retrieval accuracy of sun-induced chlorophyll fluorescence. Remote Sensing of Environment, 2011, 115(8):1882~1892.
[11]Meroni M, Busetto L, Colombo R, et al. Performance of Spectral Fitting Methods for vegetation fluorescence quantification.Remote Sensing of Environment, 2010, 114(2):363~374.
[12]Zhou X, Liu Z, Xu S, et al. An automated comparative observation system for sun-induced chlorophyll fluorescence of vegetation canopies. Sensors, 2016, 16(6):775.
[13]张立福,王思恒,黄长平.太阳诱导叶绿素荧光的卫星遥感反演方法.遥感学报, 2018, 22(1):1~12.
[14]Frankenberg C, K?hler P, Magney T S, et al. The Chlorophyll Fluorescence Imaging Spectrometer(CFIS), mapping far red fluorescence from aircraft. Remote Sensing of Environment, 2018, 217:523~536.
[15]Miao G, Guan K, Yang X, et al. Sun‐induced chlorophyll fluorescence, photosynthesis, and light use efficiency of a soybean field from seasonally continuous measurements. Journal of Geophysical Research:Biogeosciences, 2018, 123(2):610~623.
[16]Yang K, Ryu Y, Dechant B, et al. Sun-induced chlorophyll fluorescence is more strongly related to absorbed light than to photosynthesis at half-hourly resolution in a rice paddy. Remote Sensing of Environment, 2018, 216:658~673.
[17]Paul-Limoges E, Damm A, Hueni A, et al. Effect of environmental conditions on sun-induced fluorescence in a mixed forest and a cropland. Remote Sensing of Environment, 2018, 219:310~323.
[18]Van der Tol C, Verhoef W, Timmermans J, et al. An integrated model of soil-canopy spectral radiances, photosytesis,fluorescence, temperature and energy balance. Biogeosciences, 2009, 6(12):3109~3129.
[19]Zhao F, Li R, Verhoef W, et al. Reconstruction of the full spectrum of solar-induced chlorophyll fluorescence:intercomparison study for a novel method. Remote Sensing of Environment, 2018, 219:233~246.
[20]Maier S W, Günther K P, Stellmes M. Sun-induced fluorescence:a new tool for precision farming. Digital Imaging and Spectral Techniques:Applications to Precision Agriculture and Crop Physiology, 2003:209~222.
[21]Alonso L., Gomez Chova L., Vila Frances J., et al. Improved fraunhofer line discrimination method for vegetation fluorescence quantification. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4):620~624.
[22]Zhao F, Guo Y, Verhoef W, et al. A method to reconstruct the solar-induced canopy fluorescence spectrum from hyperspectral measurements. Remote Sensing, 2014, 6, 10171~10192.
[2]Sun Y, Frankenberg C, Jung M, et al. Overview of Solar-Induced Chlorophyll Fluorescence(SIF)from the Orbiting Carbon Observatory-2:Retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sensing of Environment, 2018,209:808~823.
[3]Zhang Y, Guanter L, Berry J A, et al. Model-based analysis of the relationship between sun-induced chlorophyll fluorescence and gross primary production for remote sensing applications. Remote Sensing of Environment, 2016, 187:145~155.
[4]Porcar-Castell A, Berry J A. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications:mechanisms and challenges. Journal of Experimental Botany, 2014, 65(15):4065~4095.
[5]Meroni M, Rossini M, Guanter L, et al. Remote sensing of solar-induced chlorophyll fluorescence:Review of methods and applications. Remote Sensing of Environment, 2009, 113:2037~2051.
[6]Frankenberg C, Berry J. Solar induced chlorophyll fluorescence:origins, relation to photosynthesis and retrieval.Amsterdam:Elsevier, 2018, 143~162.
[7]章钊颖,王松寒,邱博,等.日光诱导叶绿素荧光遥感反演及碳循环应用进展.遥感学报, 2019, 23(1):37~52.
[8]Plascyk J A. The MK II Fraunhofer line discriminator(FLD-II)for airborne and orbital remote sensing of solar-stimulated luminescence. Optical Engineering, 1975, 14(4):144339.
[9]Guanter L, Rossini M, Colombo R, et al. Using field spectroscopy to assess the potential of statistical approaches for the retrieval of sun-induced chlorophyll fluorescence from ground and space. Remote Sensing of Environment, 2013, 133(7253):52~61.
[10]Damm A, Erler A, Hillen W, et al. Modeling the impact of spectral sensor configurations on the FLD retrieval accuracy of sun-induced chlorophyll fluorescence. Remote Sensing of Environment, 2011, 115(8):1882~1892.
[11]Meroni M, Busetto L, Colombo R, et al. Performance of Spectral Fitting Methods for vegetation fluorescence quantification.Remote Sensing of Environment, 2010, 114(2):363~374.
[12]Zhou X, Liu Z, Xu S, et al. An automated comparative observation system for sun-induced chlorophyll fluorescence of vegetation canopies. Sensors, 2016, 16(6):775.
[13]张立福,王思恒,黄长平.太阳诱导叶绿素荧光的卫星遥感反演方法.遥感学报, 2018, 22(1):1~12.
[14]Frankenberg C, K?hler P, Magney T S, et al. The Chlorophyll Fluorescence Imaging Spectrometer(CFIS), mapping far red fluorescence from aircraft. Remote Sensing of Environment, 2018, 217:523~536.
[15]Miao G, Guan K, Yang X, et al. Sun‐induced chlorophyll fluorescence, photosynthesis, and light use efficiency of a soybean field from seasonally continuous measurements. Journal of Geophysical Research:Biogeosciences, 2018, 123(2):610~623.
[16]Yang K, Ryu Y, Dechant B, et al. Sun-induced chlorophyll fluorescence is more strongly related to absorbed light than to photosynthesis at half-hourly resolution in a rice paddy. Remote Sensing of Environment, 2018, 216:658~673.
[17]Paul-Limoges E, Damm A, Hueni A, et al. Effect of environmental conditions on sun-induced fluorescence in a mixed forest and a cropland. Remote Sensing of Environment, 2018, 219:310~323.
[18]Van der Tol C, Verhoef W, Timmermans J, et al. An integrated model of soil-canopy spectral radiances, photosytesis,fluorescence, temperature and energy balance. Biogeosciences, 2009, 6(12):3109~3129.
[19]Zhao F, Li R, Verhoef W, et al. Reconstruction of the full spectrum of solar-induced chlorophyll fluorescence:intercomparison study for a novel method. Remote Sensing of Environment, 2018, 219:233~246.
[20]Maier S W, Günther K P, Stellmes M. Sun-induced fluorescence:a new tool for precision farming. Digital Imaging and Spectral Techniques:Applications to Precision Agriculture and Crop Physiology, 2003:209~222.
[21]Alonso L., Gomez Chova L., Vila Frances J., et al. Improved fraunhofer line discrimination method for vegetation fluorescence quantification. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4):620~624.
[22]Zhao F, Guo Y, Verhoef W, et al. A method to reconstruct the solar-induced canopy fluorescence spectrum from hyperspectral measurements. Remote Sensing, 2014, 6, 10171~10192.
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