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Published： 2018-1 ，
Accepted： 26 September 2017
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张立福, 王思恒, 黄长平. 2018. 太阳诱导叶绿素荧光的卫星遥感反演方法. 遥感学报, 22(1): 1–12
Zhang L F, Wang S H and Huang C P. 2018. Top-of-atmosphere hyperspectral remote sensing of solar-induced chlorophyll fluorescence: A review of methods. Journal of Remote Sensing, 22(1): 1–12
利用卫星遥感探测区域和全球尺度太阳诱导叶绿素荧光SIF(Solar-Induced chlorophyll Fluorescence)近年来成为研究热点。由于地球大气吸收和散射的影响，卫星尺度的SIF反演问题较为复杂，科学界对该问题一直存在争议，不同科学团队提出了众多方法。本文介绍了大气层顶SIF反演的机理、难点及思路，总结了近10年来最新发展的大气层顶SIF反演算法，并将这些算法归纳为3类：基于辐射传输方程的算法、简化的物理模型算法和数据驱动算法，分析讨论了各算法的特点及适用性；以应用最广泛的数据驱动算法为例，基于GOME-2数据详细介绍了算法的中间环节及注意事项；最后回顾了卫星遥感反演SIF的发展历程，汇总了目前及未来具有荧光探测能力的星载传感器，并依据数据源的特点相应地给出了适用的SIF反演算法，为今后基于航空和卫星高光谱数据的SIF反演提供了依据。
Solar-Induced chlorophyll Fluorescence (SIF) is directly related to photosynthesis and therefore considered a promising tool for grossprimary productivityestimation and vegetation environmental stress monitoring. Interest in SIF has increased since satellite remote sensing of SIF became feasible
especially after the first global SIF map was depicted in 2011. However
methods for retrieving SIF at Top-Of-Atmosphere (TOA) are still under investigation and argumentation
as decoupling SIF from total at-sensor radiance is challenging in the presence of atmospheric scattering and absorption. This paper aims to review the methods proposed for SIF retrieval at TOA over the past 10 years
to illustratethe advantages/disadvantages of those methods and to provide a technical instruction for remote sensingof SIF at airborne/space level. All the methods were categorized into three types: methods based on Radiative Transfer (RT) calculations
simplified physically-based methods and data-driven approaches. Methods based on RT calculations
including improved Fraunhofer Line Discrimination (FLD) methods and Spectral Fitting Methods (SFM)
aim to retrieve SIF using atmospheric absorption lines. Atmosphere is characterized through RT calculations and then the TOA problem is converted to Bottom-Of-Atmosphere (BOA). These methods are applicable in situations with medium to low spectral resolution (0.3—5 nm) whereas imperfect characterization of atmosphere and RT process will lead to retrieval errors. Physically-based methods utilize single or several solar Fraunhofer Lines located in atmospheric windows
using solar irradiance spectra (measured or simulated through spectra convolution) as reference
decoupling SIF signal from earth radiances. Atmosphere scattering and absorption are neglected under these situations. Physically-based methods were developed for high spectral resolution measurements (e.g. 0.025 nm for GOSAT) and are sensitive to noise. Data driven approaches consider any fluorescent radiance spectrum consists non-fluorescent portion and SIF signal. Features extracted from large training dataset consisting non-fluorescent spectra (cloud
ice/snow
desert…) are used to express the non-fluorescent portion in the fluorescent spectra while SIF signal is expressed as mathematical function or spectra with fixed shape. Data driven approaches are popular because they do not require RT calculations while are applicable for medium to high spectral resolution situations. The performance of data driven approaches depends on the representativeness of training dataset and other empirical settings of the model
including the number of features used
function used to describe SIF spectrum and retrieval window selected. With several satellite sensors with medium spectral resolution (0.3—0.5 nm) being available for SIF retrieval (including currently available ones and scheduled to be launched in near future ones)
including MetOp-GOME-2
Sentinel-5-TROPOMI and the FLuorescence EXplorer (FLEX) mission
RT-based methods and data-driven approaches are considered most promising SIF retrieval methods in the future. The RT-based methods are mainly developed by the FLEX team and are applicable for low spectral resolution and airborne data
but the performance of these methods on global SIF retrieval needs to be validated with real satellite data. On the other hand
several global SIF products have been generated using data driven approaches. However
representative training dataset needs to be built carefully and optimal parameters need to be determined according to different sensors.
太阳诱导叶绿素荧光(SIF)高光谱遥感辐射传输方程数据驱动算法
SIF retrievalhyperspectral remote sensingradiative transferdata-driven approaches
Cogliati S, Verhoef W, Kraft S, Sabater N, Alonso L, Vicent J, Moreno J, Drusch M and Colombo R. 2015. Retrieval of sun-induced fluorescence using advanced spectral fitting methods. Remote Sensing of Environment, 169: 344–357
Damm A, Guanter L, Laurent V C E, Schaepman M E, Schickling A and Rascher U. 2014. FLD-based retrieval of sun-induced chlorophyll fluorescence from medium spectral resolution airborne spectroscopy data. Remote Sensing of Environment, 147: 256–266
Frankenberg C, Butz A and Toon G C. 2011. Disentangling chlorophyll fluorescence from atmospheric scattering effects in O2A-band spectra of reflected sun-light. Geophysical Research Letters, 38: L03801
Frankenberg C, O’dell C, Berry J, Guanter L, Joiner J, Köhler P, Pollock R and Taylor T E. 2014. Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sensing of Environment, 147: 1–12
Guan K, Berry J A, Zhang Y, Joiner J, Guanter L, Badgley G and Lobell D B. 2016. Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence. Glob Chang Biol, 22: 716–26
Guanter L, Aben I, Tol P, Krijger J M, Hollstein A, Köhler P, Damm A, Joiner J, Frankenberg C and Landgraf J. 2015. Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence. Atmospheric Measurement Techniques, 8: 1337–1352
Guanter L, Alonso L, Gómez-Chova L, Amorós-López J, Vila J and Moreno J. 2007. Estimation of solar-induced vegetation fluorescence from space measurements. Geophysical Research Letters, 34: L08401
Guanter L, Alonso L, Gómez-Chova L, Meroni M, Preusker R, Fischer J and Moreno J. 2010. Developments for vegetation fluorescence retrieval from spaceborne high-resolution spectrometry in the O2-A and O2-B absorption bands. Journal of Geophysical Research, 115: D19303
Guanter L, Frankenberg C, Dudhia A, Lewis P E, Gómez-Dans J, Kuze A, Suto H and Grainger R G. 2012. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sensing of Environment, 121: 236–251
Guanter L, Rossini M, Colombo R, Meroni M, Frankenberg C, Lee J-E and Joiner J. 2013. 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, 133: 52–61
Guanter L, Zhang Y, Jung M, Joiner J, Voigt M, Berry J A, Frankenberg C, Huete A R, Zarco-Tejada P and Lee J-E. 2014. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences, 111: E1327–E1333
胡姣婵, 刘良云, 刘新杰. 2015. FluorMOD模拟叶绿素荧光夫琅和费暗线反演算法不确定性分析. 遥感学报, 19(4): 594–608
Hu J C, Liu L Y and Liu X J. 2015. Assessing uncertainties of sun-induced chlorophyll fluorescence retrieval using FluorMOD model. Journal of Remote Sensing, 19(4): 594–608 (
Jeong S-J, Schimel D, Frankenberg C, Drewry D T, Fisher J B, Verma M, Berry J A., Lee J-E and Joiner J 2017. Application of satellite solar-induced chlorophyll fluorescence to understanding large-scale variations in vegetation phenology and function over northern high latitude forests. Remote Sensing of Environment, 190: 178–187
Joiner J, Guanter L, Lindstrot R, Voigt M, Vasilkov A P, Middleton E M, Huemmrich K F, Yoshida Y and Frankenberg C. 2013. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmospheric Measurement Techniques, 6: 2803–2823
Joiner J, Yoshida Y, Guanter L and Middleton E. M. 2016. New methods for retrieval of chlorophyll red fluorescence from hyper-spectral satellite instruments: simulations and application to GOME-2 and SCIAMACHY[J]. Atmospheric Measurement Techniques Discussions, 1–41 [DOI: 10.5194/amt-2015-387, 2016]
Joiner J, Yoshida Y, Vasilkov A P, Yoshida Y, Corp L A and Middleton E M. 2011. First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences, 8: 637–651
Köhler P, Guanter L and Frankenberg C. 2015. Simplified physically based retrieval of sun-induced chlorophyll fluorescence from GOSAT data. IEEE Geoscience and Remote Sensing Letters, 12: 1446–1450
Köhler P, Guanter L and Joiner J. 2015. A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data. Atmospheric Measurement Techniques, 8: 2589–2608
刘良云, 张永江, 王纪华, 赵春江. 2006. 利用夫琅和费暗线探测自然光条件下的植被光合作用荧光研究. 遥感学报, 10(1): 130–137
Liu L Y, Zhang Y J, Wang J H and Zhao C J. 2006. Detecting photosynthesis fluorescence under natural sunlight based on fraunhofer line. Journal of Remote Sensing, 10(1): 130–137 (
Liu X, Liu L. Assessing band sensitivity to atmospheric radiation transfer for space-based retrieval of solar-induced chlorophyll fluorescence[J]. Remote Sensing 2014. Assessing band sensitivity to atmospheric radiation transfer for space-based retrieval of solar-induced chlorophyll fluorescence. Remote Sensing, 6(11): 10656–10675
刘新杰, 刘良云. 2013. 叶绿素荧光的GOSAT卫星遥感反演. 遥感学报, 17(6): 1518–1532
Liu X J and Liu L Y. 2013. Retrieval of chlorophyll fluorescence from GOSAT TANSO-FTS data based on weighted least square fitting. Journal of Remote Sensing, 17(6): 1518–1532 (
Meroni M, Rossini M, Guanter L, Alonso L, Rascher U, Colombo R. and Moreno J 2009. Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sensing of Environment, 113: 2037–2051
Porcar-Castell A, Tyystjarvi E, Atherton J, Van Der Tol C, Flexas J, Pfundel E E, Moreno J, Frankenberg C and Berry J A. 2014. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J Exp Bot, 65: 4065–95
Sanders A F, Verstraeten W W, Kooreman M L, Van Leth T C, Beringer J and Joiner J. 2016. Spaceborne Sun-Induced Vegetation Fluorescence Time Series from 2007 to 2015 Evaluated with Australian Flux Tower Measurements. Remote Sensing, 8: 895
Sun Y, Fu R, Dickinson R, Joiner J, Frankenberg C, Gu L, Xia Y and Fernando N. 2015. Drought onset mechanisms revealed by satellite solar-induced chlorophyll fluorescence: Insights from two contrasting extreme events. Journal of Geophysical Research: Biogeosciences, 120: 2427–2440
Van Der Tol C Berry J A, Campbell P K E and Rascher U. 2014. Models of fluorescence and photosynthesis for interpreting measurements of solar-induced chlorophyll fluorescence. Journal of Geophysical Research: Biogeosciences, 119: 2312–2327
Vasilkov A Joiner J and Spurr R. 2013. Note on rotational-Raman scattering in the O2 A- and B-bands. Atmospheric Measurement Techniques, 6: 981–990
Vicent J, Sabater N, Tenjo C, Acarreta J R, Manzano M, Rivera J P, Jurado P, Franco R, Alonso L and Verrelst J. 2016. FLEX end-to-end mission performance simulator. IEEE Transactions on Geoscience and Remote Sensing, 54: 4215–4223
Walther S, Voigt M, Thum T, Gonsamo A, Zhang Y, Kohler P, Jung M, Varlagin A and Guanter L. 2016. Satellite chlorophyll fluorescence measurements reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests. Glob Chang Biol, 22: 2979–96
Wang S H, Huang C P, Zhang L F, Lin Y, Cen Y and Wu T X. 2016. Monitoring and Assessing the 2012 Drought in the Great Plains: Analyzing Satellite-Retrieved Solar-Induced Chlorophyll Fluorescence, Drought Indices, and Gross Primary Production. Remote Sensing, 8: 61
Yoshida Y, Joiner J, Tucker C, Berry J, Lee J E, Walker G, Reichle R, Koster R, Lyapustin A and Wang Y. 2015. The 2010 Russian drought impact on satellite measurements of solar-induced chlorophyll fluorescence: Insights from modeling and comparisons with parameters derived from satellite reflectances. Remote Sensing of Environment, 166: 163–177
Zhang L F, Wang S H, Huang C P, Cen Y., Zhai Y G and Tong Q X 2017. Retrieval of sun-induced fluorescence using statistical method without synchronous irradiance data. IEEE Geoscience and Remote Sensing Letters, 14(3): 384–388
Zhang Y G, Guanter, L., Berry, J., Van Der Tol, C., Yang X., Tang J W and Zhang F 2016. Model-based analysis of the relationship between sun-induced chlorophyll fluorescence and gross primary production for remote sensing applications. Remote Sensing of Environment, 187: 145–155
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