سابِق|تال

Top-of-atmosphere hyperspectral remote sensing of solar-induced chlorophyll fluorescence: A review of methods

  • role： First author第一作者
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：zhanglf@radi.ac.cn
  • Introduction：张立福(1967—　)，男，研究员，研究方向为高光谱遥感技术和应用。E-mail：zhanglf@radi.ac.cn
Lifu ZHANG1,  
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

Siheng WANG12,  
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Changping HUANG1

ملخص

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.

مفهوم

SIF retrieval;hyperspectral remote sensing;radiative transfer;data-driven approaches

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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 (
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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]
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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 (
  20. 20.
    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
  21. 21.
    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 (
  22. 22.
    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
  23. 23.
    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
  24. 24.
    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
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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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