Früher|Nächster

Tower-based automatic observation methods and systems of solar-induced chlorophyll fluorescence in vegetation canopy

  • role： First author第一作者
  • Affiliation：

    International Institute for Earth System Sciences, Nanjing University, Nanjing 210023, China

  • Email：mg1727058@smail.nju.edu.cn
  • Introduction：李朝晖，1995年生，男，硕士研究生,研究方向为植被定量遥感、冠层荧光和光合耦合关系。E-mail: mg1727058@smail.nju.edu.cn
LI Zhaohui1,  
  • role： Corresponding author通信作者
  • Affiliation：

    International Institute for Earth System Sciences, Nanjing University, Nanjing 210023, China

    Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

  • Email：yongguang_zhang@nju.edu.cn
  • Introduction：张永光，1980年生，男，教授，研究方向为植被叶绿素荧光遥感和全球碳循环等。E-mail: yongguang_zhang@nju.edu.cn
ZHANG Yongguang12*,  
  • Affiliation：

    International Institute for Earth System Sciences, Nanjing University, Nanjing 210023, China

ZHANG Qian1,  
  • Affiliation：

    International Institute for Earth System Sciences, Nanjing University, Nanjing 210023, China

WU Yunfei1,  
  • Affiliation：

    International Institute for Earth System Sciences, Nanjing University, Nanjing 210023, China

ZHANG Xiaokang1,  
  • Affiliation：

    International Institute for Earth System Sciences, Nanjing University, Nanjing 210023, China

ZHANG Zhaoying1

Resümee

Sun-Induced chlorophyll Fluorescence (SIF) is a by-product of plant photosynthesis and is closely related to plant photosynthesis. The study on SIF and its relationship with Gross Primary Productivity (GPP) is of great significance in understanding the mechanism of photosynthesis. Recent instrumental and methodological developments of the tower-based SIF observation system provide a complementary capacity for measuring and interpreting chlorophyll fluorescence in the context of physiological processes. In addition, a tower-based system can also support satellite-based measurements through validation, interpretation, and data inputs provision for models. Recently, the tower-based SIF observation system has developed rapidly with varied observation methods and system characteristics. In this paper, we discuss and summarize the recent developments of tower-based SIF observation methods and propose technical specifications by comparing different tower-based SIF observation systems.Tower-based SIF observation systema can be built with either two spectrometers or one spectrometer combined with an optical path switching trigger. A two-spectrometer SIF system measures the solar incident radiance and the radiance reflected by the canopy independently to realize synchronous measurement. This system can obtain high frequency spectral data, and nearly no time gap exists between the solar incident spectrum and the spectrum reflected by the canopy, reducing the uncertainty of the retrieved SIF caused by the mismatch between the two optical channels under varied weather conditions. However, the spectral response characteristics of the two spectrometers are not completely consistent. The spectral drift between the two optical channels is difficult to correct, which may lead to the increase of the Sif retrieval uncertainty. A single-spectrometer SIF system realizes the sequential switching between the two optical channels by using an optical path switch, which allows the measurement of the solar incident radiance and the canopy reflected radiance with reliable data quality. Although a certain time gap exists between the solar incident spectrum and the reflected spectrum, it can be used for SIF retrieval because of the second disparity. In cloudy and other rapidly changing light conditions, the acquisition time gap between the spectra from the two optical channels may increase the SIF retrieval uncertainty. Compared with the dual spectrometer system, the single spectrometer system is simpler, has lower cost, and avoids the risk of spectral drift, which is the mainstream tower-based SIF system.The tower-based SIF system can be employed with bi-hemispherical and hemispherical-conical observation configurations for field installation. The bi-hemispherical observation mode refers to the configuration in which both downwelling and upwelling bare fibers are equipped with cosine correctors, while the hemispherical-conical observation mode refers to the configuration in which only the upwelling bare fiber is equipped with a cosine corrector. The bi-hemispherical observation mode has a larger field of view, which is suitable for canopy measurements with high canopy heterogeneity or height with a limited installation height. The hemispherical-conical observation mode is suitable for low canopy, homogeneous canopy, and multi angle observation. In addition, if the canopy area is limited or the experimental observations have control factors, hemispherical-conical observation is more appropriate.The SIFprism system is a novel optical-prism-based SIF automatic observation system. This article introduces the software and hardware components and the flow of spectral data collection of the SIFprism system. Taking the SIFprism system as an example, the spectral data processing process is expounded, and the potential uncertainty of SIF retrieval is analyzed.The tower-based SIF observation system has experienced rapid development in recent years. Despite the essential and incremental research on near-surface SIF, further development of hardware and mechanistic theory is still urgently required. Several prospective areas for future work include improving the signal-to-noise ratio and radiation stability of the spectrometer and appraising the capabilities and efficacy of different retrieval algorithms in varied light conditions. Finally, research should strengthen the cooperation with industry to jointly develop a more efficient and stable field tower-based SIF system and formulate corresponding field observation technical specifications.

Schlüsselwort

Solar-Induced chlorophyll Fluorescence;tower-based SIF measurements;SIFprism system;measurement protocol;field data collection

References

  1. 1.
    Baker N R. 2008. Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annual Review of Plant Biology, 59: 89-113
  2. 2.
    Cogliati S, Rossini M, Julitta T, Meroni M, Schickling A, Burkart A, Pinto F, Rascher U and Colombo R. 2015. Continuous and long-term measurements of reflectance and sun-induced chlorophyll fluorescence by using novel automated field spectroscopy systems. Remote Sensing of Environment, 164: 270-281
  3. 3.
    Damm A, Erler A, Hillen W, Meroni M, Schaepman M E, Verhoef W and Rascher U. 2011. Modeling the impact of spectral sensor configurations on the FLD retrieval accuracy of sun-induced chlorophyll fluorescence. Remote Sensing of Environment, 115(8): 1882-1892
  4. 4.
    Damm A, Guanter L, Paul-Limoges E, Van der Tol C, Hueni A, Buchmann N, Eugster W, Ammann C and Schaepman M E. 2015a. Far-red sun-induced chlorophyll fluorescence shows ecosystem-specific relationships to gross primary production: an assessment based on observational and modeling approaches. Remote Sensing of Environment, 166(6): 91-105
  5. 5.
    Damm A, Guanter L, Verhoef W, Schläpfer D, Garbari S and Schaepman M E. 2015b. Impact of varying irradiance on vegetation indices and chlorophyll fluorescence derived from spectroscopy data. Remote Sensing of Environment, 156: 202-215
  6. 6.
    Daumard F, Champagne S, Fournier A, Goulas Y, Ounis A, Hanocq J F and Moya I. 2010. A feld platform for continuous measurement of canopy fluorescence. IEEE Transactions on Geoscience and Remote Sensing, 48(9): 3358-3368
  7. 7.
    Du S S, Liu L Y, Liu X J, Zhang X, Zhang X Y, Bi Y M and Zhang L C. 2018. Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite. Science Bulletin, 63(22): 1502-1512
  8. 8.
    Du S S, Liu L Y, Liu X J, Guo J, Hu J C, Wang S Q and Zhang Y G. 2019. SIFSpec: measuring solar-induced chlorophyll fluorescence observations for remote sensing of photosynthesis. Sensors, 19(13): 3009
  9. 9.
    Frankenberg C, Fisher J B, Worden J, Badgley G, Saatchi S S, Lee J E, Toon G C, Butz A, Jung M, Kuze A and Yokota T. 2011. New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophysical Research Letters, 38(17): L17706
  10. 10.
    Frankenberg C, O’Dell C, Berry J, Guanter L, Joiner J, Köhler P, Pollack 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
  11. 11.
    Frankenberg C and Berry J. 2018. Solar induced chlorophyll fluorescence: origins, relation to photosynthesis and retrieval//Reference Module in Earth Systems and Environmental Sciences. vol. 3. Oxford: Elsevier: 143-162
  12. 12.
    Gastellu-Etchegorry J P, Lauret N, Yin T G, Landier L, Kallel A, Malenovský Z, Al Bitar A, Aval J, Benhmida S, Qi J B, Medjdoub G, Guilleux J, Chavanon E, Cook B, Morton D, Chrysoulakis N and Mitraka Z. 2017. DART: recent advances in remote sensing data modeling with atmosphere, polarization, and chlorophyll fluorescence. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(6): 2640-2649
  13. 13.
    Grossmann K, Frankenberg C, Magney T S, Hurlock S C, Seibt U and Stutz J. 2018. PhotoSpec: a new instrument to measure spatially distributed red and far-red solar-induced chlorophyll fluorescence. Remote Sensing of Environment, 216: 311-327
  14. 14.
    Gu L, Wood J D, Chang C Y Y, Sun Y and Riggs J S. 2019. Advancing terrestrial ecosystem science with a novel automated measurement system for sun-induced chlorophyll fluorescence for integration with eddy covariance flux networks. Journal of Geophysical Research: Biogeosciences, 124: 127-146
  15. 15.
    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(8): L08401
  16. 16.
    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
  17. 17.
    Guanter L, Zhang Y, Jung M, Joiner J, Voigt M, Berry J A, Frankenberg C, Huete A R, Zarco-Tejada P, Lee J E, Moran M S, Ponce-Campos G, Beer C, Camps-Valls G, Buchmann N, Dianelle D, Klumpp K, Cescatti A, Baker J M and Grifs T J. 2014. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences of the United States of America, 111(14): E1327-E1333
  18. 18.
    He L M, Chen J M, Liu J, Mo G and Joiner J. 2017. Angular normalization of GOME-2 sun-induced chlorophyll fluorescence observation as a better proxy of vegetation productivity. Geophysical Research Letters, 44(11): 5691-5699
  19. 19.
    Hernández-Clemente R, North P R J, Hornero A and Zarco-Tejada P J. 2017. Assessing the effects of forest health on sun-induced chlorophyll fluorescence using the FluorFLIGHT 3-D radiative transfer model to account for forest structure. Remote Sensing of Environment, 193: 165-179
  20. 20.
    Hu J C, Liu L Y, Guo J, Du S S and Liu X J. 2018. Upscaling solar-induced chlorophyll fluorescence from an instantaneous to daily scale gives an improved estimation of the gross primary productivity. Remote Sensing, 10(10): 1663
  21. 21.
    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(3): 637-651
  22. 22.
    Joiner J, Yoshida Y, Vasilkov A P, Middleton E M, Campbell P K E, Yoshida Y, Kuze A and Corp L A. 2012. Filling-in of near-infrared solar lines by terrestrial fluorescence and other geophysical effects: simulations and space-based observations from SCIAMACHY and GOSAT. Atmospheric Measurement Techniques, 5(4): 809-829
  23. 23.
    Julitta T, Burkart A, Colombo R, Rossini M, Schickling A, Migliavacca M, Cogliati S, Wutzler T and Rascher U. 2017. Accurate measurements of fluorescence in the O2A and O2B band using the FloX spectroscopy system - results and prospects//Proceedings of the Potsdam GHG Flux Workshop: From Photosystems to Ecosystems. Potsdam, Germany: [s.n.]
  24. 24.
    Köhler P, Frankenberg C, Magney T S, Guanter L, Joiner J and Landgraf J. 2018a. Global retrievals of solar-induced chlorophyll fluorescence with TROPOMI: first results and intersensor comparison to OCO-2. Geophysical Research Letters, 45(19): 10456-10463
  25. 25.
    Köhler P, Guanter L, Kobayashi H, Walther S and Yang W. 2018b. Assessing the potential of sun-induced fluorescence and the canopy scattering coefficient to track large-scale vegetation dynamics in Amazon forests. Remote Sensing of Environment, 204: 769-785
  26. 26.
    Li X, Xiao J F, He B B, Arain M A, Beringer J, Desai A R, Emmel C, Hollinger D Y, Krasnova A, Mammarella I, Noe S M, Ortiz P S, Rey-Sanchez A C, Rocha A V and Varlagin A. 2018. Solar-induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: first global analysis based on OCO-2 and flux tower observations. Global Change Biology, 24(9): 3990-4008
  27. 27.
    Li Z H, Zhang Q, Li J, Yang X, Wu Y F, Zhang Z Y, Wang S H, Wang H Z and Zhang Y G. 2020. Solar-induced chlorophyll fluorescence and its link to canopy photosynthesis in maize from continuous ground measurements. Remote Sensing of Environment, 236: 111420
  28. 28.
    Liu L Y, Guan L L and Liu X J. 2017. Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence. Agricultural and Forest Meteorology, 232: 1-9
  29. 29.
    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
  30. 30.
    Liu X J, Liu L Y, Zhang S and Zhou X F. 2015. New spectral fitting method for full-spectrum solar-induced chlorophyll fluorescence retrieval based on principal components analysis. Remote Sensing, 7(8): 10626-10645
  31. 31.
    Mazzoni M, Falorni P and Verhoef W. 2010. High-resolution methods for fluorescence retrieval from space. Optics Express, 18(15): 15649-15663
  32. 32.
    Mazzoni M, Meroni M, Fortunato C, Colombo R and Verhoef W. 2012. Retrieval of maize canopy fluorescence and reflectance by spectral fitting in the O2–A absorption band. Remote Sensing of Environment, 124: 72-82
  33. 33.
    Meroni M, Busetto L, Colombo R, Guanter L, Moreno J and Verhoef W. 2010. Performance of Spectral Fitting Methods for vegetation fluorescence quantification. Remote Sensing of Environment, 114: 363-374
  34. 34.
    Meroni M and Colombo R. 2006. Leaf level detection of solar induced chlorophyll fluorescence by means of a subnanometer resolution spectroradiometer. Remote Sensing of Environment, 103(4): 438-448
  35. 35.
    Middleton E M, Huemmrich K F, Zhang Q, Campbell P K E and Landis D R. 2018. Spectral bio-indicators of photosynthetic efficiency and vegetation stress//Thenkabail P S, Lyon J G, Huete A, eds. Hyperspectral Remote Sensing of Vegetation, Vol. III: Biophysical and Biochemical Characterization and Plant Species Studies. 2nd ed. New York: CRC Press: 133-179
  36. 36.
    Nichol C J, Drolet G, Porcar-Castell A, Wade T, Sabater N, Middleton E M, MacLellan C, Levula J, Mammarella I, Vesala T and Atherton J. 2019. Diurnal and seasonal solar induced chlorophyll fluorescence and photosynthesis in a boreal Scots pine canopy. Remote Sensing, 11(3): 273
  37. 37.
    Pacheco-Labrador J, Hueni A, Mihai L, Sakowska K, Julitta T, Kuusk J, Sporea D, Alonso L, Burkart A, Cendrero-Mateo M P, Aasen H, Goulas Y and Arthur A M. 2019. Sun-induced chlorophyll fluorescence I: instrumental considerations for proximal spectroradiometers. Remote Sensing, 11(8): 960
  38. 38.
    Paul-Limoges E, Damm A, Hueni A, Liebische F, Eugster W, Schaepman M E and Buchmann N. 2018. Effect of environmental conditions on sun-induced fluorescence in a mixed forest and a cropland. Remote Sensing of Environment, 219: 310-323
  39. 39.
    Pérez-Priego O, Guan J, Rossini M, Fava F, Wutzler T, Moreno G, Carvalhais N, Carrara A, Kolle O, Julitta T, Schrumpf M, Reichstein M and Migliavacca M. 2015. Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem. Biogeosciences, 12(21): 6351-6367
  40. 40.
    Plascyk J A and Gabriel F C. 1975. The Fraunhofer line discriminator MKII—An airborne instrument for precise and standardized ecological luminescence measurement. IEEE Transactions on Instrumentation and Measurement, 24(4): 306-313
  41. 41.
    Qiu B, Xue Y K, Fisher J B, Guo W D, Berry J A and Zhang Y G. 2018. Satellite chlorophyll fluorescence and soil moisture observations lead to advances in the predictive understanding of global terrestrial coupled carbon-water cycles. Global Biogeochemical Cycles, 32(3): 360-375
  42. 42.
    Schlau-Cohen G S and Berry J. 2015. Photosynthetic fluorescence, from molecule to planet. Physics Today, 68(9): 66
  43. 43.
    Song L, Guanter L, Guan K Y, You L Z, Huete A, Ju W M and Zhang Y G. 2018. Satellite sun-induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo-Gangetic Plains. Global Change Biology, 24(9): 4023-4037
  44. 44.
    Sun Y, Frankenberg C, Jung M, Joiner J, Guanter L, Köhler P and Magney T. 2018. 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, 209: 808-823
  45. 45.
    Verma M, Schimel D, Evans B, Frankenberg C, Beringer J, Drewry D T, Magney T, Marang I, Hutley L, Moore C and Eldering A. 2017. Effect of environmental conditions on the relationship between solar-induced fluorescence and gross primary productivity at an OzFlux grassland site. Journal of Geophysical Research: Biogeosciences, 122: 716-733
  46. 46.
    Xu S, Liu Z G, Zhao L, Zhao H R and Ren S X. 2018. Diurnal response of sun-induced fluorescence and PRI to water stress in maize using a near-surface remote sensing platform. Remote Sensing, 10(10): 1510
  47. 47.
    Yang X, Shi H Y, Stovall A, Guan K Y, Miao G F, Zhang Y G, Zhang Y, Xiao X M, Ryu Y and Lee J E. 2018. FluoSpec2—an automated field spectroscopy system to monitor canopy solar-induced fluorescence. Sensors, 18(7): 2063
  48. 48.
    Yang X, Tang J W, Mustard J F, Lee J E, Rossini M, Joiner J, Munger J W, Kornfeld A and Richardson A D. 2015. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophysical Research Letters, 42(8): 2977-2987
  49. 49.
    Zhang Q, Zhang X K, Li Z H, Wu Y F and Zhang Y G. 2019. Comparison of Bi-hemispherical and hemispherical-conical configurations for in situ measurements of solar-induced chlorophyll fluorescence. Remote Sensing, 11(22): 2642
  50. 50.
    Zhang Y, Xiao X M, Zhang Y G, Wolf S, Zhou S, Joiner J, Guanter L, Verma M, Sun Y, Yang X, Paul-Limoges E, Gough C M, Wohlfahrt G, Gioli B, van der Tol C, Yann N, Lund M and de Grandcourt A. 2018. On the relationship between sub-daily instantaneous and daily total gross primary production: implications for interpreting satellite-based SIF retrievals. Remote Sensing of Environment, 205: 276-289
  51. 51.
    Zhang Y G, Guanter L, Berry J A, Van der Tol C, Yang X, Tang J W and Zhang F M. 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
  52. 52.
    Zhao F, Dai X, Verhoef W, Guo Y Q, Van der Tol C, Li Y G and Huang Y B. 2016. FluorWPS: a Monte Carlo ray-tracing model to compute sun-induced chlorophyll fluorescence of three-dimensional canopy. Remote Sensing of Environment, 187: 385-399
  53. 53.
    Zhou X J, Liu Z G, Xu S, Zhang W W and Wu J. 2016. An automated comparative observation system for sun-induced chlorophyll fluorescence of vegetation canopies. Sensors, 16(6): 775

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website