이전|다음

탑에서의 태양광유발 엽록소 형광의 다중 역방향 알고리즘 비교 분석

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

  • Email：chenjh.14b@igsnrr.ac.cn
  • Introduction：陈敬华，研究方向为生态遥感与生态系统模拟。E-mail： chenjh.14b@igsnrr.ac.cn
CHEN Jinghua1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Hubei Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China

    Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks, Ministry of Natural Resources, Wuhan 430074, China

  • Email：dengzy@cug.edu.cn
  • Introduction：邓濯缨，研究方向为植被高光谱遥感。E-mail： dengzy@cug.edu.cn
DENG Zhuoying23*,  
  • Affiliation：

    Hubei Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China

    Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks, Ministry of Natural Resources, Wuhan 430074, China

RAO Yuanyi23,  
  • Affiliation：

    Hubei Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China

    Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks, Ministry of Natural Resources, Wuhan 430074, China

HU Zhuoran23,  
  • Affiliation：

    Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    Hubei Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China

    Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks, Ministry of Natural Resources, Wuhan 430074, China

WANG Shaoqiang123,  
  • Affiliation：

    Hubei Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China

    Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks, Ministry of Natural Resources, Wuhan 430074, China

CHEN Xuan23,  
  • Affiliation：

    Hubei Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China

    Engineering Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sinks, Ministry of Natural Resources, Wuhan 430074, China

GU Peng23,  
  • Affiliation：

    Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

WANG Jing1

추상적인

태양 광유발 엽록소 형광 (Solar-induced Chlorophyll Fluorescence, SIF)은 식물이 자연 광 아래 광합성을 할 때 방출되는 약한 광학 신호입니다. 식물 광합성의 비파괴적 조사기로써 SIF는 최근에 식물 생산성 추정, 스트레스 모니터링 및 현상 관찰 등 여러 분야에 광범위하게 적용되고 있습니다. 지표 근접 탑에서의 SIF 관측은 식물의 생리 생화학 과정을 보다 정교한 척도에서 이해하는 데 도움이 되지만, SIF 신호가 매우 약하고 식물 반사광과 혼합되어 있기 때문에 전통적인 방법으로 SIF를 직접 관찰하기 어렵습니다. 현재 식물 스펙트럼을 기반으로 한 여러 SIF 역방향 알고리즘이 있지만, 현재의 연구는 이러한 알고리즘의 차이와 다양한 조건에서의 적응성에 대한 이해와 토의 부족한 상태입니다. 따라서 본 연구에서는 산림 지역 탑 관측에서 얻은 고선명 스펙트럼 데이터를 기반으로 여섯 가지 SIF 역방향 알고리즘 (sFLD, 3FLD, iFLD, SFM, SVD 및 BSF)에 대한 비교 분석 및 평가를 수행했습니다. 결과는 다음을 보여줍니다. (1) 전통적인 FLD 시리즈 알고리즘 (sFLD, 3FLD 및 iFLD)의 역방향 결과는 상당히 유사합니다 (R2>0.86), SVD 알고리즘의 역방향 결과는 다른 알고리즘보다 유의하게 높으며, SFM 및 BSF 알고리즘은 다른 알고리즘들과 높은 상관 관계를 유지합니다. (2) SFM, SVD 및 BSF 알고리즘은 전통적인 FLD 시리즈 알고리즘에 비해 더 높은 정확성과 안정성을 보이며 (NIRvR과 비교: 평균 ΔR2=0.29, GPP와 비교: 평균 ΔR2=0.10), 구름이 많거나 날씨가 불안정한 경우에도 SFM 알고리즘은 여전히 GPP와 높은 상관 관계를 유지하므로 복잡한 환경에서의 SIF 역방향에 가장 적합하며, SVD 알고리즘은 식물의 일일 규모에서 태양 광선의 변화를 가장 잘 나타낼 수 있습니다. (3) BSF 알고리즘은 관측 높이 및 사전 가중치 설정에 민감하지만 온도 매개 변수에는 민감하지 않으며, BSF 알고리즘을 사용하여 SIF를 역방향으로 추정할 때 사용 상황과 데이터 특성에 따라 조정해야 합니다. 본 연구는 여섯 가지 전형적인 SIF 역방향 알고리즘 간의 차이를 명확히하고, 미래의 다양한 상황에서의 SIF 역방향 알고리즘 선택에 대한 참고 자료를 제공합니다.

키워드

태양 광유발 엽록소 형광 (SIF); 역방향 알고리즘 평가; 탑 관측; 고선명 스펙트럼 데이터; 총 일차생산량 (GPP); 산림

References

  1. 1.
    Alonso L, Gomez-Chova L, Vila-Frances J, Amoros-Lopez J, Guanter L, Calpe J and Moreno J. 2008. Improved fraunhofer line discrimination method for vegetation fluorescence quantification. IEEE Geoscience and Remote Sensing Letters, 5(4): 620-624
  2. 2.
    Amiro B D, Barr A G, Barr J G, Black T A, Bracho R, Brown M, Chen J, Clark K L, Davis K J, Desai A R, Dore S, Engel V, Fuentes J D, Goldstein A H, Goulden M L, Kolb T E, Lavigne M B, Law B E, Margolis H A, Martin T, McCaughey J H, Misson L, Montes-helu M, Noormets A, Randerson J T, Starr G and Xiao J. 2010. Ecosystem carbon dioxide fluxes after disturbance in forests of North America. Journal of Geophysical Research: Biogeosciences, 115(G4): G00K02
  3. 3.
    Cao J J, An Q, Zhang X, Xu S, Si T and Niyogi D. 2021. Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition? Science of the Total Environment, 792: 148396
  4. 4.
    Cendrero-Mateo M P, Wieneke S, Damm A, Alonso L, Pinto F, Moreno J, Guanter L, Celesti M, Rossini M, Sabater N, Cogliati S, Julitta T, Rascher U, Goulas Y, Aasen H, Pacheco-Labrador J and Mac Arthur A. 2019. Sun-induced chlorophyll fluorescence III: benchmarking retrieval methods and sensor characteristics for proximal sensing. Remote Sensing, 11(8): 962
  5. 5.
    Chang C Y, Guanter L, Frankenberg C, Köhler P, Gu L H, Magney T S, Grossmann K and Sun Y. 2020. Systematic assessment of retrieval methods for canopy far-red solar-induced chlorophyll fluorescence using high-frequency automated field spectroscopy. Journal of Geophysical Research: Biogeosciences, 125(7): e2019JG005533
  6. 6.
    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
  7. 7.
    Du S S. 2020. Retrieval of Solar-Induced Chlorophyll Fluorescence Based on Domestic Satellites. Beijing: University of Chinese Academy of Sciences (Aerospace Information Research Institute, Chinese Academy of Sciences)
  8. 8.
    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
  9. 9.
    Fang J Y. 2021. Ecological perspectives of carbon neutrality. Chinese Journal of Plant Ecology, 45(11): 1173-1176
  10. 10.
    Fu B J, Liu Y S, Cao Z, Wang Z Z and Wu X T. 2023. Current conditions, issues, and suggestions for ecological protection and high-quality development in Loess Plateau. Bulletin of Chinese Academy of Sciences, 38(8): 1110-1117
  11. 11.
    Gu L H, Han J M, Wood J D, Chang C Y Y and Sun Y. 2019. Sun-induced Chl fluorescence and its importance for biophysical modeling of photosynthesis based on light reactions. New Phytologist, 223(3): 1179-1191
  12. 12.
    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
  13. 13.
    Hu J C, Liu L Y and Liu X J. 2015. Assessing uncertainties of sun-induced chlorophyll fluorescence retrieval using Fluor MOD model. Journal of Remote Sensing (in Chinese), 19(4): 594-608
  14. 14.
    Ji M H, Tang B H and Li Z L. 2019. Review of solar-induced chlorophyll fluorescence retrieval methods from satellite data. Remote Sensing Technology and Application, 34(3): 455-466
  15. 15.
    Li S L, Gao M F and Li Z L. 2021a. Retrieving sun-induced chlorophyll fluorescence from hyperspectral data with TanSat satellite. Sensors, 21(14): 4886
  16. 16.
    Li S L, Gao M F, Li Z L, Duan S B and Leng P. 2021b. Uncertainty analysis of SVD-based spaceborne far-red sun-induced chlorophyll fluorescence retrieval using TanSat satellite data. International Journal of Applied Earth Observation and Geoinformation, 103: 102517
  17. 17.
    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
  18. 18.
    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
  19. 19.
    Liu L Y. 2014. Principles and applications of quantitative remote sensing in vegetation. Beijing: Science Press.
  20. 20.
    Liu O Y, Liu L Y, Hu J C, Liu X J and Jiang J B. 2019. Assessment of solar-induced chlorophyll fluorescence retrieval from the atmospheric H2O absorption bands at 719 nm. Remote Sensing Technology and Application, 34(3): 500-510
  21. 21.
    Liu X J, Liu L Y, Du S S and Qi M J. 2025. Inconsistent diurnal patterns of far-red solar-induced chlorophyll fluorescence retrieved with different algorithms from tower-based observations. Journal of Remote Sensing, 5: 0429
  22. 22.
    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
  23. 23.
    Magney T S, Barnes M L and Yang X. 2020. On the covariation of chlorophyll fluorescence and photosynthesis across scales. Geophysical Research Letters, 47(23): e2020GL091098
  24. 24.
    Maier S W, Günther K P and Stellmes M. 2004. Sun-induced fluorescence: a new tool for precision farming//VanToai T, Major D, McDonald M, Schepers J and Tarpley L, eds. Digital Imaging and Spectral Techniques: Applications to Precision Agriculture and Crop Physiology. [s.l.]: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America: 207-222
  25. 25.
    Masters G M. 2013. Renewable and Efficient Electric Power Systems. 2nd ed. Hoboken: John Wiley and Sons Inc.
  26. 26.
    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(2): 363-374
  27. 27.
    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
  28. 28.
    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(10): 2037-2051
  29. 29.
    Mohammed G H, Colombo R, Middleton E M, Rascher U, van der Tol C, Nedbal L, Goulas Y, Pérez-Priego O, Damm A, Meroni M, Joiner J, Cogliati S, Verhoef W, Malenovský Z, Gastellu-Etchegorry J P, Miller J R, Guanter L, Moreno J, Moya I, Berry J A, Frankenberg C and Zarco-Tejada P J. 2019. Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress. Remote Sensing of Environment, 231: 111177
  30. 30.
    Plascyk J A. 1975. The MK II Fraunhofer line discriminator (FLD-II) for airborne and orbital remote sensing of solar-stimulated luminescence. Optical Engineering, 14(4): 144339
  31. 31.
    Porcar-Castell A, Tyystjärvi E, Atherton J, Van der Tol C, Flexas J, Pfündel E E, Moreno J, Frankenberg C and Berry J A. 2014. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. Journal of Experimental Botany, 65(15): 4065-4095
  32. 32.
    Qiu B and Guo W D. 2022. Progresses in solar-induced chlorophyll fluorescence and its applications in terrestrial ecosystem carbon cycling and land-atmosphere interaction. Transactions of Atmospheric Sciences, 45(6): 801-814
  33. 33.
    Reda I and Andreas A. 2004. Solar position algorithm for solar radiation applications. Solar Energy, 76(5): 577-589
  34. 34.
    Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Gilmanov T, Granier A, Grünwald T, Havránková K, Ilvesniemi H, Janous D, Knohl A, Laurila T, Lohila A, Loustau D, Matteucci G, Meyers T, Miglietta F, Ourcival J M, Pumpanen J, Rambal S, Rotenberg E, Sanz M, Tenhunen J, Seufert G, Vaccari F, Vesala T, Yakir D and Valentini R. 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, 11(9): 1424-1439
  35. 35.
    van der Tol C, Julitta T, Yang P Q, Sabater N, Reiter I, Tudoroiu M, Schuettemeyer D and Drusch M. 2023. Retrieval of chlorophyll fluorescence from a large distance using oxygen absorption bands. Remote Sensing of Environment, 284: 113304
  36. 36.
    van der Tol C, Verhoef W, Timmermans J, Verhoef A and Su Z. 2009. An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance. Biogeosciences, 6(12): 3109-3129
  37. 37.
    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(3): 716-733
  38. 38.
    Vitrack-Tamam S, Yasuor H, Hamus-Cohen D, Erel R, Rubinovich L and Liran O. 2025. Solar induced fluorescence retrieved from avocado (Persea americana Mill.) canopies along the season correlates with sugar levels in the developing fruit. European Journal of Remote Sensing, 58(1): 2449940
  39. 39.
    Wang X, Wang C Z, Wu J, Miao G F, Chen M, Chen S L, Wang S H, Guo Z F, Wang Z H, Wang B, Li J, Zhao Y J, Wu X L, Zhao C F, Lin W L, Zhang Y G and Liu L L. 2021. Intermediate aerosol loading enhances photosynthetic activity of croplands. Geophysical Research Letters, 48(7): e2020GL091893
  40. 40.
    Wang Y N, Sun Y, Chen Y N, Wu C Y, Huang C P, Li C and Tang X G. 2023a. Non-linear correlations exist between solar-induced chlorophyll fluorescence and canopy photosynthesis in a subtropical evergreen forest in Southwest China. Ecological Indicators, 157: 111311
  41. 41.
    Wang Y Q, Leng P, Shang G F, Zhang X and Li Z L. 2023b. Sun-induced chlorophyll fluorescence is superior to satellite vegetation indices for predicting summer maize yield under drought conditions. Computers and Electronics in Agriculture, 205: 107615
  42. 42.
    Wu L S, Zhang Y G, Zhang Z Y, Zhang X K and Wu Y F. 2022. Remote sensing of solar-induced chlorophyll fluorescence and its applications in terrestrial ecosystem monitoring. Chinese Journal of Plant Ecology, 46(10): 1167-1199
  43. 43.
    Yan L S, Liu X J, Chen J D, Zou C, Du K Q and Liu L Y. 2023. Performance of data-driven algorithm for SIF retrieval from tower-based observation. Remote Sensing Technology and Application, 38(4): 924-934
  44. 44.
    Yu G R and Wang Q F. 2010. Ecophysiology of Plant Photosynthesis, Transpiration, and Water Use. Beijing: Science Press
  45. 45.
    Yu L and Piao S L. 2014. Key scientific points on carbon and other biogeochemical cycles from the IPCC Fifth assessment report. Progressus Inquisitiones de Mutatione Climatis, 10(1): 33-36
  46. 46.
    Zeng Y L, Chen M, Hao D L, Damm A, Badgley G, Rascher U, Johnson J E, Dechant B, Siegmann B, Ryu Y, Qiu H, Krieger V, Panigada C, Celesti M, Miglietta F, Yang X and Berry J A. 2022. Combining near-infrared radiance of vegetation and fluorescence spectroscopy to detect effects of abiotic changes and stresses. Remote Sensing of Environment, 270: 112856
  47. 47.
    Zeng, Y L, Badgley G, Dechant B, Ryu Y, Chen M, Berry J A. 2019. A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence. Remote Sensing of Environment 232:111209
  48. 48.
    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
  49. 49.
    Zhang Z Y, Wang S H, Qiu B, Song L and Zhang Y G. 2019. Retrieval of sun-induced chlorophyll fluorescence and advancements in carbon cycle application. Journal of Remote Sensing, 23(1): 37-52

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