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Solar-induced chlorophyll fluorescence data-based study on the spatial and temporal patterns of vegetation phenology in the Northern Hemisphere during the period of 2007—2018

  • role： First author第一作者
  • Affiliation：

    School of Earth and Space Science, Peking University, Beijing 100871, China

  • Email：haoran.zhou@pku.edu.cn
  • Introduction：周浩然，研究方向为摄影测量与遥感。E-mail： haoran.zhou@pku.edu.cn
ZHOU Haoran1,  
  • Affiliation：

    PopSmart Technology Co., Ltd, Ningbo 315100, China

SUN Hua2,  
  • Affiliation：

    School of Earth and Space Science, Peking University, Beijing 100871, China

SHI Zhenwei1,  
  • Affiliation：

    School of Earth and Space Science, Peking University, Beijing 100871, China

PENG Fei1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Earth and Space Science, Peking University, Beijing 100871, China

  • Email：yi.lin@pku.edu.cn
  • Introduction：林沂，研究方向为激光遥感、陆地生态系统遥感和森林生态遥感等。E-mail： yi.lin@pku.edu.cn
LIN Yi1*

resumen

Vegetation phenology refers to the specific timing of periodic events in plants and how these timings are adapted by periodic variations in climate and environmental factors such as air temperature and soil moisture content. Vegetation phenology change trends are closely related to global climate change; therefore, studies on vegetation phenology can help us better understand global climate change and how vegetation reacts to climate change. Remote sensing technology has been the main means for large-scale vegetation research; however, there have been problems when using remote sensing Vegetation Indexes (VIs) to monitor vegetation phenology due to the discrepancies between the vegetation greenness index and photosynthesis. Especially in evergreen forests, the periodic change in VI datasets is weak; thus, it is difficult to capture phenology metrics based on these VI datasets. Therefore, there is an urgent need to develop new technology to better monitor vegetation phenology. Recently, Sun-Induced Chlorophyll Fluorescence (SIF) has attracted increasing attention since it is strongly coupled with photosynthesis and has good performance in estimating vegetation Gross Primary Productivity (GPP). Due to its strong correlation with GPP, SIF is capable of capturing the rapid change in GPP over time and has great potential for monitoring vegetation phenology. Based on GOME-2 SIF, GOSIF, and CSIF data in the Northern Hemisphere during 2007—2018, this study mainly calculated the vegetation phenology metrics by using a double logistic model and analyzed the vegetation phenology change trends by using Sen’s slope trend test. The results showed the following: (1) The double logistic model used in this study could capture the start of the growing season (SOS) better than the end of the growing season (EOS), and vegetation phenology metrics derived from SIF data have stronger correlations with vegetation phenology metrics derived from GPP data than those derived from VI data, especially for SOS. (2) In the Northern Hemisphere, the multiannual average SOS was mainly (>90%) concentrated in 100—170 days, while the multiannual average EOS was mainly concentrated in 220—270 days. The SOS occurred later in high latitude areas and high-altitude areas, while the EOS showed the opposite trend. (3) From 2007 to 2018, the SOS derived from GOME-2 SIF data in the Northern Hemisphere showed a significant advancing trend (Senslope was -0.173), and the EOS showed an insignificant advancing trend (Senslope was -0.002). (4) The vegetation phenology in high latitude cold areas was mainly affected by air temperature, while the vegetation phenology in middle and low latitude arid areas was mainly affected by precipitation. SIF has great potential to calculate phenological characteristics based on vegetation photosynthesis, and vegetation phenology derived from GOME-2 SIF data showed a weaker change trend over the last 10 years compared with that of the period from 1980 to 2010. Overall, this study first analyzed the vegetation phenology change trends in the past 10 years based on long-term GOME-2 SIF datasets, and the results in this study could promote our understanding of global climate change and the terrestrial carbon cycle.

palabra clave

remote sensing;solar-induced chlorophyll fluorescence;vegetation phenology;spatial pattern;trend;climatic factors

References

  1. 1.
    Balzarolo M, Vicca S, Nguy-Robertson A L, Bonal D, Elbers J A, Fu Y H, Grünwald T, Horemans J A, Papale D, Peñuelas J, Suyker A and Veroustraete F. 2016. Matching the phenology of Net Ecosystem Exchange and vegetation indices estimated with MODIS and FLUXNET in-situ observations. Remote Sensing of Environment, 174: 290-300
  2. 2.
    Cao C and Jiang X. 2017. Response of plant distribution to climate change. Journal of Green Science and Technology, (16): 111-113
  3. 3.
    Dechant B, Ryu Y, Badgley G, Zeng Y L, Berry J A, Zhang Y G, Goulas Y, Li Z H, Zhang Q, Kang M, Li J and Moya I. 2020. Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops. Remote Sensing of Environment, 241: 111733
  4. 4.
    Fisher J I, Mustard J F and Vadeboncoeur M A. 2006. Green leaf phenology at Landsat resolution: scaling from the field to the satellite. Remote Sensing of Environment, 100(2): 265-279
  5. 5.
    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
  6. 6.
    Gilbert R O. 1987. Statistical Methods for Environmental Pollution Monitoring. Hoboken: John Wiley and Sons
  7. 7.
    Hmimina G, Dufrêne E, Pontailler J Y, Delpierre N, Aubinet M, Caquet B, De Grandcourt A, Burban B, Flechard C, Granier A, Gross P, Heinesch B, Longdoz B, Moureaux C, Ourcival J M, Rambal S, Saint André L and Soudani K. 2013. Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: an investigation using ground-based NDVI measurements. Remote Sensing of Environment, 132: 145-158
  8. 8.
    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
  9. 9.
    Joiner J, Yoshida Y, Vasilkov A P, Schaefer K, Jung M, Guanter L, Zhang Y, Garrity S, Middleton E M, Huemmrich K F, Gu L and Marchesini L B. 2014. The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange. Remote Sensing of Environment, 152: 375-391
  10. 10.
    Jung M, Schwalm C, Migliavacca M, Walther S, Camps-Valls G, Koirala S, Anthoni P, Besnard S, Bodesheim P, Carvalhais N, Chevallier F, Gans F, Goll D S, Haverd V, Köhler P, Ichii K, Jain A K, Liu J Z, Lombardozzi D, Nabel J E M S, Nelson J A, O’Sullivan M, Pallandt M, Papale D, Peters W, Pongratz J, Rödenbeck C, Sitch S, Tramontana G, Walker A, Weber U and Reichstein M. 2020. Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach. Biogeosciences, 17(5): 1343-1365
  11. 11.
    Kato S and Komiyama A. 2002. Spatial and seasonal heterogeneity in understory light conditions caused by differential leaf flushing of deciduous overstory trees. Ecological Research, 17(6): 687-693
  12. 12.
    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(6): 2589-2608
  13. 13.
    Li X and Xiao J F. 2019. A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data. Remote Sensing, 11(5): 517
  14. 14.
    Liu G H and Fu B J. 2001. Effects of global climate change on forest ecosystems. Journal of Natural Resources, 16(1): 71-78
  15. 15.
    Liu X T, Zhou L, Shi H, Wang S Q and Chi Y G. 2018. Phenological characteristics of temperate coniferous and broad-leaved mixed forests based on multiple remote sensing vegetation indices, chlorophyll fluorescence and CO2 flux data. Acta Ecologica Sinica, 38(10): 3482-3494
  16. 16.
    Lu X L, Liu Z Q, Zhou Y Y, Liu Y L, An S Q and Tang J W. 2018. Comparison of phenology estimated from reflectance-based indices and solar-induced chlorophyll fluorescence (SIF) observations in a temperate forest using GPP-based phenology as the standard. Remote Sensing, 10(6): 932
  17. 17.
    Luus K A, Commane R, Parazoo N C, Benmergui J, Euskirchen E S, Frankenberg C, Joiner J, Lindaas J, Miller C E, Oechel W C, Zona D, Wofsy S and Lin J C. 2017. Tundra photosynthesis captured by satellite-observed solar-induced chlorophyll fluorescence. Geophysical Research Letters, 44(3): 1564-1573
  18. 18.
    Meehl G A and Tebaldi C. 2004. More intense, more frequent, and longer lasting heat waves in the 21st century. Science, 305(5686): 994-997
  19. 19.
    Melaas E K, Friedl M A and Zhu Z. 2013. Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM + data. Remote Sensing of Environment, 132: 176-185
  20. 20.
    Morecroft M D, Stokes V J and Morison J I L. 2003. Seasonal changes in the photosynthetic capacity of canopy oak (Quercus robur) leaves: the impact of slow development on annual carbon uptake. International Journal of Biometeorology, 47(4): 221-226
  21. 21.
    Piao S, Liu Q, Chen A P, Janssens I A, Fu Y S, Dai J H, Liu L L, Lian X, Shen M G and Zhu X L. 2019. Plant phenology and global climate change: current progresses and challenges. Global Change Biology, 25(6): 1922-1940
  22. 22.
    Richardson A D, Braswell B H, Hollinger D Y, Jenkins J P and Ollinger S V. 2009a. Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecological Applications, 19(6): 1417-1428
  23. 23.
    Richardson A D, Hollinger D Y, Dail D B, Lee J T, Munger J W and O’Keefe J. 2009b. Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Physiology, 29(3): 321-331
  24. 24.
    Richardson A D, Black T A, Ciais P, Delbart N, Friedl M A, Gobron N, Hollinger D Y, Kutsch W L, Longdoz B, Luyssaert S, Migliavacca M, Montagnani L, Munger J W, Moors E, Piao S, Rebmann C, Reichstein M, Saigusa N, Tomelleri E, Vargas R and Varlagin A. 2010. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1555): 3227-3246
  25. 25.
    Sen P K. 1968. Estimates of the regression coefficient based on Kendall’s Tau. Journal of the American Statistical Association, 63(324): 1379-1389
  26. 26.
    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
  27. 27.
    Tang J W, Körner C, Muraoka H, Piao S, Shen M G, Thackeray S J and Yang X. 2016. Emerging opportunities and challenges in phenology: a review. Ecosphere, 7(8): e01436
  28. 28.
    Testa S, Soudani K, Boschetti L and Borgogno Mondino E. 2018. MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests. International Journal of Applied Earth Observation and Geoinformation, 64: 132-144
  29. 29.
    Tong L M, Zeng B and Wang X. 2016. Phenological variation of different vegetation types and its response to climate changes in Shanxi province from 2000 to 2012. Research of Soil and Water Conservation, 23(2): 194-200
  30. 30.
    Tramontana G, Jung M, Schwalm C R, Ichii K, Camps-Valls G, Ráduly B, Reichstein M, Arain M A, Cescatti A, Kiely G, Merbold L, Serrano-Ortiz P, Sickert S, Wolf S and Papale D. 2016. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences, 13(14): 4291-4313
  31. 31.
    Turner A J, Köhler P, Magney T S, Frankenberg C, Fung I and Cohen R C. 2020. A double peak in the seasonality of California’s photosynthesis as observed from space. Biogeosciences, 17(2): 405-422
  32. 32.
    Urban D, Guan K Y and Jain M. 2018. Estimating sowing dates from satellite data over the U.S. Midwest: a comparison of multiple sensors and metrics. Remote Sensing of Environment, 211: 400-412
  33. 33.
    Walther G R, Post E, Convey P, Menzel A, Parmesan C, Beebee T J C, Fromentin J M, Hoegh-Guldberg O and Bairlein F. 2002. Ecological responses to recent climate change. Nature, 416(6879): 389-395
  34. 34.
    Walther S, Voigt M, Thum T, Gonsamo A, Zhang Y G, Köhler 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. Global Change Biology, 22(9): 2979-2996
  35. 35.
    Wang C, Beringer J, Hutley L B, Cleverly J, Li J, Liu Q H and Sun Y. 2019. Phenology dynamics of dryland ecosystems along the North Australian tropical transect revealed by satellite solar-induced chlorophyll fluorescence. Geophysical Research Letters, 46(10): 5294-5302
  36. 36.
    Xia C F, Li J and Liu Q H. 2013. Review of advances in vegetation phenology monitoring by remote sensing. Journal of Remote Sensing, 17(1): 1-16
  37. 37.
    Yang H L, Yang X, Zhang Y G, Heskel M A, Lu X L, Munger J W, Sun S C and Tang J W. 2017. Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest. Global Change Biology, 23(7): 2874-2886
  38. 38.
    Yang J, Guo N, Huang L N and Jia J H. 2008. Analyses on MODIS-NDVI Index Saturation in Northwest China. Plateau Meteorology, 27(4): 896-903
  39. 39.
    Yang X, Mustard J F, Tang J W and Xu H. 2012. Regional-scale phenology modeling based on meteorological records and remote sensing observations. Journal of Geophysical Research: Biogeosciences, 117(G3): G03029
  40. 40.
    Yuan W P, Liu S G, Yu G R, Bonnefond J M, Chen J Q, Davis K, Desai A R, Goldstein A H, Gianelle D, Rossi F, Suyker A E and Verma S B. 2010. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sensing of Environment, 114(7): 1416-1431
  41. 41.
    Zarco-Tejada P J, Pushnik J C, Dobrowski S and Ustin S L. 2003. Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sensing of Environment, 84(2): 283-294
  42. 42.
    Zhang X Y, Friedl M A, Schaaf C B, Strahler A H, Hodges J C F, Gao F, Reed B C and Huete A. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3): 471-475
  43. 43.
    Zhang X Y, Friedl M A, Schaaf C B and Strahler A H. 2004. Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Global Change Biology, 10(7): 1133-1145
  44. 44.
    Zhang X Y, Friedl M A and Schaaf C B. 2009. Sensitivity of vegetation phenology detection to the temporal resolution of satellite data. International Journal of Remote Sensing, 30(8): 2061-2074
  45. 45.
    Zhang Y, Joiner J, Alemohammad S H, Zhou S and Gentine P. 2018a. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences, 15(19): 5779-5800
  46. 46.
    Zhang Y, Joiner J, Gentine P and Zhou S. 2018b. Reduced solar-induced chlorophyll fluorescence from GOME-2 during Amazon drought caused by dataset artifacts. Global Change Biology, 24(6): 2229-2230
  47. 47.
    Zhou L, Chi Y G, Liu X T, Dai X Q and Yang F T. 2020. Land surface phenology tracked by remotely sensed sun-induced chlorophyll fluorescence in subtropical evergreen coniferous forests. Acta Ecologica Sinica, 40(12): 4114-4125
  48. 48.
    Zou J M. 1983. Agriculture Phenology. Beijing: Agriculture Press

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