Remote sensing of solar-induced chlorophyll fluorescence and its applications in terrestrial ecosystem monitoring

doi:10.17521/cjpe.2022.0233

Abstract

Abstract:

Recent advances in solar-induced chlorophyll fluorescence (SIF), which is a complement to optical remote sensing based on greenness observation, have made it possible to monitor the photosynthesis of plants in terrestrial ecosystems using state-of-the-art technologies. With the rapid development of tower-based, unmanned aerial vehicle (UAV), airborne and space-borne SIF observation technology and improving understanding of SIF mechanism, SIF is providing essential data support and mechanism understanding for the estimation of biological traits and gross primary production of terrestrial ecosystem, early detection of abiotic stress, extraction of photosynthetic phenology and monitoring of transpiration. In this review, we first introduce the fundamental theory, the observation systems and technologies and the retrieval method of SIF. Then, we review the applications of SIF in terrestrial ecosystem monitoring. Finally, we propose a roadmap of activities to facilitate future directions and discuss critical emerging applications of SIF in terrestrial ecosystem monitoring that can benefit from cross-disciplinary expertise.

Key words: solar-induced chlorophyll fluorescence (SIF), terrestrial ecosystem, photosynthesis, abiotic stress, phenology, transpiration

WU Lin-Sheng, ZHANG Yong-Guang, ZHANG Zhao-Ying, ZHANG Xiao-Kang, WU Yun-Fei. Remote sensing of solar-induced chlorophyll fluorescence and its applications in terrestrial ecosystem monitoring[J]. Chin J Plant Ecol, 2022, 46(10): 1167-1199.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2022.0233

https://www.plant-ecology.com/EN/Y2022/V46/I10/1167

Figures/Tables 7

References 279

[1]	Ač A, Malenovský Z, Olejníčková J, Gallé A, Rascher U, Mohammed G. (2015). Meta-analysis assessing potential of steady-state chlorophyll fluorescence for remote sensing detection of plant water, temperature and nitrogen stress. Remote Sensing of Environment, 168, 420-436. DOI URL
[2]	Alemohammad SH, Fang B, Konings AG, Aires F, Green JK, Kolassa J, Miralles D, Prigent C, Gentine P. (2017). Water, energy, and carbon with Artificial Neural Networks (WECANN): a statistically-based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence. Biogeosciences, 14, 4101-4124. DOI PMID
[3]	Alonso L, Gomez-Chova L, Vila-Frances J, Amoros-Lopez J, Guanter L, Calpe J, Moreno J. (2008). Improved Fraunhofer line discrimination method for vegetation fluorescence quantification. IEEE Geoscience and Remote Sensing Letters, 5, 620-624. DOI URL
[4]	Alonso L, Gómez-Chova L, Vila-Francés J, Amorós-López J, Guanter L, Calpe J, Moreno J. (2007). Sensitivity analysis of the Fraunhofer line discrimination method for the measurement of chlorophyll fluorescence using a field spectroradiometer//IEEE. 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, Barcelona, Spain.
[5]	Anav A, Friedlingstein P, Beer C, Ciais P, Harper A, Jones C, Murray-Tortarolo G, Papale D, Parazoo NC, Peylin P, Piao S, Sitch S, Viovy N, Wiltshire A, Zhao M. (2015). Spatiotemporal patterns of terrestrial gross primary production: a review. Reviews of Geophysics, 53, 785-818. DOI URL
[6]	Atherton J, MacArthur A, Hakala T, Maseyk K, Robinson I, Liu W, Honkavaara E, Porcar-Castell A, IEEE. (2018). Drone measurements of solar-induced chlorophyll fluorescence acquired with a low-weight DFOV spectrometer system//IEEE. 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, Valencia, Spain.
[7]	Baker NR. (2008). Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annual Review of Plant Biology, 5, 89-113.
[8]	Baldocchi DD. (2003). Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Global Change Biology, 9, 479-492. DOI URL
[9]	Belwalkar A, Poblete T, Longmire A, Hornero A, Hernandez-Clemente R, Zarco-Tejada PJ. (2022). Evaluation of SIF retrievals from narrow-band and sub-nanometer airborne hyperspectral imagers flown in tandem: modelling and validation in the context of plant phenotyping. Remote Sensing of Environment, 273, 112986. DOI: 10.1016/j.rse.2022.112986. DOI
[10]	Bendig J, Malenovský Z, Gautam D, Lucieer A. (2020). Solar-induced chlorophyll fluorescence measured from an unmanned aircraft system: sensor etaloning and platform motion correction. IEEE Transactions on Geoscience and Remote Sensing, 58, 3437-3444. DOI URL
[11]	Berger K, Verrelst J, Féret JB, Wang ZH, Wocher M, Strathmann M, Danner M, Mauser W, Hank T. (2020). Crop nitrogen monitoring: recent progress and principal developments in the context of imaging spectroscopy missions. Remote Sensing of Environment, 242, 111758. DOI: 10.1016/j.rse.2020.111758. DOI
[12]	Berry J, Bjorkman O. (1980). Photosynthetic response and adaptation to temperature in higher plants. Annual Review of Plant Physiology, 31, 491-543. DOI URL
[13]	Berry JA, Beerling DJ, Franks PJ. (2010). Stomata: key players in the earth system, past and present. Current Opinion in Plant Biology, 13, 233-240. PMID
[14]	Bertani G, Wagner F, Anderson L, Aragão L. (2017). Chlorophyll fluorescence data reveals climate-related photosynthesis seasonality in Amazonian forests. Remote Sensing, 9, 1275. DOI: 10.3390/rs9121275. DOI
[15]	Biriukova K, Pacheco-Labrador J, Migliavacca M, Mahecha MD, Gonzalez-Cascon R, Martín MP, Rossini M. (2021). Performance of singular spectrum analysis in separating seasonal and fast physiological dynamics of solar-induced chlorophyll fluorescence and PRI optical signals. Journal of Geophysical Research: Biogeosciences, 126, e2020JG006158. DOI: 10.1029/2020JG006158. DOI
[16]	Breshears DD, Cobb NS, Rich PM, Price KP, Allen CD, Balice RG, Romme WH, Kastens JH, Floyd ML, Belnap J, Anderson JJ, Myers OB, Meyer CW. (2005). Regional vegetation die-off in response to global-change-type drought. Proceedings of the National Academy of Sciences of the United States of America, 102, 15144-15148. PMID
[17]	Buman B, Hueni A, Colombo R, Cogliati S, Celesti M, Julitta T, Burkart A, Siegmann B, Rascher U, Drusch M, Damm A. (2022). Towards consistent assessments of in situ radiometric measurements for the validation of fluorescence satellite missions. Remote Sensing of Environment, 274, 112984. DOI: 10.1016/j.rse.2022.112984. DOI
[18]	Burkart A, Schickling A, Mateo MPC, Wrobel TJ, Rossini M, Cogliati S, Julitta T, Rascher U. (2015). A method for uncertainty assessment of passive sun-induced chlorophyll fluorescence retrieval using an infrared reference light. IEEE Sensors Journal, 15, 4603-4611. DOI URL
[19]	Camino C, González-Dugo V, Hernández P, Sillero JC, Zarco-Tejada PJ. (2018). Improved nitrogen retrievals with airborne-derived fluorescence and plant traits quantified from VNIR-SWIR hyperspectral imagery in the context of precision agriculture. International Journal of Applied Earth Observation and Geoinformation, 70, 105-117. DOI URL
[20]	Camino C, Gonzalez-Dugo V, Hernandez P, Zarco-Tejada PJ. (2019). Radiative transfer Vcmax estimation from hyperspectral imagery and SIF retrievals to assess photosynthetic performance in rainfed and irrigated plant phenotyping trials. Remote Sensing of Environment, 231, 111186. DOI: 10.1016/j.rse.2019.05.005. DOI
[21]	Cavender-Bares J, Schneider FD, Santos MJ, Armstrong A, Carnaval A, Dahlin KM, Fatoyinbo L, Hurtt GC, Schimel D, Townsend PA, Ustin SL, Wang ZH, Wilson AM. (2022). Integrating remote sensing with ecology and evolution to advance biodiversity conservation. Nature Ecology & Evolution, 6, 506-519.
[22]	Celesti M, van der Tol C, Cogliati S, Panigada C, Yang PQ, Pinto F, Rascher U, Miglietta F, Colombo R, Rossini M. (2018). Exploring the physiological information of Sun-induced chlorophyll fluorescence through radiative transfer model inversion. Remote Sensing of Environment, 215, 97-108. DOI URL
[23]	Chambers JQ, Asner GP, Morton DC, Anderson LO, Saatchi SS, Espírito-Santo FD, Palace M, Souza Jr C. (2007). Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. Trends in Ecology & Evolution, 22, 414-423. DOI URL
[24]	Chang CY, Wen JM, Han JM, Kira O, LeVonne J, Melkonian J, Riha SJ, Skovira J, Ng S, Gu LH, Wood JD, Näthe P, Sun Y. (2021). Unpacking the drivers of diurnal dynamics of sun-induced chlorophyll fluorescence (SIF): canopy structure, plant physiology, instrument configuration and retrieval methods. Remote Sensing of Environment, 265, 112672. DOI: 10.1016/j.rse.2021.112672. DOI
[25]	Chang CY, Zhou RQ, Kira O, Marri S, Skovira J, Gu LH, Sun Y. (2020). An Unmanned Aerial System (UAS) for concurrent measurements of solar-induced chlorophyll fluorescence and hyperspectral reflectance toward improving crop monitoring. Agricultural and Forest Meteorology, 294, 108145. DOI: 10.1016/j.agrformet.2020.108145. DOI
[26]	Chapin F, Matson P, Mooney H. (2002). Principles of Terrestrial Ecosystem Ecology. 2nd ed. Springer, New York.
[27]	Chen JD, Liu XJ, Du SS, Ma Y, Liu LY. (2021). Effects of drought on the relationship between photosynthesis and chlorophyll fluorescence for maize. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 11148-11161. DOI URL
[28]	Cogliati S, Rossini M, Julitta T, Meroni M, Schickling A, Burkart A, Pinto F, Rascher U, Colombo R. (2015a). 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. DOI URL
[29]	Cogliati S, Verhoef W, Kraft S, Sabater N, Alonso L, Vicent J, Moreno J, Drusch M, Colombo R. (2015b). Retrieval of sun-induced fluorescence using advanced spectral fitting methods. Remote Sensing of Environment, 169, 344-357. DOI URL
[30]	Colombo R, Celesti M, Bianchi RM, Campbell PKE, Cogliati S, Cook BD, Corp LA, Damm A, Domec JC, Guanter L, Julitta T, Middleton EM, Noormets A, Panigada C, Pinto F, et al. (2018). Variability of sun-induced chlorophyll fluorescence according to stand age-related processes in a managed loblolly pine forest. Global Change Biology, 24, 2980-2996. DOI PMID
[31]	Coppo P, Taiti A, Pettinato L, Francois M, Taccola M, Drusch M. (2017). Fluorescence imaging spectrometer (FLORIS) for ESA FLEX mission. Remote Sensing, 9, 649. DOI: 10.3390/rs9070649. DOI
[32]	Corp L, Middleton E, Daughtry C, Campbell P. (2006). Solar induced fluorescence and reflectance sensing techniques for monitoring nitrogen utilization in corn//IEEE. 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, Denver, USA.2267-2270.
[33]	Croft H, Chen JM, Wang R, Mo G, Luo S, Luo X, He L, Gonsamo A, Arabian J, Zhang Y, Simic-Milas A, Noland TL, He Y, Homolová L, Malenovský Z, et al. (2020). The global distribution of leaf chlorophyll content. Remote Sensing of Environment, 236, 111479. DOI: 10.1016/j.rse.2019.111479. DOI
[34]	Culpepper AS, York AC. (1999). Weed management in glufosinate-resistant corn (Zea mays). Weed Technology, 13, 324-333. DOI URL
[35]	Damm A, Cogliati S, Colombo R, Fritsche L, Genangeli A, Genesio L, Hanus J, Peressotti A, Rademske P, Rascher U, Schuettemeyer D, Siegmann B, Sturm J, Miglietta F. (2022). Response times of remote sensing measured sun-induced chlorophyll fluorescence, surface temperature and vegetation indices to evolving soil water limitation in a crop canopy. Remote Sensing of Environment, 273, 112957. DOI: 10.1016/j.rse.2022.112957. DOI
[36]	Damm A, Elber J, Erler A, Gioli B, Hamdi K, Hutjes R, Kosvancova M, Meroni M, Miglietta F, Moersch A, Moreno J, Schickling A, Sonnenschein R, Udelhoven T, van der Linden S, et al. (2010). Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP). Global Change Biology, 16, 171-186. DOI URL
[37]	Damm A, Guanter L, Laurent VCE, Schaepman ME, Schickling A, 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. DOI URL
[38]	Damm A, Guanter L, Paul-Limoges E, van der Tol C, Hueni A, Buchmann N, Eugster W, Ammann C, Schaepman ME. (2015). 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, 91-105. DOI URL
[39]	Damm A, Haghighi E, Paul-Limoges E, van der Tol C. (2021). On the seasonal relation of sun-induced chlorophyll fluorescence and transpiration in a temperate mixed forest. Agricultural and Forest Meteorology, 304-305, 108386. DOI: 10.1016/j.agrformet.2021.108386. DOI
[40]	Damm A, Roethlin S, Fritsche L. (2018). Towards advanced retrievals of plant transpiration using sun-induced chlorophyll fluorescence: first considerations//IEEE. 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, Valencia, Spain. 5983-5986.
[41]	Dannenberg M, Wang X, Yan D, Smith W. (2020). Phenological characteristics of global ecosystems based on optical, fluorescence, and microwave remote sensing. Remote Sensing, 12, 671. DOI: 10.3390/rs12040671. DOI
[42]	Daumard F, Champagne S, Fournier A, Goulas Y, Ounis A, Hanocq JF, Moya I. (2010). A field platform for continuous measurement of canopy fluorescence. IEEE Transactions on Geoscience and Remote Sensing, 48, 3358-3368. DOI URL
[43]	Daumard F, Goulas Y, Champagne S, Fournier A, Ounis A, Olioso A, Moya I. (2012). Continuous monitoring of canopy level sun-induced chlorophyll fluorescence during the growth of a sorghum field. IEEE Transactions on Geoscience and Remote Sensing, 50, 4292-4300. DOI URL
[44]	Dı́az S, Cabido M. (2001). Vive la différence: plant functional diversity matters to ecosystem processes. Trends in Ecology & Evolution, 16, 646-655. DOI URL
[45]	Dobrowski SZ, Pushnik JC, Zarco-Tejada PJ, Ustin SL. (2005). Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale. Remote Sensing of Environment, 97, 403-414. DOI URL
[46]	Doughty R, Köhler P, Frankenberg C, Magney TS, Xiao XM, Qin YW, Wu XC, Moore III B. (2019). TROPOMI reveals dry-season increase of solar-induced chlorophyll fluorescence in the Amazon forest. Proceedings of the National Academy of Sciences of the United States of America, 116, 22393-22398. DOI PMID
[47]	Drusch M, Moreno J, del Bello U, Franco R, Goulas Y, Huth A, Kraft S, Middleton EM, Miglietta F, Mohammed G, Nedbal L, Rascher U, Schüttemeyer D, Verhoef W. (2017). The FLuorescence EXplorer mission concept—ESA’s earth explorer 8. IEEE Transactions on Geoscience and Remote Sensing, 55, 1273-1284. DOI URL
[48]	Du SS, Liu LY, Liu XJ, Guo J, Hu JC, Wang SQ, Zhang YG. (2019). SIFSpec: measuring solar-induced chlorophyll fluorescence observations for remote sensing of photosynthesis. Sensors, 19, 3009. DOI: 10.3390/s19133009. DOI
[49]	Du SS, Liu LY, Liu XJ, Zhang X, Zhang XY, Bi YM, Zhang LC. (2018). Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite. Science Bulletin, 63, 1502-1512. DOI URL
[50]	Duveiller G, Filipponi F, Walther S, Köhler P, Frankenberg C, Guanter L, Cescatti A. (2020). A spatially downscaled Sun-induced fluorescence global product for enhanced monitoring of vegetation productivity. Earth System Science Data, 12, 1101-1116. DOI URL
[51]	Evans JR. (1989). Photosynthesis and nitrogen relationships in leaves of C₃ plants. Oecologia, 78, 9-19. DOI PMID
[52]	Field CB, Randerson JT, Malmström CM. (1995). Global net primary production: combining ecology and remote sensing. Remote Sensing of Environment, 51, 74-88. DOI URL
[53]	Fisher JB, Melton F, Middleton E, Hain C, Anderson MC, Allen R, McCabe M, Hook S, Baldocchi D, Townsend P, Kilic A, Tu K, Miralles DD, Perret J, Lagouarde J, et al. (2017). The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resources Research, 53, 2618-2626. DOI URL
[54]	Fournier A, Daumard F, Champagne S, Ounis A, Goulas Y, Moya I. (2012). Effect of canopy structure on sun-induced chlorophyll fluorescence. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 112-120. DOI URL
[55]	Frankenberg C, Fisher J, Worden J, Badgley G, Saatchi S, Lee J, Toon G, Butz A, Jung M, Kuze A, 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. L17706. DOI: 10.1029/2011GL048738. DOI
[56]	Frankenberg C, Köhler P, Magney TS, Geier S, Lawson P, Schwochert M, McDuffie J, Drewry DT, Pavlick R, Kuhnert A. (2018). The Chlorophyll Fluorescence Imaging Spectrometer (CFIS), mapping far red fluorescence from aircraft. Remote Sensing of Environment, 217, 523-536. DOI URL
[57]	Freedman A, Cavender-Bares J, Kebabian P, Bhaskar R, Scott H, Bazzaz F. (2002). Remote sensing of solar-excited plant fluorescence as a measure of photosynthetic rate. Photosynthetica, 40, 127-132. DOI URL
[58]	Fu P, Meacham-Hensold K, Siebers MH, Bernacchi CJ. (2020). The inverse relationship between solar-induced fluorescence yield and photosynthetic capacity: benefits for field phenotyping. Journal of Experimental Botany, 72, 1295-1306. DOI URL
[59]	Garzonio R, Colombo R, Cogliati S, Mauro BD. (2017). Surface reflectance and Sun-induced fluorescence spectroscopy measurements using a small hyperspectral UAS. Remote Sensing, 9, 472. DOI: 10.3390/rs9050472. DOI
[60]	Gastellu-Etchegorry JP, Lauret N, Yin TG, Landier L, Kallel A, Malenovský Z, Bitar AA, Aval J, Benhmida S, Qi JB, Medjdoub G, Guilleux J, Chavanon E, Cook B, Morton D, et al. (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, 2640-2649. DOI URL
[61]	Gautam D, Lucieer A, Bendig J, Malenovský Z. (2020). Footprint determination of a spectroradiometer mounted on an unmanned aircraft system. IEEE Transactions on Geoscience and Remote Sensing, 58, 3085-3096. DOI URL
[62]	Ge QS, Dai JH, Zheng JY. (2010). The progress of phenology studies and challenges to modern phenology research in China. Bulletin of Chinese Academy of Sciences, 25, 310-316.
	[葛全胜, 戴君虎, 郑景云 (2010). 物候学研究进展及中国现代物候学面临的挑战. 中国科学院院刊, 25, 310-316.]
[63]	Gentine P, Alemohammad SH. (2018). Reconstructed solar-induced fluorescence: a machine learning vegetation product based on MODIS surface reflectance to reproduce GOME-2 solar-induced fluorescence. Geophysical Research Letters, 45, 3136-3146. DOI PMID
[64]	Gensheimer J, Turner AJ, Köhler P, Frankenberg C, Chen J. (2022). A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet). Biogeosciences, 19, 1777-1793. DOI URL
[65]	Gerhards M, Schlerf M, Rascher U, Udelhoven T, Juszczak R, Alberti G, Miglietta F, Inoue Y. (2018). Analysis of airborne optical and thermal imagery for detection of water stress symptoms. Remote Sensing, 10, 1139. DOI: 10.3390/rs10071139. DOI
[66]	Gitelson AA, Buschmann C, Lichtenthaler HK. (1998). Leaf chlorophyll fluorescence corrected for re-absorption by means of absorption and reflectance measurements. Journal of Plant Physiology, 152, 283-296. DOI URL
[67]	Gitelson AA, Viña A, Ciganda V, Rundquist DC, Arkebauer TJ. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32, 1-4.
[68]	GomezChova L, AlonsoChorda L, Lopez JA, Frances JV, del ValleTascon S, Calpe J, Moreno J. (2006). Solar induced fluorescence measurements using a field spectroradiometer// AIP. AIP Conference Proceedings. AIP, Cairo, Egypt. 274-281.
[69]	Goulas Y, Fournier A, Daumard F, Champagne S, Ounis A, Marloie O, Moya I. (2017). Gross primary production of a wheat canopy relates stronger to far red than to red solar-induced chlorophyll fluorescence. Remote Sensing, 9, 97. DOI: 10.3390/rs9010097. DOI
[70]	Grace J, Nichol C, Disney M, Lewis P, Quaife T, Bowyer P. (2007). Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence? Global Change Biology, 13, 1484-1497. DOI URL
[71]	Grossmann K, Frankenberg C, Magney TS, Hurlock SC, Seibt U, 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. DOI URL
[72]	Gu L, Wood J, Chang CY, Sun Y, Riggs J. (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. DOI URL
[73]	Guanter L, Alonso L, Gómez-Chova L, Amorós-López J, Vila J, Moreno J. (2007). Estimation of solar-induced vegetation fluorescence from space measurements. Geophysical Research Letters, 34, L08401. DOI: 10.1029/2007GL029289. DOI
[74]	Guanter L, Alonso L, Gómez-Chova L, Meroni M, Preusker R, Fischer J, 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, Atmospheres, 115, D19303. DOI: 10.1029/2009JD013716. DOI
[75]	Guanter L, Bacour C, Schneider A, Aben I, van Kempen T, Maignan F, Retscher C, Köhler P, Frankenberg C, Joiner J, Zhang Y. (2021). The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission. Earth System Science Data, 13, 5423-5440. DOI URL
[76]	Guanter L, Frankenberg C, Dudhia A, Lewis PE, Gómez-Dans J, Kuze A, Suto H, Grainger RG. (2012). Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sensing of Environment, 121, 236-251. DOI URL
[77]	Guanter L, Rossini M, Colombo R, Meroni M, Frankenberg C, Lee JE, 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. DOI URL
[78]	Guanter L, Zhang YG, Jung M, Joiner J, Voigt M, Berry JA, Frankenberg C, Huete AR, Zarco-Tejada P, Lee JE, Moran MS, Ponce-Campos G, Beer C, Camps-Valls G, Buchmann N, et al. (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, E1327-E1333.
[79]	Guo QH, Hu TY, Ma Q, Xu KX, Yang QL, Sun QH, Li YM, Su YJ. (2020). Advances for the new remote sensing technology in ecosystem ecology research. Chinese Journal of Plant Ecology, 44, 418-435. DOI URL
	[郭庆华, 胡天宇, 马勤, 徐可心, 杨秋丽, 孙千惠, 李玉美, 苏艳军 (2020). 新一代遥感技术助力生态系统生态学研究. 植物生态学报, 44, 418-435.] DOI
[80]	Han JM, Chang CYY, Gu LH, Zhang YJ, Meeker EW, Magney TS, Walker AP, Wen JM, Kira O, McNaull S, Sun Y. (2022a). The physiological basis for estimating photosynthesis from Chla fluorescence. New Phytologist, 234, 1206-1219. DOI URL
[81]	Han JM, Gu LH, Wen JM, Sun Y. (2022b). Inference of photosynthetic capacity parameters from chlorophyll a fluorescence is affected by redox state of PSII reaction centers. Plant, Cell & Environment, 45, 1298-1314.
[82]	Hao D, Asrar G, Zeng YL, Yang X, Li X, Xiao JF, Guan K, Wen JG, Xiao Q, Berry J, Chen M. (2021a). Potential of hotspot solar-induced chlorophyll fluorescence for better tracking terrestrial photosynthesis. Global Change Biology, 27, 2144-2158. DOI URL
[83]	Hao DL, Zeng YL, Zhang ZY, Zhang YG, Qiu H, Biriukova K, Celesti M, Rossini M, Zhu P, Asrar GR, Chen M. (2022). Adjusting solar-induced fluorescence to nadir-viewing provides a better proxy for GPP. ISPRS Journal of Photogrammetry and Remote Sensing, 186, 157-169. DOI URL
[84]	Hao DL, Zeng, YL, Qiu H, Biriukova K, Celesti M, Migliavacca M, Rossini M, Asrar GR, Chen M. (2021b). Practical approaches for normalizing directional solar-induced fluorescence to a standard viewing geometry. Remote Sensing of Environment, 255, 112171. DOI: 10.1016/j.rse.2020.112171. DOI
[85]	Helm LT, Shi HY, Lerdau MT, Yang X. (2020). Solar-induced chlorophyll fluorescence and short-term photosynthetic response to drought. Ecological Applications, 30, e02101. DOI: 10.1002/eap.2101. DOI
[86]	Hernández-Clemente R, North PRJ, Hornero A, Zarco-Tejada PJ. (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. DOI URL
[87]	Hou P, Yang M, Zhai J, Liu XM, Wan HW, Li J, Cai MY, Liu HM. (2017). Discussion about natural reserve and construction of national ecological security pattern. Geographical Research, 36, 420-428.
	[侯鹏, 杨旻, 翟俊, 刘晓曼, 万华伟, 李静, 蔡明勇, 刘慧明 (2017). 论自然保护地与国家生态安全格局构建. 地理研究, 36, 420-428.] DOI
[88]	Hu JC, Liu LY, Guo J, Du SS, Liu XJ. (2018). Upscaling solar-induced chlorophyll fluorescence from an instantaneous to daily scale gives an improved estimation of the gross primary productivity. Remote Sensing, 10, 1663. DOI: 10.3390/rs10101663. DOI
[89]	Huang BR, Ma YH, Huang K, Su LY, Zhang CL, Cheng DW, Wang Y. (2018). Strategic approach on promoting reform of China’s natural protected areas system with National Parks as backbone. Bulletin of Chinese Academy of Sciences, 33, 1342-1351.
	[黄宝荣, 马永欢, 黄凯, 苏利阳, 张丛林, 程多威, 王毅 (2018). 推动以国家公园为主体的自然保护地体系改革的思考. 中国科学院院刊, 33, 1342-1351.]
[90]	Huang Y, Zhou C, Du MH, Wu PF, Yuan L, Tang JW. (2022). Tidal influence on the relationship between solar-induced chlorophyll fluorescence and canopy photosynthesis in a coastal salt marsh. Remote Sensing of Environment, 270, 112865. DOI: 10.1016/j.rse.2021.112865. DOI
[91]	Jeong SJ, Schimel D, Frankenberg C, Drewry DT, Fisher JB, Verma M, Berry JA, Lee JE, 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. DOI URL
[92]	Ji MH, Tang BH, Li ZL. (2019). Review of solar-induced chlorophyll fluorescence retrieval methods from satellite data. Remote Sensing Technology and Application, 34, 455-466.
	[纪梦豪, 唐伯惠, 李召良 (2019). 太阳诱导叶绿素荧光的卫星遥感反演方法研究进展. 遥感技术与应用, 34, 455-466.]
[93]	Jia M, Colombo R, Rossini M, Celesti M, Zhu J, Cogliati S, Cheng T, Tian YC, Zhu Y, Cao WX, Yao X. (2021). Estimation of leaf nitrogen content and photosynthetic nitrogen use efficiency in wheat using sun-induced chlorophyll fluorescence at the leaf and canopy scales. European Journal of Agronomy, 122, 126192. DOI: 10.1016/j.eja.2020.126192. DOI
[94]	Jia M, Zhu J, Ma CC, Alonso L, Li D, Cheng T, Tian YC, Zhu Y, Yao X, Cao WX. (2018). Difference and potential of the upward and downward sun-induced chlorophyll fluorescence on detecting leaf nitrogen concentration in wheat. Remote Sensing, 10, 1315. DOI: 10.3390/rs10081315. DOI
[95]	Jin SC, Sun XL, Wu FF, Su YJ, Li YM, Song SL, Xu KX, Ma Q, Baret F, Jiang D, Ding YF, Guo QH. (2021). Lidar sheds new light on plant phenomics for plant breeding and management: recent advances and future prospects. ISPRS Journal of Photogrammetry and Remote Sensing, 171, 202-223. DOI URL
[96]	Jing X, Bai ZF, Gao Y, Liu LY. (2019). Wheat stripe rust monitoring by random forest algorithm combined with SIF and reflectance spectrum. Transactions of the Chinese Society of Agricultural Engineering, 35(13), 154-161.
	[竞霞, 白宗璠, 高媛, 刘良云 (2019). 利用随机森林法协同SIF和反射率光谱监测小麦条锈病. 农业工程学报, 35(13), 154-161.]
[97]	Joiner J, Guanter L, Lindstrot R, Voigt M, Vasilkov AP, Middleton EM, Huemmrich KF, Yoshida Y, 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 Discussions, 6, 3883-3930. DOI: 10.5194/amt-6-2803-2013. DOI
[98]	Joiner J, Yoshida Y, Guanter L, Middleton EM. (2016). New methods for retrieval of chlorophyll red fluorescence from hyper-spectral satellite instruments: simulations and application to GOME-2 and SCIAMACHY. Atmospheric Measurement Techniques Discussions, 2016, 1-41.
[99]	Joiner J, Yoshida Y, Vasilkov AP, Corp LA, Middleton EM. (2011). First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences, 8, 637-651. DOI URL
[100]	Joiner J, Yoshida Y, Vasilkov AP, Middleton EM, Kuze A, Corp LA. (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, 809-829. DOI URL
[101]	Joiner J, Yoshida Y, Vasilkov AP, Schaefer K, Jung M, Guanter L, Zhang Y, Garrity S, Middleton EM, Huemmrich KF, Gu L, Belelli Marchesini L (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. DOI URL
[102]	Jonard F, Brüggemann N, Gentine P, Short Gianotti DJ, Lobet G, Miralles DG, Montzka C, Pagán BR, Rascher U, Vereecken H. (2020). Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: current status and challenges. Agricultural and Forest Meteorology, 291, 108088. DOI: 10.1016/j.agrformet.2020.108088. DOI
[103]	Julitta T, Burkart A, Colombo R, Rossini M, Schickling A, Migliavacca M, Cogliati S, Wutzler T, Rascher U. (2017). Accurate measurements of fluorescence in the O₂A and O₂B band using the FloX spectroscopy system—Results and prospects. 2022-06-06. http://refhub.elsevier.com/S0168-1923(22)00251-9/sbref0034.
[104]	Kimm H, Guan KY, Burroughs CH, Peng B, Ainsworth EA, Bernacchi CJ, Moore CE, Kumagai E, Yang X, Berry JA, Wu GH. (2021). Quantifying high-temperature stress on soybean canopy photosynthesis: the unique role of sun-induced chlorophyll fluorescence. Global Change Biology, 27, 2403-2415. DOI PMID
[105]	Koffi EN, Rayner PJ, Norton AJ, Frankenberg C, Scholze M. (2015). Investigating the usefulness of satellite-derived fluorescence data in inferring gross primary productivity within the carbon cycle data assimilation system. Biogeosciences, 12, 4067-4084. DOI URL
[106]	Köhler P, Frankenberg C, Magney TS, Guanter L, Joiner J, Landgraf J. (2018). Global retrievals of solar induced chlorophyll fluorescence with TROPOMI: first results and inter-sensor comparison to OCO-2. Geophysical Research Letters, 45, 10456-10463.
[107]	Köhler P, Guanter L, Frankenberg C. (2015a). Simplified physically based retrieval of sun-induced chlorophyll fluorescence from GOSAT data. IEEE Geoscience and Remote Sensing Letters, 12, 1446-1450. DOI URL
[108]	Köhler P, Guanter L, Joiner J. (2015b). A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data. Atmospheric Measurement Techniques, 8, 2589-2608. DOI URL
[109]	Lee JE, Berry JA, van der Tol C, Yang X, Guanter L, Damm A, Baker I, Frankenberg C. (2015). Simulations of chlorophyll fluorescence incorporated into the Community Land Model version 4. Global Change Biology, 21, 3469-3477. DOI URL
[110]	Lee JE, Frankenberg C, van der Tol C, Berry JA, Guanter L, Boyce CK, Fisher JB, Morrow E, Worden JR, Asefi S, Badgley G, Saatchi S. (2013). Forest productivity and water stress in Amazonia: observations from GOSAT chlorophyll fluorescence. Proceedings of the Royal Society B: Biological Sciences, 280, 20130171. DOI: 10.1098/rspb.2013.0171. DOI
[111]	Legendre P, Fortin MJ. (1989). Spatial pattern and ecological analysis. Vegetatio, 80, 107-138. DOI URL
[112]	Li J, Zhang YG, Gu LH, Li ZH, Li J, Zhang Q, Zhang ZY, Song L. (2020a). Seasonal variations in the relationship between sun-induced chlorophyll fluorescence and photosynthetic capacity from the leaf to canopy level in a rice crop. Journal of Experimental Botany, 71, 7179-7197. DOI URL
[113]	Li Q, Zhu JH, Xiao WF. (2019). Relationships and trade-offs between, and management of biodiversity and ecosystem services. Acta Ecologica Sinica, 39, 2655-2666.
	[李奇, 朱建华, 肖文发 (2019). 生物多样性与生态系统服务——关系、权衡与管理. 生态学报, 39, 2655-2666.]
[114]	Li X, Xiao JF. (2019). A global 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data. Remote Sensing, 11, 517. DOI: 10.3390/rs11050517. DOI
[115]	Li X, Xiao JF. (2022). TROPOMI observations allow for robust exploration of the relationship between solar-induced chlorophyll fluorescence and terrestrial gross primary production. Remote Sensing of Environment, 268, 112748. DOI: 10.1016/j.rse.2021.112748. DOI
[116]	Li X, Xiao JF, Kimball JS, Reichle RH, Scott RL, Litvak ME, Bohrer G, Frankenberg C. (2020b). Synergistic use of SMAP and OCO-2 data in assessing the responses of ecosystem productivity to the 2018 US drought. Remote Sensing of Environment, 251, 112062. DOI: 10.1016/j.rse.2020.112062. DOI
[117]	Li ZH, Zhang Q, Li J, Yang X, Wu YF, Zhang ZY, Wang SH, Wang HZ, Zhang YG. (2020c). Solar-induced chlorophyll fluorescence and its link to canopy photosynthesis in maize from continuous ground measurements. Remote Sensing of Environment, 236, 111420. DOI: 10.1016/j.rse.2019.111420. DOI
[118]	Li ZH, Zhang YG, Zhang Q, Wu YF, Zhang XK, Zhang ZY. (2021). Tower-based automatic observation methods and systems of solar-induced chlorophyll fluorescence in vegetation canopy. National Remote Sensing Bulletin, 25, 1152-1168.
	[李朝晖, 张永光, 张乾, 吴云飞, 张小康, 章钊颖 (2021). 植被冠层日光诱导叶绿素荧光塔基自动观测方法及系统介绍. 遥感学报, 25, 1152-1168.]
[119]	Lichtenthaler HK, Rinderle U. (1988). The role of chlorophyll fluorescence in the detection of stress conditions in plants. CRC Critical Reviews in Analytical Chemistry, 19, S29-S85.
[120]	Liu GH, Wang YS, Chen YN, Tong XQ, Wang YD, Xie J, Tang XG. (2022). Remotely monitoring vegetation productivity in two contrasting subtropical forest ecosystems using solar-induced chlorophyll fluorescence. Remote Sensing, 14, 1328. DOI: 10.3390/rs14061328. DOI
[121]	Liu LY, Chen LF, Liu Y, Yang DX, Zhang XY, Lu NM, Ju WM, Jiang F, Yin ZS, Liu GH, Tian LF, Hu DH, Mao HQ, Liu SH, Zhang JH, et al. (2022). Satellite remote sensing for global stocktaking: methods, progress and perspectives. National Remote Sensing Bulletin, 26, 243-267.
	[刘良云, 陈良富, 刘毅, 杨东旭, 张兴嬴, 卢乃锰, 居为民, 江飞, 尹增山, 刘国华, 田龙飞, 胡登辉, 毛慧琴, 刘思含, 张建辉, 等 (2022). 全球碳盘点卫星遥感监测方法、进展与挑战. 遥感学报, 26, 243-267.]
[122]	Liu LY, Cheng ZH. (2010). Detection of vegetation light-use efficiency based on solar-induced chlorophyll fluorescence separated from canopy radiance spectrum. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3, 306-312. DOI URL
[123]	Liu LY, Guan LL, Liu XJ. (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. DOI URL
[124]	Liu LY, Zhang YJ, Jiao QJ, Peng DL. (2013a). Assessing photosynthetic light-use efficiency using a solar-induced chlorophyll fluorescence and photochemical reflectance index. International Journal of Remote Sensing, 34, 4264-4280. DOI URL
[125]	Liu LY, Zhao JJ, Guan LL. (2013b). Tracking photosynthetic injury of paraquat-treated crop using chlorophyll fluorescence from hyperspectral data. European Journal of Remote Sensing, 46, 459-473. DOI URL
[126]	Liu LZ, Teng YG, Wu JJ, Zhao WH, Liu SS, Shen Q. (2020a). Soil water deficit promotes the effect of atmospheric water deficit on solar-induced chlorophyll fluorescence. Science of the Total Environment, 720, 137408. DOI: 10.1016/j.scitotenv.2020.137408. DOI
[127]	Liu WW, Atherton J, Mõttus M, Gastellu-Etchegorry JP, Malenovský Z, Raumonen P, Åkerblom M, Mäkipää R, Porcar-Castell A. (2019a). Simulating solar-induced chlorophyll fluorescence in a boreal forest stand reconstructed from terrestrial laser scanning measurements. Remote Sensing of Environment, 232, 111274. DOI: 10.1016/j.rse.2019.111274. DOI
[128]	Liu X, Guo J, Hu J, Liu L. (2019b). Atmospheric correction for tower-based solar-induced chlorophyll fluorescence observations at O₂-A band. Remote Sensing, 11, 355. DOI: 10.3390/rs11030355. DOI
[129]	Liu XJ, Guanter L, Liu LY, Damm A, Malenovský Z, Rascher U, Peng DL, Du SS, Gastellu-Etchegorry JP. (2019c). Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model. Remote Sensing of Environment, 231, 110772. DOI: 10.1016/j.rse.2018.05.035. DOI
[130]	Liu XJ, Liu LY. (2015). Improving chlorophyll fluorescence retrieval using reflectance reconstruction based on principal components analysis. IEEE Geoscience and Remote Sensing Letters, 12, 1645-1649. DOI URL
[131]	Liu XJ, Liu LY, Hu JC, Guo J, Du SS. (2020b). Improving the potential of red SIF for estimating GPP by downscaling from the canopy level to the photosystem level. Agricultural and Forest Meteorology, 281, 107846. DOI: 10.1016/j.agrformet.2019.107846. DOI
[132]	Liu XJ, Liu LY, Zhang S, Zhou XF. (2015). New spectral fitting method for full-spectrum solar-induced chlorophyll fluorescence retrieval based on principal components analysis. Remote Sensing, 7, 10626-10645. DOI URL
[133]	Liu XT, Zhou L, Shi H, Wang SQ, Chi YG. (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, 3482-3494.
	[刘啸添, 周蕾, 石浩, 王绍强, 迟永刚 (2018). 基于多种遥感植被指数、叶绿素荧光与CO₂通量数据的温带针阔混交林物候特征对比分析. 生态学报, 38, 3482-3494.]
[134]	Liu YJ, You CH, Zhang YG, Chen SP, Zhang ZY, Li J, Wu YF. (2021). Resistance and resilience of grasslands to drought detected by SIF in Inner Mongolia, China. Agricultural and Forest Meteorology, 308-309, 108567. DOI: 10.1016/j.agrformet.2021.108567. DOI
[135]	Lu XL, Liu ZQ, An SQ, Miralles DG, Maes W, Liu YL, Tang JW. (2018). Potential of solar-induced chlorophyll fluorescence to estimate transpiration in a temperate forest. Agricultural and Forest Meteorology, 252, 75-87. DOI URL
[136]	Ma KP. (1993). On the concept of biodiversity. Chinese Biodiversity, 1, 20-22.
	[马克平 (1993). 试论生物多样性的概念. 生物多样性, 1, 20-22.]
[137]	Ma Y, Liu LY, Chen RN, Du SS, Liu XJ. (2020). Generation of a global spatially continuous TanSat solar-induced chlorophyll fluorescence product by considering the impact of the solar radiation intensity. Remote Sensing, 12, 2167. DOI: 10.3390/rs12132167. DOI
[138]	Ma Y, Liu LY, Liu XJ, Chen JD. (2022). An improved downscaled sun-induced chlorophyll fluorescence (DSIF) product of GOME-2 dataset. European Journal of Remote Sensing, 55, 168-180. DOI URL
[139]	MacArthur A, Robinson I, Rossini M, Davis N, MacDonald K. (2014). A dual-field-of-view spectrometer system for reflectance and fluorescence measurements (Piccolo Doppio) and correction of etaloning//ESA. Fifth International Workshop on Remote Sensing of Vegetation Fluorescence. European Space Agenccy, Paris, France.
[140]	Maes WH, Pagán BR, Martens B, Gentine P, Guanter L, Steppe K, Verhoest NEC, Dorigo W, Li X, Xiao JF, Miralles DG. (2020). Sun-induced fluorescence closely linked to ecosystem transpiration as evidenced by satellite data and radiative transfer models. Remote Sensing of Environment, 249, 112030. DOI: 10.1016/j.rse.2020.112030. DOI
[141]	Magney TS, Barnes ML, Xi Y. (2020). On the covariation of chlorophyll fluorescence and photosynthesis across scales. Geophysical Research Letters, 47, e2020GL091098. DOI: 10.1029/2020GL091098. DOI
[142]	Magney TS, Bowling DR, Logan BA, Grossmann K, Stutz J, Blanken PD, Burns SP, Cheng R, Garcia MA, Kӧhler P, Lopez S, Parazoo NC, Raczka B, Schimel D, Frankenberg C. (2019). Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proceedings of the National Academy of Sciences of the United States of America, 116, 11640-11645. DOI PMID
[143]	Maier SW, Günther KP, Stellmes M. (2004). Sun-induced fluorescence: a new tool for precision farming. Digital Imaging and Spectral Techniques: Applications to Precision Agriculture, 66, 209-222.
[144]	Marrs JK, Reblin JS, Logan BA, Allen DW, Reinmann AB, Bombard DM, Tabachnik D, Hutyra LR. (2020). Solar-induced fluorescence does not track photosynthetic carbon assimilation following induced stomatal closure. Geophysical Research Letters, 47, e2020GL087956. DOI: 10.1029/2020gl087956. DOI
[145]	Mazzoni M, Meroni M, Fortunato C, Colombo R, 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. DOI URL
[146]	Meng TT, Ni J, Wang GH. (2007). Plant functional traits, environments and ecosystem functioning. Journal of Plant Ecology (Chinese Version), 31, 150-165.
	[孟婷婷, 倪健, 王国宏 (2007). 植物功能性状与环境和生态系统功能. 植物生态学报, 31, 150-165.] DOI
[147]	Meroni M, Barducci A, Cogliati S, Castagnoli F, Rossini M, Busetto L, Migliavacca M, Cremonese E, Galvagno M, Colombo R, di Cella UM (2011). The hyperspectral irradiometer, a new instrument for long-term and unattended field spectroscopy measurements. Review of Scientific Instruments, 82, 043106. DOI: 10.1063/1.3574360. DOI
[148]	Meroni M, Busetto L, Colombo R, Guanter L, Moreno J, Verhoef W. (2010). Performance of spectral fitting methods for vegetation fluorescence quantification. Remote Sensing of Environment, 114, 363-374. DOI URL
[149]	Meroni M, Colombo R. (2006). Leaf level detection of solar induced chlorophyll fluorescence by means of a subnanometer resolution spectroradiometer. Remote Sensing of Environment, 103, 438-448. DOI URL
[150]	Meroni M, Rossini M, Guanter L, Alonso L, Rascher U, Colombo R, Moreno J. (2009). Remote sensing of solar-induced chlorophyll fluorescence: review of methods and applications. Remote Sensing of Environment, 113, 2037-2051. DOI URL
[151]	Miao G, Guan K, Yang X, Bernacchi C, Berry J, DeLucia E, Wu J, Moore C, Meacham K, Cai YP, Peng B, Kimm H, Masters M. (2018). Sun-induced chlorophyll fluorescence, photosynthesis, and light use efficiency of a soybean field from seasonally continuous measurements. Journal of Geophysical Research: Biogeosciences, 123, 610-623. DOI URL
[152]	Middleton EM, Huemmrich KF, Zhang Q, Campbell PK, Landis DR. (2018). Photosynthetic efficiency and vegetation stress//Thenkabail PS, Lyon JG, Huete A. Biophysical and Biochemical Characterization and Plant Species Studies. CRC Press, Boca Raton, USA. 133-179.
[153]	Mohammed GH, Colombo R, Middleton EM, 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 JP, et al. (2019). Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50  years of progress. Remote Sensing of Environment, 231, 111177. DOI: 10.1016/j.rse.2019.04.030. DOI
[154]	Monteith JL. (1972). Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 9, 747-766. DOI URL
[155]	Moore B, Crowell SMR, Rayner PJ, Kumer J, O’dell CW, O’Brien D, Utembe S, Polonsky I, Schimel D, Lemen J. (2018). The potential of the geostationary carbon cycle observatory (GeoCarb) to provide multi-scale constraints on the carbon cycle in the americas. Frontiers in Environmental Science, 6, 109. DOI: 10.3389/fenvs.2018.00109. DOI
[156]	Moya I, Daumard F, Moise N, Ounis A, Goulas Y. (2006). First airborne multiwavelength passive chlorophyll fluorescence measurements over La Mancha (Spain) fields//Sobrino JA. Second Recent Advances in Quantitative Remote Sensing. Publicacions de la Universitat de València, Torrent, Spain. 820-825.
[157]	Moya I, Loayza H, López ML, Quiroz R, Ounis A, Goulas Y. (2019). Canopy chlorophyll fluorescence applied to stress detection using an easy-to-build micro-lidar. Photosynthesis Research, 142, 1-15. DOI PMID
[158]	Ni ZY, Liu ZG, Li ZL, Nerry F, Huo HY, Sun R, Yang PQ, Zhang WW. (2016). Investigation of atmospheric effects on retrieval of sun-induced fluorescence using hyperspectral imagery. Sensors, 16, 480. DOI: 10.3390/s16040480. DOI
[159]	Nichol C, Drolet G, Porcar-Castell A, Wade T, Sabater N, Middleton E, MacLellan C, Levula J, Mammarella I, Vesala T, Atherton J. (2019). Diurnal and seasonal solar induced chlorophyll fluorescence and photosynthesis in a boreal Scots pine canopy. Remote Sensing, 11, 273. DOI: 10.3390/rs11030273. DOI
[160]	Odum EP, Barrett GW. (1971). Fundamentals of Ecology. Saunders, Philadelphia, USA.
[161]	Pacheco-Labrador J, Hueni A, Mihai L, Sakowska K, Julitta T, Kuusk J, Sporea D, Alonso L, Burkart A, Mateo MC, Aasen H, Goulas Y, Arthur A. (2019). Sun-induced chlorophyll fluorescence I: instrumental considerations for proximal spectroradiometers. Remote Sensing, 11, 960. DOI: 10.3390/rs11080960. DOI
[162]	Pagán B, Maes W, Gentine P, Martens B, Miralles D. (2019). Exploring the potential of satellite solar-induced fluorescence to constrain global transpiration estimates. Remote Sensing, 11, 413. DOI: 10.3390/rs11040413. DOI
[163]	Paynter I, Cook B, Corp L, Nagol J, McCorkel J. (2020). Characterization of FIREFLY, an imaging spectrometer designed for remote sensing of solar induced fluorescence. Sensors, 20, 4682. DOI: 10.3390/s20174682. DOI
[164]	Peng YJ, Fan J, Xing SH, Cui GF. (2018). Overview and classification outlook of natural protected areas in China’s mainland. Biodiversity Science, 26, 315-325. DOI URL
	[彭杨靖, 樊简, 邢韶华, 崔国发 (2018). 中国大陆自然保护地概况及分类体系构想. 生物多样性, 26, 315-325.] DOI
[165]	Perez-Priego O, Zarco-Tejada PJ, Miller JR, Sepulcre-Canto G, Fereres E. (2005). Detection of water stress in orchard trees with a high-resolution spectrometer through chlorophyll fluorescence in-filling of the O₂-A band. IEEE Transactions on Geoscience and Remote Sensing, 43, 2860-2869. DOI URL
[166]	Peterson RB, Oja V, Eichelmann H, Bichele I, Dall’Osto L, Laisk A. (2014). Fluorescence F₀ of photosystems II and I in developing C₃ and C₄ leaves, and implications on regulation of excitation balance. Photosynthesis Research, 122, 41-56. DOI PMID
[167]	Pierrat Z, Magney T, Parazoo NC, Grossmann K, Bowling DR, Seibt U, Johnson B, Helgason W, Barr A, Bortnik J, Norton A, Maguire A, Frankenberg C, Stutz J. (2022). Diurnal and seasonal dynamics of solar-induced chlorophyll fluorescence, vegetation indices, and gross primary productivity in the boreal forest. Journal of Geophysical Research: Biogeosciences, 127, e2021JG006588. DOI: 10.1029/2021jg006588. DOI
[168]	Pinto F, Celesti M, Acebron K, Alberti G, Cogliati S, Colombo R, Juszczak R, Matsubara S, Miglietta F, Palombo A, Panigada C, Pignatti S, Rossini M, Sakowska K, Schickling A, et al. (2020). Dynamics of Sun-induced chlorophyll fluorescence and reflectance to detect stress-induced variations in canopy photosynthesis. Plant, Cell & Environment, 43, 1637-1654.
[169]	Pinto F, Damm A, Schickling A, Panigada C, Cogliati S, Müller-Linow M, Balvora A, Rascher U. (2016). Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies. Plant, Cell & Environment, 39, 1500-1512.
[170]	Plascyk JA. (1975). The MK II Fraunhofer line discriminator (FLD-II) for airborne and orbital remote sensing of solar-stimulated luminescence. Optical Engineering, 14, 339-346.
[171]	Platt U, Stutz J. (2008). Differential absorption spectroscopy// Physics Editorial Department. Physics of Earth and Space Environments. Springer, Berlin. 135-174.
[172]	Porcar-Castell A, Malenovský Z, Magney T, van Wittenberghe S, Fernández-Marín B, Maignan F, Zhang YG, Maseyk K, Atherton J, Albert LP, Robson TM, Zhao F, Garcia-Plazaola JI, Ensminger I, Rajewicz PA, et al. (2021). Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science. Nature Plants, 7, 998-1009. DOI PMID
[173]	Porcar-Castell A, Tyystjärvi E, Atherton J, van der Tol C, Flexas J, Pfündel EE, Moreno J, Frankenberg C, Berry JA. (2014). Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. Journal of Experimental Botany, 65, 4065-4095. DOI PMID
[174]	Qin QM, Chen J, Zhang YG, Ren HZ, Wu ZH, Zhang CS, Wu LS, Liu JL. (2020). A discussion on some frontier directions of quantitative remote sensing. Remote Sensing for Land ＆ Resources, 32, 8-15.
	[秦其明, 陈晋, 张永光, 任华忠, 吴自华, 张赤山, 吴霖升, 刘见礼 (2020). 定量遥感若干前沿方向探讨. 国土资源遥感, 32, 8-15.]
[175]	Qin YW, Xiao XM, Wigneron JP, Ciais P, Canadell JG, Brandt M, Li XJ, Fan L, Wu XC, Tang H, Dubayah R, Doughty R, Crowell S, Zheng B, Moore B III. (2022). Large loss and rapid recovery of vegetation cover and aboveground biomass over forest areas in Australia during 2019-2020. Remote Sensing of Environment, 278, 113087. DOI: 10.1016/j.rse.2022.113087. DOI
[176]	Qiu B, Chen JM, Ju WM, Zhang Q, Zhang YG. (2019). Simulating emission and scattering of solar-induced chlorophyll fluorescence at far-red band in global vegetation with different canopy structures. Remote Sensing of Environment, 233, 111373. DOI: 10.1016/j.rse.2019.111373. DOI
[177]	Qiu B, Ge J, Guo WD, Pitman AJ, Mu MY. (2020). Responses of Australian dryland vegetation to the 2019 heat wave at a subdaily scale. Geophysical Research Letters, 47, e2019GL086569. DOI: 10.1029/2019gl086569. DOI
[178]	Qiu RN, Li X, Han G, Xiao JF, Ma X, Gong W. (2022). Monitoring drought impacts on crop productivity of the US Midwest with solar-induced fluorescence: GOSIF outperforms GOME-2 SIF and MODIS NDVI, EVI, and NIRv. Agricultural and Forest Meteorology, 323, 109038. DOI: 10.1016/j.agrformet.2022.109038. DOI
[179]	Rascher U, Alonso L, Burkart A, Cilia C, Cogliati S, Colombo R, Damm A, Drusch M, Guanter L, Hanus J, Hyvärinen T, Julitta T, Jussila J, Kataja K, Kokkalis P, et al. (2015). Sun-induced fluorescence—A new probe of photosynthesis: first maps from the imaging spectrometer HyPlant. Global Change Biology, 21, 4673-4684. DOI PMID
[180]	Rietkerk M, van de Koppel J (2008). Regular pattern formation in real ecosystems. Trends in Ecology & Evolution, 23, 169-175. DOI URL
[181]	Rigden AJ, Salvucci GD, Entekhabi D, Short Gianotti DJ (2018). Partitioning evapotranspiration over the continental United States using weather station data. Geophysical Research Letters, 45, 9605-9613. DOI URL
[182]	Rossini M, Nedbal L, Guanter L, Ač A, Alonso L, Burkart A, Cogliati S, Colombo R, Damm A, Drusch M, Hanus J, Janoutova R, Julitta T, Kokkalis P, Moreno J, et al. (2015a). Red and far red sun-induced chlorophyll fluorescence as a measure of plant photosynthesis. Geophysical Research Letters, 42, 1632-1639. DOI URL
[183]	Rossini M, Panigada C, Cilia C, Meroni M, Busetto L, Cogliati S, Amaducci S, Colombo R. (2015b). Discriminating irrigated and rainfed maize with diurnal fluorescence and canopy temperature airborne maps. ISPRS International Journal of Geo-Information, 4, 626-646. DOI URL
[184]	Ryu Y, Berry JA, Baldocchi DD. (2019). What is global photosynthesis? History, uncertainties and opportunities. Remote Sensing of Environment, 223, 95-114. DOI
[185]	Schlesinger WH, Jasechko S. (2014). Transpiration in the global water cycle. Agricultural and Forest Meteorology, 189-190, 115-117.
[186]	Schreiber U, Schliwa U, Bilger W. (1986). Continuous recording of photochemical and non-photochemical chlorophyll fluorescence quenching with a new type of modulation fluorometer. Photosynthesis Research, 10, 51-62. DOI PMID
[187]	Shan N, Ju WM, Migliavacca M, Martini D, Guanter L, Chen JM, Goulas Y, Zhang YG. (2019). Modeling canopy conductance and transpiration from solar-induced chlorophyll fluorescence. Agricultural and Forest Meteorology, 268, 189-201. DOI
[188]	Shan N, Zhang YG, Chen JM, Ju WM, Migliavacca M, Peñuelas J, Yang X, Zhang ZY, Nelson JA, Goulas Y. (2021). A model for estimating transpiration from remotely sensed solar-induced chlorophyll fluorescence. Remote Sensing of Environment, 252, 112134. DOI: 10.1016/j.rse.2020.112134. DOI
[189]	Shi GY, Huang H, Sang YQ, Cai LL, Zhang JS, Cheng XF, Meng P, Sun SJ, Li JX, Qiao YS. (2022). Solar-induced chlorophyll fluorescence intensity has a significant correlation with negative air ion release in forest canopy. Atmospheric Environment, 269, 118873. DOI: 10.1016/j.atmosenv.2021.118873. DOI
[190]	Smith WK, Biederman JA, Scott RL, Moore DJP, He M, Kimball JS, Yan D, Hudson A, Barnes ML, MacBean N, Fox AM, Litvak ME. (2018). Chlorophyll fluorescence better captures seasonal and interannual gross primary productivity dynamics across dryland ecosystems of southwestern north America. Geophysical Research Letters, 45, 748-757. DOI URL
[191]	Smith WK, Dannenberg MP, Yan D, Herrmann S, Barnes ML, Barron-Gafford GA, Biederman JA, Ferrenberg S, Fox AM, Hudson A, Knowles JF, MacBean N, Moore DJP, Nagler PL, Reed SC, et al. (2019). Remote sensing of dryland ecosystem structure and function: progress, challenges, and opportunities. Remote Sensing of Environment, 233, 111401. DOI: 10.1016/j.rse.2019. 111401. DOI
[192]	Song L, Guanter L, Guan KY, You LZ, Huete A, Ju WM, Zhang YG. (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, 4023-4037. DOI PMID
[193]	Song Y, Wang J, Wang LX. (2020). Satellite solar-induced chlorophyll fluorescence reveals heat stress impacts on wheat yield in India. Remote Sensing, 12, 3277. DOI: 10.3390/rs12203277. DOI
[194]	Stoy P, El-Madany T, Fisher J, Gentine P, Gerken T, Good S, Klosterhalfen A, Liu SG, Miralles D, Pérez-Priego Ó, Rigden A, Skaggs T, Wohlfahrt G, Anderson R, Coenders-Gerrits A, et al. (2019). Reviews and syntheses: turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities. Biogeosciences, 16, 3747-3775. DOI URL
[195]	Su YJ, Wu FF, Ao ZR, Jin SC, Qin F, Liu BX, Pang SX, Liu LL, Guo QH. (2019). Evaluating maize phenotype dynamics under drought stress using terrestrial lidar. Plant Methods, 15, 1-16. DOI URL
[196]	Sun Y, Frankenberg C, Jung M, Joiner J, Guanter L, Köhler P, 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. DOI URL
[197]	Sun Y, Frankenberg C, Wood JD, Schimel DS, Jung M, Guanter L, Drewry DT, Verma M, Porcar-Castell A, Griffis TJ, Gu L, Magney TS, Köhler P, Evans B, Yuen K. (2017). OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science, 358, eaam5747. DOI: 10.1126/science.aam5747. DOI
[198]	Sun Y, Fu R, Dickinson R, Joiner J, Frankenberg C, Gu LH, Xia YL, 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. DOI URL
[199]	Tagliabue G, Panigada C, Celesti M, Cogliati S, Colombo R, Migliavacca M, Rascher U, Rocchini D, Schüttemeyer D, Rossini M. (2020). Sun-induced fluorescence heterogeneity as a measure of functional diversity. Remote Sensing of Environment, 247, 111934. DOI: 10.1016/j.rse.2020.111934. DOI
[200]	Taiz L, Zeiger E, Møller IM, Murphy A. (2015). Plant Physiology and Development. Sinauer Associates Incorporated, Sunderland, USA.
[201]	Tang XG, Xiao JF, Ma MG, Yang H, Li X, Ding Z, Yu PJ, Zhang YG, Wu CY, Huang J, Thompson JR. (2022). Satellite evidence for China’s leading role in restoring vegetation productivity over global karst ecosystems. Forest Ecology and Management, 507, 120000. DOI: 10.1016/j.foreco.2021.120000. DOI
[202]	Taylor TE, Eldering A, Merrelli A, Kiel M, Somkuti P, Cheng C, Rosenberg R, Fisher B, Crisp D, Basilio R, Bennett M, Cervantes D, Chang A, Dang L, Frankenberg C, et al. (2020). OCO-3 early mission operations and initial (vEarly) XCO₂ and SIF retrievals. Remote Sensing of Environment, 251, 112032. DOI: 10.1016/j.rse.2020.112032. DOI
[203]	Tubuxin B, Rahimzadeh-Bajgiran P, Ginnan Y, Hosoi F, Omasa K. (2015). Estimating chlorophyll content and photochemical yield of photosystem II (ΦPSII) using solar-induced chlorophyll fluorescence measurements at different growing stages of attached leaves. Journal of Experimental Botany, 66, 5595-5603. DOI PMID
[204]	van der Tol C, Rossini M, Cogliati S, Verhoef W, Colombo R, Rascher U, Mohammed G. (2016). A model and measurement comparison of diurnal cycles of sun-induced chlorophyll fluorescence of crops. Remote Sensing of Environment, 186, 663-677. DOI URL
[205]	van der Tol C, Verhoef W, Timmermans J, Verhoef A, Su Z. (2009). An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance. Biogeosciences, 6, 3109-3129. DOI URL
[206]	Verrelst J, Rivera JP, van der Tol C, Magnani F, Mohammed G, Moreno J. (2015). Global sensitivity analysis of the SCOPE model: What drives simulated canopy-leaving sun-induced fluorescence? Remote Sensing of Environment, 166, 8-21. DOI URL
[207]	von Hebel C, Matveeva M, Verweij E, Rademske P, Kaufmann M, Brogi C, Vereecken H, Rascher U, van der Kruk J. (2018). Understanding soil and plant interaction by combining ground-based quantitative electromagnetic induction and airborne hyperspectral data. Geophysical Research Letters, 45, 7571-7579. DOI URL
[208]	Walther S, Voigt M, Thum T, Gonsamo A, Zhang YG, Köhler P, Jung M, Varlagin A, Guanter L. (2016). Satellite chlorophyll fluorescence measurements reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests. Global Change Biology, 22, 2979-2996. DOI PMID
[209]	Wang C, Beringer J, Hutley L, Cleverly J, Li J, Liu QH, Sun Y. (2019a). Phenology dynamics of dryland ecosystems along the north Australian tropical transect revealed by satellite solar-induced chlorophyll fluorescence. Geophysical Research Letters, 46, 5294-5302. DOI URL
[210]	Wang J, Zhong XM, Lv XL, Shi ZS, Li FH. (2018). Photosynthesis and physiology responses of paired near-isogenic lines in waxy maize (Zea mays L.) to nicosulfuron. Photosynthetica, 56, 1059-1068. DOI URL
[211]	Wang MY, Luo Y, Zhang ZY, Xie QY, Wu XD, Ma XL. (2022). Recent advances in remote sensing of vegetation phenology: retrieval algorithm and validation strategy. National Remote Sensing Bulletin, 26, 431-455.
	[王敏钰, 罗毅, 张正阳, 谢巧云, 吴小丹, 马轩龙 (2022). 植被物候参数遥感提取与验证方法研究进展. 遥感学报, 26, 431-455.]
[212]	Wang N, Suomalainen J, Bartholomeus H, Kooistra L, Masiliūnas D, Clevers JGPW. (2021). Diurnal variation of Sun-induced chlorophyll fluorescence of agricultural crops observed from a point-based spectrometer on a UAV. International Journal of Applied Earth Observation and Geoinformation, 96, 102276. DOI: 10.1016/j.jag.2020.102276. DOI
[213]	Wang SH, Huang CP, Zhang LF, Gao XL, Fu AM. (2019). Designment and assessment of far-red solar-induced chlorophyll fluorescence retrieval method for the terrestrial ecosystem carbon inventory satellite. Remote Sensing Technology and Application, 34, 476-487.
	[王思恒, 黄长平, 张立福, 高显连, 付安民 (2019). 陆地生态系统碳监测卫星远红波段叶绿素荧光反演算法设计. 遥感技术与应用, 34, 476-487.]
[214]	Wang SH, Ju WM, Peñuelas J, Cescatti A, Zhou YY, Fu YS, Huete A, Liu M, Zhang YG. (2019b). Urban-rural gradients reveal joint control of elevated CO₂ and temperature on extended photosynthetic seasons. Nature Ecology & Evolution, 3, 1076-1085.
[215]	Wang SH, Zhang YG, Ju W, Chen J, Ciais P, Cescatti A, Sardans J, Janssens I, Wu MS, Berry J, Campbell E, Fernández-Martínez M, Alkama R, Sitch S, Friedlingstein P, et al. (2020). Recent global decline of CO₂ fertilization effects on vegetation photosynthesis. Science, 370, 1295-1300. DOI URL
[216]	Wang X, Biederman JA, Knowles JF, Scott RL, Turner AJ, Dannenberg MP, Köhler P, Frankenberg C, Litvak ME, Flerchinger GN, Law BE, Kwon H, Reed SC, Parton WJ, Barron-Gafford GA, Smith WK. (2022). Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics. Remote Sensing of Environment, 270, 112858. DOI: 10.1016/j.rse.2021.112858. DOI
[217]	Wang XR, Qiu B, Li WK, Zhang Q. (2019c). Impacts of drought and heatwave on the terrestrial ecosystem in China as revealed by satellite solar-induced chlorophyll fluorescence. Science of the Total Environment, 693, 133627. DOI: 10.1016/j.scitotenv.2019.133627. DOI
[218]	Wang YJ, Frankenberg C. (2022). On the impact of canopy model complexity on simulated carbon, water, and solar-induced chlorophyll fluorescence fluxes. Biogeosciences, 19, 29-45. DOI URL
[219]	Wen J, Köhler P, Duveiller G, Parazoo NC, Magney TS, Hooker G, Yu L, Chang CY, Sun Y. (2020). A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF). Remote Sensing of Environment, 239, 111644. DOI: 10.1016/j.rse.2020.111644. DOI
[220]	Wieneke S, Ahrends H, Damm A, Pinto F, Stadler A, Rossini M, Rascher U. (2016). Airborne based spectroscopy of red and far-red sun-induced chlorophyll fluorescence: implications for improved estimates of gross primary productivity. Remote Sensing of Environment, 184, 654-667. DOI URL
[221]	Wieneke S, Burkart A, Cendrero-Mateo MP, Julitta T, Rossini M, Schickling A, Schmidt M, Rascher U. (2018). Linking photosynthesis and sun-induced fluorescence at sub-daily to seasonal scales. Remote Sensing of Environment, 219, 247-258. DOI URL
[222]	Wolanin A, Rozanov VV, Dinter T, Noël S, Vountas M, Burrows JP, Bracher A. (2015). Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results. Remote Sensing of Environment, 166, 243-261. DOI URL
[223]	Wu JP, Su YX, Chen XZ, Liu LY, Yang XQ, Gong FX, Zhang HO, Xiong X, Zhang DQ. (2021). Leaf shedding of Pan-Asian tropical evergreen forests depends on the synchrony of seasonal variations of rainfall and incoming solar radiation. Agricultural and Forest Meteorology, 311, 108691. DOI: 10.1016/j.agrformet.2021.108691. DOI
[224]	Wu LS, Wang L, Shi C, Yin DM. (2022a). Detecting mangrove photosynthesis with solar-induced chlorophyll fluorescence. International Journal of Remote Sensing, 43, 1037-1053. DOI URL
[225]	Wu LS, Zhang XK, Rossini M, Wu YF, Zhang ZY, Zhang YG. (2022b). Physiological dynamics dominate the response of canopy far-red solar-induced fluorescence to herbicide treatment. Agricultural and Forest Meteorology, 323, 109063. DOI: 10.1016/j.agrformet.2022.109063. DOI
[226]	Xia JY, Lu RL, Zhu C, Cui EQ, Du Y, Huang K, Sun BY. (2020). Response and adaptation of terrestrial ecosystem processes to climate warming. Chinese Journal of Plant Ecology, 44, 494-514. DOI URL
	[夏建阳, 鲁芮伶, 朱辰, 崔二乾, 杜莹, 黄昆, 孙宝玉 (2020). 陆地生态系统过程对气候变暖的响应与适应. 植物生态学报, 44, 494-514.] DOI
[227]	Xiao JF, Fisher JB, Hashimoto H, Ichii K, Parazoo NC. (2021). Emerging satellite observations for diurnal cycling of ecosystem processes. Nature Plants, 7, 877-887. DOI PMID
[228]	Xie XM, He B, Guo LL, Huang L, Hao XM, Zhang YF, Liu XB, Tang R, Wang SF. (2022). Revisiting dry season vegetation dynamics in the Amazon rainforest using different satellite vegetation datasets. Agricultural and Forest Meteorology, 312, 108704. DOI: 10.1016/j.agrformet.2021.108704. DOI
[229]	Xu S, Atherton J, Riikonen A, Zhang C, Oivukkamäki J, MacArthur A, Honkavaara E, Hakala T, Koivumäki N, Liu ZG, Porcar-Castell A. (2021). Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop. Remote Sensing of Environment, 263, 112555. DOI: 10.1016/j.rse.2021.112555. DOI
[230]	Xu S, Liu ZG, Zhao L, Zhao HR, Ren SX. (2018). Diurnal response of sun-induced fluorescence and PRI to water stress in maize using a near-surface remote sensing platform. Remote Sensing, 10, 1510. DOI: 10.3390/rs10101510. DOI
[231]	Yan S, Zhang L, Jing YS, He HL, Yu GR. (2014). Variations in the relationship between maximum leaf carboxylation rate and leaf nitrogen concentration. Chinese Journal of Plant Ecology, 38, 640-652. DOI
	[闫霜, 张黎, 景元书, 何洪林, 于贵瑞 (2014). 植物叶片最大羧化速率与叶氮含量关系的变异性. 植物生态学报, 38, 640-652.] DOI
[232]	Yang JC, Magney T, Albert L, Richardson AD, Frankenberg C, Stutz J, Grossmann K, Burns S, Seyednasrollah B, Blanken P, Bowling D. (2022). Gross primary production (GPP) and red solar induced fluorescence (SIF) respond differently to light and seasonal environmental conditions in a subalpine conifer forest. Agricultural and Forest Meteorology, 317, 108904. DOI: 10.1016/j.agrformet.2022.108904. DOI
[233]	Yang KG, Ryu Y, Dechant B, Berry JA, Hwang Y, Jiang CY, Kang M, Kim J, Kimm H, Kornfeld A, Yang X. (2018a). Sun-induced chlorophyll fluorescence is more strongly related to absorbed light than to photosynthesis at half-hourly resolution in a rice paddy. Remote Sensing of Environment, 216, 658-673. DOI URL
[234]	Yang P, van der Tol C, Campbell P, Middleton E. (2021). Unraveling the physical and physiological basis for the solar-induced chlorophyll fluorescence and photosynthesis relationship using continuous leaf and canopy measurements of a corn crop. Biogeosciences, 18, 441-465. DOI URL
[235]	Yang PQ, van der Tol C. (2018). Linking canopy scattering of far-red sun-induced chlorophyll fluorescence with reflectance. Remote Sensing of Environment, 209, 456-467. DOI URL
[236]	Yang PQ, van der Tol C, Campbell PKE, Middleton EM. (2020). Fluorescence Correction Vegetation Index (FCVI): a physically based reflectance index to separate physiological and non-physiological information in far-red sun-induced chlorophyll fluorescence. Remote Sensing of Environment, 240, 111676. DOI: 10.1016/j.rse.2020.111676. DOI
[237]	Yang X, Shi HY, Stovall A, Guan KY, Miao GF, Zhang YG, Zhang Y, Xiao XM, Ryu Y, Lee JE. (2018b). FluoSpec 2—An automated field spectroscopy system to monitor canopy solar-induced fluorescence. Sensors, 18, 2063. DOI: 10.3390/s18072063. DOI
[238]	Yang X, Tang JW, Mustard JF, Lee JE, Rossini M, Joiner J, Munger JW, Kornfeld A, Richardson AD. (2015). Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophysical Research Letters, 42, 2977-2987. DOI URL
[239]	Yao L, Liu Y, Yang DX, Cai Z, Wang J, Lin C, Lu N, Lyu D, Tian L, Wang MH, Yin Z, Zheng YQ, Wang SS. (2022). Retrieval of solar-induced chlorophyll fluorescence (SIF) from satellite measurements: comparison of SIF between TanSat and OCO-2. Atmospheric Measurement Techniques, 15, 2125-2137. DOI URL
[240]	Yao L, Yang DX, Liu Y, Wang J, Liu LY, Du SS, Cai ZN, Lu NM, Lyu DR, Wang MH, Yin ZS, Zheng YQ. (2021). A new global solar-induced chlorophyll fluorescence (SIF) data product from TanSat measurements. Advances in Atmospheric Sciences, 38, 341-345. DOI URL
[241]	Yoshida Y, Joiner J, Tucker C, Berry J, Lee JE, Walker G, Reichle R, Koster R, Lyapustin A, 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. DOI URL
[242]	Yu GR, Zhang L, He HL, Yang M. (2021). A process-based model and simulation system of dynamic change and spatial variation in large-scale terrestrial ecosystems. Chinese Journal of Applied Ecology, 32, 2653-2665.
	[于贵瑞, 张黎, 何洪林, 杨萌 (2021). 大尺度陆地生态系统动态变化与空间变异的过程模型及模拟系统. 应用生态学报, 32, 2653-2665.] DOI
[243]	Yu L, Wen J, Chang CY, Frankenberg C, Sun Y. (2019). High-resolution global contiguous SIF of OCO-2. Geophysical Research Letters, 46, 1449-1458. DOI
[244]	Yue YM, Wang KL, Zhang B, Chen ZC. (2008). Applications of hyperspectral remote sensing in ecosystem: a review. Remote Sensing Technology and Application, 23, 471-478.
	[岳跃民, 王克林, 张兵, 陈正超 (2008). 高光谱遥感在生态系统研究中的应用进展. 遥感技术与应用, 23, 471-478.]
[245]	Zarco-Tajeda PJ, Miller JR, Haboudane D, Tremblay N, Apostol S. (2003). Detection of chlorophyll fluorescence in vegetation from airborne hyperspectral CASI imagery in the red edge spectral region//IEEE. 2003 IEEE International Geoscience and Remote Sensing Symposium Proceedings. IEEE, Toulouse, France. 598-600.
[246]	Zarco-Tejada PJ, Berni JAJ, Suárez L, Sepulcre-Cantó G, Morales F, Miller JR. (2009). Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection. Remote Sensing of Environment, 113, 1262-1275. DOI URL
[247]	Zarco-Tejada PJ, González-Dugo V, Berni JAJ. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment, 117, 322-337. DOI URL
[248]	Zarco-Tejada PJ, Poblete T, Camino C, Gonzalez-Dugo V, Calderon R, Hornero A, Hernandez-Clemente R, Román-Écija M, Velasco-Amo MP, Landa BB, Beck PSA, Saponari M, Boscia D, Navas-Cortes JA. (2021). Divergent abiotic spectral pathways unravel pathogen stress signals across species. Nature Communications, 12, 6088. DOI: 10.1038/s41467-021-26335-3. DOI
[249]	Zeng YL, Badgley G, Chen M, Li J, Anderegg LDL, Kornfeld A, Liu QH, Xu BD, Yang B, Yan K, Berry JA. (2020). A radiative transfer model for solar induced fluorescence using spectral invariants theory. Remote Sensing of Environment, 240, 111678. DOI: 10.1016/j.rse.2020.111678. DOI
[250]	Zeng YL, Badgley G, Dechant B, Ryu Y, Chen M, Berry JA. (2019). A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence. Remote Sensing of Environment, 232, 111209. DOI: 10.1016/j.rse.2019.05.028. DOI
[251]	Zeng YL, Chen M, Hao DL, Damm A, Badgley G, Rascher U, Johnson JE, Dechant B, Siegmann B, Ryu Y, Qiu H, Krieger V, Panigada C, Celesti M, Miglietta F, et al. (2022a). Combining near-infrared radiance of vegetation and fluorescence spectroscopy to detect effects of abiotic changes and stresses. Remote Sensing of Environment, 270, 112856. DOI: 10.1016/j.rse.2021.112856. DOI
[252]	Zeng YL, Hao DL, Huete A, Dechant B, Berry J, Chen JM, Joiner J, Frankenberg C, Bond-Lamberty B, Ryu Y, Xiao JF, Asrar GR, Chen M. (2022b). Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth & Environment, 3, 477-493.
[253]	Zhang LF, Qiao N, Huang CP, Wang SH. (2019a). Monitoring drought effects on vegetation productivity using satellite solar-induced chlorophyll fluorescence. Remote Sensing, 11, 378. DOI: 10.3390/rs11040378. DOI
[254]	Zhang XK, Zhang ZY, Zhang YG, Zhang Q, Liu XJ, Chen JD, Wu YF, Wu LS. (2022a). Influences of fractional vegetation cover on the spatial variability of canopy SIF from unmanned aerial vehicle observations. International Journal of Applied Earth Observation and Geoinformation, 107, 102712. DOI: 10.1016/j.jag.2022.102712. DOI
[255]	Zhang Y, Joiner J, Alemohammad SH, Zhou S, Gentine P. (2018a). A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences, 15, 5779-5800. DOI URL
[256]	Zhang Y, Xiao XM, Wolf S, Wu JY, Wu XC, Gioli B, Wohlfahrt G, Cescatti A, Tol C, Zhou S, Gough C, Gentine P, Zhang YG, Steinbrecher R, Ardö J. (2018b). Spatio-temporal convergence of maximum daily light-use efficiency based on radiation absorption by canopy chlorophyll. Geophysical Research Letters, 45, 3508-3519. DOI URL
[257]	Zhang YG, Guanter L, Berry JA, Joiner J, van der Tol C, Huete A, Gitelson A, Voigt M, Köhler P. (2014). Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere models. Global Change Biology, 20, 3727-3742. DOI PMID
[258]	Zhang YG, Guanter L, Berry JA, van der Tol C, Yang X, Tang JW, Zhang FM. (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. DOI URL
[259]	Zhang YG, Guanter L, Joiner J, Song L, Guan KY. (2018c). Spatially-explicit monitoring of crop photosynthetic capacity through the use of space-based chlorophyll fluorescence data. Remote Sensing of Environment, 210, 362-374. DOI URL
[260]	Zhang YG, Zhang Q, Liu LY, Zhang YJ, Wang SQ, Ju WM, Zhou GS, Zhou L, Tang JW, Zhu XD, Wang F, Huang Y, Zhang ZZ, Qiu B, Zhang XK, et al. (2021a). ChinaSpec: a network for long-term ground-based measurements of solar-induced fluorescence in China. Journal of Geophysical Research: Biogeosciences, 126, e2020JG006042. DOI: 10.1029/2020jg006042. DOI
[261]	Zhang YJ, Fan CK, Huang K, Liu YJ, Zu JX, Zhu JT. (2017). Opportunities and challenges in remote sensing applications to ecosystem ecology. Chinese Journal of Ecology, 36, 809-823.
	[张扬建, 范春捆, 黄珂, 刘瑶杰, 俎佳星, 朱军涛 (2017). 遥感在生态系统生态学上应用的机遇与挑战. 生态学杂志, 36, 809-823.]
[262]	Zhang YM, Zhou GS. (2012). Advances in leaf maximum carboxylation rate and its response to environmental factors. Acta Ecologica Sinica, 32, 5907-5917. DOI URL
	[张彦敏, 周广胜 (2012). 植物叶片最大羧化速率及其对环境因子响应的研究进展. 生态学报, 32, 5907-5917.]
[263]	Zhang ZX, Xu W, Qin QM, Chen YJ. (2020a). Monitoring and assessment of agricultural drought based on solar-induced chlorophyll fluorescence during growing season in North China Plain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 775-790. DOI URL
[264]	Zhang ZY, Wang SH, Qiu B, Song L, Zhang YG. (2019). Retrieval of sun-induced chlorophyll fluorescence and advancements in carbon cycle application. Journal of Remote Sensing, 23, 37-52.
	[章钊颖, 王松寒, 邱博, 宋练, 张永光 (2019). 日光诱导叶绿素荧光遥感反演及碳循环应用进展. 遥感学报, 23, 37-52.]
[265]	Zhang ZY, Zhang XK, Albert PC, Chen JM, Ju WM, Wu LS, Wu YF, Zhang YG. (2022b). Sun-induced chlorophyll fluorescence is more strongly related to photosynthesis with hemispherical than nadir measurements: evidence from field observations and model simulations. Remote Sensing of Environment, 279, 113118. DOI: 10.1016/j.rse.2022.113118. DOI
[266]	Zhang ZY, Zhang YG, Joiner J, Migliavacca M. (2018d). Angle matters: Bidirectional effects impact the slope of relationship between gross primary productivity and sun-induced chlorophyll fluorescence from Orbiting Carbon Observatory-2 across biomes. Global Change Biology, 24, 5017-5020. DOI URL
[267]	Zhang ZY, Zhang YG, Porcar-Castell A, Joiner J, Guanter L, Yang X, Migliavacca M, Ju WM, Sun ZG, Chen SP, Martini D, Zhang Q, Li ZH, Cleverly J, Wang HZ, Goulas Y. (2020b). Reduction of structural impacts and distinction of photosynthetic pathways in a global estimation of GPP from space-borne solar-induced chlorophyll fluorescence. Remote Sensing of Environment, 240, 111722. DOI: 10.1016/j.rse.2020.111722. DOI
[268]	Zhang ZY, Zhang YG, Zhang Q, Chen JM, Porcar-Castell A, Guanter L, Wu YF, Zhang XK, Wang HZ, Ding DW, Li ZY. (2020c). Assessing bi-directional effects on the diurnal cycle of measured solar-induced chlorophyll fluorescence in crop canopies. Agricultural and Forest Meteorology, 295, 108147. DOI: 10.1016/j.agrformet.2020.108147. DOI
[269]	Zhang ZZ, Chen JM, Guanter L, He LM, Zhang YG. (2019b). From canopy-leaving to total canopy far-red fluorescence emission for remote sensing of photosynthesis: first results from TROPOMI. Geophysical Research Letters, 46, 12030-12040. DOI URL
[270]	Zhang ZZ, Zhang YG, Chen JM, Ju WM, Migliavacca M, El-Madany TS. (2021b). Sensitivity of estimated total canopy SIF emission to eemotely sensed LAI and BRDF products. Journal of Remote Sensing, 1, 145-162.
[271]	Zhang, Zhang, Li, Wu, Zhang (2019c). Comparison of Bi-hemispherical and hemispherical-conical configurations for in situ measurements of solar-induced chlorophyll fluorescence. Remote Sensing, 11, 2642. DOI: 10.3390/rs11222642. DOI
[272]	Zhao DY, Hou YQ, Zhang ZY, Wu YF, Zhang XK, Wu LS, Zhu XL, Zhang YG. (2022a). Temporal resolution of vegetation indices and solar-induced chlorophyll fluorescence data affects the accuracy of vegetation phenology estimation: a study using in situ measurements. Ecological Indicators, 136, 108673. DOI: 10.1016/j.ecolind.2022.108673. DOI
[273]	Zhao F, Guo YQ, Verhoef W, Gu XF, Liu LY, Yang GJ. (2014). A method to reconstruct the solar-induced canopy fluorescence spectrum from hyperspectral measurements. Remote Sensing, 6, 10171-10192. DOI URL
[274]	Zhao F, Li R, Verhoef W, Cogliati S, Liu XJ, Huang YB, Guo YQ, Huang JX. (2018). Reconstruction of the full spectrum of solar-induced chlorophyll fluorescence: intercomparison study for a novel method. Remote Sensing of Environment, 219, 233-246. DOI URL
[275]	Zhao F, Li ZJ, Verhoef W, Fan CR, Luan HX, Yin TG, Zhang J, Liu ZQ, Tong CM, Bao YF. (2022b). Simulation of solar-induced chlorophyll fluorescence by modeling radiative coupling between vegetation and atmosphere with WPS. Remote Sensing of Environment, 277, 113075. DOI: 10.1016/j.rse.2022.113075. DOI
[276]	Zheng SX, Shangguan ZP. (2007). Photosynthetic characteristics and their relationships with leaf nitrogen content and leaf mass per area in different plant functional types. Acta Ecologica Sinica, 27, 171-181.
	[郑淑霞, 上官周平 (2007). 不同功能型植物光合特性及其与叶氮含量、比叶重的关系. 生态学报, 27, 171-181.]
[277]	Zhou L, Chi YG, Liu XT, Dai XQ, Yang FT. (2020). Land surface phenology tracked by remotely sensed sun-induced chlorophyll fluorescence in subtropical evergreen coniferous forests. Acta Ecologica Sinica, 40, 4114-4125.
	[周蕾, 迟永刚, 刘啸添, 戴晓琴, 杨风亭 (2020). 日光诱导叶绿素荧光对亚热带常绿针叶林物候的追踪. 生态学报, 40, 4114-4125.]
[278]	Zhou YY. (2022). Understanding urban plant phenology for sustainable cities and planet. Nature Climate Change, 12, 302-304. DOI URL
[279]	Zhu XD, Hou YW, Zhang YG, Lu XL, Liu ZQ, Weng QH. (2021). Potential of Sun-induced chlorophyll fluorescence for indicating mangrove canopy photosynthesis. Journal of Geophysical Research: Biogeosciences, 126, e2020JG006159. DOI: 10.1029/2020jg006159. DOI

观测系统 Observation system	光谱仪 Spectrometer	波段范围 Band range (nm)	光谱分辨率 Spectral resolution (nm)	SIF波段 SIF bands (nm)	参考文献 Reference
TriFLEX	HR2000+	630-815	0.50	687, 760	Daumard et al., 2010
	HR2000+	630-815	0.50
	HR2000+	300-900	2.00
SpectroFLEX	HR2000+	630-820	0.20	687, 760	Fournier et al., 2012
SFLUOR	HR4000	700-800	0.10	760	Cogliati et al., 2015a
SFLUOR	HR4000	400-1 000	1.00	760	Cogliati et al., 2015a
FluoSpec	HR2000+	680-775	0.13	760	Yang et al., 2015
SIF-Sys	STS-VIS	337-823	3.00	760	Burkart et al., 2015
FloX	QEpro	650-800	0.30	687, 760	Julitta et al., 2017
FloX	FLAME-S	400-1 000	1.50	687, 760	Julitta et al., 2017
FluoSpec2	QEpro	730-780	0.17	760	Yang et al., 2018b
FluoSpec2	HR2000+	350-1 100	1.10	760	Yang et al., 2018b
AutoSIF	QE65pro	645-805	0.30	687, 760	Hu et al., 2018
PhotoSpec	QEpro 1	670-732	0.30	680-686, 745-758	Grossmann et al., 2018
	QEpro 2	729-784	0.30
	FLAME	177-874	1.20
FAME	QEpro	730-786	0.15	760	Gu et al., 2019
SIFspec	QE65pro	649-805	0.30	687, 760	Du et al., 2019
SIFprism	QEpro	650-800	0.30	687, 760	Zhang et al., 2019c
SIFmotor	QEpro	650-800	0.30	687, 760	Zhang et al., 2022b

观测系统 Observation system	光谱仪 Spectrometer	波段范围 Band range (nm)	光谱分辨率 Spectral resolution (nm)	SIF波段 SIF bands (nm)	参考文献 Reference
TriFLEX	HR2000+	630-815	0.50	687, 760	Daumard et al., 2010
	HR2000+	630-815	0.50
	HR2000+	300-900	2.00
SpectroFLEX	HR2000+	630-820	0.20	687, 760	Fournier et al., 2012
SFLUOR	HR4000	700-800	0.10	760	Cogliati et al., 2015a
SFLUOR	HR4000	400-1 000	1.00	760	Cogliati et al., 2015a
FluoSpec	HR2000+	680-775	0.13	760	Yang et al., 2015
SIF-Sys	STS-VIS	337-823	3.00	760	Burkart et al., 2015
FloX	QEpro	650-800	0.30	687, 760	Julitta et al., 2017
FloX	FLAME-S	400-1 000	1.50	687, 760	Julitta et al., 2017
FluoSpec2	QEpro	730-780	0.17	760	Yang et al., 2018b
FluoSpec2	HR2000+	350-1 100	1.10	760	Yang et al., 2018b
AutoSIF	QE65pro	645-805	0.30	687, 760	Hu et al., 2018
PhotoSpec	QEpro 1	670-732	0.30	680-686, 745-758	Grossmann et al., 2018
	QEpro 2	729-784	0.30
	FLAME	177-874	1.20
FAME	QEpro	730-786	0.15	760	Gu et al., 2019
SIFspec	QE65pro	649-805	0.30	687, 760	Du et al., 2019
SIFprism	QEpro	650-800	0.30	687, 760	Zhang et al., 2019c
SIFmotor	QEpro	650-800	0.30	687, 760	Zhang et al., 2022b

观测系统 Observation system	光谱仪 Spectrometer	波段范围 Band range (nm)	光谱分辨率 Spectral resolution (nm)	搭载平台 Platform	成像或非成像 Imaging or non-imaging	参考文献 Reference
Piccolo Doppio	QEpro	650-800	0.31	UAV	非成像 Non-imaging	MacArthur et al., 2014
Piccolo Doppio	FLAME	400-950	1.30	UAV	非成像 Non-imaging	MacArthur et al., 2014
HyUAS	USB4000	350-1 000	1.50	UAV	非成像 Non-imaging	Garzonio et al., 2017
AirSIF	QEpro	498-877	0.80	UAV	非成像 Non-imaging	Bendig et al., 2020
FAME-UAV	QEpro	730-784	0.15	UAV	非成像 Non-imaging	Chang et al., 2020
FAME-UAV	FLAME	350-1 000	1.30	UAV	非成像 Non-imaging	Chang et al., 2020
FluorSpec	QEpro	630-800	0.30	UAV	非成像 Non-imaging	Wang et al., 2021
CASI	CASI	408-947	7.50	机载 Airborne	成像 Imaging	Zarco-Tajeda et al., 2003
ROSIS	ROSIS	430-860	~7.00	机载 Airborne	成像 Imaging	Maier et al., 2004
AISA	AISA	520-884	1.60	机载 Airborne	成像 Imaging	Corp et al., 2006
AIRFLEX	AIRFLEX	687.3	0.50	机载 Airborne	非成像 Non-imaging	Moya et al., 2006
AIRFLEX	AIRFLEX	760.7	1.00	机载 Airborne	非成像 Non-imaging	Moya et al., 2006
MCA-6	MCA-6	757.42	1.60	UAV	成像 Imaging	Zarco-Tejada et al., 2009
MCA-6	MCA-6	760.47	1.57	UAV	成像 Imaging	Zarco-Tejada et al., 2009
Micro-Hyperspec	VNIR	400-885	6.40	UAV	成像 Imaging	Zarco-Tejada et al., 2012
APEX	APEX	400-2 500	5.70	机载 Airborne	成像 Imaging	Damm et al., 2015
HyPlant	FLUO DUAL	670-800	0.25	机载 Airborne	成像 Imaging	Rascher et al., 2015
HyPlant	FLUO DUAL	370-2 500	3.00/10.00	机载 Airborne	成像 Imaging	Rascher et al., 2015
AisaEAGLE	AisaEAGLE	400-970	3.30	飞艇 Airship	成像 Imaging	Ni et al., 2016
CFIS	CSIF	737-772	<0.10	机载 Airborne	成像 Imaging	Frankenberg et al., 2018
Nano-Hyperspec	VNIR	400-1 000	6.00	UAV	成像 Imaging	Wu et al., 2022a
FIREFLY	Fluorescence	670-780	0.10-0.20	机载 Airborne	成像 Imaging	Paynter et al., 2020
VNIR E-Series	VNIR	400-1 000	5.80	机载 Airborne	成像 Imaging	Belwalkar et al., 2022

观测系统 Observation system	光谱仪 Spectrometer	波段范围 Band range (nm)	光谱分辨率 Spectral resolution (nm)	搭载平台 Platform	成像或非成像 Imaging or non-imaging	参考文献 Reference
Piccolo Doppio	QEpro	650-800	0.31	UAV	非成像 Non-imaging	MacArthur et al., 2014
Piccolo Doppio	FLAME	400-950	1.30	UAV	非成像 Non-imaging	MacArthur et al., 2014
HyUAS	USB4000	350-1 000	1.50	UAV	非成像 Non-imaging	Garzonio et al., 2017
AirSIF	QEpro	498-877	0.80	UAV	非成像 Non-imaging	Bendig et al., 2020
FAME-UAV	QEpro	730-784	0.15	UAV	非成像 Non-imaging	Chang et al., 2020
FAME-UAV	FLAME	350-1 000	1.30	UAV	非成像 Non-imaging	Chang et al., 2020
FluorSpec	QEpro	630-800	0.30	UAV	非成像 Non-imaging	Wang et al., 2021
CASI	CASI	408-947	7.50	机载 Airborne	成像 Imaging	Zarco-Tajeda et al., 2003
ROSIS	ROSIS	430-860	~7.00	机载 Airborne	成像 Imaging	Maier et al., 2004
AISA	AISA	520-884	1.60	机载 Airborne	成像 Imaging	Corp et al., 2006
AIRFLEX	AIRFLEX	687.3	0.50	机载 Airborne	非成像 Non-imaging	Moya et al., 2006
AIRFLEX	AIRFLEX	760.7	1.00	机载 Airborne	非成像 Non-imaging	Moya et al., 2006
MCA-6	MCA-6	757.42	1.60	UAV	成像 Imaging	Zarco-Tejada et al., 2009
MCA-6	MCA-6	760.47	1.57	UAV	成像 Imaging	Zarco-Tejada et al., 2009
Micro-Hyperspec	VNIR	400-885	6.40	UAV	成像 Imaging	Zarco-Tejada et al., 2012
APEX	APEX	400-2 500	5.70	机载 Airborne	成像 Imaging	Damm et al., 2015
HyPlant	FLUO DUAL	670-800	0.25	机载 Airborne	成像 Imaging	Rascher et al., 2015
HyPlant	FLUO DUAL	370-2 500	3.00/10.00	机载 Airborne	成像 Imaging	Rascher et al., 2015
AisaEAGLE	AisaEAGLE	400-970	3.30	飞艇 Airship	成像 Imaging	Ni et al., 2016
CFIS	CSIF	737-772	<0.10	机载 Airborne	成像 Imaging	Frankenberg et al., 2018
Nano-Hyperspec	VNIR	400-1 000	6.00	UAV	成像 Imaging	Wu et al., 2022a
FIREFLY	Fluorescence	670-780	0.10-0.20	机载 Airborne	成像 Imaging	Paynter et al., 2020
VNIR E-Series	VNIR	400-1 000	5.80	机载 Airborne	成像 Imaging	Belwalkar et al., 2022

数据产品 Data product	传感器 Sensor	时间分辨率 Temporal resolution (d)	空间分辨率 Spatial resolution	时段 Time period	参考文献 Reference
GOSAT-Caltech	GOSAT	3	直径10 km Diameter 10 km	2009-2020	Frankenberg et al., 2011
SCIAMACHY-GFZ	SCIAMACHY	~3	1.5° × 1.5°	2002-2012	K?hler et al., 2015b
GOME-F	GOME-1	3	40 km × 40 km	1995-2003	Joiner et al., 2013
GOME-F	GOME-2			2007-2019	Joiner et al., 2013
GOME-2-GFZ	GOME-2	1	0.5° × 0.5°	2007-2012	K?hler et al., 2015b
GOME-2-Caltech	GOME-2	1	0.5° × 0.5°	2007-2018	K?hler et al., 2015b
Downscaled-GOME2-SIF^*	GOME-2	8	0.05° × 0.05°	2007-2018	Duveiller et al., 2020
RSIF^*	GOME-2	14	0.5° × 0.5°	2007-2017	Gentine & Alemohammad, 2018
Harmonized SIF^*	SCIAMACHY	~30	0.05° × 0.05°	2002-2018	Wen et al., 2020
Harmonized SIF^*	GOME-2	~30	0.05° × 0.05°	2002-2018	Wen et al., 2020
DSIF^*	GOME-2	16	0.05° × 0.05°	2007-2019	Ma et al., 2022
OCO-2_L2_Lite_SIF	OCO-2	16	2.25 km × 1.29 km	2014-2022	Sun et al., 2018
CSIF^*	OCO-2	4	0.05° × 0.05°	2000-2020	Zhang et al., 2018a
SIF_{oco2_005}^*	OCO-2	16	0.05° × 0.05°	2014-2021	Yu et al., 2019
GOSIF^*	OCO-2	8	0.05° × 0.05°	2000-2020	Li & Xiao, 2019
TanSat SIF	TanSat	16	2.0 km × 2.0 km	2017-2019	Du et al., 2018
Continuous TanSat SIF^*	TanSat	4	0.05° × 0.05°	2017-2019	Ma et al., 2020
IAPCAS/SIF	TanSat	16	1.0° × 1.0°	2017-2018	Yao et al., 2021
TROPOMI-Caltech	TROPOMI	8	0.05° × 0.05°	2018-2021	K?hler et al., 2018
TROPOSIF	TROPOMI	~1	3.5 km × 5.5 km	2018-2021	Guanter et al., 2021
SIFnet^*	TROPOMI	16	0.005° × 0.005°	2018-2021	Gensheimer et al., 2022
OCO3_L2_Lite_SIF	OCO-3	16	2.25 km × 1.29 km	2019-2022	Taylor et al., 2020