高寒草甸生态系统极易受到气候变化的影响，准确估计该生态系统的总初级生产力（gross primary production，GPP）对了解全球碳循环至关重要。日光诱导叶绿素荧光（solar-induced chlorophyll fluorescence，SIF），作为植物光合作用过程的伴生产物，已经成为准确估计GPP的遥感新方法。当前有关SIF与GPP关系的研究表明，SIF-GPP的关系具有生态系统和物种依赖性。因此，有必要研究不同生态系统，尤其是一些对环境和气候变化敏感的特殊生态系统中SIF-GPP的关系。而且，针对同一生态系统，同时探究红光和远红波段SIF与GPP关系的研究还很缺乏。鉴于此，本文选择高寒草甸这一独特生态系统，利用塔基平台三年长时序冠层光谱观测、通量观测及气象观测资料，开展了冠层和光系统两个尺度的红光和远红光波段SIF与GPP关系的分析以及 SIF耦合GPP关系对环境因子变化的响应规律分析。

主要研究结果和结论如下：

（1）红光和远红光SIF在冠层（SIF_Red，SIF_Far-red）及光系统（TSIF_Red，TSIF_Far-red）水平与GPP均存在可靠统计关系，且红光波段性能更优。在季节尺度上，冠层SIF、光系统SIF（TSIF）与GPP有一致的单峰季节变化模式；在日尺度上，SIF、TSIF与GPP呈现相似的单峰日变化模式，但是SIF、TSIF比GPP更早达到日峰值。对于半小时观测数据，SIF与GPP在晴天和阴天均呈现显著非线性相关关系，且二者的相关性在阴天强于晴天；对于日平均观测数据，SIF-GPP在晴天和阴天均呈现显著线性相关关系，线性拟合斜率阴天比晴天更高，证实了散射光的施肥效应。TSIF-GPP关系及其变化规律与冠层SIF-GPP关系类似，但二者相关性略低于冠层SIF-GPP。从红光和远红光SIF与GPP关系对比来看，SIF_Red和TSIF_Red与GPP之间的相关性始终对应强于SIF_Far-red和TSIF_Far-red。研究结果表明，在高寒草甸生态系统中，红光波段SIF能够比远红光波段SIF更好地捕获GPP动态变化。

（2）光能利用率（light use efficiency，LUE）与红光和远红光荧光量子产率ΦF（ΦFRed，ΦFFar–red）以及表观荧光量子产率SIFyield（SIFyieldRed，SIFyieldFar–red）存在可靠统计关系，且红光SIF量子产率能更好追踪LUE的变化。在季节尺度上，LUE、ΦFRed和εFar–red呈现单峰季节变化模式，εRed呈现单谷季节变化模式，ΦFFar–red则无明显季节变化；在日尺度上，LUE，εRed，和εFar–red呈现“碗状”日变化模式，而ΦFRed和ΦFFar–red在一天内数值相对稳定，且没有明显日变化模式。对于不同的时间分辨率数据和不同天气条件、 ΦFRed与LUE的相关性比ΦFFar–red强，εRed与LUE的相关性比εFar–red弱，SIFyieldRed与LUE的相关性比SIFyieldFar–red强。该结果表明，在追踪GPP方面，红光波段SIF的生理贡献大于结构贡献，远红光波段SIF的结构贡献大于生理贡献。并且，红光波段SIF在生理方面的优势弥补了其在结构方面的劣势。

（3）SIF和GPP耦合关系对环境变化响应存在显著差异。本研究考虑了光合有效辐射（photosynthetically active radiation，PAR）、温度（air temperature，Ta）、饱和水汽压差（vapor pressure deficit，VPD）、土壤温度（soil temperature，Ts）和土壤水分含量（soil moisture content，SM）等涉及光照、温度和水分三个方面的五个环境因子。利用PAR归一化方法（SIF/PAR、GPP/PAR），消除了入射辐射对SIF和GPP数据本身的贡献。在季节尺度上，对比分析SIF_Far-red/PAR、SIF_Red/PAR和GPP/PAR对五个环境因子变化的响应。结果表明：SIF_Far-red/PAR、SIF_Red/PAR和GPP/PAR对VPD、Ts和SM的变化响应一致，但是，对于PAR和Ta的变化，SIF_Red/PAR和GPP/PAR响应基本一致，而SIF_Far-red/PAR存在明显差异。在日尺度上，PAR是导致SIF耦合GPP关系变化的主导因素。并且当环境因子在日尺度上发生突然变化时，SIF_Red能更敏感地追踪GPP的变化。进一步分析了SIFyield与LUE对环境因子的响应，发现SIFyieldRed和LUE对环境因子变化的响应比SIFyieldFar–red和LUE的同步性明显更强。这些结果均表明在高寒草甸生态系统中SIF_Red与GPP的关系比SIF_Far-red更加稳定可靠。

Alpine meadow ecosystems are extremely vulnerable to climate change, and accurate estimation of gross primary production (GPP) in this ecosystem is essential to understand the global carbon cycle. Solar-induced chlorophyll fluorescence (SIF), a by-product of plant photosynthesis, has become a new remote sensing method to accurately estimate GPP. Present studies on the relationship between SIF and GPP indicate that the SIF-GPP relationship is ecosystem- and species-dependent. Therefore, it is necessary to study the SIF-GPP relationship in different ecosystems, especially some special ecosystems sensitive to environmental and climate change. Moreover, there is a lack of studies exploring the relationship between SIF and GPP in both red and far-red bands for the same ecosystem. In view of this, this paper selects one unique ecosystem, alpine meadow, using three-year long time series canopy spectral observation, flux observation and meteorological observation data from the tower base platform, and carried out the analysis of the relationship between SIF and GPP in the red and far-red band at two scales of canopy and photosystem, analyzing the response pattern of SIF-coupled GPP relationship to variations in environmental factors.

The main findings and conclusions are as follows：

(1) Red and far-red band SIF had reliable statistical relationships with GPP at both canopy (SIFRed, SIFFar-red) and photosystem (TSIFRed, TSIFFar-red)levels, and the red band had better performance. At the seasonal scale, canopy SIF, photosystem SIF (TSIF) showed a consistent single-peaked seasonal pattern with GPP; at the diurnal scale, SIF, TSIF showed a similar single-peaked daily pattern with GPP, but SIF, TSIF reached the diurnal peak earlier than GPP. For half-hourly observations, SIF and GPP showed significant nonlinear correlations in both sunny and cloudy days, and their correlations were stronger in cloudy days than in sunny days; for daily observations, SIF-GPP showed significant linear correlations in both sunny and cloudy days, and the slope of the linear fit was steeper in cloudy days than in sunny days, which confirmed the fertilization effect of scattered light. The variation pattern of TSIF-GPP relationship is consistent with SIF-GPP under different weather conditions in different time resolution data, but the correlation is uniformly lower than that of SIF-GPP. Moreover, in the comparison of red and far-red band SIF with GPP, the correlation of SIFRed and TSIFRed with GPP always corresponds to stronger than that of SIFFar-redand TSIFFar-red. The results show that SIF in the red band can capture GPP dynamics better than SIF in the far-red band in alpine meadow ecosystems.

(2) There existed a reliable statistical relationship between light use efficiency (LUE) with red and far-red fluorescence quantum yield and apparent fluorescence quantum yield, and the red SIF quantum yield better tracked the variation of LUE. At the seasonal scale, LUE，ΦFRed，εFar–redexhibited a single-peak seasonal variation pattern, εRedexhibited a single-valley seasonal variation pattern, and ΦFFar–redshowed no significant seasonal variation; At the diurnal scale, LUE, εRed, and εFar–red showed a " bowl-shaped" pattern of diurnal variation, while ΦFRed and ΦFFar–red were relatively stable within a day and had no obvious diurnal pattern. For different temporal data resolutions and different weather conditions, the correlation between ΦFRed and LUE is stronger than ΦFFar–red, the correlation between εRed and LUE is weaker than εFar–red, and the correlation between SIFyieldRed and LUE is stronger than SIFyieldFar–red. These results indicate that the physiological contribution of red band SIF is greater than the structural contribution in tracking GPP, and the structural contribution of far-red band SIF is greater than the physiological contribution. Moreover, the physiological advantages of red band SIF compensate for its structural disadvantages.

(3) There was a significant difference in the response of SIF and GPP coupling relationship to environmental change. In this study, five environmental factors involving three aspects of light, temperature and moisture, such as photosynthetically active radiation (PAR), air temperature (Ta), vapor pressure deficit (VPD), soil temperature (Ts) and soil moisture content (SM), were considered. The contribution of incident radiation to the SIF and GPP data itself was eliminated using the PAR normalization method (SIF/PAR, GPP/PAR). At the seasonal scale, the responses of SIFFar-red/PAR，SIFRed/PAR and GPP/PAR to the changes of five environmental factors were comparatively analyzed. It was found that SIFFar-red/PAR，SIFRed/PAR and GPP/PAR responded consistently to the changes of VPD, Ts and SM, but SIFRed/PAR and GPP/PAR responded consistently to the changes of PAR and Ta, while SIFFar-red/PAR was significantly different from them. At the diurnal scale, PAR was found to be the dominant factor causing the change of SIF-GPP daily diurnal relationship. When the environmental factor changes abruptly on the diurnal scale, SIFRed could track the change of GPP more sensitively. Further analysis of SIFyield and LUE responses to environmental factors revealed that the responses of SIFyieldRed and LUE to changes in environmental factors were significantly more synchronized than those of SIFyieldFar–redand LUE. These results all indicate that the relationship between SIFRedand GPP is more stable and robust than SIFFar-red in alpine meadow ecosystem.

[1] Kato T, Tang Y, Gu S, et al. Carbon dioxide exchange between the atmosphere and an alpine meadow ecosystem on the Qinghai–Tibetan Plateau, China [J]. Agricultural and Forest Meteorology, 2004, 124(1-2): 121-134.

[2] Sun J, Qin X, Yang J. The response of vegetation dynamics of the different alpine grassland types to temperature and precipitation on the Tibetan Plateau [J]. Environmental Monitoring and Assessment, 2016, 188(1): 20.

[3] Kato T, Tang Y H, Gu S, et al. Temperature and biomass influences on interannual changes in CO2 exchange in an alpine meadow on the Qinghai-Tibetan Plateau [J]. Global Change Biology, 2006, 12(7): 1285-1298.

[4] Gu S, Tang Y H, Du M Y, et al. Short-term variation of CO2flux in relation to environmental controls in an alpine meadow on the Qinghai-Tibetan Plateau [J]. Journal of Geophysical Research: Atmospheres, 2003, 108(D21).

[5] Wang Y Y, Xiao J F, Ma Y M, et al. Carbon fluxes and environmental controls across different alpine grassland types on the Tibetan Plateau [J]. Agricultural and Forest Meteorology, 2021, 311.

[6] Sun S B, Che T, Li H Y, et al. Water and carbon dioxide exchange of an alpine meadow ecosystem in the northeastern Tibetan Plateau is energy-limited [J]. Agricultural and Forest Meteorology, 2019, 275: 283-295.

[7] Quan Q, Zhang F Y, Tian D S, et al. Transpiration Dominates Ecosystem Water-Use Efficiency in Response to Warming in an Alpine Meadow [J]. Journal of Geophysical Research: Biogeosciences, 2018, 123(2): 453-462.

[8] Zhang T, Zhang Y J, Xu M J, et al. Water availability is more important than temperature in driving the carbon fluxes of an alpine meadow on the Tibetan Plateau [J]. Agricultural and Forest Meteorology, 2018, 256-257: 22-31.

[9] Wang Y Y, Ma Y M, Li H X, et al. Carbon and water fluxes and their coupling in an alpine meadow ecosystem on the northeastern Tibetan Plateau [J]. Theoretical and Applied Climatology, 2020, 142(1-2): 1-18.

[10] Pepin N, Bradley R S, Diaz H F, et al. Elevation-dependent warming in mountain regions of the world [J]. Nature Climate Change, 2015, 5(5): 424-430.

[11] Thompson D W, Solomon S. Interpretation of recent Southern Hemisphere climate change [J]. Science, 2002, 296(5569): 895-899.

[12] Graven H D, Keeling R F, Piper S C, et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960 [J]. Science, 2013, 341(6150): 1085-1089.

[13] Piao S L, Tan K, Nan H J, et al. Impacts of climate and CO2 changes on the vegetation growth and carbon balance of Qinghai–Tibetan grasslands over the past five decades [J]. Global and Planetary Change, 2012, 98-99: 73-80.

[14] Baldocchi D, Chu H, Reichstein M. Inter-annual variability of net and gross ecosystem carbon fluxes: A review [J]. Agricultural and Forest Meteorology, 2018, 249: 520-533.

[15] Liu X J, Liu Z Q, Liu L Y, et al. Modelling the influence of incident radiation on the SIF-based GPP estimation for maize [J]. Agricultural and Forest Meteorology, 2021, 307.

[16] Guanter L, Zhang Y G, Jung M, et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence [J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(14): E1327-1333.

[17] Hilker T, Coops N C, Wulder M A, et al. The use of remote sensing in light use efficiency based models of gross primary production: a review of current status and future requirements [J]. Science of The Total Environment, 2008, 404(2-3): 411-423.

[18] Wu C Y, Munger J W, Niu Z, et al. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest [J]. Remote Sensing of Environment, 2010, 114(12): 2925-2939.

[19] Pokhariyal S, Patel N R. Comparison of empirical remote-sensing based models for the estimation of gross primary productivity using eddy covariance and satellite data over agroecosystem [J]. Tropical Ecology, 2021, 62(4): 600-611.

[20] Running S W, Baldocchi D D, Turner D P, et al. A Global Terrestrial Monitoring Network Integrating Tower Fluxes, Flask Sampling,Ecosystem Modeling and EOS Satellite Data [J]. Remote Sensing of Environment 1999, 70(1): 108-127.

[21] Baldocchi D. Assessing the eddy covariance technique for evaluating carbon dioxide rates of ecosystems Past, present [J]. Global Change Biology, 2003, 9(4).

[22] Kljun N, Calanca P, Rotach M W, et al. A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP) [J]. Geoscientific Model Development, 2015, 8(11): 3695-3713.

[23] Chen B Z, Black T A, Coops N C, et al. Assessing Tower Flux Footprint Climatology and Scaling Between Remotely Sensed and Eddy Covariance Measurements [J]. Boundary-Layer Meteorology, 2008, 130(2): 137-167.

[24] Damm A, Elbers J A N, Erler A, et al. Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP) [J]. Global Change Biology, 2010, 16(1): 171-186.

[25] Osmond B, Ananyev G, Berry J, et al. Changing the way we think about global change research: scaling up in experimental ecosystem science [J]. Global Change Biology, 2004, 10(4): 393-407.

[26] Porcar-Castell A, Tyystjarvi E, Atherton J, et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges [J]. Journal of Experimental Botany, 2014, 65(15): 4065-4095.

[27] 章钊颖, 王松寒, 邱博, 等. 日光诱导叶绿素荧光遥感反演及碳循环应用进展 [J]. 遥感学报, 2019, 23(01): 37-52.

[28] Baker N R. Chlorophyll fluorescence: a probe of photosynthesis in vivo [J]. The Annual Review ofPlant Biology 2008, 59: 89-113.

[29] Krause G H, Weis E. Chlorophyll fluorescence and photosynthesis the basics [J]. Annual Review of Plant Physiology, 1991, 42: 313-349.

[30] Frankenberg C, Fisher J B, Worden J, et al. New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity [J]. Geophysical Research Letters, 2011, 38(17).

[31] Yang P Q, van der Tol C, Verhoef W, et al. Using reflectance to explain vegetation biochemical and structural effects on sun-induced chlorophyll fluorescence [J]. Remote Sensing of Environment, 2019, 231.

[32] Li X, Xiao J F, He B B, et al. Solar-induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO-2 and flux tower observations [J]. Global Change Biology, 2018, 24(9): 3990-4008.

[33] Goulas Y, Fournier A, Daumard F, et al. Gross Primary Production of a Wheat Canopy Relates Stronger to Far Red Than to Red Solar-Induced Chlorophyll Fluorescence [J]. Remote Sensing, 2017, 9(1).

[34] Liu L Y, Zhang Y J, Wang J H, et al. Detecting solar-induced chlorophyll fluorescence from field radiance spectra based on the Fraunhofer line principle [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 827-832.

[35] Liu L, Liu X, Hu J. Effects of spectral resolution and SNR on the vegetation solar-induced fluorescence retrieval using FLD-based methods at canopy level [J]. European Journal of Remote Sensing, 2017, 48(1): 743-762.

[36] Wyber R, Malenovský Z, Ashcroft M, et al. Do Daily and Seasonal Trends in Leaf Solar Induced Fluorescence Reflect Changes in Photosynthesis, Growth or Light Exposure? [J]. Remote Sensing, 2017, 9(6).

[37] Burkart A, Schickling A, Mateo M P C, et al. A Method for Uncertainty Assessment of Passive Sun-Induced Chlorophyll Fluorescence Retrieval Using an Infrared Reference Light [J]. IEEE Sensors Journal, 2015, 15(8): 4603-4611.

[38] Daumard F, Champagne S, Fournier A, et al. A Field Platform for Continuous Measurement of Canopy Fluorescence [J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(9): 3358-3368.

[39] Meroni M, Barducci A, Cogliati S, et al. The hyperspectral irradiometer, a new instrument for long-term and unattended field spectroscopy measurements [J]. Rev Sci Instrum, 2011, 82(4): S92.

[40] Cogliati S, Rossini M, Julitta T, et al. Continuous and long-term measurements of reflectance and sun-induced chlorophyll fluorescence by using novel automated field spectroscopy systems [J]. Remote Sensing of Environment, 2015, 164: 270-281.

[41] Grossmann K, Frankenberg C, Magney T S, et al. PhotoSpec: A new instrument to measure spatially distributed red and far-red Solar-Induced Chlorophyll Fluorescence [J]. Remote Sensing of Environment, 2018, 216: 311-327.

[42] Du S S, Liu L Y, Liu X J, et al. SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis [J]. Sensors, 2019, 19(13): 3009.

[43] Damm A, Guanter L, Laurent V C E, et al. FLD-based retrieval of sun-induced chlorophyll fluorescence from medium spectral resolution airborne spectroscopy data [J]. Remote Sensing of Environment, 2014, 147: 256-266.

[44] Rascher U, Alonso L, Burkart A, et al. Sun-induced fluorescence - a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant [J]. Glob Chang Biol, 2015, 21(12): 4673-4684.

[45] Wieneke S, Ahrends H, Damm A, et al. Airborne based spectroscopy of red and far-red sun-induced chlorophyll fluorescence: Implications for improved estimates of gross primary productivity [J]. Remote Sensing of Environment, 2016, 184: 654-667.

[46] Guanter L, Frankenberg C, Dudhia A, et al. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements [J]. Remote Sensing of Environment, 2012, 121: 236-251.

[47] Joiner J, Yoshida Y, Vasilkov A P, et al. First observations of global and seasonal terrestrial chlorophyll fluorescence from space [J]. Biogeosciences, 2011, 8(3): 637-651.

[48] Köhler P, Guanter L, Joiner J. A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data [J]. Atmospheric Measurement Techniques, 2015, 8(6): 2589-2608.

[49] 刘良云. 植被定量遥感原理与应用 [M]. 科学出版社, 2014.

[50] 武维华. 植物生理学 [M]. 科学出版社, 2003.

[51] Grace J, Nichol C, Disney M, et al. Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence? [J]. Global Change Biology, 2007, 13(7): 1484-1497.

[52] Zarco-Tejada P J, Catalina A, González M R, et al. Relationships between net photosynthesis and steady-state chlorophyll fluorescence retrieved from airborne hyperspectral imagery [J]. Remote Sensing of Environment, 2013, 136: 247-258.

[53] Meroni M, Rossini M, Guanter L, et al. Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications [J]. Remote Sensing of Environment, 2009, 113(10): 2037-2051.

[54] Perez-Priego O, Zarco-Tejada P J, Miller J R, et al. Detection of water stress in orchard trees with a high-resolution spectrometer through chlorophyll fluorescence in-filling of the O2-A band [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(12): 2860-2869.

[55] Meroni M, Colombo R. Leaf level detection of solar induced chlorophyll fluorescence by means of a subnanometer resolution spectroradiometer [J]. Remote Sensing of Environment, 2006, 103(4): 438-448.

[56] PLASCYK J A. The MK II Fraunhofer Line Discriminator (FLD -II) for Airborne and Orbital Remote Sensing of Solar -Stimulated Luminescence [J]. 1975, 14(4).

[57] Mcfarlane J C, Watson R D, Theisen A F, et al. Plant stress detection by remote measurement of fluorescence [J]. Applied Optics, 1980, 19(19): 3287.

[58] 刘良云, 张永江, 王纪华, 等. 利用夫琅和费暗线探测自然光条件下的植被光合作用荧光研究 [J]. 遥 感 学 报, 2006, 10(1): 130-137.

[59] 张永江, 黄文江, 王纪华, 等. 基于Fraunhofer线的小麦条锈病荧光遥感探测 [J]. 中国农业科学, 2007, 40(1): 78-83.

[60] Maier S, Günther K P. Sun induced fluorescence A new tool for precision farming [M]. Madison, WI, USA: American Society of Agronomy Special Publication, 2003.

[61] Meroni M, Busetto L, Colombo R, et al. Performance of Spectral Fitting Methods for vegetation fluorescence quantification [J]. Remote Sensing of Environment, 2010, 114(2): 363-374.

[62] Mazzoni M, Falorni P, Bianco S D. Sun-induced leaf fluorescence retrieval in the O2-B atmospheric absorption band [J]. Optics Express, 2008, 16(10): 7014.

[63] Guanter L, Richter R, Kaufmann H. On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing [J]. International Journal of Remote Sensing, 2009, 30(6): 1407-1424.

[64] Hurley J, Dudhia A, Grainger R G. Cloud detection for MIPAS using singular vector decomposition [J]. Atmospheric Measurement Techniques Discussions, 2009, 2(2): 1185-1219.

[65] Klüser L, Martynenko D, Holzer-Popp T. Thermal infrared remote sensing of mineral dust over land and ocean: a spectral SVD based retrieval approach for IASI [J]. Atmospheric Measurement Techniques, 2011, 4(5): 757-773.

[66] Guanter L, Rossini M, Colombo R, et al. Using field spectroscopy to assess the potential of statistical approaches for the retrieval of sun-induced chlorophyll fluorescence from ground and space [J]. Remote Sensing of Environment, 2013, 133: 52-61.

[67] Khosravi N, Vountas M, Rozanov V V, et al. Retrieval of Terrestrial Plant Fluorescence Based on the In-Filling of Far-Red Fraunhofer Lines Using SCIAMACHY Observations [J]. Frontiers in Environmental Science, 2015, 3.

[68] Chang C Y, Guanter L, Frankenberg C, et al. Systematic Assessment of Retrieval Methods for Canopy Far‐Red Solar‐Induced Chlorophyll Fluorescence Using High‐Frequency Automated Field Spectroscopy [J]. Journal of Geophysical Research: Biogeosciences, 2020, 125(7).

[69] Yang X, Shi H, Stovall A, et al. FluoSpec 2-An Automated Field Spectroscopy System to Monitor Canopy Solar-Induced Fluorescence [J]. Sensors (Basel), 2018, 18(7).

[70] Zhang Q, Zhang X K, Li Z H, et al. Comparison of Bi-Hemispherical and Hemispherical-Conical Configurations for In Situ Measurements of Solar-Induced Chlorophyll Fluorescence [J]. Remote Sensing, 2019, 11(22).

[71] Monteith J L. Solar Radiation and Productivity in Tropical Ecosystems [J]. Journal of Applied Ecology, 1972, 9(3): 747-766.

[72] Potter C S, Randerson J T, Field C B, et al. Terrestrial ecosystem production a process model based on global satellite and surface data [J]. Global Biogeochemical Cycles, 1993, 7(4): 811-841.

[73] Field C B, Behrenfeld M J, Randerson J T, et al. Primary production of the biosphere integrating terrestrial and oceanic components [J]. Science, 1998, 281(5374): 237-240.

[74] Ruimy A, Saugie B, Dedieu G. Methodology for the estimation of terrestrial net primary production from remotely sensed data [J]. Journal of Geophysical Research Atmospheres 1994, 99: 5263-5283.

[75] Xiao X M, Hollinger D, Aber J, et al. Satellite-based modeling of gross primary production in an evergreen needleleaf forest [J]. Remote Sensing of Environment, 2004, 89(4): 519-534.

[76] Yuan W P, Liu S G, Zhou G S, et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes [J]. Agricultural and Forest Meteorology, 2007, 143(3-4): 189-207.

[77] Zhang Y, Xiao X M, Jin C, et al. Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America [J]. Remote Sensing of Environment, 2016, 183: 154-169.

[78] Turner D P, Ritts W D, Cohen W B, et al. Evaluation of MODIS NPP and GPP products across multiple biomes [J]. Remote Sensing of Environment, 2006, 102(3-4): 282-292.

[79] Wu J, Albert L P, Lopes A P, et al. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests [J]. Science 2016, 351(6276): 972-976.

[80] Yuan W P, Cai W W, Xia J Z, et al. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database [J]. Agricultural and Forest Meteorology, 2014, 192-193: 108-120.

[81] Beer C, Reichstein M, Tomelleri E, et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate [J]. Science, 2010, 329(5993): 834-838.

[82] Jung M, Reichstein M, Bondeau A. Towards global empirical upscaling of FLUXNET eddy covariance observations validation of a model tree ensemble approach using a biosphere model [J]. Biogeosciences, 2009, 6(10).

[83] Zhang L, Wylie B, Loveland T, et al. Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands [J]. Remote Sensing of Environment, 2007, 106(2): 173-189.

[84] Lieth H, Whittaker R H. Primary productivity of the biosphere [J]. Springer-Verlag, 1975.

[85] Yang F, Ichii K, White M A, et al. Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach [J]. Remote Sensing of Environment, 2007, 110(1): 109-122.

[86] Xiao J F, Zhuang Q L, Law B E, et al. A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data [J]. Remote Sensing of Environment, 2010, 114(3): 576-591.

[87] Jung M, Reichstein M, Margolis H A, et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations [J]. Journal of Geophysical Research, 2011, 116.

[88] Xiao J, Chevallier F, Gomez C, et al. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years [J]. Remote Sensing of Environment, 2019, 233.

[89] Papale D, Black T A, Carvalhais N, et al. Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks [J]. Journal of Geophysical Research: Biogeosciences, 2015, 120(10): 1941-1957.

[90] 冯险峰, 刘高焕, 陈述彭, 等. 陆地生态系统净第一性生产力过程模型研究综述 [J]. 自 然 资 源 学 报, 2004, 19(3): 369-378.

[91] Yu G R, Zhu X J, Fu Y L, et al. Spatial patterns and climate drivers of carbon fluxes in terrestrial ecosystems of China [J]. Glob Chang Biol, 2013, 19(3): 798-810.

[92] Sellers P J, Randall D A, Collatz G J, et al. A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS Part I Model Formulation [J]. Journal of Climate, 1996.

[93] Wang S, Grant R F, Verseghy D L, et al. Modelling plant carbon and nitrogen dynamics of a boreal aspen forest in CLASS — the Canadian Land Surface Scheme [J]. Ecological Modelling, 2001.

[94] 关琳琳. 基于叶绿素荧光的植被总初级生产力估算 [D]. 中国科学院大学, 2017.

[95] Chen J M, Mo G, Pisek J, et al. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity [J]. Global Biogeochemical Cycles, 2012, 26(1).

[96] Oleson K, Lawrence D, Flanner M G, et al. Technical description of version 4.0 of the community land model (clm) [J]. geophysical research letters, 2010.

[97] Tian H Q, Chen G S, Liu M L, et al. Model estimates of net primary productivity, evapotranspiration, and water use efficiency in the terrestrial ecosystems of the southern United States during 1895–2007 [J]. Forest Ecology and Management, 2010, 259(7): 1311-1327.

[98] Bonan G B. Land-atmosphere COz exchange simulated by a land surface process model coupled to an atmospheric general circulation model [J]. Journal of Geophysical Research, 1995, 100(D2): 2817.

[99] Sinclair T R, Murphy C E, Knoerr K R. Development and Evaluation of Simplified Models for Simulating Canopy Photosynthesis and Transpiration [J]. Journal of applied ecology, 1976, 13: 813-829.

[100] Baldocchi D. A Lagrangian random-walk model for simulating water vapor, CO2 and sensible heat flux densities and scalar profiles over and within a soybean canopy [J]. Boundary-Layer Meteorology, 1992, 61(1): 113-144.

[101] Dechant B, Ryu Y, Badgley G, et al. Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops [J]. Remote Sensing of Environment, 2020, 241.

[102] Berry J A, Frankenberg C, Wennberg P. New Methods for Measurements of Photosynthesis from Space [J]. Geophysical Research Letters, 2012, 38.

[103] Liu X J, Guanter L, Liu L Y, et al. Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model [J]. Remote Sensing of Environment, 2019, 231.

[104] Miao G F, Guan K Y, Yang X, et al. Sun-Induced Chlorophyll Fluorescence, Photosynthesis, and Light Use Efficiency of a Soybean Field from Seasonally Continuous Measurements [J]. Journal of Geophysical Research: Biogeosciences, 2018, 123(2): 610-623.

[105] Chen J D, Liu X J, Du S S, et al. Integrating SIF and Clearness Index to Improve Maize GPP Estimation Using Continuous Tower-Based Observations [J]. Sensors, 2020, 20(9).

[106] Yang K G, Ryu Y, Dechant B, et al. Sun-induced chlorophyll fluorescence is more strongly related to absorbed light than to photosynthesis at half-hourly resolution in a rice paddy [J]. Remote Sensing of Environment, 2018, 216: 658-673.

[107] Miao G F, Guan K Y, Suyker A E, et al. Varying Contributions of Drivers to the Relationship Between Canopy Photosynthesis and Far‐Red Sun‐Induced Fluorescence for Two Maize Sites at Different Temporal Scales [J]. Journal of Geophysical Research: Biogeosciences, 2020, 125(2).

[108] Li Z H, Zhang Q, Li J, et al. Solar-induced chlorophyll fluorescence and its link to canopy photosynthesis in maize from continuous ground measurements [J]. Remote Sensing of Environment, 2020, 236.

[109] Liu L Y, Guan L L, Liu X J. Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence [J]. Agricultural and Forest Meteorology, 2017, 232: 1-9.

[110] Magney T, Bowling D R, Logan B, et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence [J]. Proceedings of the National Academy of Sciences, 2019, 116.

[111] Wang S H, Zhang Y G, Ju W, et al. Warmer spring alleviated the impacts of 2018 European summer heatwave and drought on vegetation photosynthesis [J]. Agricultural and Forest Meteorology, 2020, 295.

[112] Yang H L, Yang X, Zhang Y G, et al. Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest [J]. Global Change Biology 2017, 23(7): 2874-2886.

[113] Kim J M, Ryu Y, Dechant B, et al. Solar-induced chlorophyll fluorescence is non-linearly related to canopy photosynthesis in a temperate evergreen needleleaf forest during the fall transition [J]. Remote Sensing of Environment, 2021, 258.

[114] Martini D, Sakowska K, Wohlfahrt G, et al. Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production [J]. New Phytologist, 2021.

[115] Cheng X F, Zhou Y, Hu M J, et al. The Links between Canopy Solar-Induced Chlorophyll Fluorescence and Gross Primary Production Responses to Meteorological Factors in the Growing Season in Deciduous Broadleaf Forest [J]. Remote Sensing, 2021, 13(12).

[116] Halubok M, Yang Z L. Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data [J]. Remote Sensing, 2020, 12(20).

[117] Huang Y, Cheng Z, Du M H, et al. Tidal influence on the relationship between solar-induced chlorophyll fluorescence and canopy photosynthesis in a coastal salt marsh [J]. Remote Sensing of Environment, 2022, 270.

[118] Zhang Y G, Zhang Q, Liu L Y, et al. ChinaSpec: A Network for Long‐Term Ground‐Based Measurements of Solar‐Induced Fluorescence in China [J]. Journal of Geophysical Research: Biogeosciences, 2021, 126(3).

[119] Lasslop G, Reichstein M, Papale D, et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation [J]. Global Change Biology, 2010, 16(1): 187-208.

[120] Alonso L, Gómez-Chova L, Vila-Francés J, et al. Improved Fraunhofer Line Discrimination Method for Vegetation Fluorescence Quantification [J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4): 620-624.

[121] Rahman M M, Lamb D W, Stanley J N. The impact of solar illumination angle when using active optical sensing of NDVI to infer fAPAR in a pasture canopy [J]. Agricultural and Forest Meteorology, 2015, 202: 39-43.

[122] Yang P Q, van der Tol C. Linking canopy scattering of far-red sun-induced chlorophyll fluorescence with reflectance [J]. Remote Sensing of Environment, 2018, 209: 456-467.

[123] Zeng Y L, Badgley G, Dechant B, et al. A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence [J]. Remote Sensing of Environment, 2019, 232.

[124] Liu X J, Liu L Y, Hu J C, et al. Improving the potential of red SIF for estimating GPP by downscaling from the canopy level to the photosystem level [J]. Agricultural and Forest Meteorology, 2020, 281.

[125] Gu L, Fuentes J D, Shugart H H, et al. Responses of net ecosystem exchanges of carbon dioxide to changes in cloudiness: Results from two North American deciduous forests [J]. Journal of Geophysical Research: Atmospheres, 1999, 104(D24): 31421-31434.

[126] Sims D A, Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages [J]. Remote Sensing of Environment, 2002, 81(2-3): 337-354.

[127] Liu Z Q, Lu X L, An S Q, et al. Advantage of multi-band solar-induced chlorophyll fluorescence to derive canopy photosynthesis in a temperate forest [J]. Agricultural and Forest Meteorology, 2019, 279.

[128] Sun Y, Frankenberg C, Wood J D, et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence [J]. Science, 2017, 358(6360).

[129] Joiner J, Yoshida Y, Guanter L, et al. New methods for retrieval of chlorophyll red fluorescence from hyper-spectral satellite instruments: simulations and application to GOME-2 and SCIAMACHY [J]. Atmospheric Measurement Techniques, 2016.

[130] Verrelst J, van der Tol C, Magnani F, et al. Evaluating the predictive power of sun-induced chlorophyll fluorescence to estimate net photosynthesis of vegetation canopies: A SCOPE modeling study [J]. Remote Sensing of Environment, 2016, 176: 139-151.

[131] Kautsky H, Appel W, Amann H. Chlorophyll fluorescence and carbon assimilation. Part XIII. The fluorescence and the photochemistry of plants [J]. Biochem Z, 1960, 332: 16.

[132] Romero J M, Cordon G B, Lagorio M G. Re-absorption and scattering of chlorophyll fluorescence in canopies: A revised approach [J]. Remote Sensing of Environment, 2020, 246.

[133] Govindjee, Papageorgiou G C. Chlorophyll A Fluorescence: A Signature of Photosynthesis [M]. 2004.

[134] Liu L Y, Liu X J, Hu J C, et al. Assessing the wavelength-dependent ability of solar-induced chlorophyll fluorescence to estimate the GPP of winter wheat at the canopy level [J]. International Journal of Remote Sensing, 2017, 38(15): 4396-4417.

[135] Saito M, Kato T, Tang Y. Temperature controls ecosystem CO2 exchange of an alpine meadow on the northeastern Tibetan Plateau [J]. Global Change Biology, 2009, 15(1): 221-228.