<p>鄱阳湖受长江江水影响水位变化巨大，高水位和低水位的交替使得鄱阳湖呈现独特的水陆相交替出现的自然景观，并对鄱阳湖湿地生态系统碳水通量等生态功能产生了巨大影响。日光诱导叶绿素荧光(Solar- Induced Chlorophyll Fluorescence，SIF)为植被和水体光合固碳能力估算提供了一种全新而直接的测量方式。因此，开展鄱阳湖湿地生态系统SIF观测研究，可为湖泊湿地生态系统碳循环提供重要参考，也能为湿地生态系统碳循环变化对气候变化响应研究提供重要科学数据。</p> <p>本研究基于鄱阳湖南矶湿地站SIF自动观测系统，连续监测了2020年4月-2021年7月鄱阳湖南矶湿地生态系统的SIF，研究了丰水期和枯水期鄱阳湖生态系统SIF的变化特征，并结合鄱阳湖南矶湿地综合试验站环境要素变化，分析并探讨了鄱阳湖湿地生态系统SIF变化的主要影响因素。论文主要结论如下：</p> <p>（1）获得了2020-2021年两年的连续观测的鄱阳湖湿地生态系统SIF、光谱、通量和气象观测资料。在鄱阳湖枯水期时(南矶湿地站为植被覆盖)，利用3FLD算法，反演得到了湿地站植被覆盖状态下的SIF连续观测数据；在鄱阳湖丰水期时(南矶湿地站为水体覆盖)，以3FLD算法反演结果作为&ldquo;真值&rdquo;，定量评价了不同光谱分辨率条件FLH算法在丰水期水体SIF探测的适用性，结果表明3nm光谱分辨率条件下的FLH算法反演的SIF与3FLD算法结果有较好一致性，并计算了湿地站水体覆盖状态下的SIF连续观测数据。</p> <p>（2）阐明了鄱阳湖湿地生态系统SIF的日变化和季节变化规律。枯水期湿地植被SIF的日变化和季节变化结果表明，湿地植被覆盖SIF的晴天日变化趋势呈现出相似的规律，即SIF日变化曲线呈单峰特征，峰值出现在正午12点左右；枯水期湿地植被SIF的季节变化较为显著，春季湿地植被SIF较高，日均SIF在0.4mW/m²/nm/sr左右；冬季湿地植被SIF较低，日均SIF在0.1mW/m²/nm/sr左右。丰水期湿地水体SIF的日变化和季节变化表明，湿地水体覆盖SIF的晴天日变化趋势也呈现出相似的规律，即SIF日变化曲线呈单峰特征，峰值出现在正午12点左右；丰水期湿地水体SIF的季节性不显著，日均SIF在0.2-0.35 mW/m²/nm/sr之间。</p> <p>（3）分析了鄱阳湖湿地生态系统SIF的环境响应规律。基于鄱阳湖湿地生态系统连续观测数据，分析了气温、降雨以及光合有效辐射等气象因子的时间动态变化，以及探究了枯水期和丰水期时这些气象因子对SIF的影响。结果表明：湿地站在枯水期植被覆盖时，PAR和SIF存在着明显的正相关关系，晴空日变化数据的R²最高可达0.98，观测期内所有数据R²为0.78，偏相关系数为0.73，与气温和降水对比，PAR是湿地植被SIF变化的主导环境因素；在丰水期湿地站水体覆盖的条件下，晴空PAR和SIF也存在着较强的正相关关系，晴空日变化数据的R²最高可达0.79，观测期内所有数据R²为0.57，偏相关系数为0.79，与气温和降水对比，PAR也是湿地水体SIF的主导环境因素。</p>

<p>Poyang Lake is affected by the Yangtze River, which results in the great change of water level. The alternation of high and low water levels leads Poyang Lake to present a unique natural landscape of alternating land and water phases, and it has a huge impact on ecological functions such as carbon and water fluxes in the Poyang Lake wetland ecosystem. Solar-Induced Chlorophyll Fluorescence (SIF) provides a new and direct measurement method for estimating the photosynthetic carbon sequestration capacity of vegetation and water bodies. Therefore, the SIF observation research of Poyang Lake wetland ecosystem can provide an important reference for the carbon cycle of the lake wetland ecosystem, and also provide important scientific data for the study of the response of the wetland ecosystem carbon cycle change to climate change.</p> <p>Based on the SIF automatic observation system of Poyang Hunanji Wetland Station, this study continuously observed the SIF of the Poyang Hunanji wetland ecosystem from April 2020 to July 2021, and analyzed the variation characteristics of the SIF of the Poyang Lake ecosystem during the wet season and the dry season. Combining with the changes of environmental elements in Poyang Hunan Ji Wetland Comprehensive Experiment Station, the main influencing factors of SIF changes in Poyang Lake wetland ecosystem were analyzed and discussed. The main conclusions of the paper are as follows:</p> <p>(1) The SIF, spectrum, flux and meteorological observation data of the Poyang Lake wetland ecosystem for two consecutive years from 2020 to 2021 were obtained. During the dry season of Poyang Lake (Nanji Wetland Station was covered by vegetation), the 3FLD algorithm was used to invert the SIF continuous observation data under the vegetation coverage of the wetland station; during the wet season of Poyang Lake (Nanji Wetland Station was covered by water body) , assuming the inversion results of the 3FLD algorithm as the &quot;true value&quot;, the applicability of the FLH algorithm for different spectral resolution conditions in the detection of SIF in water bodies during the wet season was quantitatively evaluated. The results indicated that the SIF inversion of FLH algorithm under the condition of 3 nm spectral resolution is in a good agreement with the results of 3FLD algorithm. The SIF continuous observation data under the water coverage state of the wetland station were calculated.</p> <p>(2) The diurnal and seasonal changes of SIF in Poyang Lake wetland ecosystem were elucidated. The results of diurnal and seasonal changes in wetland vegetation SIF in dry season showed that the diurnal variation trend of wetland vegetation coverage SIF in sunny days presents a similar law, that is, the diurnal variation curve of SIF exhibits a single-peak feature, and the peak appeared around 12:00 noon; The seasonal variation of wetland vegetation SIF was more significant in the dry season, the SIF of wetland vegetation was higher in spring, and the daily average SIF was about 0.4mW/m²/nm/sr; the SIF of wetland vegetation in winter was lower, and the daily average SIF was about 0.1mW/m²/nm/sr. The diurnal and seasonal changes of SIF in wetland water body during wet season showed that the diurnal variation trend of SIF covered by wetland water body also gave a similar law, that is, the diurnal variation curve of SIF showed a single peak characteristic, and the peak appeared around 12:00 noon; The seasonality of SIF in wetland water body was not significant during wet season, and the daily average SIF was between 0.2 and 0.35 mW/m²/nm/sr.</p> <p>(3) The environmental response law of Poyang Lake wetland ecosystem SIF was analyzed. Based on the continuous observation data of the Poyang Lake wetland ecosystem, the temporal dynamic changes of meteorological factors such as temperature, rainfall, and photosynthetically active radiation were analyzed, and the effects of these meteorological factors on SIF during dry and wet periods were explored. The results showed that when the wetland station was covered by vegetation in the dry season, there was an obvious positive correlation between PAR and SIF. The R² of the clear sky diurnal variation data can reach a maximum of 0.98, the R² of all the data during the observation period was 0.78, and the partial correlation coefficient was 0.73. Compared with temperature and precipitation, PAR was the dominant environmental factor for the change of wetland vegetation SIF; under the condition of water coverage of wetland stations in wet season, there was also a strong positive correlation between clear sky PAR and SIF, and the R² of clear sky diurnal variation data was 0.79, the R² of all data during the observation period was 0.57, and the partial correlation coefficient was 0.79, which indicated that PAR was also the dominant environmental factor of wetland water body SIF, compared with temperature and precipitation.</p>

[1] Griggs D J, Noguer M. Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [J]. Weather, 2002, 57(8): 267-269.

[2] 刘子刚. 湿地生态系统碳储存和温室气体排放研究[J]. 地理科学, 2004, 24(5): 634-639.

[3] Matthews E, Fung I. Methane Emission from Natural Wetlands: Global Distribution, Area, and Environmental Characteristics of Sources[J]. Global biogeochemical cycles, 1987, 1(1): 61-86.

[4] Aselmann I, Crutzen P. Global Distribution of Natural Freshwater Wetlands and Rice Paddies, Their Net Primary Productivity, Seasonality and Possible Methane Emissions[J]. Journal of Atmospheric chemistry, 1989, 8(4): 307-358.

[5] Cole J J, Prairie Y T, Caraco N F, et al. Plumbing the Global Carbon Cycle: Integrating Inland Waters into the Terrestrial Carbon Budget[J]. Ecosystems, 2007, 10(1): 172-185.

[6] 牛振国, 张海英, 王显威, 等. 1978~ 2008 年中国湿地类型变化[J]. 2012.

[7] 刘信中. 江西湿地 [M]. 中国林业出版社, 2000.

[8] 闵骞. 鄱阳湖水位变化规律的研究[J]. 湖泊科学, 1995, 7(003): 281-288.

[9] Yuan S, Tang H, Li K, et al. Hydrodynamics, Sediment Transport and Morphological Features at the Confluence between the Yangtze River and the Poyang Lake[J]. Water Resources Research, 2021, 57(3): e2020WR028284.

[10] Agati G, Cerovic Z G, Moya I. The Effect of Decreasing Temperature up to Chilling Values on the in Vivo F685/F735 Chlorophyll Fluorescence Ratio in Phaseolus Vulgaris and Pisum Sativum: The Role of the Photosystem I Contribution to the 735 Nm Fluorescence Band[J]. Photochemistry and Photobiology, 2000, 72(1): 75-84.

[11] Damm A, Elbers J, 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.

[12] 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).

[13] Lichtenthaler H K, Rinderle U. The Role of Chlorophyll Fluorescence in the Detection of Stress Conditions in Plants[J]. CRC Critical Reviews in Analytical Chemistry, 1988, 19(sup1): S29-S85.

[14] Liu L, Cheng Z. Detection of Vegetation Light-Use Efficiency Based on Solar-Induced Chlorophyll Fluorescence Separated from Canopy Radiance Spectrum[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2010, 3(3): 306-312.

[15] Zarco-Tejada P J, Berni J A, Suárez L, et al. Imaging Chlorophyll Fluorescence with an Airborne Narrow-Band Multispectral Camera for Vegetation Stress Detection[J]. Remote Sensing of Environment, 2009, 113(6): 1262-1275.

[16] 胡启武, 幸瑞新, 朱丽丽, 等. 鄱阳湖苔草湿地非淹水期 Co2 释放特征[J]. 应用生态学报, 2011, 22(6): 1431-1436.

[17] Porcar-Castell A, Mac Arthur A, Rossini M, et al. Eurospec: At the Interface between Remote-Sensing and Ecosystem Co 2 Flux Measurements in Europe[J]. Biogeosciences, 2015, 12(20): 6103-6124.

[18] Liu X, Liu L, Hu J, et al. Modeling the Footprint and Equivalent Radiance Transfer Path Length for Tower-Based Hemispherical Observations of Chlorophyll Fluorescence[J]. Sensors, 2017, 17(5): 1131.

[19] 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.

[20] Xu S, Liu Z, Zhao L, et al. Diurnal Response of Sun-Induced Fluorescence and Pri to Water Stress in Maize Using a near-Surface Remote Sensing Platform[J]. Remote Sensing, 2018, 10(10): 1510.

[21] Balzarolo M, Anderson K, Nichol C, et al. Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies[J]. Sensors, 2011, 11(8): 7954-7981.

[22] Eklundh L, Jin H, Schubert P, et al. An Optical Sensor Network for Vegetation Phenology Monitoring and Satellite Data Calibration[J]. Sensors, 2011, 11(8): 7678-7709.

[23] Detmers R, Hasekamp O, Aben I, et al. Anomalous Carbon Uptake in Australia as Seen by Gosat[J]. Geophysical Research Letters, 2015, 42(19): 8177-8184.

[24] Green J K, Konings A G, Alemohammad S H, et al. Regionally Strong Feedbacks between the Atmosphere and Terrestrial Biosphere[J]. Nature geoscience, 2017, 10(6): 410-414.

[25] Castro A O, Chen J, Zang C S, et al. Oco-2 Solar-Induced Chlorophyll Fluorescence Variability across Ecoregions of the Amazon Basin and the Extreme Drought Effects of El Niño (2015–2016)[J]. Remote Sensing, 2020, 12(7): 1202.

[26] Chen X, Mo X, Zhang Y, et al. Drought Detection and Assessment with Solar-Induced Chlorophyll Fluorescence in Summer Maize Growth Period over North China Plain[J]. Ecological Indicators, 2019, 104: 347-356.

[27] 黄长平. 太阳光诱导下的植被叶绿素荧光遥感反演方法研究[D]; 中国科学院大学, 2013.

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

[29] 张永江, 刘良云, 侯名语, 等. 植物叶绿素荧光遥感研究进展[J]. 遥感学报, 2009, 13(005): 963-978.

[30] Porcar-Castell A, Tyystjärvi 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.

[31] Albert P C, Esa T, Jon A, et al. Linking Chlorophyll a Fluorescence to Photosynthesis for Remote Sensing Applications: Mechanisms and Challenges[J]. Journal of Experimental Botany, (15): 4065-4095.

[32] Baldocchi D, Falge E, Gu L, et al. Fluxnet: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities[J]. Bulletin of the American Meteorological Society, 2001, 82(11): 2415-2434.

[33] Baldocchi D. ‘Breathing’of the Terrestrial Biosphere: Lessons Learned from a Global Network of Carbon Dioxide Flux Measurement Systems[J]. Australian Journal of Botany, 2008, 56(1): 1-26.

[34] Gamon J A, Rahman A, Dungan J, et al. Spectral Network (Specnet)—What Is It and Why Do We Need It? [J]. Remote Sensing of Environment, 2006, 103(3): 227-235.

[35] Porcar-Castell A, Mac Arthur A, Rossini M, et al. Eurospec: At the Interface between Remote-Sensing and Ecosystem Co 2 Flux Measurements in Europe [J]. Biogeosciences, 2015, 12(20): 6103-6124.

[36] Porcar-Castell A, Mac Arthur A, Rossini M, et al. Eurospec: At the Interface between Remote-Sensing and Ecosystem Co2 Flux Easurements in Europe[J]. 2015.

[37] 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.

[38] Zarco-Tejada P J, Catalina A, González M, 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.

[39] Plascyk J A. The Mk Ii Fraunhofer Line Discriminator (Fld-Ii) for Airborne and Orbital Remote Sensing of Solar-Stimulated Luminescence[J]. Optical Engineering, 1975, 14(4): 144339.

[40] 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, 023(001): 37-52.

[41] Maier S W, Günther K P, Stellmes M. Sun‐Induced Fluorescence: A New Tool for Precision Farming[J]. Digital imaging and spectral techniques: Applications to precision agriculture and crop physiology, 2004, 66: 207-222.

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

[43] 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.

[44] Behrenfeld M J, Westberry T K, Boss E S, et al. Satellite-Detected Fluorescence Reveals Global Physiology of Ocean Phytoplankton[J]. Biogeosciences, 2009, 6(5): 779-794.

[45] Ling Z, Sun D, Wang S, et al. Retrievals of Phytoplankton Community Structures from in Situ Fluorescence Measurements by Hs-6p[J]. Optics express, 2018, 26(23): 30556-30575.

[46] 赵冬至. 渤海赤潮灾害监测与评估研究文集[M]. 海洋出版社, 2000.

[47] Xie H, Wen J, Chen Q, et al. Evaluating the Landscape Ecological Risk Based on Gis: A Case‐Study in the Poyang Lake Region of China[J]. Land Degradation & Development, 2021, 32(9): 2762-2774.

[48] 王莉莉, 杨涛, 高晨, 等. 鄱阳湖南矶湿地净生态系统co_2交换量的日变化特征[J]. 生态与农村环境学报, 2017, 33(11): 6.

[49] 王晓鸿, 樊哲文, 崔丽娟, 等. 鄱阳湖湿地生态系统评估[M]. 鄱阳湖湿地生态系统评估, 2004.

[50] 刘信中, 樊三宝, 胡斌华. 江西南矶山湿地自然保护区综合科学考察[M]. 江西南矶山湿地自然保护区综合科学考察, 2006.

[51] 张全军, 于秀波, 胡斌华. 鄱阳湖南矶湿地植物群落分布特征研究[J]. 资源科学, 2013, 35(1): 8.

[52] Covariance E. A Practical Guide to Measurement and Data Analysis[J]. Series: Springer Atmospheric Sciences, edited by: Aubinet, M, Vesala, T, and Papale, D, XXII, 2012.

[53] Liu L, Guan L, Liu X. 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.

[54] Liu L, Liu X, Wang Z, et al. Measurement and Analysis of Bidirectional Sif Emissions in Wheat Canopies[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 54(5): 2640-2651.

[55] 王林, 杨建洪, 赵冬至, 等. 大连湾及邻近海域水体叶绿素浓度荧光遥感算法研究[J]. 应用海洋学学报, 2014, 33(1): 7.

[56] 陈明华. 鄱阳湖浮游植物时空格局及环境解析[D]; 南昌大学, 2019.

[57] Liu X, Liu Z, Liu L, et al. Modelling the Influence of Incident Radiation on the Sif-Based Gpp Estimation for Maize[J]. Agricultural and Forest Meteorology, 2021, 307: 108522.

[58] Waldorp L, Marsman M. Relations between Networks, Regression, Partial Correlation, and the Latent Variable Model[J]. Multivariate Behavioral Research, 2021: 1-13.