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遥感技术与应用  2019, Vol. 34 Issue (3): 488-499    DOI: 10.11873/j.issn.1004-0323.2019.3.0488
 (1.南京信息工程大学应用气象学院,江苏 南京 210044;
2.国际地球系统科学研究所 南京大学,江苏 南京 210033)
Sun-induced Chlorophyll Fluorescence and GPP Simulations with SCOPE Model for Paddy Rice Under Different Growing Stages
Xi Lei1,2,Shan Nan1,Yang Shenbin2,Zhang Yongguang1
 (1.School of Applied Meteorology,Nanjing University of Information,Science and Technology,Nanjing 210044,China;
2.International Institute for Earth System Science,Nanjing University,Nanjing 210023,China)
 全文: PDF(1966 KB)  
摘要: 日光诱导叶绿素荧光(Sun-induced Chlorophyll Fluorescence,SIF)作为监测总初级生产力(Gross Primary Production,GPP)最有效的手段之一,相对于传统的绿度植被指标,能直接反映光合作用的动态变化。SCOPE(Soil Canopy Observation,Photochemistry,and Energy fluxes)模型可以用于同时模拟荧光和GPP,但其在不同生育期和天气条件下的模拟效果仍有待验证。基于2016年水稻生育期的生理参数和气象观测数据,利用冠层SIF760和GPP的观测值验证了SCOPE模型模拟的不同生育期和天气条件下的SIF760和GPP。研究结果表明:SCOPE模型可以模拟季节尺度上的SIF760和GPP(R2=0.44和R2=0.67);从日尺度上来看,SCOPE模型在不同生育期有不同的表现,成熟期时SCOPE模型估算的SIF760和GPP效果最好(R2=0.99和R2=0.96),而抽穗—开花期模型的模拟效果最差。从整个生育期来看,SCOPE模型模拟的SIF760值低于实际观测值,而GPP的模拟值偏高,但整体趋势一致。同时,天气状况也会对SCOPE模型的模拟SIF760结果产生影响,晴天SCOPE模型模拟的SIF760要优于多云(R2=0.64和R2=0.46)。因此,SCOPE模型可以用于模拟水稻在不同生育期的SIF760和GPP。该研究结果为荧光遥感监测农田生态系统生产力以及对其环境因子的响应研究提供了一定的模型基础。
关键词: SIFGPPSCOPE模型水稻生育期    
Abstract: Sun-induced Chlorophyll Fluorescence and GPP Simulations
with SCOPE Model for Paddy Rice Under Different Growing StagesCompared with traditional greenness-based vegetation indicators,Sun-induced Chlorophyll Fluorescence (SIF),as one of the most effective tools in monitoring Gross Primary Production(GPP),can be used to directly reflect the dynamic changes of photosynthesis.The SCOPE (Soil Canopy Observation,Photochemistry,and Energy Fluxes) model has been widely used to simulate the SIF and GPP across multiple scales.However,the accuracy and ability of the SCOPE model on the simulations under different growth stages and weathers remains unclear.In this study,we investigated the performance of SCOPE in SIF760 and GPP based on the physiological parameters and meteorological data of paddy rice in 2016.Then we compared the simulations with the measured SIF760and GPP under different growing stages and weather conditions.The results showed that the SCOPE model could simulate well for SIF760 and GPP at the seasonal scale (R2=0.44 and R2=0.67).However,the SCOPE model had different performances under different growth stages at the diurnal scale A better performance was obtained in maturity stage of rice (R2=0.99 and R2=0.96),while a lower performance was at the heading-flowering stage.During the whole growth period,the SIF760 simulated by the SCOPE model was lower than the measured SIF760while the simulated GPP was higher.In addition,weather conditions significantly affect the simulations from the SCOPE model.The accuracy of the SIF760 simulations on sunny days was better than that in cloudy days (R2=0.64 and R2=0.46,respectively).Our quantitative assessment of the SCOPE model supported its usefulness for interpreting SIF and GPP under different growth stages.These results provided the model evidence for remote sensing of SIF to monitor crop photosynthesis and its response on environmental factors.
Key words: SIF    GPP    SCOPE model    Paddy rice    Growth stage
收稿日期: 2019-01-06 出版日期: 2019-07-01
基金资助: 国家重点研发计划(2016YFA0600202),江苏省杰出青年基金项目(BK20170018),国家自然科学基金项目(41671421、41761134082)。
作者简介: 奚雷(1994-),女,江苏常州人,硕士研究生,主要从事叶绿素荧光模拟研究。。
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奚雷, 单楠, 杨沈斌, 张永光. 基于SCOPE模型的水稻不同生育期日光诱导叶绿素荧光及GPP模拟研究[J]. 遥感技术与应用, 2019, 34(3): 488-499.

Xi Lei, Shan Nan, Yang Shenbin, Zhang Yongguang. Sun-induced Chlorophyll Fluorescence and GPP Simulations with SCOPE Model for Paddy Rice Under Different Growing Stages . Remote Sensing Technology and Application, 2019, 34(3): 488-499.


<p> [1]Heimann M,Reichstein M.Terrestrial Ecosystem Carbon Dynamics and Climate Feedbacks[J].Nature,2008,451(7176):289-292.doi:10.1038/nature06591.<br /> [2]Lieth H.Primary Production:Terrestrial Ecosystems[J].Human Ecology,1973,1(4):303-332.<br /> [3]Mkel A,Pulkkinen M,Kolari P,et al.Developing an Empirical Model of Stand GPP with the LUE Approach:Analysis of Eddy Covariance Data at Five Contrasting Conifer Sites in Europe[J].Global Change Biology,2008,14(1):92-108.<br /> [4]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.<br /> [5]Liu L,Yang X,Zhou H,et al.Evaluating the Utility of Solar-Induced Chlorophyll Fluorescence for Drought Monitoring by Comparison with NDVI derived from Wheat Canopy[J].Science of the Total Environment,2018,625:1208-1217.<br /> [6]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):339-346.<br /> [7]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.<br /> [8]Zhang Zhaoyin,Wang Songha,Qiu Bo,et al.Retrieval of Sun-Induced Chlorophyll Fluorescence and Advancements in Carbon Cycle Application[J].Journal of Remote Sensing,2019,23(1):37-52.[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(1):37-52.]<br /> [9]Guanter L,Zhang Y,Jung M,et al.Global and Time-Resolved Monitoring of Crop Photosynthesis with Chlorophyll Fluorescence[J].Proceedings of the National Academy of Sciences,2014,111(14):E1327-E1333.<br /> [10]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):351-365.<br /> [11]Porcar-Castell A,Tyystjrvi 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.<br /> [12]Guan K,Berry J A,Zhang Y,et al.Improving the Monitoring of Crop Productivity Using Spaceborne Solar-Induced Fluorescence[J].Global Change Biology,2016,22(2):716-726.<br /> [13]Joiner J,Yoshida Y,Vasilkov A P,et al.The Seasonal Cycle of Satellite Chlorophyll Fluorescence Observations and its Relationship to Vegetation Phenology and Ecosystem Atmosphere Carbon Exchange[J].Remote Sensing of Environment,2014,152:375-391.<br /> [14]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.<br /> [15]Cendrero-Mateo M P,Carmo-Silva A E,Porcar-Castell A,et al.Dynamic Response of Plant Chlorophyll Fluorescence to Light,Water and Nutrient Availability[J].Functional Plant Biology,2015,42(8):746-757.<br /> [16]Perez-Priego O,Guan J,Rossini M,et al.Sun-induced Chlorophyll Fluorescence and PRI Improve Remote Sensing GPP Estimates under Varying Nutrient Availability in a Typical Mediterranean Savanna Ecosystem[J].Biogeosciences,2015,12(21):6351-6367.<br /> [17]Van der Tol C,Verhoef W,Timmermans J,et al.An Integrated Model of Soil-Canopy Spectral Radiances,Photosynthesis,Fluorescence,Temperature and Energy Balance[J].Biogeosciences,2009,6(12):3109-3129.<br /> [18]Hu J,Liu X,Liu L,et al.Evaluating the Performance of the Scope Model in Simulating Canopy Solar-induced Chlorophyll Fluorescence[J].Remote Sensing,2018,10(2):250-276.doi:10.3390/rs10020250.<br /> [19]Van Der Tol C,Rossini M,Cogliati S,et al.A Model and Measurement Comparison of Diurnal Cycles of Sun-induced Chlorophyll Fluorescence of Crops[J].Remote Sensing of Environment,2016,186:663-677.<br /> [20]Verrelst J,Rivera J P,Van Der Tol C,et al.Global Sensitivity Analysis of the SCOPE Model:What Drives Simulated Canopy-Leaving Sun-Induced Fluorescence?[J].Remote Sensing of Environment,2015,166:8-21.<br /> [21]Fang F.The Retrieval of Leaf Inclination Angle and Leaf Area Index in Maize[M].University of Twente Faculty of Geo-Information and Earth Observation (ITC),2015.<br /> [22]Genty B,Briantais J M,Baker N R.The Relationship between the Quantum Yield of Photosynthetic Electron Transport and Quenching of Chlorophyll Fluorescence[J].Biochimica et Biophysica Acta (BBA)-General Subjects,1989,990(1):87-92.<br /> [23]Lichtenthaler H K,Wellburn A R.Determinations of Total Carotenoids and Chlorophylls a and b of Leaf Extracts in Different Solvents[J].Biochemical Society Transactions,1983,11(5):591-592.<br /> [24]Chou Shuren.Relating Crop Photosynthesis to Remotely Sensed Photochemical Reflectance Index and Sun-induced Chlorophyll Fluorescence[D].Nanjing:Nanjing University,2018.[丑述仁.光化学植被指数和日光诱导叶绿素荧光与作物光合作用的关系[D].南京:南京大学,2018.]<br /> [25]Yan Min,Li Zengyun,Tian Xin,et al.Remote Sensing Estimation of Gross Primary Productivity and Its Response to Climate Change in the Upstream of Heihe River Basin[J].Chinese Journal of Plant Ecology,2016,40(1):1-12.[闫敏,李增元,田昕,等.黑河上游植被总初级生产力遥感估算及其对气候变化的响应[J].植物生态学报,2016,40(1):1-12.]<br /> [26]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.<br /> [27]Yusuf A.Characterization of Sky Conditions Using Clearnesss Index and Relative Sunshine Duration for Iseyin,Nigeria.[J].International Journal of Physical Sciences Research,2017,1(1):53-60.<br /> [28]Wei Nan,Zhang Mi,Wang Huiming,et al.The Impacts of Changes in Diffuse Radiation on Light Use Efficiency in a Subtropical Plantation Coniferous Forest[J].Acta Ecologica Sinica,2017,37(10):3403-3414.[卫楠,张弥,王辉民,等.散射辐射对亚热带人工针叶林光能利用率的影响[J].生态学报,2017,37(10):3403-3414.]<br /> [29]Kuye A,Jagtap S S.Analysis of Solar Radiation Data for Port Harcourt,Nigeria[J].Solar Energy,1992,49(2):139-145.<br /> [30]Yang J M,Yang J Y,Liu S,et al.An Evaluation of the Statistical Methods for Testing the Performance of Crop Models with Observed Data[J].Agricultural Systems,2014,127:81-89.<br /> [31]Smith P,Smith J U,Powlson D S,et al.A Comparison of the Performance of Nine Soil Organic Matter Models Using Datasets from Seven Long-Term Experiments[J].Geoderma,1997,81(1-2):153-225.<br /> [32]Gao Xiaoye,Yuan Shili,Lü Aimin,et al.Effects of Alfalfa Green Manure on Rice Production and Greenhouse Gas Emissions based on a DNDC Model Simulation[J].Acta Prataculturae Sinica,2016,25(12):14-26.[高小叶,袁世力,吕爱敏,等.DNDC模型评估苜蓿绿肥对水稻产量和温室气体排放的影响[J].草业学报,2016,25(12):14-26.]<br /> [33]Miao G,Guan K,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.<br /> [34]Yang P,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,2018.doi:10.1016/j.rse.2018.11.039.<br /> [35]Lin Shangrong,Li Jing,Lin Qinhuo,et al.Overview on Estimation Accuracy of Gross Primary Productivity with Remote Sensing Methods[J].Journal of Remote Sensing,2018,22(2):234-252.[林尚荣,李静,柳钦火.陆地总初级生产力遥感估算精度分析[J].遥感学报,2018,22(2):234-252.]<br /> [36]Zhang Lifu,Wang Siheng,Huang Changping.Top-of-Atmosphere Hyperspectral Remote Sensing of Solar-Induced Chlorophyll Fluorescence:A Review of Methods[J].Journal of Remote Sensing,2018,22(1):1-12.[张立福,王思恒,黄长平.太阳诱导叶绿素荧光的卫星遥感反演方法[J].遥感学报,2018,22(1):1-12.]<br /> [37]Moya I,Camenen L,Evain S,et al.A New Instrument for Passive Remote Sensing:1.Measurements of Sunlight-Induced Chlorophyll Fluorescence[J].Remote Sensing of Environment,2004,91(2):186-197.<br /> [38]Goulas Y,Fournier A,Daumard F,et al.GrossPrimary Production of a Wheat Canopy Relates Stronger to Far Red than to Red Solar-Induced Chlorophyll Fluorescence[J].Remote Sensing,2017,9(1):97-128.doi:10.3390/rs9010097.<br /> [39]Wei Jiabin,Xu Huaqin,Zhou Linghong,et al.Seasonal Variation in Carbon Exchange and Its Modulating Factors of a Double Cropping Rice Ecosystem in Southern China[J].Journal of Agro-Environment Science,2018,37(5):1035-1044.[魏甲彬,徐华勤,周玲红,等.“双季稻-冬闲田”生态系统碳交换动态变化及其影响因素[J].农业环境科学学报,2018,37(5):1035-1044.]<br /> [40]Xu Xiaojun,Zhou Guomo,Du Huaqiang,et al.Interannual Variability of Moso Bamboo Forest GPP and Its Driving Factors:A Case Study of Anji County[J].Acta Ecologica Sinica,2016,36(6):1636-1644.[徐小军,周国模,杜华强,等.毛竹林总初级生产力年际变化及其驱动因素——以安吉县为例[J].生态学报,2016,36(6):1636-1644.]<br /> [41]Zhang Xiaosui,Lu Chuangen,Hu Ning,et al.Simulation of Leaf Inclination Angle Distribution for Rice with Different Plant Types[J].Chinese Journal of Rice Science,2012,26(2):205-210.[张晓翠,吕川根,胡凝,等.不同株型水稻叶倾角群体分布的模拟[J].中国水稻科学,2012,26(2):205-210.]<br /> [42][JP2]Bjrkman O,Demmig B.Photon Yield of O2 Evolution and Chlorophyll Fluorescence Characteristics at 77 K among Vascular Plants of Diverse Origins[J].Planta,1987,170(4):489-504.<br /> [43]Cao Jixin,Tian Bin,Wang Xiaoping,et al.Estimation Methods of Forest Sequestration and Their Prospects[J].Ecology and Environmental Sciences,2009,18(5):2001-2005.[曹吉鑫,田赟,王小平,等.森林碳汇的估算方法及其发展趋势[J].生态环境学报,2009,18(5):2001-2005.]<br /> [44]Zhao Haiyan,Yao Fenghai,Zhang Yong,et al.Correlation Analysis of Rice Seed Setting Rate and Weight of 1000-Grain and Agro-meteorology over the Middle and Lower Reaches of the Yangtze River[J].Scientia Agricultura Sinica,2006(9):1765-1771.[赵海燕,姚凤梅,张勇,等.长江中下游水稻开花灌浆期气象要素与结实率和粒重的相关性分析[J].中国农业科学,2006(9):1765-1771.]<br /> [45]Li min,Ma Jun,Wang Hezheng,et al.Relationship between Some Physiological and Biochemical Characteristics and Heat Tolerance at Flowering Stage in Rice[J].Hybrid Rice,2007(6):62-66.[李敏,马均,王贺正,等.水稻开花期高温胁迫条件下生理生化特性的变化及其与品种耐热性的关系[J].杂交水稻,2007(6):62-66.]<br /> [46]Zhang Guilian,Chen Liyun,Zhang Shuntang,et al.Effects of High Temperature on the Physiological and Biochemical Characteristics in Flag Leaves of Rice at Heading and Flowering Period[J].Scientia Agricultura Sinica,2007,40(7):1345-1352.[张桂莲,陈立云,张顺堂,等.抽穗开花期高温对水稻剑叶理化特性的影响[J].中国农业科学,2007,40(7):1345-1352.]<br /> [47]Li Chengde.Analysis of Massive Empty Shell Caused by High Temperature[J].Shaanxi Journal of Agricultural Sciences,2003(5):45-47.[李成德.高温导致水稻出现大量空壳分析[J].陕西农业科学,2003(5):45-47.]<br /> [48]Hu J,Liu L,Guo J,et al.Upscaling Solar-Induced Chlorophyll Fluorescence from an Instantaneous to Daily Scale Gives an Improved Estimation of the Gross Primary Productivity[J].Remote Sensing,2018,10(10):1663-1683.doi:10.3390/rs10101663.<br /> [49]Zhang Y,Guanter L,Berry J A,et al.Model-based Analysis of the Relationship between Sun-Induced Chlorophyll Fluorescence and Gross Primary Production for Remote Sensing Applications[J].Remote Sensing of Environment,2016,187:145-155. </p>
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