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
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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SIF_(760)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SIF_(760)和GPP。研究结果表明:SCOPE模型可以模拟季节尺度上的SIF_(760)和GPP(R~2=0.44和R~2=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SIF_(760)和GPP效果最好(R~2=0.99和R~2=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SIF_(760)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SIF_(760)结果产生影响,晴天SCOPE模型模拟的SIF_(760)要优于多云(R~2=0.64和R~2=0.46)。因此,SCOPE模型可以用于模拟水稻在不同生育期的SIF_(760)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。
Compared 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 SIF_(760) and GPP based on the physiological parameters and meteorological data of paddy rice in 2016.Then we compared the simulations with the measured SIF_(760) and GPP under different growing stages and weather conditions.The results showed that the SCOPE model could simulate well for SIF_(760) and GPP at the seasonal scale(R~2=0.44 and R~2=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(R~2=0.99 and R~2=0.96),while a lower performance was at the heading-flowering stage.During the whole growth period,the SIF_(760) simulated by the SCOPE model was lower than the measured SIF_(760) while the simulated GPP was higher.In addition,weather conditions significantly affect the simulations from the SCOPE model.The accuracy of the SIF_(760) simulations on sunny days was better than that in cloudy days(R~2=0.64 and R~2=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.
Compared 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 SIF_(760) and GPP based on the physiological parameters and meteorological data of paddy rice in 2016.Then we compared the simulations with the measured SIF_(760) and GPP under different growing stages and weather conditions.The results showed that the SCOPE model could simulate well for SIF_(760) and GPP at the seasonal scale(R~2=0.44 and R~2=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(R~2=0.99 and R~2=0.96),while a lower performance was at the heading-flowering stage.During the whole growth period,the SIF_(760) simulated by the SCOPE model was lower than the measured SIF_(760) while the simulated GPP was higher.In addition,weather conditions significantly affect the simulations from the SCOPE model.The accuracy of the SIF_(760) simulations on sunny days was better than that in cloudy days(R~2=0.64 and R~2=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.
引文
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[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.
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[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.
[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.
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[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.]
[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.]
[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.
[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.
[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.]
[29] Kuye A,Jagtap S S.Analysis of Solar Radiation Data for Port Harcourt,Nigeria[J].Solar Energy,1992,49(2):139-145.
[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.
[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.
[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.]
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