Please wait a minute...
img

Wechat

Remote Sensing Technology and Application  2020, Vol. 35 Issue (5): 975-989    DOI: 10.11873/j.issn.1004-0323.2020.5.0975
    
Progress of Using the Chlorophyll Fluorescence to Estimate Terrestrial Gross Primary Production
Ya'nan Wang1,2(),Jin Wei1,2,Xuguang Tang1,2(),Xujun Han1,2,Mingguo Ma1,2
1.Chongqing Jinfo Mountain Field Scientific Observation and Research Station for Karst Ecosystem,Ministry of Education,School of Geographical Sciences,Southwest University,Chongqing 400715,China
2.Chongqing Engineering Research Center for Remote Sensing Big Data Application,School of Geographical Sciences,Southwest University,Chongqing 400715,China
Download:  HTML  PDF (1483KB) 
Export:  BibTeX | EndNote (RIS)      
Abstract  

As an accompanying product of the photosynthesis of leaves, solar-induced chlorophyll fluorescence contains abundant photosynthetic information, so it is considered as a fast and non-destructive indicator that can well reflect the photosynthesis of plants. Chlorophyll fluorescence plays a unique role in studying plant stress, monitoring plant diseases and insect pests, and also estimating the gross primary production. Gross Primary Production (GPP) is an important part of the researches on global climate, carbon cycle change and the global ecosystem. Grasping the spatial and temporal distribution characteristics of GPP accurately and timely is conducive to an in-depth understanding of the interactions between biosphere and atmosphere. It can provide corresponding suggestions and policies for the ecological process management of global climate change mitigation. Compared with vegetation index, chlorophyll fluorescence is more sensitive to photosynthesis, which has been proved to be a more direct estimation method of GPP. The chlorophyll fluorescence model has significant advantages over other traditional estimation methods. It is of profound importance to discuss the basic principle, methods, uncertain, latest breakthrough, the challenges and future trend of solar-induced chlorophyll fluorescence in the field of remote sensing estimation of GPP.

Key words:  Chlorophyll fluorescence      Gross Primary Production(GPP)      Photosynthesis      Ecosystem     
Received:  14 May 2019      Published:  26 November 2020
ZTFLH:  TP79  
Corresponding Authors:  Xuguang Tang     E-mail:  15515986972@163.com;xgtang@swu.edu.cn
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Ya'nan Wang
Jin Wei
Xuguang Tang
Xujun Han
Mingguo Ma

Cite this article: 

Ya'nan Wang,Jin Wei,Xuguang Tang,Xujun Han,Mingguo Ma. Progress of Using the Chlorophyll Fluorescence to Estimate Terrestrial Gross Primary Production. Remote Sensing Technology and Application, 2020, 35(5): 975-989.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.5.0975     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I5/975

数据数据获取手段反演模型特征与光合作用相关性适用性精度
SIF数据温室气体卫星传感器、地面SIF自动观测系统获取作为生理信号,快速而直接相关性较强,与植被生理过程高度耦合全球和区域均适用精度高
植被指数数据MODIS等光学卫星获取无法直接反演,需要利用间接数据无法反映瞬时光合作用,会有延时效应对某些生态系统会存在误判部分产品 精度较差
通量站点观测数据FLUXNET通量塔获取预测能力差相关性较强,直接反应光合作用固碳情况区域性强精度较高
Table 1  Characteristics of the basic data used for GPP estimation
涡度相关法光能利用率模型陆地生态过程模型动态全球植被模型
优点

百米尺度观测、

精度较高

模型参数少、

计算简单

贴近光合作用过程大尺度、直观
缺点

站点依赖性强、

空间代表性受限制

不同植被类型精度差异较大

参数较难获取、

模型复杂

由于植物性状对环境的响应不同 而存在不确定性、精度低
Table 2  Common methods for GPP estimation
1 Gitelson A A, Buschmann C, Lichtenthaler H K. Leaf Chlorophyll Fluorescence Corrected for Re-absorption by Means of Absorption and Reflectance Measurements [J]. Plant Physiology, 1998, 152(2-3): 283-296.
2 Lichtenthaler H K, Buschmann C, Rinderle U, et al. Application of Chlorophyll Fluorescence in Ecophysiology [J]. Radiation and Environmental Biophysics, 1986, 25(4): 297-308.
3 Genty B, Briantai J M, Baker N R, et al. The Relationship between the Quantum Yield of Photosynthetic Electron Trans-port and Quenching of Chlorophyll Fluorescence [J]. Biochimica Biophysica Acta, 1998, 990(1): 87-92.
4 Van Kooten O, Snel J F H. The Use of Chlorophyll Fluorescence Nomenclature in Plant Stress Physiology [J]. Photosynthesis Research, 1990, 25(4): 147-150.
5 Fang Jingyun, Ke Jinhu, Tang Zhiyao, et al. Implications and Estimations of Four Terrestrial Productivity Parameters [J]. Chinese Journal of Plant Ecology, 2001, 25(4): 414-419.
5 方精云, 柯金虎, 唐志尧, 等. 生物生产力的“4p”概念、估算及其相互关系 [J]. 植物生态学报, 2001, 25(4): 414-419.
6 John R, Chen J, Lu N, et al. Predicting Plant Diversity based on Remote Sensing Products in the Semi-arid Region of Inner Mongolia [J]. Remote Sensing of Environment, 2008, 112(5): 2018-2032.
7 Yuan Wenping, Cai Wenwen, Liu Dan, et al. Satellite-based Vegetation Production Models of Terrestrial Ecosystem: An Overview [J]. Advances in Earth Science, 2014, 29(5): 541-550.
7 袁文平, 蔡文文, 刘丹, 等. 陆地生态系统植被生产力遥感模型研究进展 [J]. 地球科学进展, 2014, 29(5): 541-550.
8 Liu Liangyun, Zhang Yongjiang, Wang Jihua, et al. Detecting Photosynthesis Fluorescence under Natural Sunlight based on Fraunhofer Line [J]. Journal of Remote Sensing, 2006, 10(1): 130-137.
8 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.
9 Wang Ran, Yang Zhigang, Yang Peiqi. Principle and Progress in Remote Sensing of Vegetation Solar-induced Chlorophyll Fluorescence [J]. Advances in Earth Science, 2012, 27(11): 1221-1228.
9 王冉, 刘志刚, 杨沛琦. 植物日光诱导叶绿素荧光的遥感原理及研究进展 [J]. 地球科学进展, 2012, 27(11): 1221-1228.
10 Garbulsky M, Filella I, Verger A, et al. Photosynthetic Light Use Efficiency from Satellite Sensors: From Global to Mediterranean Vegetation [J]. Environmental and Experimental Botany,2014, 103: 3-11.doi: .
doi: 10.1016/j.envexpbot.2013. 10.009
11 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: 2589-2608.doi: .
doi: 10.5194/amt-8-2589-2015
12 Frankenberg C, Butz A, Toon G C. Disentangling Chlorophyll Fluorescence from Atmospheric Scattering Effects in O2A-band Spectra of Reflected Sun-light [J]. Geophysical Research Letters, 2011, 38(3): L03801. doi:.
doi: 10.1029/2010GL045896
13 Du S, Liu L, Liu X, et al. Retrieval of Global Terrestrial Solar-induced Chlorophyll Fluorescence from TanSat Satellite [J]. Science Bulletin, 2018, 63(22): 1502-1512.
14 Köhler P, Frankenberg C, Magney T S, et al. Global Retrievals of Solar-induced Chlorophyll Fluorescence with TROPOMI: First Results and Intersensor Comparison to OCO-2 [J].Geophysical Research Letters,2018,45(19):10456-10463.
15 Ryu Y, Berry J A, Baldocchi D D. What is Global Photosynthesis? History, Uncertainties and Opportunities [J]. Remote Sensing of Environment, 2019, 223: 95-114.doi: .
doi: 10.1016/j.rse.2019.01.016
16 Zhang Y G, Guanter L, Berry J A, et al. Estimation of Vegetation Photosynthetic Capacity from Space-based Measurements of Chlorophyll Fluorescence for Terrestrial Biosphere Models [J]. Global Change Biology, 2014, 20(12): 3727-3742.
17 Zaeco-Tejadea P J, Morales A, Testi L, et al. Spatio-temporal Patterns of Chlorophyll Fluorescence and Physiological and Structural Indices Acquired from Hyperspectral Imagery as Compared with Carbon Fluxes Measured with Eddy Covariance [J]. Remote Sensing of Environment, 2013, 133: 102-115. doi: .
doi: 10.1016/j.rse.2013.02.003
18 Zhang Zhaoying, Wang Songhan, 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.
18 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.
19 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.
20 Wood J D, Griffis T J, Baker J M, et al. Multiscale Analyses of Solar-induced Florescence and Gross Primary Production [J]. Geophysical Research Letters, 2017, 44(1): 533-541.
21 Wagle P, Zhang Y, Jin C, et al. Comparison of Solar-induced Chlorophyll Fluorescence, Light-use Efficiency, and Process-based GPP Models in Maize [J]. Ecological Application, 2016, 26(4): 1211-1222.
22 Zhang Yongjiang. Studies on Passive Sensing of Plant Chlorophyll Fluorescence and Application of Stress Detection [D]. Hangzhou: Zhejiang University, 2006.
22 张永江.植物叶绿素荧光被动遥感探测及应用研究 [D]. 杭州: 浙江大学, 2006.
23 Joiner J, Guanter L, Lindstrot, R, et al. Global Monitoring of Terrestrial Chlorophyll Fluorescence from Moderate-spectral-resolution Near-infrared Satellite Measurements: Methodology, Simulations, and Application to GOME-2 [J]. Atmospheric Measurement Techniques, 2013, 6: 2803–2823. doi:.
doi: 10.5194/amt-6-2803-2013
24 Zhang Yongjiang, Liu Liangyun, Wang Jihua, et al. Detection of Leaf Fluorescence from Reflectance Using Hyper-spectrometer [J]. Optical Technique, 2007, 33(1): 119-123.
24 张永江, 刘良云, 王纪华, 等. 应用高光谱仪探测叶片反射光谱中的荧光 [J]. 光学技术, 2007, 33(1): 119-123.
25 Wang Ran, Liu Zhigang, Feng Haikuan, et al. Extraction and Analysis of Solar-Induced Chlorophyll Fluorescence of Wheat with Ground-based Hyperspectral Imaging System [J]. Spectroscopy and Spectral Analysis, 2013, 33(9): 2451-2454.
25 王冉, 刘志刚, 冯海宽, 等. 基于近地面高光谱影像的冬小麦日光引诱叶绿素荧光提取与分析 [J]. 光谱学与光谱分析, 2013, 33(9): 2451-2454.
26 Hu Jiaochan, Liu Liangyun, Liu Xinjie. Assessing Uncertainties of Sun-induced Chlorophyll Fluorescence Retrieval Using Fluor MOD Model [J]. Journal of Remote Sensing, 2015,19(4): 594-608.
26 胡姣婵, 刘良云, 刘新杰. Fluor MOD模拟叶绿素荧光夫琅和费暗线反演算法不确定性分析 [J]. 遥感学报, 2015, 19(4): 594-608.
27 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.
28 Maier S W, Gunther K P, Stellmes M. Sun-induced Fluorescence: A New Tool for Precision Farming [M]. Madison: American Society of Agronomy, 2003.
29 Damm A, Erler A, Hillen W, et al. Modeling the Impact of Spectral Sensor Configurations on the FLD Retrieval Accuracy of Sun-induced Chlorophyll Fluorescence [J]. Remote Sensing of Environment, 2011, 115(8): 1882-1892.
30 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.doi: .
doi: 10.1016/j.rse.2015. 06.002
31 Meroni M, Picchi V, Rossini M, et al. Leaf Level Early Assessment of Ozone Injuries by Passive Fluorescence and Photochemical Reflectance Index [J]. International Journal of Remote Sensing, 2008, 29(17-18): 5409-5422.
32 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.
33 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.
33 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.
34 Cogliati S, Verhoef W, Kraft S, et al. Retrieval of Sun-induced Fluorescence Using Advanced Spectral Fitting Methods [J]. Remote Sensing of Environment, 2015, 169: 344-357. doi: .
doi: 10.1016/j.rse.2015.08.022
35 Vicent J, Sabater N, Tenjo C, et al. FLEX End-To-End Mission Performance Simulator [J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(7): 4215-4223.
36 Jonier 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, 9(8): 3939-3967.
37 Yang Yanzheng, Wang Han, Zhu Qiuan, et al. Research Progress in Improving Dynamic Global Vegetation Models (DGVMs) with Plant Functional Traits [J]. Chinese Science Bulletin, 2018, 63(25): 2599-2611.
37 杨延征, 王焓, 朱求安, 等. 植物功能性状对动态全球植被模型改进研究进展 [J]. 科学通报, 2018, 63(25): 2599-2611.
38 Yu Guirui, Fang Huajun, Fu Yuling, et al. Research on Carbon Budget and Carbon Cycle of Terrestrial Ecosystems in Regional Scale: A Review [J]. Acta Ecologica Sinica, 2011, 31(19): 5449–5459.
38 于贵瑞, 方华军, 伏玉玲, 等. 区域尺度陆地生态系统碳收支及其循环过程研究进展 [J]. 生态学报, 2011, 31(19): 5449–5459.
39 Schimel D, Pavlick R, Fisher J B, et al. Observing Terrestrial Ecosystems and the Carbon Cycle from Space [J]. Global Change Biology, 2015, 21(5): 1762-1776.
40 Lieth H, Whittaker R H. Primary Productivity of the Biosphere [M]. New York: Springer Berlin Heidelberg, 1975.
41 Keenan T F, Baker I, Barr A, et al. Terrestrial Biosphere Model Performance for Inter-annual Variability of Land-atmosphere CO2 Exchange [J]. Global Change Biology, 2012, 18(6): 1971–1987.
42 Yu Guirui, Sun Xiaomin. Principles of Flux Measurement in Terrestrial Ecosystems [M]. Beijing: Higher Education Press, 2006.
42 于贵瑞,孙晓敏. 陆地生态系统通量观测的原理与方法 [M]. 北京: 高等教育出版社, 2006.
43 Wang Z. Sunlit Leaf Photosynthesis Rate Correlates Best with Chlorophyll Fluorescence of Terrestrial Ecosystems [D]. Toronto: University of Toronto, 2014.
44 Piao S L, Fang J Y, Ciais P, et al. The Carbon Balance of Terrestrial Ecosystems in China [J]. Nature, 2009, 458: 1009-1013.doi: .
doi: 10.1038/nature07944
45 Meroni M, Rossino M, Guanter L, et al. Remote Sensing of Solar-induced Chlorophyll Fluorescence: Review of Methods and Application [J]. Remote Sensing of Environment, 2009, 113(10): 2037-2051.
46 Rossini M, Meroni M, Migliavancca M, et al. High Resolution Field Spectroscopy Measurements for Estimating Gross Ecosystem Production in a Rice Field [J]. Agricultural and Forest Meteorology, 2010, 150(9): 1283-1296
47 Perez-Priego O, Guan J, Rossini M, et al. Sun-induced Chlorophyll Fluorescence and Photochemical Reflectance Index Improve Remote Sensing GPP Estimates under Varying Nutrient Availability in a Typical Mediterrancan Savanna Ecosystem [J] Biogeosciences Discuss, 2015, 12(14): 11891-11934.
48 Guan Linlin. Estimation of Gross Primary Production Using Sun-induced Chlorophyll Fluorescence [D]. Beijing: University of Chinese Academy of Sciences, 2017.
48 关琳琳. 基于叶绿素荧光的植被总初级生产力估算 [D]. 北京: 中国科学院大学, 2017.
49 Guanter L, Zhang Y, Jung M, et al. Reply to Magnaniet al: Linking Large-scale Chlorophyll Fluorescence Observation with Cropland Gross Primary Production [J]. Proceedings of the National Academy of Sciences, 2014, 111 (25): E2511. doi: .
doi: 10.1073/pnas.1406996111
50 Knyazikhin Y, Schull M A, Stenberg P, et al. Hyperspectral Remote Sensing of Foliar Nitrogen Content [J]. Proceedings of the National Academy of Sciences, 2013, 110(3): E185–E192.
51 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(1): 111420. doi: .
doi: 10.1016/j.rse.2019.111420
52 Liu X J, Guanter L, Liu L Y, et al. Downscaling of Solar-induced Chlorophyll Fluorescence from Canopy Level Photosystem Level Using a Random Forest Model [J]. Remote Sensing of Environment, 2019, 231: 110772. doi: .
doi: 10.1016/j.rse.2018.05.035
53 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,2019,232:111209.doi: .
doi: 10.1016/j.rse.2019.05.028
54 Damm A, Guanter L. Far-red Sun-induced Chlorophyll Fluorescence Shows Ecosystem-specific Relationship to Gross Primary Production: An Assessment based on Observational and Modeling Approaches [J]. Remote Sensing of Environment, 2015, 166: 91-105. doi: .
doi: 10.1016/j.rse.2019.05.028
55 Zhang Z Y, Zhang Y G, Joiner J, et al. 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 [J]. Global Change Biology, 2018, 24(11): 5017-5020.
56 Jacquemoud S, Ustin S L, Verdebout J, et al. Estimating Leaf Biochemistry Using the Prospect Leaf Optical Properties Model [J]. Remote Sensing of Environment, 1996, 56 (3): 194-202.
57 Yang X, Tang J W, Mustard J F, et al. Solar-induced Chlorophyll Fluorescence Correlates with Canopy Photosynthesis on Diurnal and Seasonal Scales in a Temperate Deciduous Forest [J]. Geophysical Research Letters, 2015, 42(8): 2977-2987.
58 Liu L Y, Liu X J, Wang Z H, et al. Measurement and Analysis of Bidirectional SIF Emissions in Wheat Canopies [J]. IEEE Transactions on Geoence and Remote Sensing, 2016, 54 (5): 2640–2651.
59 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.
60 Zhang Y, Joiner J, Gentine P, et al. Reduced Solar-induced Chlorophyll Fluorescence from GOME-2 during Amazon Drought Caused by Dataset Artifacts [J]. Global Change Biology, 2018, 24(6): 2229-2230.
61 Ji Menghao, Tang Bohui, Li Zhaoliang. Review of Solar-induced Chlorophyll Fluorescence Retrieval Methods from Satellite Data [J]. Remote Sensing Technology and Application, 2019, 34(3): 455-466.
61 纪梦豪, 唐伯慧, 李召良. 太阳诱导叶绿素荧光的卫星遥感反演方法研究进展 [J]. 遥感技术与应用, 2019, 34(3): 455-466.
62 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. doi: .
doi: 10.1016/j.rse.2018.07.008
63 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: 110996. doi:.
doi: 10.1016/j.rse.2018.11.039
64 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.doi:.
doi: 10.1016/j.agrformet. 2016. 06.014
65 Hao Yong, Jiang Haimei, Ye Haootian, et al. Application of Sun-Induced Chlorophyll Fluorescence in Estimating Gross Primary Productivity of a Semi-Arid Grassland Ecosystem [J]. Journal of Inner Mongolia University(Natural Science Edition), 2020, 51(2): 154-162.
65 郝勇, 姜海梅, 叶昊天, 等. 日光诱导叶绿素荧光在估算半干旱草原生态系统总初级生产力中的应用 [J]. 内蒙古大学学报(自然科学版), 2020,51(2): 154-162.
66 Wieneke S, Burkart A, Cendrero-Mateo M P, et al. Linking Photosynthesis and Sun-induced Fluorescence at Sub-daily to Seasonal Scales [J]. Remote Sensing of Environment, 2018, 219: 247-258.doi: .
doi: 10.1016 / j.rse.2018.10.019
67 Yang J, Tian H Q, Pan S F, et al. Amazon Drought and Forest Response: Largely Reduced Forest Photosynthesis but Slightly Increased Canopy Greenness during the Extreme Drought of 2015/2016 [J]. Global Change Biology, 2018, 24(5): 1919-1934.
68 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.
69 Dong Bin, Lan Laijiao, Huang Yongfang, et al. Effects of Drought Stress on Photosynthetic Pigments and Chlorophyll Fluorescence Characteristics in Leaves of Camellia Oleifera [J]. Non-wood Forest Research, 2020, 38(3): 16-25.
69 董斌, 蓝来娇, 黄永芳, 等. 干旱胁迫对油茶叶片叶绿素含量和叶绿素荧光参数的影响[J]. 经济林研究, 2020, 38(3): 16-25.
70 Pinto F, Celesti M, Acebron K, et al. Dynamics of Sun-induced Chlorophyll Fluorescence and Reflectance to Detect Stress-induced Variations in Canopy Photosynthesis [J]. Plant, Cell & Environment, 2020, 43(7): 1637-1654.
71 Gentine P, Alemohammad S. Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence [J]. Geophysical Research Letters, 2018, 45(7): 3136-3146.
72 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, 2014, 111(14): E1327-E1333.
73 Drusch M, Moreno J, Bello U D, et al. The Fluorscence Explorer Mission Concept – ESA’s Earth Explorer 8 [J]. IEEE Transactions on Geoscience and Remote Sensing, 55(3): 1273-1284.
74 Lu X C, Cheng X, Li X L, et al. Seasonal Patterns of Canopy Photosynthesis Captured by Remotely Sensed Sun-induced Fluorescence and Vegetation Indexes in Mid-to-high Latitude Forests: A Cross-platform Comparison [J]. Science of the Total Environment, 2018, 644: 439-451.
75 Wei X X, Wang X F, Wei W, et al. Use of Sun-Induced Chlorophyll Fluorescence Obtained by OCO-2 and GOME-2 for GPP Estimates of the Heihe River Basin, China [J]. Remote Sensing, 2018, 10(12): 2039. doi: .
doi: 10.3390/rs10122039
76 Wang X P, Chen J M, Ju W M. Photochemical Reflectance Index (PRI) Can be used to Improve the Relationship between Gross Primary Productivity (GPP) and Sun-induced Chlorophyll Fluorescence (SIF) [J]. Remote Sensing of Environment,2020,246:111888. doi:.
doi: 10.1016/j.rse.2020.111888
77 Wen J, Köhler P, Duveiller G, et al. A Framework for Harmonizing Multiple Satellite Instruments to Generate a Long-term Global High Spatial-resolution Solar-induced Chlorophyll Fluorescence (SIF) [J]. Remote Sensing of Environment, 2020, 239: 111644. doi: .
doi: 10.1016/j.rse.2020.111644
78 Paul-Limoges E, Damm A, Hueni A, et al. Effect of Environmental Conditions on Sun-induced Fluorescence in a Mixed Forest and a Cropland [J]. Remote Sensing of Environment,2018,219:310-323.doi: .
doi: 10.1016/j.rse.2018.10.018
79 Zhou Lei, Chi Yonggang, Liu Xiaotian, et al. Land Surface Phenology Tracked by Remotely Sensed Sun-induced Chlorophyll Fluorescence in Subtropical Evergreen Coniferous Forests [J]. Acta Ecologica Sinica, 2020, 40(12): 1-12.
79 周蕾, 迟永刚, 刘啸添, 等. 日光诱导叶绿素荧光对亚热带常绿针叶林物候的追踪 [J]. 生态学报, 2020, 40(12): 1-12.
80 Chen S L, Huang Y F, Gao S, et al. Impact of Physiological and Phrenological Change on Carbon Uptake on the Tibetan Plateau Revealed through GPP Estimation based on Space Borne Solar-induced Fluorescence [J]. Science of The Total Environment, 2019, 663: 45-59.doi: .
doi: 10.1016/j.scitotenv. 2019.01.324
81 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.doi: .
doi: 10.1016/j.rse.2018.02.029
82 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: 107846. doi: .
doi: 10.1016/j.agrformet.2019.107846
83 Zhang Z Y, Zhang Y G, Porcar-Castell A, et al. Reduction of Structural Impacts and Distinction of Photosynthetic Pathways in a Global Estimation of GPP from Space-borne Solar-induced Chlorophyll Fluorescence [J]. Remote Sensing of Environment,2020, 240: 111722. doi: .
doi: 10.1016/j.rse.2020. 111722
84 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.
85 Bodesheim P, Jung M, Gans F, et al. Unscaled Diurnal Cycles of Land–atmosphere Fluxes: A New Global Half-hourly Data Product [J]. Earth System Science Data, 2018, 10: 1327-1365.doi: .
doi: 10.5194/essd-10-1327-2018
86 Zhang Y, Joiner J, Alemohammad S H, et al. A Global Spatially Continuous Solar Induced Fluorescence (CSIF) Dataset Using Neural Networks [J]. Biogeosciences Discussions, 2018, 15(19): 5779-5800.
87 Gentine P, Alemohammad S H. Reconstructed Solar-induced Fluorescence: A Machine Learning Vegetation Product based on MODIS Surface Reflectance to Reproduce GOME-2 Solar-induced Fluorescence [J]. Geographical Research Letters, 2018, 45(7): 3136–3146.
88 Li X, Xiao J F. A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence derived from OCO-2, MODIS, and Reanalysis Data [J]. Remote Sensing, 2019, 11(5): 517-530.
89 Lee J, Berry J A, Van der Tol C, et al. Simulations of Chlorophyll Fluorescence Incorporated into the Community Land Model Version 4 [J].Global Change Biology, 2016, 21(9): 3469-3477.
90 Parazoo N C, Bowman K, Frankenberg C, et al. Interpreting Seasonal Changes in the Carbon Balance of Southern Amazonia Using Measurements of XCO2 and Chlorophyll Fluorescence from GOSAT [J]. Geophysical Research Letters, 2013, 40(11): 2829-2933.
91 Geruo A, Velicogna I, Kimball J S, et al. Satellite-observed Changes in Vegetation Sensitivities to Surface Soil Moisture and Total Water Storage Variations Since the 2011 Texas Drought [J]. Environmental Research Letters, 2017, 12: 054006. doi: .
doi: 10.1088/1748-9326/aa6965
92 Gu L H, Wood J D, Chang Y Y, et al. Advancing Terrestrial Ecosystem Science with a Novel Automated Measurement System for Sun‐induced Chlorophyll Fluorescence for Integration with Eddy Covariance Flux Networks [J]. Journal of Geophysical Research: Biogeosciences, 2018,124(1):127-146.
93 Ryu Y, Baldocchi D D, Black T A, et al. On the Temporal Upscaling of Evapotranspiration from Instantaneous Remote Sensing Measurements to 8-day Mean Daily-sums [J]. Journal of Agricultural Metrology, 2012, 152: 212–222.
No Suggested Reading articles found!