浏览全部资源
扫码关注微信
Published： 07 May 2021 ，
扫 描 看 全 文
李朝晖，张永光，张乾，吴云飞，张小康，章钊颖.2021.植被冠层日光诱导叶绿素荧光塔基自动观测方法及系统介绍.遥感学报，25（5）： 1152-1168
Li Z H，Zhang Y G，Zhang Q，Wu Y F，Zhang X K and Zhang Z Y. 2021. Tower-based automatic observation methods and systems of solar-induced chlorophyll fluorescence in vegetation canopy. National Remote Sensing Bulletin， 25（5）：1152-1168
植物光合作用所提供的物质和能量是人类赖以生存的关键因素，而日光诱导叶绿素荧光SIF（Sun-Induced chlorophyll Fluorescence）是植物光合作用的副产品，与光合作用关系密切，深入研究SIF将对于更加深入理解光合作用机制有着重要的意义。目前，近地面植被冠层SIF遥感观测发展迅速，但不同SIF观测系统间差异较大。本文通过比较分析不同塔基SIF观测系统及其特征，归纳了塔基SIF观测方式和方法，提出了塔基SIF观测技术规范。塔基SIF观测主要有两台光谱仪和一台光谱仪结合光路切换开关的观测方法，可以采取双半球和半球—锥体两种观测方式。SIFprism系统是一种新的基于光学棱镜的SIF自动观测系统，本文介绍了SIFprism系统软硬件组成和光谱数据采集流程，并以SIFprism系统为例阐述了塔基观测系统光谱数据处理流程，分析了SIF反演过程可能存在的不确定性，最后对近地面SIF观测进行了展望。
Sun-Induced chlorophyll Fluorescence (SIF) is a by-product of plant photosynthesis and is closely related to plant photosynthesis. The study on SIF and its relationship with Gross Primary Productivity (GPP) is of great significance in understanding the mechanism of photosynthesis. Recent instrumental and methodological developments of the tower-based SIF observation system provide a complementary capacity for measuring and interpreting chlorophyll fluorescence in the context of physiological processes. In addition
a tower-based system can also support satellite-based measurements through validation
interpretation
and data inputs provision for models. Recently
the tower-based SIF observation system has developed rapidly with varied observation methods and system characteristics. In this paper
we discuss and summarize the recent developments of tower-based SIF observation methods and propose technical specifications by comparing different tower-based SIF observation systems.
Tower-based SIF observation systema can be built with either two spectrometers or one spectrometer combined with an optical path switching trigger. A two-spectrometer SIF system measures the solar incident radiance and the radiance reflected by the canopy independently to realize synchronous measurement. This system can obtain high frequency spectral data
and nearly no time gap exists between the solar incident spectrum and the spectrum reflected by the canopy
reducing the uncertainty of the retrieved SIF caused by the mismatch between the two optical channels under varied weather conditions. However
the spectral response characteristics of the two spectrometers are not completely consistent. The spectral drift between the two optical channels is difficult to correct
which may lead to the increase of the Sif retrieval uncertainty. A single-spectrometer SIF system realizes the sequential switching between the two optical channels by using an optical path switch
which allows the measurement of the solar incident radiance and the canopy reflected radiance with reliable data quality. Although a certain time gap exists between the solar incident spectrum and the reflected spectrum
it can be used for SIF retrieval because of the second disparity. In cloudy and other rapidly changing light conditions
the acquisition time gap between the spectra from the two optical channels may increase the SIF retrieval uncertainty. Compared with the dual spectrometer system
the single spectrometer system is simpler
has lower cost
and avoids the risk of spectral drift
which is the mainstream tower-based SIF system.
The tower-based SIF system can be employed with bi-hemispherical and hemispherical-conical observation configurations for field installation. The bi-hemispherical observation mode refers to the configuration in which both downwelling and upwelling bare fibers are equipped with cosine correctors
while the hemispherical-conical observation mode refers to the configuration in which only the upwelling bare fiber is equipped with a cosine corrector. The bi-hemispherical observation mode has a larger field of view
which is suitable for canopy measurements with high canopy heterogeneity or height with a limited installation height. The hemispherical-conical observation mode is suitable for low canopy
homogeneous canopy
and multi angle observation. In addition
if the canopy area is limited or the experimental observations have control factors
hemispherical-conical observation is more appropriate.
The SIFprism system is a novel optical-prism-based SIF automatic observation system. This article introduces the software and hardware components and the flow of spectral data collection of the SIFprism system. Taking the SIFprism system as an example
the spectral data processing process is expounded
and the potential uncertainty of SIF retrieval is analyzed.
The tower-based SIF observation system has experienced rapid development in recent years. Despite the essential and incremental research on near-surface SIF
further development of hardware and mechanistic theory is still urgently required. Several prospective areas for future work include improving the signal-to-noise ratio and radiation stability of the spectrometer and appraising the capabilities and efficacy of different retrieval algorithms in varied light conditions. Finally
research should strengthen the cooperation with industry to jointly develop a more efficient and stable field tower-based SIF system and formulate corresponding field observation technical specifications.
日光诱导叶绿素荧光SIF塔基SIF观测SIFprism系统观测规范数据采集流程
Solar-Induced chlorophyll Fluorescencetower-based SIF measurementsSIFprism systemmeasurement protocolfield data collection
Baker N R. 2008. Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annual Review of Plant Biology, 59: 89-113 [DOI: 10.1146/annurev.arplant.59.032607.092759http://dx.doi.org/10.1146/annurev.arplant.59.032607.092759]
Cogliati S, Rossini M, Julitta T, Meroni M, Schickling A, Burkart A, Pinto F, Rascher U and Colombo R. 2015. Continuous and long-term measurements of reflectance and sun-induced chlorophyll fluorescence by using novel automated field spectroscopy systems. Remote Sensing of Environment, 164: 270-281 [DOI: 10.1016/j.rse.2015.03.027http://dx.doi.org/10.1016/j.rse.2015.03.027]
Damm A, Erler A, Hillen W, Meroni M, Schaepman M E, Verhoef W and Rascher U. 2011. Modeling the impact of spectral sensor configurations on the FLD retrieval accuracy of sun-induced chlorophyll fluorescence. Remote Sensing of Environment, 115(8): 1882-1892 [DOI: 10.1016/j.rse.2011.03.011http://dx.doi.org/10.1016/j.rse.2011.03.011]
Damm A, Guanter L, Paul-Limoges E, Van der Tol C, Hueni A, Buchmann N, Eugster W, Ammann C and Schaepman M E. 2015a. Far-red sun-induced chlorophyll fluorescence shows ecosystem-specific relationships to gross primary production: an assessment based on observational and modeling approaches. Remote Sensing of Environment, 166(6): 91-105 [DOI: 10.1016/j.rse.2015.06.004http://dx.doi.org/10.1016/j.rse.2015.06.004]
Damm A, Guanter L, Verhoef W, Schläpfer D, Garbari S and Schaepman M E. 2015b. Impact of varying irradiance on vegetation indices and chlorophyll fluorescence derived from spectroscopy data. Remote Sensing of Environment, 156: 202-215 [DOI: 10.1016/j.rse.2014.09.031http://dx.doi.org/10.1016/j.rse.2014.09.031]
Daumard F, Champagne S, Fournier A, Goulas Y, Ounis A, Hanocq J F and Moya I. 2010. A feld platform for continuous measurement of canopy fluorescence. IEEE Transactions on Geoscience and Remote Sensing, 48(9): 3358-3368 [DOI: 10.1109/TGRS.2010.2046420http://dx.doi.org/10.1109/TGRS.2010.2046420]
Du S S, Liu L Y, Liu X J, Zhang X, Zhang X Y, Bi Y M and Zhang L C. 2018. Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite. Science Bulletin, 63(22): 1502-1512 [DOI: 10.1016/j.scib.2018.10.003http://dx.doi.org/10.1016/j.scib.2018.10.003]
Du S S, Liu L Y, Liu X J, Guo J, Hu J C, Wang S Q and Zhang Y G. 2019. SIFSpec: measuring solar-induced chlorophyll fluorescence observations for remote sensing of photosynthesis. Sensors, 19(13): 3009 [DOI: 10.3390/s19133009http://dx.doi.org/10.3390/s19133009]
Frankenberg C, Fisher J B, Worden J, Badgley G, Saatchi S S, Lee J E, Toon G C, Butz A, Jung M, Kuze A and Yokota T. 2011. New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophysical Research Letters, 38(17): L17706 [DOI: 10.1029/2011GL048738http://dx.doi.org/10.1029/2011GL048738]
Frankenberg C, O’Dell C, Berry J, Guanter L, Joiner J, Köhler P, Pollack R and Taylor T E. 2014. Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sensing of Environment, 147: 1-12 [DOI: 10.1016/j.rse.2014.02.007http://dx.doi.org/10.1016/j.rse.2014.02.007]
Frankenberg C and Berry J. 2018. Solar induced chlorophyll fluorescence: origins, relation to photosynthesis and retrieval//Reference Module in Earth Systems and Environmental Sciences. vol. 3. Oxford: Elsevier: 143-162
Gastellu-Etchegorry J P, Lauret N, Yin T G, Landier L, Kallel A, Malenovský Z, Al Bitar A, Aval J, Benhmida S, Qi J B, Medjdoub G, Guilleux J, Chavanon E, Cook B, Morton D, Chrysoulakis N and Mitraka Z. 2017. DART: recent advances in remote sensing data modeling with atmosphere, polarization, and chlorophyll fluorescence. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(6): 2640-2649 [DOI: 10.1109/JSTARS.2017.2685528http://dx.doi.org/10.1109/JSTARS.2017.2685528]
Grossmann K, Frankenberg C, Magney T S, Hurlock S C, Seibt U and Stutz J. 2018. PhotoSpec: a new instrument to measure spatially distributed red and far-red solar-induced chlorophyll fluorescence. Remote Sensing of Environment, 216: 311-327 [DOI: 10.1016/j.rse.2018.07.002http://dx.doi.org/10.1016/j.rse.2018.07.002]
Gu L, Wood J D, Chang C Y Y, Sun Y and Riggs J S. 2019. Advancing terrestrial ecosystem science with a novel automated measurement system for sun-induced chlorophyll fluorescence for integration with eddy covariance flux networks. Journal of Geophysical Research: Biogeosciences, 124: 127-146 [DOI: 10.1029/2018JG004742http://dx.doi.org/10.1029/2018JG004742]
Guanter L, Alonso L, Gómez-Chova L, Amorós-López J, Vila J and Moreno J. 2007. Estimation of solar-induced vegetation fluorescence from space measurements. Geophysical Research Letters, 34(8): L08401 [DOI: 10.1029/2007GL029289http://dx.doi.org/10.1029/2007GL029289]
Guanter L, Frankenberg C, Dudhia A, Lewis P E, Gómez-Dans J, Kuze A, Suto H and Grainger R G. 2012. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sensing of Environment, 121: 236-251 [DOI: 10.1016/j.rse.2012.02.006http://dx.doi.org/10.1016/j.rse.2012.02.006]
Guanter L, Zhang Y, Jung M, Joiner J, Voigt M, Berry J A, Frankenberg C, Huete A R, Zarco-Tejada P, Lee J E, Moran M S, Ponce-Campos G, Beer C, Camps-Valls G, Buchmann N, Dianelle D, Klumpp K, Cescatti A, Baker J M and Grifs T J. 2014. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences of the United States of America, 111(14): E1327-E1333 [DOI: 10.1073/pnas.1320008111http://dx.doi.org/10.1073/pnas.1320008111]
He L M, Chen J M, Liu J, Mo G and Joiner J. 2017. Angular normalization of GOME-2 sun-induced chlorophyll fluorescence observation as a better proxy of vegetation productivity. Geophysical Research Letters, 44(11): 5691-5699 [DOI: 10.1002/2017GL073708http://dx.doi.org/10.1002/2017GL073708]
Hernández-Clemente R, North P R J, Hornero A and Zarco-Tejada P J. 2017. Assessing the effects of forest health on sun-induced chlorophyll fluorescence using the FluorFLIGHT 3-D radiative transfer model to account for forest structure. Remote Sensing of Environment, 193: 165-179 [DOI: 10.1016/j.rse.2017.02.012http://dx.doi.org/10.1016/j.rse.2017.02.012]
Hu J C, Liu L Y, Guo J, Du S S and Liu X J. 2018. Upscaling solar-induced chlorophyll fluorescence from an instantaneous to daily scale gives an improved estimation of the gross primary productivity. Remote Sensing, 10(10): 1663 [DOI: 10.3390/rs10101663http://dx.doi.org/10.3390/rs10101663]
Joiner J, Yoshida Y, Vasilkov A P, Yoshida Y, Corp L A and Middleton E M. 2011. First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences, 8(3): 637-651 [DOI: 10.5194/bg-8-637-2011http://dx.doi.org/10.5194/bg-8-637-2011]
Joiner J, Yoshida Y, Vasilkov A P, Middleton E M, Campbell P K E, Yoshida Y, Kuze A and Corp L A. 2012. Filling-in of near-infrared solar lines by terrestrial fluorescence and other geophysical effects: simulations and space-based observations from SCIAMACHY and GOSAT. Atmospheric Measurement Techniques, 5(4): 809-829 [DOI: 10.5194/amt-5-809-2012http://dx.doi.org/10.5194/amt-5-809-2012]
Julitta T, Burkart A, Colombo R, Rossini M, Schickling A, Migliavacca M, Cogliati S, Wutzler T and Rascher U. 2017. Accurate measurements of fluorescence in the O2A and O2B band using the FloX spectroscopy system - results and prospects//Proceedings of the Potsdam GHG Flux Workshop: From Photosystems to Ecosystems. Potsdam, Germany: [s.n.]
Köhler P, Frankenberg C, Magney T S, Guanter L, Joiner J and Landgraf J. 2018a. Global retrievals of solar-induced chlorophyll fluorescence with TROPOMI: first results and intersensor comparison to OCO-2. Geophysical Research Letters, 45(19): 10456-10463 [DOI: 10.1029/2018GL079031http://dx.doi.org/10.1029/2018GL079031]
Köhler P, Guanter L, Kobayashi H, Walther S and Yang W. 2018b. Assessing the potential of sun-induced fluorescence and the canopy scattering coefficient to track large-scale vegetation dynamics in Amazon forests. Remote Sensing of Environment, 204: 769-785 [DOI:10.1016/j.rse.2017.09.025http://dx.doi.org/10.1016/j.rse.2017.09.025]
Li X, Xiao J F, He B B, Arain M A, Beringer J, Desai A R, Emmel C, Hollinger D Y, Krasnova A, Mammarella I, Noe S M, Ortiz P S, Rey-Sanchez A C, Rocha A V and Varlagin A. 2018. 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. Global Change Biology, 24(9): 3990-4008 [DOI: 10.1111/gcb.14297http://dx.doi.org/10.1111/gcb.14297]
Li Z H, Zhang Q, Li J, Yang X, Wu Y F, Zhang Z Y, Wang S H, Wang H Z and Zhang Y G. 2020. Solar-induced chlorophyll fluorescence and its link to canopy photosynthesis in maize from continuous ground measurements. Remote Sensing of Environment, 236: 111420 [DOI: 10.1016/j.rse.2019.111420http://dx.doi.org/10.1016/j.rse.2019.111420]
Liu L Y, Guan L L and Liu X J. 2017. Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence. Agricultural and Forest Meteorology, 232: 1-9 [DOI: 10.1016/j.agrformet.2016.06.014http://dx.doi.org/10.1016/j.agrformet.2016.06.014]
Liu L Y, Zhang Y J, Wang J H and Zhao C J. 2006. Detecting photosynthesis fluorescence under natural sunlight based on fraunhofer line. Journal of Remote Sensing, 10(1): 130-137
刘良云, 张永江, 王纪华, 赵春江 2006. 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. 遥感学报, 10(1): 130-137 [DOI: 10.3321/j.issn:1007-4619.2006.01.020http://dx.doi.org/10.3321/j.issn:1007-4619.2006.01.020]
Liu X J, Liu L Y, Zhang S and Zhou X F. 2015. New spectral fitting method for full-spectrum solar-induced chlorophyll fluorescence retrieval based on principal components analysis. Remote Sensing, 7(8): 10626-10645 [DOI: 10.3390/rs70810626http://dx.doi.org/10.3390/rs70810626]
Mazzoni M, Falorni P and Verhoef W. 2010. High-resolution methods for fluorescence retrieval from space. Optics Express, 18(15): 15649-15663 [DOI: 10.1364/OE.18.015649http://dx.doi.org/10.1364/OE.18.015649]
Mazzoni M, Meroni M, Fortunato C, Colombo R and Verhoef W. 2012. Retrieval of maize canopy fluorescence and reflectance by spectral fitting in the O2–A absorption band. Remote Sensing of Environment, 124: 72-82 [DOI: 10.1016/j.rse.2012.04.025http://dx.doi.org/10.1016/j.rse.2012.04.025]
Meroni M, Busetto L, Colombo R, Guanter L, Moreno J and Verhoef W. 2010. Performance of Spectral Fitting Methods for vegetation fluorescence quantification. Remote Sensing of Environment, 114: 363-374 [DOI: 10.1016/j.rse.2009.09.010http://dx.doi.org/10.1016/j.rse.2009.09.010]
Meroni M and Colombo R. 2006. Leaf level detection of solar induced chlorophyll fluorescence by means of a subnanometer resolution spectroradiometer. Remote Sensing of Environment, 103(4): 438-448 [DOI: 10.1016/j.rse.2006.03.016http://dx.doi.org/10.1016/j.rse.2006.03.016]
Middleton E M, Huemmrich K F, Zhang Q, Campbell P K E and Landis D R. 2018. Spectral bio-indicators of photosynthetic efficiency and vegetation stress//Thenkabail P S, Lyon J G, Huete A, eds. Hyperspectral Remote Sensing of Vegetation, Vol. III: Biophysical and Biochemical Characterization and Plant Species Studies. 2nd ed. New York: CRC Press: 133-179
Nichol C J, Drolet G, Porcar-Castell A, Wade T, Sabater N, Middleton E M, MacLellan C, Levula J, Mammarella I, Vesala T and Atherton J. 2019. Diurnal and seasonal solar induced chlorophyll fluorescence and photosynthesis in a boreal Scots pine canopy. Remote Sensing, 11(3): 273 [DOI: 10.3390/rs11030273http://dx.doi.org/10.3390/rs11030273]
Pacheco-Labrador J, Hueni A, Mihai L, Sakowska K, Julitta T, Kuusk J, Sporea D, Alonso L, Burkart A, Cendrero-Mateo M P, Aasen H, Goulas Y and Arthur A M. 2019. Sun-induced chlorophyll fluorescence I: instrumental considerations for proximal spectroradiometers. Remote Sensing, 11(8): 960 [DOI: 10.3390/rs11080960http://dx.doi.org/10.3390/rs11080960]
Paul-Limoges E, Damm A, Hueni A, Liebische F, Eugster W, Schaepman M E and Buchmann N. 2018. Effect of environmental conditions on sun-induced fluorescence in a mixed forest and a cropland. Remote Sensing of Environment, 219: 310-323 [DOI: 10.1016/j.rse.2018.10.018http://dx.doi.org/10.1016/j.rse.2018.10.018]
Pérez-Priego O, Guan J, Rossini M, Fava F, Wutzler T, Moreno G, Carvalhais N, Carrara A, Kolle O, Julitta T, Schrumpf M, Reichstein M and Migliavacca M. 2015. Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem. Biogeosciences, 12(21): 6351-6367 [DOI: 10.5194/bg-12-6351-2015http://dx.doi.org/10.5194/bg-12-6351-2015]
Plascyk J A and Gabriel F C. 1975. The Fraunhofer line discriminator MKII—An airborne instrument for precise and standardized ecological luminescence measurement. IEEE Transactions on Instrumentation and Measurement, 24(4): 306-313 [DOI: 10.1109/TIM.1975.4314448http://dx.doi.org/10.1109/TIM.1975.4314448]
Qiu B, Xue Y K, Fisher J B, Guo W D, Berry J A and Zhang Y G. 2018. Satellite chlorophyll fluorescence and soil moisture observations lead to advances in the predictive understanding of global terrestrial coupled carbon-water cycles. Global Biogeochemical Cycles, 32(3): 360-375 [DOI: 10.1002/2017GB005744http://dx.doi.org/10.1002/2017GB005744]
Schlau-Cohen G S and Berry J. 2015. Photosynthetic fluorescence, from molecule to planet. Physics Today, 68(9): 66 [DOI: 10.1063/PT.3.2924http://dx.doi.org/10.1063/PT.3.2924]
Song L, Guanter L, Guan K Y, You L Z, Huete A, Ju W M and Zhang Y G. 2018. Satellite sun-induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo-Gangetic Plains. Global Change Biology, 24(9): 4023-4037 [DOI: 10.1111/GCB.14302http://dx.doi.org/10.1111/GCB.14302]
Sun Y, Frankenberg C, Jung M, Joiner J, Guanter L, Köhler P and Magney T. 2018. Overview of Solar-Induced chlorophyll Fluorescence (SIF) from the Orbiting Carbon Observatory-2: retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sensing of Environment, 209: 808-823 [DOI: 10.1016/j.rse.2018.02.016http://dx.doi.org/10.1016/j.rse.2018.02.016]
Verma M, Schimel D, Evans B, Frankenberg C, Beringer J, Drewry D T, Magney T, Marang I, Hutley L, Moore C and Eldering A. 2017. Effect of environmental conditions on the relationship between solar-induced fluorescence and gross primary productivity at an OzFlux grassland site. Journal of Geophysical Research: Biogeosciences, 122: 716-733 [DOI: 10.1002/2016JG003580http://dx.doi.org/10.1002/2016JG003580]
Xu S, Liu Z G, Zhao L, Zhao H R and Ren S X. 2018. Diurnal response of sun-induced fluorescence and PRI to water stress in maize using a near-surface remote sensing platform. Remote Sensing, 10(10): 1510 [DOI: 10.3390/rs10101510http://dx.doi.org/10.3390/rs10101510]
Yang X, Shi H Y, Stovall A, Guan K Y, Miao G F, Zhang Y G, Zhang Y, Xiao X M, Ryu Y and Lee J E. 2018. FluoSpec2—an automated field spectroscopy system to monitor canopy solar-induced fluorescence. Sensors, 18(7): 2063 [DOI: 10.3390/s18072063http://dx.doi.org/10.3390/s18072063]
Yang X, Tang J W, Mustard J F, Lee J E, Rossini M, Joiner J, Munger J W, Kornfeld A and Richardson A D. 2015. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophysical Research Letters, 42(8): 2977-2987 [DOI: 10.1002/2015GL063201http://dx.doi.org/10.1002/2015GL063201]
Zhang Q, Zhang X K, Li Z H, Wu Y F and Zhang Y G. 2019. Comparison of Bi-hemispherical and hemispherical-conical configurations for in situ measurements of solar-induced chlorophyll fluorescence. Remote Sensing, 11(22): 2642 [DOI: 10.3390/rs11222642http://dx.doi.org/10.3390/rs11222642]
Zhang Y, Xiao X M, Zhang Y G, Wolf S, Zhou S, Joiner J, Guanter L, Verma M, Sun Y, Yang X, Paul-Limoges E, Gough C M, Wohlfahrt G, Gioli B, van der Tol C, Yann N, Lund M and de Grandcourt A. 2018. On the relationship between sub-daily instantaneous and daily total gross primary production: implications for interpreting satellite-based SIF retrievals. Remote Sensing of Environment, 205: 276-289 [DOI: 10.1016/j.rse.2017.12.009http://dx.doi.org/10.1016/j.rse.2017.12.009]
Zhang Y G, Guanter L, Berry J A, Van der Tol C, Yang X, Tang J W and Zhang F M. 2016. Model-based analysis of the relationship between sun-induced chlorophyll fluorescence and gross primary production for remote sensing applications. Remote Sensing of Environment, 187: 145-155 [DOI: 10.1016/j.rse.2016.10.016http://dx.doi.org/10.1016/j.rse.2016.10.016]
Zhao F, Dai X, Verhoef W, Guo Y Q, Van der Tol C, Li Y G and Huang Y B. 2016. FluorWPS: a Monte Carlo ray-tracing model to compute sun-induced chlorophyll fluorescence of three-dimensional canopy. Remote Sensing of Environment, 187: 385-399 [DOI: 10.1016/j.rse.2016.10.036http://dx.doi.org/10.1016/j.rse.2016.10.036]
Zhou X J, Liu Z G, Xu S, Zhang W W and Wu J. 2016. An automated comparative observation system for sun-induced chlorophyll fluorescence of vegetation canopies. Sensors, 16(6): 775 [DOI: 10.3390/s16060775http://dx.doi.org/10.3390/s16060775]
相关文章
相关作者
相关机构
京公网安备11010802024621