植被生态遥感参数定量反演研究方法进展

赵燕红; 侯鹏; 蒋金豹; 姜赟; 张兵; 白君君; 徐海涛

doi:10.11834/jrs.20210402

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植被生态遥感参数定量反演研究方法进展
Progress in quantitative inversion of vegetation ecological remote sensing parameters
2021年25卷第11期页码：2173-2197
纸质出版日期： 2021-11-07 ，
DOI： 10.11834/jrs.20210402

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赵燕红，侯鹏，蒋金豹，姜赟，张兵，白君君，徐海涛.2021.植被生态遥感参数定量反演研究方法进展.遥感学报，25（11）： 2173-2197

Zhao Y H，Hou P，Jiang J B，Jiang Y，Zhang B，Bai J J and Xu H T. 2021. Progress in quantitative inversion of vegetation ecological remote sensing parameters. National Remote Sensing Bulletin， 25（11）：2173-2197
赵燕红，侯鹏，蒋金豹，姜赟，张兵，白君君，徐海涛.2021.植被生态遥感参数定量反演研究方法进展.遥感学报，25（11）： 2173-2197 DOI： 10.11834/jrs.20210402.

Zhao Y H，Hou P，Jiang J B，Jiang Y，Zhang B，Bai J J and Xu H T. 2021. Progress in quantitative inversion of vegetation ecological remote sensing parameters. National Remote Sensing Bulletin， 25（11）：2173-2197 DOI： 10.11834/jrs.20210402.

摘要

植被参数是生态遥感定量反演的热点和难点，也是生态系统研究的基础性参数。本文在广泛阅读国内外公开发表的文献资料基础上，将现有的植被生态遥感参数概括为物理类、生化组分类、能量和功能类3大类，系统梳理了每类参数定量反演的主要模型方法，进行优缺点和适用性分析，对现阶段存在的不足和未来发展趋势进行了探讨。物理类植被生态遥感参数主要介绍了植被覆盖度、生物量、叶面积指数、树高等研究进展，生化组分类植被生态遥感参数主要介绍了植被含水量、叶绿素含量和光合能力等研究进展，能量类植被生态遥感参数介绍了光合有效辐射、植被吸收光合有效辐射等研究进展，功能类植被生态遥感参数主要介绍了植被生产力和碳交换量等研究进展。存在的主要问题包括混合像元分解、病态反演、物理模型应用过程中的误差传递、数据融合的尺度效应和空间变异性、模型耦合的最优方案确定问题。

Abstract

Vegetation parameters are the hotspots and difficulties in the quantitative inversion of ecological remote sensing

and they are also the basic parameters of ecosystem research

as well as the basic parameters of ecosystem research. They have an important impact on the structure

process and function of ecosystems

and have always been hot issues in ecology and remote sensing technology research. However

in actual work

the relevant parameters of vegetation are not obtained through direct interpretation of remote sensing data

but based on the spectral characteristics of vegetation to establish a data model for quantitative inversion calculation

and there are obvious differences in the retrieval results of different inversion methods. Based on extensive reading of publicly published documents at home and abroad

this paper summarizes the existing vegetation ecological remote sensing parameters into three categories: physical

biochemical

energy and functional categories

and systematically sorts out the main quantitative inversion methods of each category of parameters’ advantages and disadvantages and applicability

and the current deficiencies and future development trends are discussed. And this article selects representative common vegetation parameters to introduce one by one

the physical vegetation ecological remote sensing parameters mainly introduce the research progress of vegetation coverage

biomass

leaf area index

tree height

etc. The biochemical group vegetation ecological remote sensing parameters mainly introduce the research progress of vegetation water content

chlorophyll content and photosynthetic capacity. Energy Vegetation ecological remote sensing parameters introduce the research progress of vegetation with Photosynthetically Active Radiation and Absorption of Photosynthetically Active Radiation

and functional vegetation ecological remote sensing parameters mainly introduce the research progress of vegetation productivity and carbon exchange capacity. Although positive progress has been made in the research on the quantitative inversion of vegetation ecological remote sensing parameters

due to the constraints of the ground sensor observation performance indicators and the insufficient knowledge of the vegetation growth and change process

there are still some problems in the research of vegetation ecological remote sensing parameters that need to be resolved. The main problems that exist include the decomposition of mixed pixels

ill-conditioned inversion

error transfer in the application of physical models

the scale effect and spatial variability of data fusion

and the determination of the optimal scheme for model coupling. Generally speaking

the inversion methods of vegetation parameters have been developing in the direction of multivariate methods

multi-method fusion

and continuous improvement of various methods. Data sources are becoming more and more abundant

which can better realize mutual verification and information complementarity between data

and solve the problems of lack of vegetation characteristic information faced in the process of parameter inversion. High-precision vegetation ecological remote sensing parameter products can better study new hot issues related to vegetation carbon sources

carbon sinks

carbon use efficiency

carbon cycle

vegetation and phenology.By comparing the inversion methods of different vegetation ecological remote sensing parameters and the relationship between different vegetation ecological remote sensing parameters

the difficulties and scientific problems existing in the vegetation quantitative remote sensing inversion are discussed

which is convenient for research and technical personnel in related fields.

关键词

陆表植被生态遥感反演方法植被参数模型方法

Keywords

land surface vegetationecological remote sensinginversion methodvegetation parametersmodel method

references

Alden C B, Miller J B, Gatti L V, Gloor M M, Guan K Y, Michalak A M, Van Der Laan-Luijkx I T, Touma D, Andrews A, Basso L S, Correia C S C, Domingues L G, Joiner J, Krol M C, Lyapustin A I, Peters W, Shiga Y P, Thoning K, Van Der Velde I R, Van Leeuwen T T, Yadav V and Diffenbaugh N S. 2016. Regional atmospheric CO2 inversion reveals seasonal and geographic differences in Amazon net biome exchange. Global Change Biology, 22(10): 3427-3443 [DOI: 10.1111/gcb.13305http://dx.doi.org/10.1111/gcb.13305]

Annala L, Honkavaara E, Tuominen S and Pölönen I. 2020. Chlorophyll concentration retrieval by training convolutional neural network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion. Remote Sensing, 12(2): 283 [DOI: 10.3390/RS12020283http://dx.doi.org/10.3390/RS12020283]

Arshad M, Ullah S, Khurshid K and Ali A. 2018. Estimation of leaf water content from mid- and thermal-infrared spectra by coupling genetic algorithm and partial least squares regression. Journal of Applied Remote Sensing, 12(2): 022203 [DOI: 10.1117/1.JRS.12.022203http://dx.doi.org/10.1117/1.JRS.12.022203]

Badawy B, Rödenbeck C, Reichstein M, Carvalhais N and Heimann M. 2012. Technical note: the Simple Diagnostic Photosynthesis and Respiration Model (SDPRM). Biogeosciences, 10(10): 6485-6508 [DOI: 10.5194/bg-10-6485-2013http://dx.doi.org/10.5194/bg-10-6485-2013]

Banks J M. 2017. Continuous excitation chlorophyll fluorescence parameters: a review for practitioners. Tree Physiology, 37(8): 1128-1136 [DOI: 10.1093/treephys/tpx059http://dx.doi.org/10.1093/treephys/tpx059]

Bannari A, Morin D, Bonn F and Huete A R. 1995. A review of vegetation indices. Remote Sensing Reviews, 13(1/2): 95-120 [DOI: 10.1080/02757259509532298http://dx.doi.org/10.1080/02757259509532298]

Baret F and Buis S. 2008. Estimating canopy characteristics from remote sensing observations: Review of methods and associated problems//Liang S L, ed. Advances in Land Remote Sensing: System, Modeling, Inversion and Application. Dordrecht: Springer: 173-201 [DOI: 10.1007/978-1-4020-6450-0_7http://dx.doi.org/10.1007/978-1-4020-6450-0_7]

Bastos A, Friedlingstein P, Sitch S, Chen C, Mialon A, Wigneron J P, Arora V K, Briggs P R, Canadell J G, Ciais P, Chevallier F, Cheng L, Delire C, Haverd V, Jain A K, Joos F, Kato E, Lienert S, Lombardozzi D, Melton J R, Myneni R, Nabel J E M S, Pongratz J, Poulter B, Rödenbeck C, Séférian R, Tian H Q, Van Eck C, Viovy N, Vuichard N, Walker A P, Wiltshire A, Yang J, Zaehle S, Zeng N and Zhu D. 2018. Impact of the 2015/2016 El Nino on the terrestrial carbon cycle constrained by bottom-up and top-down approaches. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1760): 20170304 [DOI: 10.1098/rstb.2017.0304http://dx.doi.org/10.1098/rstb.2017.0304]

Bazame H C, Althoff D, Filgueiras R, Calijuri M L and De Oliveira J C. 2019. Modeling the net primary productivity: a study case in the Brazilian territory. Journal of the Indian Society of Remote Sensing, 47(10): 1727-1735 [DOI: 10.1007/s12524-019-01024-3http://dx.doi.org/10.1007/s12524-019-01024-3]

Box E. 1978. Geographical dimensions of terrestrial net and gross primary productivity. Radiation and Environmental Biophysics, 15(4): 305-322 [DOI: 10.1007/BF01323458http://dx.doi.org/10.1007/BF01323458]

Brienen R J W, Phillips O L, Feldpausch T, Gloor E, Baker T R, Lloyd J, Lopez-Gonzalez G, Monteagudo-Mendoza A, Malhi Y, Lewis S L, Martinez R V, Alexiades M, Dávila E Á, Alvarez-Loayza P, Andrade A, Aragão L E O C, Araujo-Murakami A, Arets E J M M, Arroyo L, Aymard G A C, Bánki O S, Baraloto C, Barroso J, Bonal D, Boot R G A, Camargo J L C, Castilho C V, Chama V, Chao K J, Chave J, Comiskey J A, Valverde F C, Da Costa L, de Oliveira E A, Di Fiore A, Erwin T L, Fauset S, Forsthofer M, Galbraith D R, Grahame E S, Groot N, Hérault B, Higuchi N, Coronado E N H, Keeling H, Killeen T J, Laurance W F, Laurance S, Licona J, Magnussen W E, Marimon B S, Marimon-Junior B H, Mendoza C, Neill D A, Nogueira E M, Núñez P, Camacho N C P, Parada A, Pardo-Molina G, Peacock J, Peña-Claros M, Pickavance G C, Pitman N C A, Poorter L, Prieto A, Quesada C A, Ramírez F, Ramírez-Angulo H, Restrepo Z, Roopsind A, Rudas A, Salomão R P, Schwarz M, Silva N, Silva-Espejo J E, Silveira M, Stropp J, Talbot J, Ter Steege H, Teran-Aguilar J, Terborgh J, Thomas-Caesar R, Toledo M, Torello-Raventos M, Umetsu R K, Van Der Heijden G M F, Van Der Hout P, Vieira I C G, Vieira S A, Vilanova E, Vos V A and Zagt R J. 2015. Long-term decline of the Amazon carbon sink. Nature, 519(7543): 344-348 [DOI: 10.1038/nature14283http://dx.doi.org/10.1038/nature14283]

Brown M G L, Skakun S, He T and Liang S L. 2020. Intercomparison of machine-learning methods for estimating surface shortwave and photosynthetically active radiation. Remote Sensing, 12(3): 372 [DOI: 10.3390/rs12030372http://dx.doi.org/10.3390/rs12030372]

Cartus O, Santoro M, Wegmüller U and Rommen B. 2019. Benchmarking the retrieval of biomass in boreal forests using P-band SAR backscatter with multi-temporal C- and l-band observations. Remote Sensing, 11(14): 1695 [DOI: 10.3390/RS11141695http://dx.doi.org/10.3390/RS11141695]

Carvalhais N, Reichstein M, Ciais P, Collatz G J, Mahecha M D, Montagnani L, Papale D, Rambal S and Seixas J. 2010. Identification of vegetation and soil carbon pools out of equilibrium in a process model via eddy covariance and biometric constraints. Global Change Biology, 16(10): 2813-2829 [DOI: 10.1111/j.1365-2486.2010.02173.xhttp://dx.doi.org/10.1111/j.1365-2486.2010.02173.x]

Chen J M and Black T A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15(4): 421-429 [DOI: 10.1111/j.1365-3040.1992.tb00992.xhttp://dx.doi.org/10.1111/j.1365-3040.1992.tb00992.x]

Chen M, Zhuang Q, Cook D R Coulter R, Pekour M, Scott R L, Munger J W and Bible K. 2011. Quantification of terrestrial ecosystem carbon dynamics in the conterminous United States combining a process-based biogeochemical model and MODIS and AmeriFlux data. Biogeosciences, 8(9): 2665-2688 [DOI: 10.5194/bg-8-2665-2011http://dx.doi.org/10.5194/bg-8-2665-2011]

Chen Z Q, Chen J M, Zheng X G, Jiang F, Qin J, Zhang S P, Yuan W P, Ju W M and Mo G. 2015. Optimizing photosynthetic and respiratory parameters based on the seasonal variation pattern in regional net ecosystem productivity obtained from atmospheric inversion. Science Bulletin, 60(22): 1954-1961 [DOI: 10.1007/s11434-015-0917-6http://dx.doi.org/10.1007/s11434-015-0917-6]

Cheng X J, Xu X G, Chen T E, Yang G J and Li Z H. 2014a. The new method monitoring crop water content based on NIR-Red spectrum feature space. Spectroscopy and Spectral Analysis, 34(6): 1542-1547

程晓娟,徐新刚,陈天恩,杨贵军,李振海.基于NIR-Red光谱特征空间的作物水分指数.光谱学与光谱分析,2014,34(06):1542-1547) [DOI: 10.3964/j.issn.1000-0593(201406-1542-06http://dx.doi.org/10.3964/j.issn.1000-0593(2014)06-1542-06]

Cheng Y B, Zarco-Tejada P J, Riaño D Rueda C A and Ustin S L. 2006. Estimating vegetation water content with hyperspectral data for different canopy scenarios: relationships between AVIRIS and MODIS indexes. Remote Sensing of Environment, 105(4): 354-366 [DOI: 10.1016/j.rse.2006.07.005http://dx.doi.org/10.1016/j.rse.2006.07.005]

Cheng Y B, Zhang Q Y, Lyapustin A I, Wang Y J and Middleton E M. 2014b. Impacts of light use efficiency and fPAR parameterization on gross primary production modeling. Agricultural and Forest Meteorology, 189-190: 187-197 [DOI: 10.1016/j.agrformet.2014.01.006http://dx.doi.org/10.1016/j.agrformet.2014.01.006]

Collier C J, Ow Y X, Langlois L, Uthicke S, Johansson C L, O'brien K R, Hrebien V and Adams M P. 2017. Optimum temperatures for net primary productivity of three tropical seagrass species. Frontiers in Plant Science, 8: 1446 [DOI: 10.3389/fpls.2017.01446http://dx.doi.org/10.3389/fpls.2017.01446]

Deng B, Yang W N, Mu N and Zhang C. 2016. The research of vegetation water content based on spectrum analysis and angle slope index. Spectroscopy and Spectral Analysis, 36(8): 2546-2552 [DOI: 10.3964/j.issn.1000-0593(2016)08-2546-07http://dx.doi.org/10.3964/j.issn.1000-0593(2016)08-2546-07]

Deng F, Chen J M, Peters W, Birdsey R, Mccullough K and Xiao J. 2013. The use of forest stand age information in an atmospheric CO2 inversion applied to North America. Biogeosciences, 10(8): 5335-5348 [DOI: 10.5194/BG-10-5335-2013http://dx.doi.org/10.5194/BG-10-5335-2013]

Di Bella C M, Paruelo J M, Becerra J E, Bacour C and Baret F. 2004. Effect of senescent leaves on NDVI-based estimates of fAPAR: Experimental and modelling evidences. International Journal of Remote Sensing, 25(23): 5415-5427 [DOI: 10.1080/01431160412331269724http://dx.doi.org/10.1080/01431160412331269724]

Dong L X. 2019. Multi-model estimation of forest leaf area index in the Three Gorges Reservoir area. Remote Sensing for Land and Resources, 31(2): 73-81

董立新. 2019. 三峡库区森林叶面积指数多模型遥感估算. 国土资源遥感, 31(2): 73-81 [DOI: 10.6046/gtzyyg.2019.02.11http://dx.doi.org/10.6046/gtzyyg.2019.02.11]

Dou Z G, Li Y Z, Cui L J, Pan X, Ma Q F, Huang Y L, Lei Y R, Li J, Zhao X S, Li W, Dou Z G, Li Y Z, Cui L J, Pan X, Ma Q F, Huang Y L, Lei Y R, Li J, Zhao X S and Li W. 2020. Hyperspectral inversion of Suaeda salsa biomass under different types of human activity in Liaohe Estuary wetland in north-eastern China. Marine and Freshwater Research, 71(4): 482-492 [DOI: 10.1071/MF19030http://dx.doi.org/10.1071/MF19030]

Dutta D, Schimel D S, Sun Y, Van Der Tol C and Frankenberg C. 2019. Optimal inverse estimation of ecosystem parameters from observations of carbon and energy fluxes. Biogeosciences, 16(1): 77-103 [DOI: 10.5194/bg-16-77-2019http://dx.doi.org/10.5194/bg-16-77-2019]

Fang M H and Ju W M. 2015. Study on inversion model of crop leaf water content based on leaf optical properties. Spectroscopy and spectral analysis,35 (01): 167-171

方美红,居为民.基于叶片光学属性的作物叶片水分含量反演模型研究.光谱学与光谱分析,2015,35(01):167-171

Field C B, Randerson J T and Malmström C M. 1995. Global net primary production: Combining ecology and remote sensing. Remote Sensing of Environment, 51(1): 74-88 [DOI: 10.1016/0034-4257(94)00066-Vhttp://dx.doi.org/10.1016/0034-4257(94)00066-V]

Flanagan L B, Sharp E J and Gamon J A. 2015. Application of the photosynthetic light-use efficiency model in a northern Great Plains grassland. Remote Sensing of Environment, 168: 239-251 [DOI: 10.1016/j.rse.2015.07.013http://dx.doi.org/10.1016/j.rse.2015.07.013]

Fu H Q, Wang C C, Zhu J J, Xie Q H and Zhang B. 2016. Estimation of pine forest height and underlying DEM using multi-baseline P-band PolInSAR data. Remote Sensing, 8(10): 820 [DOI: 10.3390/rs8100820http://dx.doi.org/10.3390/rs8100820]

Garestier F, Dubois-Fernandez P C, Guyon D and Toan T L. 2009. Forest biophysical parameter estimation using L- and P-Band polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 47(10): 3379-3388 [DOI: 10.1109/TGRS.2009.2022947http://dx.doi.org/10.1109/TGRS.2009.2022947]

Garestier F and Le Toan T L. 2010. Forest modeling for height inversion using single-baseline InSAR/Pol-InSAR data. IEEE Transactions on Geoscience and Remote Sensing, 48(3): 1528-1539.

Ghasemi N, Tolpekin V and Stein A. 2018. A modified model for estimating tree height from PolInSAR with compensation for temporal decorrelation. International Journal of Applied Earth Observation and Geoinformation, 73: 313-322 [DOI: 10.1016/j.jag.2018.06.022http://dx.doi.org/10.1016/j.jag.2018.06.022]

Gitelson A, Via A, Solovchenko A, Arkebauer T and Inoue Y. 2019. Derivation of canopy light absorption coefficient from reflectance spectra. Remote Sensing of Environment, 231: 111276 [DOI: 10.1016/j.rse.2019.111276http://dx.doi.org/10.1016/j.rse.2019.111276]

Guo B, Han F and Jiang L. 2018. An improved dimidiated pixel model for vegetation fraction in the Yarlung Zangbo River basin of Qinghai-Tibet Plateau. Journal of the Indian Society of Remote Sensing, 46(2/3): 219-231 [DOI: 10.1007/s12524-017-0692-8http://dx.doi.org/10.1007/s12524-017-0692-8]

He L, Li A N, Yin G F, Nan X and Bian J H. 2019. Retrieval of grassland aboveground biomass through inversion of the PROSAIL model with MODIS imagery. Remote Sensing, 11(13): 1597 [DOI: 10.3390/rs11131597http://dx.doi.org/10.3390/rs11131597]

He L M, Chen J M, Liu J, Mo G, Bélair S, Zheng T, Wang R, Chen B, Croft H, Arain M A and Barr A G. 2014. Optimization of water uptake and photosynthetic parameters in an ecosystem model using tower flux data. Ecological Modelling, 294: 94-104 [DOI: 10.1016/J.ECOLMODEL.2014.09.019http://dx.doi.org/10.1016/J.ECOLMODEL.2014.09.019]

He M Z, Ju W M, Zhou Y L, Chen J M, He H L, Wang S Q, Wang H M, Guan D X, Yan J H, Li Y N, Hao Y B and Zhao F H. 2013. Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity. Agricultural and Forest Meteorology, 173: 28-39 [DOI: 10.1016/J.AGRFORMET.2013.01.003http://dx.doi.org/10.1016/J.AGRFORMET.2013.01.003]

Hilton T W, Davis K J, Keller K and Urban N M. 2013. Improving North American terrestrial CO2 flux diagnosis using spatial structure in land surface model residuals. Biogeosciences, 10(7): 4607-4625 [DOI: 10.5194/bg-10-4607-2013http://dx.doi.org/10.5194/bg-10-4607-2013]

Hou W M, Su J, Xu W B and Li X Y. 2019. Inversion of the fraction of absorbed Photosynthetically Active Radiation (FPAR) from FY-3C MERSI data. Remote Sensing, 12(1): 67 [DOI: 10.3390/rs12010067http://dx.doi.org/10.3390/rs12010067]

Huete A, Justice C and Van Leeuwen W. 1999. MODIS Vegetation Index (MOD13). Algorithm Theoretical Basis Document. Charlottesville: University of Arizona, Tucson, University of Virginia Department of Environmental Sciences.

Huete A R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295-309 [DOI: 10.1016/0034-4257(88)90106-Xhttp://dx.doi.org/10.1016/0034-4257(88)90106-X]

Jackson R D, Slater P N and Pinter P J. 1983. Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sensing of Environment, 13(3): 187-208 [DOI: 10.1016/0034-4257(83)90039-1http://dx.doi.org/10.1016/0034-4257(83)90039-1]

Jacquemoud S, Baret F, Andrieu B, Danson F M and Jaggard K. 1995. Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors. Remote Sensing of Environment, 52(3): 163-172 [DOI: 10.1016/0034-4257(95)00018-Vhttp://dx.doi.org/10.1016/0034-4257(95)00018-V]

Jacquemoud S, Bacour C, Poilvé H and Frangi J P. 2000. Comparison of four radiative transfer models to simulate plant canopies reflectance: Direct and inverse mode. Remote Sensing of Environment, 74(3): 471-481 [DOI: 10.1016/S0034-4257(00)00139-5http://dx.doi.org/10.1016/S0034-4257(00)00139-5]

Jenerette G D, Scott R L, Barron-Gafford G A and Huxman T E. 2009. Gross primary production variability associated with meteorology, physiology, leaf area, and water supply in contrasting woodland and grassland semiarid riparian ecosystems. Journal of Geophysical Research: Biogeosciences, 114(G4): G04010 [DOI: 10.1029/2009JG001074http://dx.doi.org/10.1029/2009JG001074]

Jiang M H, Lin T C, Shaner P J L, Lyu M K, Xu C, Xie J S, Lin C F, Yang Z J and Yang Y S. 2019. Understory interception contributed to the convergence of surface runoff between a Chinese fir plantation and a secondary broadleaf forest. Journal of Hydrology, 574: 862-871 [DOI: 10.1016/j.jhydrol.2019.04.088http://dx.doi.org/10.1016/j.jhydrol.2019.04.088]

Jiapaer G, Chen X and Bao A M. 2011. A comparison of methods for estimating fractional vegetation cover in arid regions. Agricultural and Forest Meteorology, 151(12): 1698-1710 [DOI: 10.1016/j.agrformet.2011.07.004http://dx.doi.org/10.1016/j.agrformet.2011.07.004]

Jin P B, Wang Q, Iio A and Tenhunen J. 2012. Retrieval of seasonal variation in photosynthetic capacity from multi-source vegetation indices. Ecological Informatics, 7(1): 7-18 [DOI: 10.1016/j.ecoinf.2011.10.004http://dx.doi.org/10.1016/j.ecoinf.2011.10.004]

Kaufman Y J. 1984. Atmospheric effects on remote sensing of surface reflectance//Proceedings of SPIE 0475, Remote Sensing: Critical Review of Technology. Arlington: SPIE: 20-33 [DOI: 10.1117/12.966238http://dx.doi.org/10.1117/12.966238]

Kaufman Y J and Tanré D. 1992. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 261-270 [DOI: 10.1109/36.134076http://dx.doi.org/10.1109/36.134076]

Kimm H, Guan K Y, Jiang C Y, Peng B, Gentry L F, Wilkin S C, Wang S B, Cai Y P, Bernacchi C J, Peng J and Luo Y A. 2020. Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data. Remote Sensing of Environment, 239: 111615 [DOI: 10.1016/j.rse.2019.111615http://dx.doi.org/10.1016/j.rse.2019.111615]

Koetz B, Morsdorf F, Sun G, Ranson K J, Itten K and Allgower B. 2006. Inversion of a lidar waveform model for forest biophysical parameter estimation. IEEE Geoscience and Remote Sensing Letters, 3(1): 49-53 [DOI: 10.1109/LGRS.2005.856706http://dx.doi.org/10.1109/LGRS.2005.856706]

Koffi E N, Rayner P J, Scholze M and Beer C. 2012. Atmospheric constraints on gross primary productivity and net ecosystem productivity: Results from a carbon-cycle data assimilation system. Global Biogeochemical Cycles, 26(1):

GB1024 [DOI: 10.1029/2010GB003900http://dx.doi.org/10.1029/2010GB003900]

Kong B, Yu H, Du R X and Wang Q. 2019. Quantitative estimation of biomass of alpine grasslands using hyperspectral remote sensing. Rangeland Ecology and Management, 72(2): 336-346 [DOI: 10.1016/j.rama.2018.10.005http://dx.doi.org/10.1016/j.rama.2018.10.005]

Konings A G, Bloom A A, Liu J J, Parazoo N C, Schimel D S and Bowman K W. 2019. Global satellite-driven estimates of heterotrophic respiration. Biogscieoences, 16(11): 2269-2284 [DOI: 10.5194/bg-16-2269-2019http://dx.doi.org/10.5194/bg-16-2269-2019]

Kumar S, Khati U G, Chandola S, Agrawal S and Kushwaha S P S. 2017. Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest. Advances in Space Research, 60(3): 571-586 [DOI: 10.1016/j.asr.2017.04.018http://dx.doi.org/10.1016/j.asr.2017.04.018]

Kuppel S, Peylin P, Maignan F, Chevallier F, Kiely G, Montagnani L and Cescatti A. 2014. Model-data fusion across ecosystems: From multisite optimizations to global simulations. Geoscientific Model Development, 7(6): 2581-2597 [DOI: 10.5194/gmd-7-2581-2014http://dx.doi.org/10.5194/gmd-7-2581-2014]

Lahivaara T, Seppanen A, Kaipio J P, Vauhkonen J, Korhonen L, Tokola T and Maltamo M. 2014. Bayesian approach to tree detection based on airborne laser scanning data. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2690-2699 [DOI: 10.1109/TGRS.2013.2264548http://dx.doi.org/10.1109/TGRS.2013.2264548]

Laine A M, Mehtätalo L, Tolvanen A, Frolking S and Tuittila E S. 2019. Impacts of drainage, restoration and warming on boreal wetland greenhouse gas fluxes. The Science of the Total Environment, 647: 169-181 [DOI: 10.1016/j.scitotenv.2018.07.390http://dx.doi.org/10.1016/j.scitotenv.2018.07.390]

Laurent V C E, Schaepman M E, Verhoef W, Weyermann J and Chávez R O. 2014. Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image. Remote Sensing of Environment, 140: 318-329 [DOI: 10.1016/J.RSE.2013.09.005http://dx.doi.org/10.1016/J.RSE.2013.09.005]

Lavalle M and Khun K. 2014. Three-baseline InSAR estimation of forest height. IEEE Geoscience and Remote Sensing Letters, 11(10): 1737-1741 [DOI: 10.1109/LGRS.2014.2307583http://dx.doi.org/10.1109/LGRS.2014.2307583]

Le Maire G, François C, Soudani K, Berveiller D, Pontailler J, Breda N, Genet H, Davi H and Dufrene E. 2008. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sensing of Environment, 112(10): 3846-3864 [DOI: 10.1016/j.rse.2008.06.005http://dx.doi.org/10.1016/j.rse.2008.06.005]

Lei X X, Zhao J, Liu H C, Zhang J Y, Liang W Y, Tian J L and Long Y B. 2019. Inversion of chlorophyll content and SPAD value of vegetable leaves based on prospect model. Spectroscopy and Spectral Analysis, 39(10): 3256-3260

雷祥祥, 赵静, 刘厚诚, 张继业, 梁文跃, 田佳灵, 龙拥兵. 2019. 基于PROSPECT模型的蔬菜叶片叶绿素含量和SPAD值反演. 光谱学与光谱分析, 39(10): 3256-3260) [DOI: 10.3964/j.issn.1000-0593(201910-3256-05]

Leith H and Wittaker R H. 1975. Modeling the primary productivity of the world//Leith H and Wittaker R H, eds. Primary Productivity of the Biosphere. Berlin: Springer: 237-263 [DOI: 10.1007/978-3-642-80913-2_12http://dx.doi.org/10.1007/978-3-642-80913-2_12]

Leprieur C, Kerr Y H, Mastorchio S and Meunier J C. 2000. Monitoring vegetation cover across semi-arid regions: Comparison of remote observations from various scales. International Journal of Remote Sensing, 21(2): 281-300 [DOI: 10.1080/014311600210830http://dx.doi.org/10.1080/014311600210830]

Li H, Liu G H, Liu Q S, Chen Z X and Huang C. 2018. Retrieval of winter wheat leaf area index from Chinese GF-1 satellite data using the PROSAIL model. Sensors, 18(4): 1120 [DOI: 10.3390/s18041120http://dx.doi.org/10.3390/s18041120]

Li L, Du Y M, Tang Y, Xin X Z, Zhang H L, Wen J G and Liu Q H. 2015. A new algorithm of the FPAR product in the Heihe River Basin considering the contributions of direct and diffuse solar radiation separately. Remote Sensing, 7(5): 6414-6432 [DOI: 10.3390/rs70506414http://dx.doi.org/10.3390/rs70506414]

Li Z , Guo X D , Gi C , Zhao J. 2017. A New Vegetation Index Infusing Visible-Infrared Spectral Absorption Feature for Natural Grassland FAPAR Retrieval. Spectroscopy and Spectral Analysis, 37(3):859-864

李喆,郭旭东,古春,赵静.融入可见光-近红外高光谱吸收特征的新型植被指数估算天然草地FAPAR.光谱学与光谱分析,2017,37(03):859-864) [DOI: 10.3964/j.issn.1000-0593(201703-0859-06http://dx.doi.org/10.3964/j.issn.1000-0593(2017)03-0859-06]

Liang S L, Cheng J, Jia K, Jiang B, Liu Q, Liu S H, Xiao Z Q, Xie X H and Yao Y J. 2016. Recent progress in land surface quantitative remote sensing. Journal of Remote Sensing, 20(5): 875-898

梁顺林, 程洁, 贾坤, 江波, 刘强, 刘素红, 肖志强, 谢先红, 姚云军. 2016. 陆表定量遥感反演方法的发展新动态. 遥感学报, 20(5): 875-898 [DOI: 10.11834/jrs.20166258http://dx.doi.org/10.11834/jrs.20166258]

Liang S L, Bai R, Chen X N, Cheng J, Fan W J, He T, Jia K, Jiang B, Jiang L M, Jiao Z T, Liu Y B, Ni W J, Qiu F, Song L L, Sun L, Tang B H, Wen J G, Wu G P, Xie D H, Yao Y J, Yuan W P, Zhang Y G, Zhang Y Z, Zhang Y T, Zhang X T, Zhao T J and Zhao X. 2020. Review of China’s land surface quantitative remote sensing development in 2019. Journal of Remote Sensing, 24(6): 618-671

梁顺林, 白瑞, 陈晓娜, 程洁, 范闻捷, 何涛, 贾坤, 江波, 蒋玲梅, 焦子锑, 刘元波, 倪文俭, 邱凤, 宋柳霖, 孙林, 唐伯惠, 闻建光, 吴桂平, 谢东辉, 姚云军, 袁文平, 张永光, 张玉珍, 张云腾, 张晓通, 赵天杰, 赵祥. 2020. 2019年中国陆表定量遥感发展综述. 遥感学报, 24(6): 618-671 [DOI: 10.11834/jrs.20209476http://dx.doi.org/10.11834/jrs.20209476]

Liu J, Meng Q Y, Ge X S, Liu S X, Chen X and Sun Y X. 2020. Leaf area index inversion of summer maize at multiple growth stages based on BP neural network. Remote Sensing Technology and Application, 35(1): 174-184

刘俊, 孟庆岩, 葛小三, 刘顺喜, 陈旭, 孙云晓. 2020. 基于BP神经网络的夏玉米多生育期叶面积指数反演研究. 遥感技术与应用, 35(1): 174-184 [DOI: 10.11873/j.issn.1004-0323.2020.1.0174http://dx.doi.org/10.11873/j.issn.1004-0323.2020.1.0174]

Locherer M, Hank T, Danner M and Mauser W. 2015. Retrieval of seasonal leaf area index from simulated EnMAP data through optimized LUT-based inversion of the PROSAIL model[J]. Remote Sensing, 7(8): 10321-10346 [DOI: 10.3390/rs70810321http://dx.doi.org/10.3390/rs70810321]

Lunagaria M M and Patel H R. 2018. Evaluation of PROSAIL inversion for retrieval of chlorophyll, leaf dry matter, leaf angle, and leaf area index of wheat using spectrodirectional measurements. International Journal of Remote Sensing, 40(21): 8125-8145 [DOI: 10.1080/01431161.2018.1524608http://dx.doi.org/10.1080/01431161.2018.1524608]

Luo S Z, Wang C, Xi X H, Nie S, Fan X Y, Chen H Y, Yang X B, Peng D L, Lin Y and Zhou G Q. 2019. Combining hyperspectral imagery and LiDAR pseudo-waveform for predicting crop LAI, canopy height and above-ground biomass. Ecological Indicators, 102: 801-812 [DOI: 10.1016/j.ecolind.2019.03.011http://dx.doi.org/10.1016/j.ecolind.2019.03.011]

Ma S F, Zhou Y T, Prasanna H, Gowda P H, Dong J W, Zhang G L, Kakani V G, Wagle P, Chen L F, Flynn K C and Jiang W G. 2019. Application of the water-related spectral reflectance indices: A review. Ecological Indicators, 98: 68-79 [DOI: 10.1016/j.ecolind.2018.10.049http://dx.doi.org/10.1016/j.ecolind.2018.10.049]

Mahadevan P, Wofsy S C, Matross D M, Xiao X M, Dunn A L, Lin J C, Gerbig C, Munger J W, Chow V Y and Gottlieb E W. 2008. A satellite-based biosphere parameterization for net ecosystem CO2 exchange: Vegetation Photosynthesis and Respiration Model (VPRM). Global Biogeochemical Cycles, 22(2):

GB2005 [DOI: 10.1029/2006GB002735http://dx.doi.org/10.1029/2006GB002735]

Maire V, Martre P, Kattge J, Gastal F, Esser G, Fontaine S and Soussana J F. 2012. The coordination of leaf photosynthesis links C and N fluxes in C3 plant species. PLoS One, 7(6): e38345 [DOI: 10.1371/JOURNAL.PONE.0038345http://dx.doi.org/10.1371/JOURNAL.PONE.0038345]

Managhebi T, Maghsoudi Y and Zoej M J V. 2018. Four-stage inversion algorithm for forest height estimation using repeat pass polarimetric SAR interferometry data. Remote Sensing, 10(8): 1174 [DOI: 10.3390/RS10081174http://dx.doi.org/10.3390/RS10081174]

Manzoni S. 2014. Integrating plant hydraulics and gas exchange along the drought-response trait spectrum. Tree Physiology, 34(10): 1031-1034 [DOI: 10.1093/treephys/tpu088http://dx.doi.org/10.1093/treephys/tpu088]

Marcolla B, Migliavacca M, Rödenbeck C and Cescatti A. 2020. Patterns and trends of the dominant environmental controls of net biome productivity. Biogeosciences, 17(8): 2365-2379 [DOI: 10.5194/bg-17-2365-2020http://dx.doi.org/10.5194/bg-17-2365-2020]

Matsushita B, Yang W, Chen J, Onda Y and Qiu G Y. 2007. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors, 7(11): 2636-2651 [DOI: 10.3390/s7112636http://dx.doi.org/10.3390/s7112636]

Monteith J L. 1972. Solar radiation and productivity in tropical ecosystems. The Journal of Applied Ecology, 9(3): 747-766 [DOI: 10.2307/2401901http://dx.doi.org/10.2307/2401901]

Ónodi G, Kertész M, Kovács-Láng E, Ódor P, Botta-Dukát Z, Lhotsky B, Barabás S, Mojzes A and Kröel-Dulay G. 2017. Estimating aboveground herbaceous plant biomass via proxies: The confounding effects of sampling year and precipitation. Ecological Indicators, 2017, 79: 355-360 [DOI: 10.1016/j.ecolind.2017.04.011http://dx.doi.org/10.1016/j.ecolind.2017.04.011]

Ogutu B O, Dash J and Dawson T P. 2013. Developing a diagnostic model for estimating terrestrial vegetation gross primary productivity using the photosynthetic quantum yield and Earth Observation data. Global Change Biology, 19(9): 2878-2892 [DOI: 10.1111/gcb.12261http://dx.doi.org/10.1111/gcb.12261]

Olesk A, Praks J, Antropov O, Zalite K, Arumäe T and Voormansik K. 2016. Interferometric SAR coherence models for characterization of hemiboreal forests using TanDEM-X data. Remote Sensing, 8(9): 700 [DOI: 10.3390/rs8090700http://dx.doi.org/10.3390/rs8090700]

Padalia H, Sinha S K, Bhave V, Trivedi N K and Kumar A S. 2019. Estimating canopy LAI and chlorophyll of tropical forest plantation (North India) using Sentinel-2 data. Advances in Space Research, 65(1): 458-469 [DOI: 10.1016/j.asr.2019.09.023http://dx.doi.org/10.1016/j.asr.2019.09.023]

Pasqualotto N, Delegido J, Van Wittenberghe S, Verrelst J, Rivera J P and Moreno J. 2018. Retrieval of canopy water content of different crop types with two new hyperspectral indices: water absorption Area Index and Depth Water Index. International Journal of Applied Earth Observation and Geoinformation, 67: 69-78 [DOI: 10.1016/j.jag.2018.01.002http://dx.doi.org/10.1016/j.jag.2018.01.002]

Pastor-Guzman J, Atkinson PM, Dash J and Rioja-Nieto R. 2015. Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8. Remote Sensing, 7(11):14530-14558 [DOI:10.3390/rs71114530http://dx.doi.org/10.3390/rs71114530]

Potter C S, Randerson J T, Field C B, Matson P A, Vitousek P M, Mooney H A and Klooster S A. 1993. Terrestrial ecosystem production: A process model based on global satellite and surface data. Global Biogeochemical Cycles, 7(4): 811-841 [DOI: 10.1029/93GB02725http://dx.doi.org/10.1029/93GB02725]

Pourshamsi M, Garcia M, Lavalle M and Balzter H. 2018. A machine-learning approach to PolInSAR and LiDAR data fusion for improved tropical forest canopy height estimation using NASA AfriSAR campaign data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10): 3453-3463 [DOI: 10.1109/JSTARS.2018.2868119http://dx.doi.org/10.1109/JSTARS.2018.2868119]

Prieto-Blanco A, North P R J, Barnsley M J and Fox N. 2009. Satellite-driven modelling of Net Primary Productivity (NPP): Theoretical analysis. Remote Sensing of Environment, 113(1): 137-147 [DOI: 10.1016/j.rse.2008.09.002http://dx.doi.org/10.1016/j.rse.2008.09.002]

Qian X J, Zhang Y J, Liu L Y and Du S S. 2019. Exploring the potential of leaf reflectance spectra for retrieving the leaf maximum carboxylation rate. International Journal of Remote Sensing, 40(14): 1-18 [DOI: 10.1080/01431161.2019.1579940http://dx.doi.org/10.1080/01431161.2019.1579940]

Qiu J X, Crow W T, Wagner W and Zhao T J. 2019. Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing. International Journal of Applied Earth Observation and Geoinformation, 80: 47-57 [DOI: 10.1016/j.jag.2019.03.015http://dx.doi.org/10.1016/j.jag.2019.03.015]

Quan X W, He B B and Li X. 2015. A bayesian network-based method to alleviate the Ill-posed inverse problem: a case study on Leaf Area index and canopy water content retrieval. IEEE Transactions on Geoscience and Remote Sensing, 53(12): 6507-6517 [DOI: 10.1109/TGRS.2015.2442999http://dx.doi.org/10.1109/TGRS.2015.2442999]

Quebbeman J A and Ramirez J A. 2016. Optimal allocation of leaf‐level nitrogen: Implications for covariation of Vcmax and Jmax and photosynthetic downregulation. Journal of Geophysical Research: Biogeoences, 121(9): 2464-2475 [DOI: 10.1002/2016JG003473http://dx.doi.org/10.1002/2016JG003473]

Rahman H. 2001. Influence of atmospheric correction on the estimation of biophysical parameters of crop canopy using satellite remote sensing. International Journal of Remote Sensing, 22(7): 1245-1268 [DOI: 10.1080/01431160151144332http://dx.doi.org/10.1080/01431160151144332]

Randerson J T, Chapin III F S, Harden J W, Neff J C and Harmon M E. 2002. Net ecosystem production: A comprehensive measure of net carbon accumulation by ecosystems. Ecological Applications, 12(4): 937-947 [DOI: 10.1890/1051-0761(2002)012http://dx.doi.org/10.1890/1051-0761(2002)012[0937:NEPACM]2.0.CO;2]

Rao K, Anderegg W R L, Sala A, Martínez-Vilalta J and Konings A G. 2019. Satellite-based vegetation optical depth as an indicator of drought-driven tree mortality. Remote Sensing of Environment, 227: 125-136 [DOI: 10.1016/j.rse.2019.03.026http://dx.doi.org/10.1016/j.rse.2019.03.026]

Richardson A J and Everitt J H. 1992. Using spectral vegetation indices to estimate rangeland productivity. Geocarto International, 7(1): 63-69 [DOI: 10.1080/10106049209354353http://dx.doi.org/10.1080/10106049209354353]

Rouse J W, Hass R W, Schell J A, Deering D W and Harlan J C. 1974. Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) Of Natural Vegetation. Greenbelt, MD: NASA/GSFC

Running S W, Thornton P E, Nemani R and Glassy J M. 2000. Global terrestrial gross and net primary productivity from the earth observing system//Sala O E, Jackson R B, Mooney H A and Howarth R W, eds. Methods in Ecosystem Science. New York: Springer: 44-57 [DOI: 10.1007/978-1-4612-1224-9_4http://dx.doi.org/10.1007/978-1-4612-1224-9_4]

Santi E, Paloscia S, Pettinato S, Cuozzo G, Padovano A, Notarnicola C and Albinet C. 2020. Machine-learning applications for the retrieval of forest biomass from airborne P-band SAR data. Remote Sensing, 12(5): 804 [DOI: 10.3390/rs12050804http://dx.doi.org/10.3390/rs12050804]

Sawada Y, Tsutsui H, Koike T, Rasmy M, Seto R and Fujii H. 2016. A field verification of an algorithm for retrieving vegetation water content from passive microwave observations. IEEE Transactions on Geoscience and Remote Sensing, 54(4): 2082-2095 [DOI: 10.1109/TGRS.2015.2495365http://dx.doi.org/10.1109/TGRS.2015.2495365]

Scholze M, Kaminski T, Knorr W, Blessing S, Vossbeck M, Grant J P and Scipal K. 2016. Simultaneous assimilation of SMOS soil moisture and atmospheric CO2 in-situ observations to constrain the global terrestrial carbon cycle. Remote Sensing of Environment, 180: 334-345 [DOI: 10.1016/j.rse.2016.02.058http://dx.doi.org/10.1016/j.rse.2016.02.058]

Sellers P J. 1985. Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing, 6: 1335-1372 [DOI: 10.1080/01431168508948283http://dx.doi.org/10.1080/01431168508948283]

Shen X, Cao L and She G H. 2016. Subtropical forest biomass estimation based on hyperspectral and high-resolution remotely sensed data. Journal of Remote Sensing, 20(6): 1446-1460

申鑫, 曹林, 佘光辉. 2016. 高光谱与高空间分辨率遥感数据的亚热带森林生物量反演. 遥感学报, 20(6): 1446-1460 [DOI: 10.11834/jrs.20165210http://dx.doi.org/10.11834/jrs.20165210]

Shi H Y, Xiao Z Q, Liang S L and Zhang X T. 2016. Consistent estimation of multiple parameters from MODIS top of atmosphere reflectance data using a coupled soil-canopy-atmosphere radiative transfer model. Remote Sensing of Environment, 184: 40-57 [DOI: 10.1016/j.rse.2016.06.008http://dx.doi.org/10.1016/j.rse.2016.06.008]

Shiga Y P, Tadić J M, Qiu X M, Yadav V, Andrews A E, Berry J A and Michalak A M. 2018. Atmospheric CO2 observations reveal strong correlation between regional net biospheric carbon uptake and solar-induced chlorophyll fluorescence. Geophysical Research Letters, 45(2): 1122-1132 [DOI: 10.1002/2017GL076630http://dx.doi.org/10.1002/2017GL076630]

Shiroma G H X, De Macedo K A C, Wimmer C, Moreira J R and Fernandes D. 2016. The dual-band PolInSAR method for forest parametrization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(7): 3189-3201 [DOI: 10.1109/JSTARS.2016.2520900http://dx.doi.org/10.1109/JSTARS.2016.2520900]

Sinha S K, Padalia H, Dasgupta A, Verrelst J and Rivera J P. 2020. Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India. International Journal of Applied Earth Observation and Geoinformation, 86: 102027 [DOI: 10.1016/j.jag.2019.102027http://dx.doi.org/10.1016/j.jag.2019.102027]

Soenen S A, Peddle D R, Hall R J, Coburn C A and Hall F G. 2010. Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain. Remote Sensing of Environment, 114(7): 1325-1337 [DOI: 10.1016/j.rse.2009.12.012http://dx.doi.org/10.1016/j.rse.2009.12.012]

Song C H, Dannenberg M P and Wang T. 2013a. Optical remote sensing of terrestrial ecosystem primary productivity. Progress in Physical Geography, 37(6): 834-854 [DOI: 10.1177/0309133313507944http://dx.doi.org/10.1177/0309133313507944]

Song X N, Ma J W, Li X T, Leng P, Zhou F C and Li S. 2013b. Estimation of vegetation canopy water content using Hyperion hyperspectral data. SPECTROSCOPY AND SPECTRAL ANALYSIS, 33(10):2833-2837

宋小宁,马建威,李小涛,冷佩,周芳成,李爽.2013.基于Hyperion高光谱数据的植被冠层含水量反演.光谱学与光谱分析, 33(10):2833-2837) [DOI: 10.3964/j.issn.1000-0593(201310-2833-05http://dx.doi.org/10.3964/j.issn.1000-0593(2013)10-2833-05]

Sui J, Qin Q M, Ren H Z, Sun Y H, Zhang T Y, Wang J D and Gong S H. 2018. Winter wheat production estimation based on environmental stress factors from satellite observations. Remote Sensing, 10(6): 962 [DOI: 10.3390/rs10060962http://dx.doi.org/10.3390/rs10060962]

Sun H L, Geng S Y, Wang X Y and Xiong Q X. 2019. Estimation method of wheat leaf area index based on Hyperspectral under late sowing conditions. Spectroscopy and Spectral Analysis, 39(4): 1199-1206

孙华林, 耿石英, 王小燕, 熊勤学. 2019. 晚播条件下基于高光谱的小麦叶面积指数估算方法. 光谱学与光谱分析, 39(4): 1199-1206) [DOI: 10.3964/j.issn.1000-0593(201904-1199-08]

Sun X F, Wang B N, Xiang M S, Jiang S and Fu X K. 2018. Forest height estimation based on constrained gaussian vertical backscatter model using multi-baseline P-band Pol-InSAR data. Remote Sensing, 11(1): 42 [DOI: 10.3390/RS11010042http://dx.doi.org/10.3390/RS11010042]

Tagliabue G, Panigada C, Dechant B, Baret F, Cogliati S, Colombo R, Migliavacca M, Rademske P, Schickling A, Schüttemeyer D, Verrelst J, Rascher U, Ryu Y and Rossini M. 2019. Exploring the spatial relationship between airborne-derived red and far-red sun-induced fluorescence and process-based GPP estimates in a forest ecosystem. Remote Sensing of Environment, 231: 111272 [DOI: 10.1016/j.rse.2019.111272http://dx.doi.org/10.1016/j.rse.2019.111272]

Tan C W, Zhou J, Luo M, Du Y, Yang X and Ma C. 2017. Using combined vegetation indices to monitor leaf chlorophyll content in winter wheat based on Hj-1a/1b images. International Journal of Agriculture and Biology, 19(6): 1576-1584 [DOI: 10.17957/IJAB/15.0475http://dx.doi.org/10.17957/IJAB/15.0475]

Tang L, He M Z and Li X R. 2020. Verification of fractional vegetation coverage and NDVI of desert vegetation via UAVRS technology. Remote Sensing, 12(11): 1742 [DOI: 10.3390/RS12111742http://dx.doi.org/10.3390/RS12111742]

Tucker C J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150 [DOI: 10.1016/0034-4257(79)90013-0http://dx.doi.org/10.1016/0034-4257(79)90013-0]

Wang H Z, Han D, Mu Y, Jiang L N, Yao X L, Bai Y F, Lu Q and Wang F. 2019a. Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform. Agricultural and Forest Meteorology, 278: 107665 [DOI: 10.1016/j.agrformet.2019.107665http://dx.doi.org/10.1016/j.agrformet.2019.107665]

Wang Q , Yi Q X , Bao A M and Zhao J. 2013. Discussion on Hyperspectral Index for the Estimation of Cotton Canopy Water Content. Spectroscopy and Spectral Analysis, 33(2):507-512

王强,易秋香,包安明,赵金.棉花冠层水分含量估算的高光谱指数研究[J].光谱学与光谱分析,2013,33(02):507-512)[DOI:10.3964/j.issn.1000-0593(2013 02-0507-06http://dx.doi.org/10.3964/j.issn.1000-0593(2013)02-0507-06]

Wang S, Zhang B, Xie G D, Zhai X and Sun H L. 2020. Vegetation cover changes and sand-fixing service responses in the Beijing-Tianjin sandstorm source control project area. Environmental Development, 34: 100455 [DOI: 10.1016/j.envdev.2019.08.002http://dx.doi.org/10.1016/j.envdev.2019.08.002]

Wang T, Kang F F, Han H R, Cheng X Q, Zhu J and Zhou W S. 2019b. Estimation of leaf area index from high resolution ZY-3 satellite imagery in a catchment dominated by Larix principis-rupprechtii, northern China. Journal of Forestry Research, 30(2): 603-615 [DOI: 10.1007/s11676-018-0617-6http://dx.doi.org/10.1007/s11676-018-0617-6]

Wei X Q, Gu X F, Meng Q Y, Yu T, Jia parative analysis of GF-1 wide field view and landsat-7 enhanced thematic mapper plus data. Journal of Applied Spectroscopy, 84K, Zhan Y L and Wang C M. 2017. Cross-com (5): 829-836 [DOI: 10.1007/s10812-017-0552-xhttp://dx.doi.org/10.1007/s10812-017-0552-x]

Wiegand C L, Richardson A J, Escobar D E and Gerbermann A H. 1991. Vegetation indices in crop assessments. Remote Sensing of Environment, 35(2/3): 105-119 [DOI: 10.1016/0034-4257(91)90004-Phttp://dx.doi.org/10.1016/0034-4257(91)90004-P]

Wu J J, Zhang J, Lv A F and Zhou L. 2012. An exploratory analysis of spectral indices to estimate vegetation water content using sensitivity function. Remote Sensing Letters, 3(2): 161-169 [DOI: 10.1080/01431161.2011.551845http://dx.doi.org/10.1080/01431161.2011.551845]

Wu X W, Luo Y Q, Weng E S, White L, Ma Y and Zhou X H. 2009. Conditional inversion to estimate parameters from eddy-flux observations. Journal of Plant Ecology, 2(2): 55-68 [DOI: 10.1093/jpe/rtp005http://dx.doi.org/10.1093/jpe/rtp005]

Xiao Q, Tao J P, Xiao Y and Qian F. 2017. Monitoring vegetation cover in Chongqing between 2001 and 2010 using remote sensing data. Environmental Monitoring and Assessment, 189: 493 [DOI: 10.1007/s10661-017-6210-1http://dx.doi.org/10.1007/s10661-017-6210-1]

Xiao W, Chen J L, Zhao Y L, Hu Z Q, Lv X J and Zhang S. 2019. Inversion of maize chlorophyll content in subsidence land of high groundwater level mining area by UAV Remote Sensing. Journal of China Coal Society, 44(1): 295-306

肖武, 陈佳乐, 赵艳玲, 胡振琪, 吕雪娇, 张硕. 2019. 利用无人机遥感反演高潜水位矿区沉陷地玉米叶绿素含量. 煤炭学报, 44(1): 295-306 [DOI: 10.13225/j.cnki.jccs.2018.0759http://dx.doi.org/10.13225/j.cnki.jccs.2018.0759]

Xie Q H, Zhu J J, Wang C C, Fu H Q, Lopez-Sanchez J M and Ballester-Berman J D. 2017. A modified dual-baseline PolInSAR method for forest height estimation. Remote Sensing, 9(8): 819 [DOI: 10.3390/RS9080819http://dx.doi.org/10.3390/RS9080819]

Xu W X, Xue H Z, Jin H A and Li A N. 2019. Retrieval of leaf area index by fusing prior information from remote sensing data. Remote Sensing Technology and Application, 34(6): 1235-1244

徐卫星, 薛华柱, 靳华安, 李爱农. 2019. 融合遥感先验信息的叶面积指数反演. 遥感技术与应用, 34(6): 1235-1244 [DOI: 10.11873/j.issn.1004-0323.2019.6.1235http://dx.doi.org/10.11873/j.issn.1004-0323.2019.6.1235]

Xu X J, Du H Q, Zhou G M, Mao F J, Li P H, Fan W L and Zhu D E. 2016. A method for daily global solar radiation estimation from two instantaneous values using MODIS atmospheric products. Energy, 111: 117-125 [DOI: 10.1016/j.energy.2016.05.095http://dx.doi.org/10.1016/j.energy.2016.05.095]

Yan G J, Hu R H, Luo J H, Weiss M, Jiang H L, Mu X H, Xie D H and Zhang W M. 2019. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology, 265: 390-411 [DOI: 10.1016/j.agrformet.2018.11.033http://dx.doi.org/10.1016/j.agrformet.2018.11.033]

Yang J, Shi S, Gong W, Du L, Ma Y Y, Zhu B and Song S L. 2015. Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content. Plant, Soil and Environment, 61(4): 182-188 [DOI: 10.17221/7/2015-PSEhttp://dx.doi.org/10.17221/7/2015-PSE]

Yi Q X, Wang F M, Bao A M and Jiapaer G. 2014. Leaf and canopy water content estimation in cotton using hyperspectral indices and radiative transfer models. International Journal of Applied Earth Observation and Geoinformation, 33: 67-75 [DOI: 10.1016/j.jag.2014.04.019http://dx.doi.org/10.1016/j.jag.2014.04.019]

Yin Y, Ciais P, Chevallier F, Li W, Bastos A, Piao S L, Wang T and Liu H Y. 2018. Changes in the response of the Northern Hemisphere carbon uptake to temperature over the last three decades. Geophysical Research Letters, 45(9): 4371-4380 [DOI: 10.1029/2018GL077316http://dx.doi.org/10.1029/2018GL077316]

Younes N, Joyce K E, Northfield T D and Maier S W. 2019. The effects of water depth on estimating Fractional Vegetation Cover in mangrove forests. International Journal of Applied Earth Observation and Geoinformation, 83: 101924 [DOI: 10.1016/j.jag.2019.101924http://dx.doi.org/10.1016/j.jag.2019.101924]

Yue J B, Yang G J, Li C C, Li Z H, Wang Y J, Feng H K and Xu B. 2017. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sensing, 9(7): 708 [DOI: 10.3390/rs9070708http://dx.doi.org/10.3390/rs9070708]

Zarco-Tejada P J, Rueda C A and Ustin S L. 2003. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85(1): 109-124 [DOI: 10.1016/S0034-4257(02)00197-9http://dx.doi.org/10.1016/S0034-4257(02)00197-9]

Zhang A W, Zhang S, Guo C F, Liu L L, Hu S X and Chai S T. 2020. Grass biomass inversion based on landsat 8 spectral derived data classification system. Spectroscopy and Spectral Analysis, 40(1): 239-246

张爱武, 张帅, 郭超凡, 刘路路, 胡少兴, 柴沙驼. 2020. Landsat8光谱衍生数据分类体系下的牧草生物量反演. 光谱学与光谱分析, 40(1): 239-246) [DOI: 10.3964/j.issn.1000-0593(202001-0239-08]

Zhang C, Cai H J and Li Z J. 2015. Estimation of fraction of absorbed photosynthetically active radiation for winter wheat based on hyperspectral characteristic parameters. Spectroscopy and Spectral Analysis, 35(9): 2644-2649

张超，蔡焕杰，李志军.2015.高光谱特征参数的冬小麦吸收性光合有效辐射分量估算模型.光谱学与光谱分析,35(09):2644-2649) [DOI: 10.3964/j.issn.1000-0593(201509-2644-06http://dx.doi.org/10.3964/j.issn.1000-0593(2015)09-2644-06]

Zhang J, Wu J J and Zhou L. 2011. Deriving vegetation leaf water content from spectrophotometric data with orthogonal signal correction-partial least square regression. International Journal of Remote Sensing, 32(22): 7557-7574 [DOI: 10.1080/01431161.2010.524677http://dx.doi.org/10.1080/01431161.2010.524677]

Zhang J H, Li L and Yao F M. 2010. Progress in retrieving vegetation water content under different vegetation coverage condition based on remote sensing spectral information. Spectroscopy and Spectral Analysis, 30(6): 1638-1642

张佳华, 李莉, 姚凤梅. 2010. 遥感光谱信息换取不同覆盖下植被水分信号的研究进展. 光谱学与光谱分析, 30(6): 1638-1642) [DOI: 10.3964/j.issn.1000-0593(201006-1638-05]

Zhang L, Yu G R, Gu F X, He H L, Zhang L M and Han S J. 2012. Uncertainty analysis of modeled carbon fluxes for a broad-leaved Korean pine mixed forest using a process-based ecosystem model. Journal of Forest Research, 17(3): 268-282 [DOI: 10.1007/s10310-011-0305-2http://dx.doi.org/10.1007/s10310-011-0305-2]

Zhang L J, Ma H Z, Zhu X B and Sun L. 2013. Retrieval of vegetation canopy water content based on spectral Index Method. Applied Mechanics and Materials, 295-298: 2446-2450

张佳华,李莉,姚凤梅.2010.遥感光谱信息提取不同覆盖下植被水分信号的研究进展.光谱学与光谱分析,30(06):1638-1642 [DOI: 10.4028/www.scientific.net/AMM.295-298.2446http://dx.doi.org/10.4028/www.scientific.net/AMM.295-298.2446]

Zhang Q Y, Xiao X M, Braswell B, Linder E, Baret F and Moore III B. 2005. Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model. Remote Sensing of Environment, 99(3): 357-371 [DOI: 10.1016/j.rse.2005.09.009http://dx.doi.org/10.1016/j.rse.2005.09.009]

Zhang Q Y, Xiao X M, Braswell B, Linder E, Ollinger S, Smith M L, Jenkins J P, Baret F, Richardson A D, Moore B and Minocha R. 2006. Characterization of seasonal variation of forest canopy in a temperate deciduous broadleaf forest, using daily MODIS data. Remote Sensing of Environment, 105(3): 189-203 [DOI: 10.1016/j.rse.2006.06.013http://dx.doi.org/10.1016/j.rse.2006.06.013]

Zhang R, Zhou Y, Luo H X, Wang F T and Wang S X. 2017. Estimation and analysis of spatiotemporal dynamics of the net primary productivity integrating efficiency model with process model in Karst area. Remote Sensing, 9(5): 477 [DOI: 10.3390/RS9050477http://dx.doi.org/10.3390/RS9050477]

Zhang S W, Hui G Y, Han Z T, Sun S S and Tian X. 2019. Estimation of large-scale forest above-ground biomass based on fast optimizing remotely sensed features from pptical multi-spectral and SAR data. Remote Sensing Technology and Application, 34(5): 925-938

张少伟, 惠刚盈, 韩宗涛, 孙珊珊, 田昕. 2019. 基于光学多光谱与SAR遥感特征快速优化的大区域森林地上生物量估测. 遥感技术与应用, 34(5): 925-938 [DOI: 10.11873/j.issn.1004-0323.2019.5.0925http://dx.doi.org/10.11873/j.issn.1004-0323.2019.5.0925]

Zhang T, Zhang Y J, Xu M J, Xi Y, Zhu J T, Zhang X Z, Wang Y F, Li Y N, Shi P L, Yu G R and Sun X M. 2016. Ecosystem response more than climate variability drives the inter-annual variability of carbon fluxes in three Chinese grasslands. Agricultural and Forest Meteorology, 225: 48-56 [DOI: 10.1016/j.agrformet.2016.05.004http://dx.doi.org/10.1016/j.agrformet.2016.05.004]

Zhang X Z, Wang Y P, Peng S S, Rayner P J, Ciais P, Silver J D, Piao S L, Zhu Z C, Lu X J and Zheng X G. 2018. Dominant regions and drivers of the variability of the global land carbon sink across timescales. Global Change Biology, 24(9): 3954-3968 [DOI: 10.1111/gcb.14275http://dx.doi.org/10.1111/gcb.14275]

Zhang Y M and Zhou G S. 2012. Advances in leaf maximum carboxylation rate and its response to environmental factors. Acta Ecologica Sinica, 32(18): 5907-5917

张彦敏, 周广胜. 2012. 植物叶片最大羧化速率及其对环境因子响应的研究进展. 生态学报, 32(18): 59017-5917 [DOI: 10.5846/stxb201108091168http://dx.doi.org/10.5846/stxb201108091168]

Zhang Y Q, Chen J M, Miller J R and Noland T L. 2008. Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery. Remote Sensing of Environment, 112(7): 3234-3247 [DOI: 10.1016/j.rse.2008.04.005http://dx.doi.org/10.1016/j.rse.2008.04.005]

Zheng Z T, Zhu W Q and Zhang Y J. 2020. Direct and lagged effects of spring phenology on net primary productivity in the alpine grasslands on the Tibetan Plateau. Remote Sensing, 12(7): 1223 [DOI: 10.3390/rs12071223http://dx.doi.org/10.3390/rs12071223]

Zhou Q W, Wei X C, Zhou X, Cai M Y and Xu Y X. 2019. Vegetation coverage change and its response to topography in a typical karst region: the Lianjiang River Basin in Southwest China. Environmental Earth Sciences, 78(6): 191 [DOI: 10.1007/s12665-019-8218-zhttp://dx.doi.org/10.1007/s12665-019-8218-z]

Zhou X H, Zhou T and Luo Y Q. 2012. Uncertainties in carbon residence time and NPP-driven carbon uptake in terrestrial ecosystems of the conterminous USA: a Bayesian approach. Tellus B: Chemical and Physical Meteorology, 64(1): 17223 [DOI: 10.3402/tellusb.v64i0.17223http://dx.doi.org/10.3402/tellusb.v64i0.17223]

Zhu A R，Sun R and Wang M J. 2021. Estimation of light use efficiency by using remote sensing data. National Remote Sensing Bulletin, 25(6) :1227-1243

朱安然, 孙睿, 王梦佳, 2021. 植被光能利用率遥感估算.遥感学报, 25(6) : 1227-1243

Zhu H C , Liu H Y , Xu Y X and Yang G J. 2018. UAV-based hyperspectral analysis and spectral indices constructing for quantitatively monitoring leaf nitrogen content of winter wheat. APPLIED OPTICS, 57: 7722-7732 [DOI:10.1364/AO.57.007722http://dx.doi.org/10.1364/AO.57.007722]

Zhu X H, Feng X M and Zhao Y S. 2012. Multi-scale MSDT inversion based on LAI spatial knowledge. Science China Earth Sciences, 55(8): 1297-1305 [DOI: 10.1007/s11430-011-4312-0http://dx.doi.org/10.1007/s11430-011-4312-0]

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