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Received：20 September 2020，
Published：07 November 2021
移动端阅览
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。
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.
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 CO 2 inversion reveals seasonal and geographic differences in Amazon net biome exchange . Global Change Biology , 22 ( 10 ): 3427 - 3443 [ DOI: 10.1111/gcb.13305 http://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/RS12020283 http://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.12.022203 http://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-10-6485-2013 http://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/tpx059 http://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: 10.1080/02757259509532298 http://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_7 http://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.2017.0304 http://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/s12524-019-01024-3 http://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/BF01323458 http://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/nature14283 http://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/rs12030372 http://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/RS11141695 http://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.x http://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.x http://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-8-2665-2011 http://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/s11434-015-0917-6 http://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(201406-1542-06 http://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.2006.07.005 http://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.2014.01.006 http://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.2017.01446 http://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)08-2546-07 http://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 CO 2 inversion applied to North America . Biogeosciences , 10 ( 8 ): 5335 - 5348 [ DOI: 10.5194/BG-10-5335-2013 http://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 f APAR: Experimental and modelling evidences . International Journal of Remote Sensing , 25 ( 23 ): 5415 - 5427 [ DOI: 10.1080/01431160412331269724 http://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.2019.02.11 http://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/MF19030 http://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-16-77-2019 http://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-V http://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.2015.07.013 http://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/rs8100820 http://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.2009.2022947 http://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.2018.06.022 http://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.2019.111276 http://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/s12524-017-0692-8 http://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/rs11131597 http://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.2014.09.019 http://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.2013.01.003 http://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 CO 2 flux diagnosis using spatial structure in land surface model residuals . Biogeosciences , 10 ( 7 ): 4607 - 4625 [ DOI: 10.5194/bg-10-4607-2013 http://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/rs12010067 http://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-X http://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)90039-1 http://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-V http://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)00139-5 http://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 ): G 04010 [ DOI: 10.1029/2009JG001074 http://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.2019.04.088 http://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.2011.07.004 http://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.2011.10.004 http://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: 10.1117/12.966238 http://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: 10.1109/36.134076 http://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.2019.111615 http://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.2005.856706 http://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 ):
GB 1024 [ DOI: 10.1029/2010GB003900 http://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.2018.10.005 http://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-16-2269-2019 http://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.2017.04.018 http://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-7-2581-2014 http://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.2013.2264548 http://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.2018.07.390 http://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.2013.09.005 http://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.2014.2307583 http://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.2008.06.005 http://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_12 http://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: 10.1080/014311600210830 http://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/s18041120 http://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/rs70506414 http://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(201703-0859-06 http://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.20166258 http://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.20209476 http://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.1004-0323.2020.1.0174 http://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/rs70810321 http://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: 10.1080/01431161.2018.1524608 http://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.2019.03.011 http://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.2018.10.049 http://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 CO 2 exchange: Vegetation Photosynthesis and Respiration Model (VPRM) . Global Biogeochemical Cycles , 22 ( 2 ):
GB 2005 [ DOI: 10.1029/2006GB002735 http://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 C 3 plant species . PLoS One , 7 ( 6 ): e 38345 [ DOI: 10.1371/JOURNAL.PONE.0038345 http://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/RS10081174 http://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/tpu088 http://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-17-2365-2020 http://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/s7112636 http://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: 10.2307/2401901 http://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.2017.04.011 http://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.12261 http://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/rs8090700 http://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.2019.09.023 http://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.2018.01.002 http://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/rs71114530 http://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: 10.1029/93GB02725 http://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.2018.2868119 http://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.2008.09.002 http://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: 10.1080/01431161.2019.1579940 http://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.2019.03.015 http://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.2015.2442999 http://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 V cmax and J max and photosynthetic downregulation . Journal of Geophysical Research : Biogeoences , 121 ( 9 ): 2464 - 2475 [ DOI: 10.1002/2016JG003473 http://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: 10.1080/01431160151144332 http://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)012 http://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.2019.03.026 http://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: 10.1080/10106049209354353 http://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_4 http://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/rs12050804 http://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.2015.2495365 http://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 CO 2 in-situ observations to constrain the global terrestrial carbon cycle . Remote Sensing of Environment , 180 : 334 - 345 [ DOI: 10.1016/j.rse.2016.02.058 http://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: 10.1080/01431168508948283 http://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.20165210 http://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.2016.06.008 http://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 CO 2 observations reveal strong correlation between regional net biospheric carbon uptake and solar-induced chlorophyll fluorescence . Geophysical Research Letters , 45 ( 2 ): 1122 - 1132 [ DOI: 10.1002/2017GL076630 http://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.2016.2520900 http://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.2019.102027 http://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.2009.12.012 http://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: 10.1177/0309133313507944 http://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(201310-2833-05 http://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/rs10060962 http://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/RS11010042 http://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.2019.111272 http://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/15.0475 http://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/RS12111742 http://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)90013-0 http://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.2019.107665 http://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 02-0507-06 http://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.2019.08.002 http://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/s11676-018-0617-6 http://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-x http://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-P http://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: 10.1080/01431161.2011.551845 http://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/rtp005 http://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/s10661-017-6210-1 http://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.2018.0759 http://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/RS9080819 http://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.1004-0323.2019.6.1235 http://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.2016.05.095 http://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.2018.11.033 http://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-PSE http://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.2014.04.019 http://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: 10.1029/2018GL077316 http://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.2019.101924 http://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/rs9070708 http://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)00197-9 http://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(201509-2644-06 http://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: 10.1080/01431161.2010.524677 http://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/s10310-011-0305-2 http://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.295-298.2446 http://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.2005.09.009 http://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.2006.06.013 http://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/RS9050477 http://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.1004-0323.2019.5.0925 http://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.2016.05.004 http://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.14275 http://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/stxb201108091168 http://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.2008.04.005 http://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/rs12071223 http://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-z http://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.v64i0.17223 http://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.57.007722 http://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/s11430-011-4312-0 http://dx.doi.org/10.1007/s11430-011-4312-0 ]
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