基于遥感与作物生长模型同化的水稻生长参数时空分析
|
摘要
遥感观测-数值动力模式同化是准确获取地表参数并进行地表状况时空分析的有效方法之一。本研究以数值动力模式之一的作物生长模型为例，探讨遥感信息与作物生长模型同化机制与方法，并基于此方法进行作物生长状况时空分析。
本文选取长春地区水稻农田作为实验样地，采集水稻多个关键生长期的ASD高光谱数据、生化数据和气象等数据，并获取准同步的环境与灾害监测小卫星CCD影像数据和MODIS影像数据。通过数据分析，提取了作为遥感信息与作物生长模型同化的同化观测量，探讨了融合遥感观测数据的时间尺度选择问题，研究了模型参数区域化方法，建立了区域尺度遥感-作物生长模型同化框架，并基于此同化框架对水稻生长状况进行时空数据分析。论文的研究工作及结论如下：
(1)借助辐射传输模型进行植被指数敏感性分析，选择对耦合参数LAI最敏感植被指数作为遥感信息与作物生长模型同化的同化观测量。结果表明：选择的同化观测量MCARI1能较好地反映LAI的变化，且将其作为同化观测量的同化框架运行效率较全波段反射率有大幅度提高。
(2)通过设置不同步长的同化时间尺度，比较不同时间尺度下的同化精度和效率，选择合理的同化时间尺度。结果表明：选择步长介于10天~20天之间的时间尺度作为遥感信息与WOFOST模型同化的同化时间尺度是较合适的。
(3)采用敏感性分析方法选择和改进植被指数，利用CCD和MODIS数据反演具有空间异质性的参数，获取其区域分布。结果表明：植被指数GNDVI和改进型植被指数NDWI2能较好地反演水稻叶绿素和水分含量，R2都在80%以上。
(4)在上述研究工作基础上，基于同化算法-微粒群算法(PSO)建立潜在生产力水平下的区域尺度遥感-作物生长模型同化框架(RS-WOPROSAIL)，并基于此同化框架对长春地区水稻生长参数进行时空分析。结果表明：同化框架生成的水稻生长参数时空数据集较好地体现了水稻生长状况时空域变化特征。
论文的创新点：借助辐射传输模型进行同化观测量的选择，并探讨了同化遥感观测的时间尺度选择。通过CCD、MODIS等多源遥感数据反演耦合模型中具有空间异质性特点的参数，为解决模型升尺度问题提供了一种有效途径。利用PSO算法构建了区域尺度遥感-作物生长模型同化框架。此方法机理性强、普适性高，在时空数据建模和分析方面具有高移植性。
基于微粒群算法构建的遥感-作物生长模型同化框架克服了作物生长模型模拟空间尺度和遥感信息反演时间尺度的不连续性问题，生成了作物生长状况时空数据集并基于此数据集实现了作物生长状况时空分析，该研究为时空数据建模与分析提供了一种参考和研究思路。
The assimilation of remote sensing observation into the numerical dynamic modelis one efficient way to obtain the surface parameter accurately and analyze the surfaceconditions on spatial-time scale. In this study, take crop growth model which is one ofnumerical dynamic model for example, I discuss the assimilation method andtechnology of remote sensing information and crop growth model, and analyze thecrop growth conditions on spatial-time scale based on this method.
In this study, I select the cropland of ChangChun area of the Northeast Plain asthe experimental field, and collect the essential data such as hyper-spectral ASD dataabout several critical rice growing period, meteorological data, Biochemical data, andget MODIS data and quasi-synchronous CCD data. Based on these data, I extract theoptimal observed quantities of the assimilation of remote sensing information intocrop growth model, discuss the problem about the time selection of the assimilation,do the research of the method of the regionalization of the model parameter, build theregional assimilation frame of remote sensing information into crop growth model, dothe dynamic analysis at spatial-time scale on the rice growth situation of ChangChunarea based on the assimilation frame. In this paper, research work and conclusions areas follows:
(1) Based on the PROSPECT+SAIL radiation transfer model analyzing thesensitivity of the vegetation index, selecting the vegetation index which is the mostsensitive to the coupling parameter LAI of the coupling modelWOFOST+PROSPECT+SAIL as the observed quantity of assimilating remotesensing information into crop growth model. The results indicate that the observedquantities MCARI1is better to reflect the variant of LAI, and as the observedquantities of the assimilation, it is more efficient than that of all band reflectance.
(2) Scheduling the assimilation of different step length observed quantities,comparing the accuracy and the efficiency of the assimilation at different time scale,selecting the proper time scale of the assimilation. The results indicate that selectingthe time scale of the step length between10days and20days about the assimilation of the remote sensing information and WOFOST model is more appropriate.
(3) Adopt the sensitivity analysis approach to select and improve currentvegetation index, obtaining spatial distribution values depended on the spatialheterogeneity characteristics parameter of the inversion model based on CCD andMODIS data. The results indicated that the GNDVI and NDWI2could do a betterinversion of chlorophyll and the moisture contents, all of R2are higher than80%.
(4) After finishing the above research, based on PSO algorithm, building thepotential productivity regional assimilation frame of remote sensing information intothe crop growth model. The spatial-temporal analyses were done on the rice growthsituation of ChangChun area with the assimilation frame. The results indicate that theregional assimilation frame RS-WOPROSAIL realizes the continuous simulation ofcrop growth parameters at spatial-time scale. The analog LAI, WSO, and TAGP areproperly reflects the change of the rice growth situation at spatial-time scale.
Main innovation points of this thesis are as follow: select the assimilationobserved quantity with radiation transfer model, and discuss the selection of theassimilation time scale of remote sensing observation; provide an efficient method tosolve the problem about increasing the scale of the model with the spatialheterogeneity characteristics parameters of the Multi-source remote sensing datainversion coupled model such as CCD and MODIS; based on PSO algorithm, buildthe regional assimilation frame of remote sensing, this method has widespreadapplicability, is very good at the mechanism and can be widely adopted on thespatial-time data model and analysis.
The regional assimilation frame of remote sensing information into crop growthmodel based on PSO algorithm solves the problem about the temporal discontinuationof remote sensing and spatial discontinuation of the crop growth model, generates thedata set of the crop growth conditions, analyzes the crop growth condition onspatial-time scale with the dataset, and also provides some reference and ideas aboutthe modeling and analyzing depended on the spatial-time data.
本文选取长春地区水稻农田作为实验样地，采集水稻多个关键生长期的ASD高光谱数据、生化数据和气象等数据，并获取准同步的环境与灾害监测小卫星CCD影像数据和MODIS影像数据。通过数据分析，提取了作为遥感信息与作物生长模型同化的同化观测量，探讨了融合遥感观测数据的时间尺度选择问题，研究了模型参数区域化方法，建立了区域尺度遥感-作物生长模型同化框架，并基于此同化框架对水稻生长状况进行时空数据分析。论文的研究工作及结论如下：
(1)借助辐射传输模型进行植被指数敏感性分析，选择对耦合参数LAI最敏感植被指数作为遥感信息与作物生长模型同化的同化观测量。结果表明：选择的同化观测量MCARI1能较好地反映LAI的变化，且将其作为同化观测量的同化框架运行效率较全波段反射率有大幅度提高。
(2)通过设置不同步长的同化时间尺度，比较不同时间尺度下的同化精度和效率，选择合理的同化时间尺度。结果表明：选择步长介于10天~20天之间的时间尺度作为遥感信息与WOFOST模型同化的同化时间尺度是较合适的。
(3)采用敏感性分析方法选择和改进植被指数，利用CCD和MODIS数据反演具有空间异质性的参数，获取其区域分布。结果表明：植被指数GNDVI和改进型植被指数NDWI2能较好地反演水稻叶绿素和水分含量，R2都在80%以上。
(4)在上述研究工作基础上，基于同化算法-微粒群算法(PSO)建立潜在生产力水平下的区域尺度遥感-作物生长模型同化框架(RS-WOPROSAIL)，并基于此同化框架对长春地区水稻生长参数进行时空分析。结果表明：同化框架生成的水稻生长参数时空数据集较好地体现了水稻生长状况时空域变化特征。
论文的创新点：借助辐射传输模型进行同化观测量的选择，并探讨了同化遥感观测的时间尺度选择。通过CCD、MODIS等多源遥感数据反演耦合模型中具有空间异质性特点的参数，为解决模型升尺度问题提供了一种有效途径。利用PSO算法构建了区域尺度遥感-作物生长模型同化框架。此方法机理性强、普适性高，在时空数据建模和分析方面具有高移植性。
基于微粒群算法构建的遥感-作物生长模型同化框架克服了作物生长模型模拟空间尺度和遥感信息反演时间尺度的不连续性问题，生成了作物生长状况时空数据集并基于此数据集实现了作物生长状况时空分析，该研究为时空数据建模与分析提供了一种参考和研究思路。
The assimilation of remote sensing observation into the numerical dynamic modelis one efficient way to obtain the surface parameter accurately and analyze the surfaceconditions on spatial-time scale. In this study, take crop growth model which is one ofnumerical dynamic model for example, I discuss the assimilation method andtechnology of remote sensing information and crop growth model, and analyze thecrop growth conditions on spatial-time scale based on this method.
In this study, I select the cropland of ChangChun area of the Northeast Plain asthe experimental field, and collect the essential data such as hyper-spectral ASD dataabout several critical rice growing period, meteorological data, Biochemical data, andget MODIS data and quasi-synchronous CCD data. Based on these data, I extract theoptimal observed quantities of the assimilation of remote sensing information intocrop growth model, discuss the problem about the time selection of the assimilation,do the research of the method of the regionalization of the model parameter, build theregional assimilation frame of remote sensing information into crop growth model, dothe dynamic analysis at spatial-time scale on the rice growth situation of ChangChunarea based on the assimilation frame. In this paper, research work and conclusions areas follows:
(1) Based on the PROSPECT+SAIL radiation transfer model analyzing thesensitivity of the vegetation index, selecting the vegetation index which is the mostsensitive to the coupling parameter LAI of the coupling modelWOFOST+PROSPECT+SAIL as the observed quantity of assimilating remotesensing information into crop growth model. The results indicate that the observedquantities MCARI1is better to reflect the variant of LAI, and as the observedquantities of the assimilation, it is more efficient than that of all band reflectance.
(2) Scheduling the assimilation of different step length observed quantities,comparing the accuracy and the efficiency of the assimilation at different time scale,selecting the proper time scale of the assimilation. The results indicate that selectingthe time scale of the step length between10days and20days about the assimilation of the remote sensing information and WOFOST model is more appropriate.
(3) Adopt the sensitivity analysis approach to select and improve currentvegetation index, obtaining spatial distribution values depended on the spatialheterogeneity characteristics parameter of the inversion model based on CCD andMODIS data. The results indicated that the GNDVI and NDWI2could do a betterinversion of chlorophyll and the moisture contents, all of R2are higher than80%.
(4) After finishing the above research, based on PSO algorithm, building thepotential productivity regional assimilation frame of remote sensing information intothe crop growth model. The spatial-temporal analyses were done on the rice growthsituation of ChangChun area with the assimilation frame. The results indicate that theregional assimilation frame RS-WOPROSAIL realizes the continuous simulation ofcrop growth parameters at spatial-time scale. The analog LAI, WSO, and TAGP areproperly reflects the change of the rice growth situation at spatial-time scale.
Main innovation points of this thesis are as follow: select the assimilationobserved quantity with radiation transfer model, and discuss the selection of theassimilation time scale of remote sensing observation; provide an efficient method tosolve the problem about increasing the scale of the model with the spatialheterogeneity characteristics parameters of the Multi-source remote sensing datainversion coupled model such as CCD and MODIS; based on PSO algorithm, buildthe regional assimilation frame of remote sensing, this method has widespreadapplicability, is very good at the mechanism and can be widely adopted on thespatial-time data model and analysis.
The regional assimilation frame of remote sensing information into crop growthmodel based on PSO algorithm solves the problem about the temporal discontinuationof remote sensing and spatial discontinuation of the crop growth model, generates thedata set of the crop growth conditions, analyzes the crop growth condition onspatial-time scale with the dataset, and also provides some reference and ideas aboutthe modeling and analyzing depended on the spatial-time data.
引文
Alagarswamy G, Ritchie J T, Godwin D C, et al. A user’s guide to CERES Sorghum. MichiganState University, ICRISAT, IFDC and IBSNAT Joint Publication,1988
Allen W A and Richardson A J. Interaction of light with a plant canopy. Journal of the OpticalSociety of America,1968,58:1023~1028
Allen W A, Gausman H W, Rchardson A J, et al. Interaction of Isotropic Light with a compactplant. Journal of the Optical Society of America,1969,59(10):1376~1379
Allen W A, Gausman H W, Rchardson A J. Willstatter-stoll theory of leaf reflectance evaluated byray-tracing. Appl.Opt,1973,12:2448~2553
Bach H, Verhoef W, Schneider K. Coupling remote sensing observation models and a growthmodel for improved retrieval of (Geo) Biophysical Information from Optical remote sensingData. In, remote sensing for agriculture, ecosystems, and hydrology II. Barcelona, Spain,2000,1~11
Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory [M]. NewJersey: PrenticeHall,1986
Boogaard H L, Van D C A, R tter R P, et al. User’s Guide for the WOFOST7.1Crop GrowthSimulation Model and WOFOST Control Center1.5. IDLO Wageningen: Winand StaringCentre:1998,1~40
Borge NH, Leblanc E. Comparing prediction power and stability of broadband and hyperspectralvegetation indices for estimation of green leaf area index and canopy chlorophyll density.Remote Sensing of Environment,2001,76:156~172
Bouman B A M. Linking physical remote sensing models with crop growth simulation models,applied for sugar beet. International Journal of Remote Sensing,1992,13:2565~2581
Bouman B A M, van Keulen H, van Laar H H. The school of de Wit crop growth simulationmodels: Apedigree and historical overview. Agricultural System,1996,52:171~198
Bouman B A M, Kropff M J, Tuong T P, et al. ORYZA2000: Modeling lowland rice. Los Banos:IRRI and Wageningen University,2001,23~77
Brisson N, Gary C, Justes E, et al. An overview of the crop model STICS. European journal ofagronomy,2003,18:309~332
Bunkei M and Masayuki T. Integrating remotely sensed data with an ecosystem model to estimatenet primary productivity in East Asia. Remote sensing of Environment,2002,81:58~6
Carbone G J, Narumalani S, King M. Application of remote sensing and GIS technologies withphysiological crop models. Photogrametric Engineering and Remote Sensing,1996,62(2):171~179
Carder K et al. Instantaneous photosynthetically available radiation and absorbed radiation byphytoplankton, ATBD-MOD-20, V5,1999
Chen J. Evaluation of vegetation indices and modified simple ratiofor boreal applications. Can. J.Remote Sens.,1996,22:229~242
Chen J M and Leblanc S G. A four-scale bidirectional reflectance model based on canopyarchitecture. IEEE Transactions on Geoscience and Remote Sensing,1997,35:1316~1337
Clevers J G P W. The application of a weighted infrared-red vegetation index for estimating leafarea index by correcting for soil moisture. Remote Sensing of Environment,1989,29:25~37
Clevers J G P W and van Leeuwen H J C. Combined use of optical and microwave remote sensingdata forcrop growth monitoring. Remote Sensing of Environment,1996,56:42~51
Clevers J G P W. A simplified approach for yield prediction of sugar beet based on optical remotesensing data. Remote Sensing of Environment,1997,61(2):221~228
Darvishzadeha R, Skidmorea A, Schlerf M, et al. LAI and chlorophyll estimation for aheterogeneous grassland using hyperspectral measurements. ISPRS Journal ofPhotogrammetry and Remote Sensing,2008,63:409~426
Daughtry C S T, Walthall C L, Kim M S, et al. Estimating corn leaf chlorophyllconcentrationfrom leaf and canopy reflectance. Remote Sensing of Environment,2000,74(2):229~239
Dawson T P, Curran P J, North P R J, et al. LIBERTY: modeling the effects of leaf biochemistryon reflectance spectra. Remote Sensing of Environment,1998,65:50~60
De Wit C T. Photosynthesis of leaf canopies. Wageningen, Netherlands: Inst Biol Chem Res FieldCrops Herb.Agric ResRep,1965,663
De Wit C T. Dynamic concepts in biology. In, Prediction and Management of PhotosyntheticProductivity. Proceedings International Biological Program Plant Production TechnicalMeeting. Wageningen, Netherlands: PUDOC,1970,17~23
De Wit C T. Simulation of assimilation, respiration and transpiration of crops. SimulationMonographs.Wageningen, Netherlands: PUDOC,1978
Delecolle R, Maas S J, Guerif M, et al. Remote sensing and crop production models: presenttrends. ISPRS Journal of Photogrammetry&Remote Sensing,1992,47(2-3):145~161
Dente L, Satalino G, Mattia F, et al. Assimilation of leaf area index derived from ASAR andMERIS data into CERES-Wheat model to map wheat yield. Remote Sensing of Environment,2008,112(4):1395~1407
Doorenbos J and Pruitt W O. Guidelines for Predicting Crop Water Requirements. Rome, Italy:Food and Agriculture Organization of the United Nations,1977.
Doraiswamy P C, Hatfield J L, Jackson T J, et al. Crop condition andyield simulation usingLandsat and MODIS. Remote Sensing of Environment,2004,92:548~55
Duncan W G, Loomis R S, Williams W A, et al. A model forsimulatingphotosynthesis in plantcommunities.Hilgardia,1967,38:181~205
Fukshanky L, Fukshansky-Kazarinova N, Remisowsky A M V. Estimation of optical Parameters ina living tissue by solving the inverse Problem of the multiflux radiative transfer.Appl.opt,1991,30:3145~3153
Gamon J A and Qiu H. Ecological applications of remote sensing at multiple scales//Pugnaire FI,Valladares F, eds. Handbook of Functional Plan t Ecology. New York: Marcel Dekker,1999:805~846
Ganapol B, Johnson L, Hammer P, et al. LEAFMOD: a new within-leaf radiative transfer model.Remote Sens Environ,1998,6:18~193
Gao B C. NDWI-a normalized difference water index for remote sensing of vegetation liquidwater from space. Remote Sensing of Environment,1996,58(3):257~266
Gao L Z, Jin Z Q, Huang Y, et al., Rice clock model-a computer model to simulatericedevelopment. Agricultural and Forest Meteorology,1992,60:1~16
Gitelson A A, Kaufman Y J, Merzlyak M N. Use of a green channel in remote sensing of globalvegetation from EOS-MODIS. Remote Sensing of Environment,1996,58(3):289~298
Goel N S and Thompson R L. A snapshot of canopy reflectance models and a universal model forthe radiation regime. Remote Sens. Reviews,2000
Govaerts Y M, Jacquemoud S, Verstraete M M, et al. Three-dimensional radiation transfermodeling in a dicotyledon leaf, Appl. Opt,1996,35(33):6585~6598
Guerif M and Duke C L. Calibration of the SUCROS emergence and early growth module forsugar beet using optical remote sensing data assimilation. European Journal of Agronomy,1998,9(2-3):127~136
Guerif M and Duke C L. Adjustment procedures of a crop model to the site specific characteristicsof soil and crop using remote sensing data assimilation. Agriculture Ecosystems&Environment,2000,81(1):57~69
Haboudane D, Miller J R, Pattey E, et al. Hyperspectral vegetation indicesand novel algorithmsfor predicting Green Lai of Crop Canopies: Modeling and Validation in the Context ofPrecision Agriculture. Remote Sensing of Environment,2004,90(3):337~352
Hosgood B, Jacquemoud S, Andreoli G, et al. Leaf Optical Properties Experiment93(LOPEX93)Report EUR-16096-EN. European Commission, Joint Research Centre, Institute for RemoteSensing Applications, Ispra, Italy,1995
Horie T, Nakagawa H, Centeno H G S. The rice simulation model SIMRIW and its testing. In:Mattews RG, et al(ed.). Modeling the impact of climate change on rice in Asia. CABinternational, Wallingford, UK.1995,51~66
Huete A R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment,1988,25(3):295~309
Jackson R D, Reginato R T, Printer P J, et al. Plant canopy information extraction from compositescene reflectance of row crops. Appl. Opt,1979,18:3775~3782
Jacquemoud S and Baret F. Prospect-a model of leaf optical-properties spectra. Remote Sensingof Environment,1990,34(2):75~91
Jones C A and Kiniry J R. CERES-Maize:A Simulation Model of MaizeGrowth and Development.Texas A&MUniversity Press,College Station,TX.1986
Kennedy J and Eberhart R. Particle swarm optimization. Proceedings of IEEE InternationalConference on Neural Networks. Piscataway, NJ: IEEE Service Center:1995,1942~1948
Kennedy J. The particle swarm: Social adaptation ofknowledge. Proceeding of1997IEEEInternational Conference on Evolutionary Computation (ICEC‘97).1997:303~308
Knyazikhin Y et al. MODIS leaf area index(LAI) and fraction of photosynthetically activeradiation absorbed by vegetation (FPAR) product (MOD15) algorithm theoretical basisdocument, V4,1999
Lemmon H. COMAX: An expert system for cotton crop management. Science,1986,233,29~33
Li X, Strahler A H, Woodcock C E. A hybrid geometric optical-radiative transfer approach formodeling albedo and directional refleetance of diseontinuous canopies. IEEE Trans. Gcosei.RemoteSens,1995,33:466~480
Maas S J, Jackso R D, Idso S B. Incorporation of remotely-sensed indicators of water stress in acropgrowth simulation model. In, Preceedings of the19th Conference on Agriculture andForest Meteorology, Charleson, S. Carolina,1989:228~231
Maas S J. Parameterized model of gramineous crop growth: II. Within season simulationcalibration.Agronomy Journal,1993,85:354~358
Maas S J. Use of remotely sensed information in agricultural crop growth models. EcologicalModelling,1998,41(3-4):274~268
Maier S W, Ludeker W, Gunther K P. SLOP: A revised version of the stochastic Model for leafoptical Properties. Remote Sens.Enviro,1999,68(3):273~280
Mcown R L, Hammer G L, Hargreaves J N G. APSIM:a novelsoftware systemfor modeldevelopment, model testing, and simulationin agricultural systems research. AgriculturalSystems,1996,50:255~271
Montieth J L. The quest for balance in modeling.AgronomyJournal,1996,88:695~697
Moran S M, Maas S J, Pinter P P J. Combining remote sensing and modeling for estimatingsurface evaporation and biomass production. Remote Sensing Reviews,1995,12:335~353
Nijland W, Addink E A, De Jong S M, et al. Optimizing spatial image support for quantitativemapping of natural vegetation: Remote Sensing of Environment,2009,113:771~780
Nouvellon Y, Susan M M, Danny L S. Coupling a grassland ecosystem model with landsatimagery for a10-year simulation of carbon and water budgets. Remote sensing ofEnvironment,2001,78:131~149
Otter-Nacke S J, Ritchie J T, Godwin D, et al. A User’s Guide toCERES Barley-V2.10.International Fertilizer Development Centre, Muscle Shoals, Alabama, USA,1991
Penuelas J, Baret F, Filella I. Semi-empirical indices to assess cartotenoids/chlorophyll a ratiofrom leaf spectral reflectance.Photosynthetica,1995,31:221~230
Penuelas J, Pinol J, Ogaya R, et al. Estimation of plant water concentration by the reflectancewater index Wi (R900/R970). International Journal of Remote Sensing,1997,18:2869~2875
Penning de Vries F W T and van Laar H H. Simulation of growth processesand themodelBACROS. In:Penning de Vries F W T and van Laar HH(eds.),Simulation of plant growthand crop production. SimulationMonographs, PUDOC, Wageningen, The Netherlands,1982,114~135
Qi J, Chehbouni A, Huete A R, et al. A Modified Soil Vegetation Adjusted Index. Remote Sensingof Environment,1994,48:119~126
Reichle R H, Walker J P, Koster R D, et al. Extended versus Ensemble Kalman Filtering for landdata assimilation. Journal of Hydrometeorology,2002,3(6):728-740
Ritchie J T and Otter S. Description and performance of CERES-Wheat: Auser-oriented wheatyield model, USDA-ARS, ARS-38.1985:159~175
Ritchie J T, Alocijia E C, Uehara G. IBSNAT/CERES Rice Model.Agrotechnology Transfer,1986,3:1~5
Richter T and Fukshansky L. Optics of a bifacial leaf:1.A novel combined Procedure for derivingthe optical parameters. Photochem. Photobiol,1996,63:507~516
Rondeaux G, Steven M, Baret F. Optimization of soil adjusted vegetation indices. Remote Sensingof Environment,1996,55:95~107
Rouse J W, Haas R H, Shell J A, et al. Monitoring Vegetation Systems in the Great Plains withErts-1. In Third Earth Resources Technology Satellite Symposium,1973,309~317
Rougean J L and Breon F M. Estimating Par Absorbed by Vegetation from BidirectionalReflectance Measurements. Remote Sensing of Environment,1995,51:375~384
Sahu S K, Yip S, Holland D M. Improved space-time forecasting of next day ozone concentrationsin the eastern US. Atmospheric Environment,2009,43(3):494-501
Sims D A and Gamon J A. Estimation of vegetation water content and photosynthetic tissue areafrom spectral reflectance: A comparison of indices based on liquid water and chlorophyllabsorption features. Remote Sensing of Environment,2003,84:526~537
Spitters C J T, van Keulen H, van Kraalingen D W G. A simple anduniversal crop growth simulator:SUCROS8. In: Rabbinge R, Ward SA, van Laar H H(Eds.), Simulation and systemsmanagement incrop protection. Simulation Monographs, PUDOC, Wageningen, TheNetherlands,1989
Spitters C J T and Schapendonk A H C M. Evaluation of breeding strategies for drought tolerancein potato by means of crop growth simulation, Plant and Soil,1990,123:193~203
Strahler A H and Jupp D L B. Modeling bidirectional reflectance of forest and woodlands usingBoolean models and gcometrice optics, Remote Sensing of Environment,1990,34:153~160
Stroosnijder L. Simulation of the soil water balance. In F.W.T. Penning de Vries&H.H. van laar(Eds.),Simulation of plant growth and crop production. Simulation Monographs, PUDOC.Wageningen, Netherlands,1982,175~193
Suits G H. The calculation of the directional reflectance of vegetation canopy. Remote Sensing ofEnvironment,1972,2:117~125
Sun Y J, Wang J F, Zhang R H, et al. Air Temperature Retrieval from Remote Sensing Data Basedon Thermo dynamics. Theoretical and Applied Climatology,2005,80:37~48
Supit I. Predicting national wheat yields using a crop simulation and trend models. Agriculturaland Forest Meteorology,1997,88(1-4):199~214
Thorndike E L. Animal Intelligence: Empirical Studies[M]. New York: MacMillan,1911
Tucker C J and Garratt M M. Leaf optical system modeled as a stochastic Process. Applied Optics,1977,16(3):635~642
Verhoef W. Earth observation modeling based on layer scattering matrices. Remote Sensing ofEnvironment,1985,17(2):165~178
Vermote E F, Tanre D, Deuz J L, et al. Second simulation of the satellite signal in the solarspectrum,6S: anoverview. IEEE Trans Geo&Remote Sensing,1997,35(3):675~686
Verstraete M M, Pinty B. Designing optimal spectral indexes for remote sensing applications.IEEE Transactions on Geoscience and Remote Sensing,1996,34(5):1254~1265
Weiss M, Troufleau D, Baret F, et al. Coupling canopy functioning and radiative transfer modelsfor remote sensing data assimilation. Agricultural and Forest Meteorology,2001,108(2):113~128
Whitaker J S, H amill T M. Ensemble data as similation with out perturbed observations. Mon.Wea. Rev.,2002,130:1913~1924
Wiegand C L, Richardson A J, Kanemasu E T, et al., Leaf area index estimates for wheat fromLANDSAT and their implications for evapotranspiration and crop modeling. AgronomyJournal,1979,71:336~342
Wilkerson G G, Jones J W, Boote K J. Modeling soybean growth for crop management. Trans.ASAE,1983,26:63~73
Wit A M and van Diepen C A. Crop model data assimilation with the ensemble kalman filter forimproving regional crop yield forecasts. Agricultural and Forest Meteorology,2007,146(1-2):38~56
Wood C W, Reeves D W, Himelrick D G. Relationship between chlorophyll meter readings andleaf chlorophyll concentration, N status, and crop yield: a review. Proceedings of theAgronomy Society of New Zealand1993,23:1~9
Wu L, Liu X N, Wang P, et al. The assimilation of spectral sensing and the WOFOST model forthe dynamic simulation of cadmium accumulation in rice tissues. International Journal ofApplied Earth Observation and Geoinformation.2013,25:66~75
Xu L, Rosmond T, Goerss J, et al. Toward a weak constraint operational4D-Var system:Application to the Burgers’ equation. Meteorologische Zeitschrift,2007,16(6):741-753
曹卫星,罗卫红.作物系统模拟及智能管理.北京：华文出版社,2000
常丽英.水稻植株形态建成的模拟模型研究:[博士学位论文].南京：南京农业大学,2007
长春市统计局,长春统计年鉴2010.北京:中国统计出版社,2010
程乾,黄敬峰,王人潮. MODIS植被指数与水稻叶面积指数及叶片叶绿素含量相关性研究.应用生态学报,2004,15(8):1363~1367
陈振平,张建平,王春乙,等.应用WOFOST模型模拟低温与干旱对玉米产量的综合影响.中国农业气象,2007,28(4):440~442
陈劲松,黄建熙,林珲,等.基于遥感信息和作物生长模型同化的水稻估产方法研究.中国科学：信息科学,2010,40(增刊):173~183
陈莹莹.基于Noah模型的陆面模拟和陆面数据同化研究:[博士学位论文].北京:中国科学院研究生院,2008
代玉丽.应用环境星监测北部湾地区采伐和造林动态的研究.林业资源管理，2011,4:122~126
董晶晶,牛铮,沈艳,等.利用反射光谱信息提取叶片水分含量的方法比较.江西农业大学学报,2006,28(4):587~592
关丽,刘湘南.两种用于作物冠层叶绿素含量提取的改进光谱指数.地球科学进展,2009,24(5):91~97
国家统计局农村社会经济调查司.中国农村统计年鉴2010.北京:中国统计出版社,2010,137~152
郭建茂.基于遥感与作物生长模型的冬小麦生长模拟研究:[博士学位论文]．南京：南京信息工程大学,2007
郭琳,裴志远,张松龄,等.基于环境星CCD图像的甘蔗叶面积指数反演方法.农业工程学报,2010,26(10):201~205
郭文娟.基于MODIS和地物光谱的华北地区作物水分定量遥感研究:[硕士学位论文].北京：中国气象科学研究院,2006
何英彬.基于MODIS与TM的冷寒影响水稻产量评估研究:[博士学位论文]．北京：中国农业科学院,2007
何维.基于ASAR和生长模拟模型的水稻长势监测研究:[博士学位论文]．北京：中国林业科学研究院,2007
胡士强,敬忠良.粒子滤波算法综述.控制与决策,2005,20(4):361~365
黄春林,李新,卢玲.基于模拟退火算法的植被参数遥感反演.遥感技术与应用,2006,21(4):271~276
黄春林,李新.基于集合卡尔曼滤波的土壤水分同化试验.高原气象,2006,25(4):665~671
李存军,王纪华,王娴,等.遥感数据和作物生长模型集成方法与应用前景.农业工程学报,2008,24(11):295~301
李新,黄春林,车涛,等.中国陆面数据同化系统研究的进展与前瞻.自然科学进展,2007,17(2):163~173
林忠辉.河北平原夏玉米生长与水分利用效率的动态模拟:[博士学位论文].北京：中国科学院地理科学与资源研究所,2003
刘布春,王石立,马玉平.国外作物生长模型区域应用中升尺度问题的研究.中国生态农业学报,2003,11(4):89~91
刘荣高,刘纪远,庄大方.基于MODIS数据估算晴空陆地光合有效辐射.地理学报,2004,59(1):64~73
刘睿,孙九林,张金区,等.中国北方草地覆被的HJ星NDVI校正研究.草业学报,2011,20(1):189~198
马玉平,王石立,张黎.基于遥感信息的华北冬小麦区域生长模型及模拟研究.气象学报,2005a,63(2):204~215
马玉平,王石立,张黎,等.针对华北小麦越冬的WOFOST模型改进.中国农业气象,2005b,26(3):145~149
孟亚丽.基于过程的水稻生长模拟模型研究:[博士学位论文].南京：南京农业大学,2002
齐述华,王军邦,张庆员,等.利用MODIS遥感影像获取近地层气温的方法研究.遥感学报,2005,9(5):570~575
沈艳,牛铮,颜春燕.植被叶片及冠层层次含水量估算模型的建立.应用生态学报,2005,16(7):1218~1223
申双和,杨沈斌,李秉柏,等.基于ENVISATASAR数据的水稻估产方案.中国科学D辑：地球科学,2009,39(6):763~773
石春林.水稻形态建成模型及虚拟生长研究:[博士学位论文].南京:南京农业大学,2006
孙建华,齐琳琳,朱江,等.三维变分系统在我国夏季降雨预测中的应用试验.气候与环境研究,2007,12(3):413~425
谭正,刘湘南,张晓倩,等.作物生长模型同化SAR数据模拟作物生物量时域变化特征.中国农学通报,2011,27(27):161~167
王纯枝,宇振荣,辛景峰,等.基于遥感和作物生长模型的作物产量差估测.农业工程学报,2005,21(7):84~89
王航.基于遥感信息与模型耦合的水稻生长预测技术研究:[硕士学位论文].南京:南京农业大学,2011
王人潮,黄敬峰.水稻遥感估产.北京:中国农业出版社,2002.
王涛,吕昌河，于伯华.基于WOFOST模型的京津冀地区冬小麦生产潜力评估.自然资源学报,2010,25(3):475~487.
邬定荣,欧阳竹,赵小敏,等.作物生长模型WOFOST在华北平原的适用性研究.植物生态学报,2003,27(5):594~602
武志海,赵国臣,徐克章,等.吉林省过去47年来水稻品种遗传改良过程中叶片光合指标的变化.中国水稻科学,2009,23(2):165~171
吴伶,刘湘南,周博天,等.利用PROSPECT+SAIL模型反演植被生化参数的植被指数优化模拟分析.应用生态学报,2012a,23(12):3250~3256
吴伶,刘湘南,周博天,等.多源遥感与作物生长模型同化模拟作物生长参数时空域连续变化.遥感学报,2012b,16(6):1173~1191
谢文霞,严力蛟,王光火.运用WOFOST模型对浙江水稻潜在生长过程的模拟与验证.中国水稻科学,2006,20(3):319~323
辛景峰.基于3S技术与生长模型的作物长势监测与估产方法研究:[博士学位论文].北京：中国农业大学,2001
颜春燕,刘强,牛铮,等.植被生化组分的遥感反演方法研究.遥感学报,2004,8(4):300-308
闫岩,柳钦火,刘强,等.基于遥感数据与作物生长模型同化的冬小麦长势监测与估产方法研究.遥感学报,2006,10(5):804~811
杨鹏,吴文斌,周清波,等.基于作物生长模型与叶面积指数遥感影像同化的区域单产估测研究.农业工程学报,2007,23:130~136
杨曦光,范文义,于颖.基于PROSPECT+SAIL模型的森林冠层叶绿素含量反演.光谱学与光谱分析,2010,30(11):3022~3026
尹芳,江东,刘磊.基于环境星HIS影像的草地叶面积指数反演.遥感技术与应用，2011,26(3):360~364
宇振荣, Driessen P M.基于遥感反演作物冠层温度的作物生长模拟和预报.中国农业大学学报,2003,8(增刊):71~75
袁金国,牛铮.基于Hyperion高光谱图像的氮和叶绿素制图.农业工程学报,2007,23(4):172~179
张竞成,顾晓鹤,王纪华,等.基于HJ-CCD与TM影像的水稻LAI估测一致性分析.农业工程学报,2010,26(7):186~193
张建平,王春乙,赵艳霞,等.基于作物生长模型的低温冷寒对我国东北三省玉米产量影响评估.生态学报,2012,32(13):4132~4138
张华,薛纪善,庄世宇,等. GRAPES三维变分同化系统的立项试验.气象学报,2004,62(1):31~41
赵艳霞.遥感信息与作物生长模型结合方法研究及初步应用:[博士学位论文].北京：北京大学,2005
钟仕全,莫建飞,陈燕丽,等.基于HJ-1B卫星遥感数据的水稻识别技术研究.遥感技术与应用,2010,25(4):464~468
朱元励,朱艳,黄彦,等.应用粒子群算法的遥感信息与水稻生长模型同化技术.遥感学报,2010,14(6):1226~1240
Allen W A and Richardson A J. Interaction of light with a plant canopy. Journal of the OpticalSociety of America,1968,58:1023~1028
Allen W A, Gausman H W, Rchardson A J, et al. Interaction of Isotropic Light with a compactplant. Journal of the Optical Society of America,1969,59(10):1376~1379
Allen W A, Gausman H W, Rchardson A J. Willstatter-stoll theory of leaf reflectance evaluated byray-tracing. Appl.Opt,1973,12:2448~2553
Bach H, Verhoef W, Schneider K. Coupling remote sensing observation models and a growthmodel for improved retrieval of (Geo) Biophysical Information from Optical remote sensingData. In, remote sensing for agriculture, ecosystems, and hydrology II. Barcelona, Spain,2000,1~11
Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory [M]. NewJersey: PrenticeHall,1986
Boogaard H L, Van D C A, R tter R P, et al. User’s Guide for the WOFOST7.1Crop GrowthSimulation Model and WOFOST Control Center1.5. IDLO Wageningen: Winand StaringCentre:1998,1~40
Borge NH, Leblanc E. Comparing prediction power and stability of broadband and hyperspectralvegetation indices for estimation of green leaf area index and canopy chlorophyll density.Remote Sensing of Environment,2001,76:156~172
Bouman B A M. Linking physical remote sensing models with crop growth simulation models,applied for sugar beet. International Journal of Remote Sensing,1992,13:2565~2581
Bouman B A M, van Keulen H, van Laar H H. The school of de Wit crop growth simulationmodels: Apedigree and historical overview. Agricultural System,1996,52:171~198
Bouman B A M, Kropff M J, Tuong T P, et al. ORYZA2000: Modeling lowland rice. Los Banos:IRRI and Wageningen University,2001,23~77
Brisson N, Gary C, Justes E, et al. An overview of the crop model STICS. European journal ofagronomy,2003,18:309~332
Bunkei M and Masayuki T. Integrating remotely sensed data with an ecosystem model to estimatenet primary productivity in East Asia. Remote sensing of Environment,2002,81:58~6
Carbone G J, Narumalani S, King M. Application of remote sensing and GIS technologies withphysiological crop models. Photogrametric Engineering and Remote Sensing,1996,62(2):171~179
Carder K et al. Instantaneous photosynthetically available radiation and absorbed radiation byphytoplankton, ATBD-MOD-20, V5,1999
Chen J. Evaluation of vegetation indices and modified simple ratiofor boreal applications. Can. J.Remote Sens.,1996,22:229~242
Chen J M and Leblanc S G. A four-scale bidirectional reflectance model based on canopyarchitecture. IEEE Transactions on Geoscience and Remote Sensing,1997,35:1316~1337
Clevers J G P W. The application of a weighted infrared-red vegetation index for estimating leafarea index by correcting for soil moisture. Remote Sensing of Environment,1989,29:25~37
Clevers J G P W and van Leeuwen H J C. Combined use of optical and microwave remote sensingdata forcrop growth monitoring. Remote Sensing of Environment,1996,56:42~51
Clevers J G P W. A simplified approach for yield prediction of sugar beet based on optical remotesensing data. Remote Sensing of Environment,1997,61(2):221~228
Darvishzadeha R, Skidmorea A, Schlerf M, et al. LAI and chlorophyll estimation for aheterogeneous grassland using hyperspectral measurements. ISPRS Journal ofPhotogrammetry and Remote Sensing,2008,63:409~426
Daughtry C S T, Walthall C L, Kim M S, et al. Estimating corn leaf chlorophyllconcentrationfrom leaf and canopy reflectance. Remote Sensing of Environment,2000,74(2):229~239
Dawson T P, Curran P J, North P R J, et al. LIBERTY: modeling the effects of leaf biochemistryon reflectance spectra. Remote Sensing of Environment,1998,65:50~60
De Wit C T. Photosynthesis of leaf canopies. Wageningen, Netherlands: Inst Biol Chem Res FieldCrops Herb.Agric ResRep,1965,663
De Wit C T. Dynamic concepts in biology. In, Prediction and Management of PhotosyntheticProductivity. Proceedings International Biological Program Plant Production TechnicalMeeting. Wageningen, Netherlands: PUDOC,1970,17~23
De Wit C T. Simulation of assimilation, respiration and transpiration of crops. SimulationMonographs.Wageningen, Netherlands: PUDOC,1978
Delecolle R, Maas S J, Guerif M, et al. Remote sensing and crop production models: presenttrends. ISPRS Journal of Photogrammetry&Remote Sensing,1992,47(2-3):145~161
Dente L, Satalino G, Mattia F, et al. Assimilation of leaf area index derived from ASAR andMERIS data into CERES-Wheat model to map wheat yield. Remote Sensing of Environment,2008,112(4):1395~1407
Doorenbos J and Pruitt W O. Guidelines for Predicting Crop Water Requirements. Rome, Italy:Food and Agriculture Organization of the United Nations,1977.
Doraiswamy P C, Hatfield J L, Jackson T J, et al. Crop condition andyield simulation usingLandsat and MODIS. Remote Sensing of Environment,2004,92:548~55
Duncan W G, Loomis R S, Williams W A, et al. A model forsimulatingphotosynthesis in plantcommunities.Hilgardia,1967,38:181~205
Fukshanky L, Fukshansky-Kazarinova N, Remisowsky A M V. Estimation of optical Parameters ina living tissue by solving the inverse Problem of the multiflux radiative transfer.Appl.opt,1991,30:3145~3153
Gamon J A and Qiu H. Ecological applications of remote sensing at multiple scales//Pugnaire FI,Valladares F, eds. Handbook of Functional Plan t Ecology. New York: Marcel Dekker,1999:805~846
Ganapol B, Johnson L, Hammer P, et al. LEAFMOD: a new within-leaf radiative transfer model.Remote Sens Environ,1998,6:18~193
Gao B C. NDWI-a normalized difference water index for remote sensing of vegetation liquidwater from space. Remote Sensing of Environment,1996,58(3):257~266
Gao L Z, Jin Z Q, Huang Y, et al., Rice clock model-a computer model to simulatericedevelopment. Agricultural and Forest Meteorology,1992,60:1~16
Gitelson A A, Kaufman Y J, Merzlyak M N. Use of a green channel in remote sensing of globalvegetation from EOS-MODIS. Remote Sensing of Environment,1996,58(3):289~298
Goel N S and Thompson R L. A snapshot of canopy reflectance models and a universal model forthe radiation regime. Remote Sens. Reviews,2000
Govaerts Y M, Jacquemoud S, Verstraete M M, et al. Three-dimensional radiation transfermodeling in a dicotyledon leaf, Appl. Opt,1996,35(33):6585~6598
Guerif M and Duke C L. Calibration of the SUCROS emergence and early growth module forsugar beet using optical remote sensing data assimilation. European Journal of Agronomy,1998,9(2-3):127~136
Guerif M and Duke C L. Adjustment procedures of a crop model to the site specific characteristicsof soil and crop using remote sensing data assimilation. Agriculture Ecosystems&Environment,2000,81(1):57~69
Haboudane D, Miller J R, Pattey E, et al. Hyperspectral vegetation indicesand novel algorithmsfor predicting Green Lai of Crop Canopies: Modeling and Validation in the Context ofPrecision Agriculture. Remote Sensing of Environment,2004,90(3):337~352
Hosgood B, Jacquemoud S, Andreoli G, et al. Leaf Optical Properties Experiment93(LOPEX93)Report EUR-16096-EN. European Commission, Joint Research Centre, Institute for RemoteSensing Applications, Ispra, Italy,1995
Horie T, Nakagawa H, Centeno H G S. The rice simulation model SIMRIW and its testing. In:Mattews RG, et al(ed.). Modeling the impact of climate change on rice in Asia. CABinternational, Wallingford, UK.1995,51~66
Huete A R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment,1988,25(3):295~309
Jackson R D, Reginato R T, Printer P J, et al. Plant canopy information extraction from compositescene reflectance of row crops. Appl. Opt,1979,18:3775~3782
Jacquemoud S and Baret F. Prospect-a model of leaf optical-properties spectra. Remote Sensingof Environment,1990,34(2):75~91
Jones C A and Kiniry J R. CERES-Maize:A Simulation Model of MaizeGrowth and Development.Texas A&MUniversity Press,College Station,TX.1986
Kennedy J and Eberhart R. Particle swarm optimization. Proceedings of IEEE InternationalConference on Neural Networks. Piscataway, NJ: IEEE Service Center:1995,1942~1948
Kennedy J. The particle swarm: Social adaptation ofknowledge. Proceeding of1997IEEEInternational Conference on Evolutionary Computation (ICEC‘97).1997:303~308
Knyazikhin Y et al. MODIS leaf area index(LAI) and fraction of photosynthetically activeradiation absorbed by vegetation (FPAR) product (MOD15) algorithm theoretical basisdocument, V4,1999
Lemmon H. COMAX: An expert system for cotton crop management. Science,1986,233,29~33
Li X, Strahler A H, Woodcock C E. A hybrid geometric optical-radiative transfer approach formodeling albedo and directional refleetance of diseontinuous canopies. IEEE Trans. Gcosei.RemoteSens,1995,33:466~480
Maas S J, Jackso R D, Idso S B. Incorporation of remotely-sensed indicators of water stress in acropgrowth simulation model. In, Preceedings of the19th Conference on Agriculture andForest Meteorology, Charleson, S. Carolina,1989:228~231
Maas S J. Parameterized model of gramineous crop growth: II. Within season simulationcalibration.Agronomy Journal,1993,85:354~358
Maas S J. Use of remotely sensed information in agricultural crop growth models. EcologicalModelling,1998,41(3-4):274~268
Maier S W, Ludeker W, Gunther K P. SLOP: A revised version of the stochastic Model for leafoptical Properties. Remote Sens.Enviro,1999,68(3):273~280
Mcown R L, Hammer G L, Hargreaves J N G. APSIM:a novelsoftware systemfor modeldevelopment, model testing, and simulationin agricultural systems research. AgriculturalSystems,1996,50:255~271
Montieth J L. The quest for balance in modeling.AgronomyJournal,1996,88:695~697
Moran S M, Maas S J, Pinter P P J. Combining remote sensing and modeling for estimatingsurface evaporation and biomass production. Remote Sensing Reviews,1995,12:335~353
Nijland W, Addink E A, De Jong S M, et al. Optimizing spatial image support for quantitativemapping of natural vegetation: Remote Sensing of Environment,2009,113:771~780
Nouvellon Y, Susan M M, Danny L S. Coupling a grassland ecosystem model with landsatimagery for a10-year simulation of carbon and water budgets. Remote sensing ofEnvironment,2001,78:131~149
Otter-Nacke S J, Ritchie J T, Godwin D, et al. A User’s Guide toCERES Barley-V2.10.International Fertilizer Development Centre, Muscle Shoals, Alabama, USA,1991
Penuelas J, Baret F, Filella I. Semi-empirical indices to assess cartotenoids/chlorophyll a ratiofrom leaf spectral reflectance.Photosynthetica,1995,31:221~230
Penuelas J, Pinol J, Ogaya R, et al. Estimation of plant water concentration by the reflectancewater index Wi (R900/R970). International Journal of Remote Sensing,1997,18:2869~2875
Penning de Vries F W T and van Laar H H. Simulation of growth processesand themodelBACROS. In:Penning de Vries F W T and van Laar HH(eds.),Simulation of plant growthand crop production. SimulationMonographs, PUDOC, Wageningen, The Netherlands,1982,114~135
Qi J, Chehbouni A, Huete A R, et al. A Modified Soil Vegetation Adjusted Index. Remote Sensingof Environment,1994,48:119~126
Reichle R H, Walker J P, Koster R D, et al. Extended versus Ensemble Kalman Filtering for landdata assimilation. Journal of Hydrometeorology,2002,3(6):728-740
Ritchie J T and Otter S. Description and performance of CERES-Wheat: Auser-oriented wheatyield model, USDA-ARS, ARS-38.1985:159~175
Ritchie J T, Alocijia E C, Uehara G. IBSNAT/CERES Rice Model.Agrotechnology Transfer,1986,3:1~5
Richter T and Fukshansky L. Optics of a bifacial leaf:1.A novel combined Procedure for derivingthe optical parameters. Photochem. Photobiol,1996,63:507~516
Rondeaux G, Steven M, Baret F. Optimization of soil adjusted vegetation indices. Remote Sensingof Environment,1996,55:95~107
Rouse J W, Haas R H, Shell J A, et al. Monitoring Vegetation Systems in the Great Plains withErts-1. In Third Earth Resources Technology Satellite Symposium,1973,309~317
Rougean J L and Breon F M. Estimating Par Absorbed by Vegetation from BidirectionalReflectance Measurements. Remote Sensing of Environment,1995,51:375~384
Sahu S K, Yip S, Holland D M. Improved space-time forecasting of next day ozone concentrationsin the eastern US. Atmospheric Environment,2009,43(3):494-501
Sims D A and Gamon J A. Estimation of vegetation water content and photosynthetic tissue areafrom spectral reflectance: A comparison of indices based on liquid water and chlorophyllabsorption features. Remote Sensing of Environment,2003,84:526~537
Spitters C J T, van Keulen H, van Kraalingen D W G. A simple anduniversal crop growth simulator:SUCROS8. In: Rabbinge R, Ward SA, van Laar H H(Eds.), Simulation and systemsmanagement incrop protection. Simulation Monographs, PUDOC, Wageningen, TheNetherlands,1989
Spitters C J T and Schapendonk A H C M. Evaluation of breeding strategies for drought tolerancein potato by means of crop growth simulation, Plant and Soil,1990,123:193~203
Strahler A H and Jupp D L B. Modeling bidirectional reflectance of forest and woodlands usingBoolean models and gcometrice optics, Remote Sensing of Environment,1990,34:153~160
Stroosnijder L. Simulation of the soil water balance. In F.W.T. Penning de Vries&H.H. van laar(Eds.),Simulation of plant growth and crop production. Simulation Monographs, PUDOC.Wageningen, Netherlands,1982,175~193
Suits G H. The calculation of the directional reflectance of vegetation canopy. Remote Sensing ofEnvironment,1972,2:117~125
Sun Y J, Wang J F, Zhang R H, et al. Air Temperature Retrieval from Remote Sensing Data Basedon Thermo dynamics. Theoretical and Applied Climatology,2005,80:37~48
Supit I. Predicting national wheat yields using a crop simulation and trend models. Agriculturaland Forest Meteorology,1997,88(1-4):199~214
Thorndike E L. Animal Intelligence: Empirical Studies[M]. New York: MacMillan,1911
Tucker C J and Garratt M M. Leaf optical system modeled as a stochastic Process. Applied Optics,1977,16(3):635~642
Verhoef W. Earth observation modeling based on layer scattering matrices. Remote Sensing ofEnvironment,1985,17(2):165~178
Vermote E F, Tanre D, Deuz J L, et al. Second simulation of the satellite signal in the solarspectrum,6S: anoverview. IEEE Trans Geo&Remote Sensing,1997,35(3):675~686
Verstraete M M, Pinty B. Designing optimal spectral indexes for remote sensing applications.IEEE Transactions on Geoscience and Remote Sensing,1996,34(5):1254~1265
Weiss M, Troufleau D, Baret F, et al. Coupling canopy functioning and radiative transfer modelsfor remote sensing data assimilation. Agricultural and Forest Meteorology,2001,108(2):113~128
Whitaker J S, H amill T M. Ensemble data as similation with out perturbed observations. Mon.Wea. Rev.,2002,130:1913~1924
Wiegand C L, Richardson A J, Kanemasu E T, et al., Leaf area index estimates for wheat fromLANDSAT and their implications for evapotranspiration and crop modeling. AgronomyJournal,1979,71:336~342
Wilkerson G G, Jones J W, Boote K J. Modeling soybean growth for crop management. Trans.ASAE,1983,26:63~73
Wit A M and van Diepen C A. Crop model data assimilation with the ensemble kalman filter forimproving regional crop yield forecasts. Agricultural and Forest Meteorology,2007,146(1-2):38~56
Wood C W, Reeves D W, Himelrick D G. Relationship between chlorophyll meter readings andleaf chlorophyll concentration, N status, and crop yield: a review. Proceedings of theAgronomy Society of New Zealand1993,23:1~9
Wu L, Liu X N, Wang P, et al. The assimilation of spectral sensing and the WOFOST model forthe dynamic simulation of cadmium accumulation in rice tissues. International Journal ofApplied Earth Observation and Geoinformation.2013,25:66~75
Xu L, Rosmond T, Goerss J, et al. Toward a weak constraint operational4D-Var system:Application to the Burgers’ equation. Meteorologische Zeitschrift,2007,16(6):741-753
曹卫星,罗卫红.作物系统模拟及智能管理.北京：华文出版社,2000
常丽英.水稻植株形态建成的模拟模型研究:[博士学位论文].南京：南京农业大学,2007
长春市统计局,长春统计年鉴2010.北京:中国统计出版社,2010
程乾,黄敬峰,王人潮. MODIS植被指数与水稻叶面积指数及叶片叶绿素含量相关性研究.应用生态学报,2004,15(8):1363~1367
陈振平,张建平,王春乙,等.应用WOFOST模型模拟低温与干旱对玉米产量的综合影响.中国农业气象,2007,28(4):440~442
陈劲松,黄建熙,林珲,等.基于遥感信息和作物生长模型同化的水稻估产方法研究.中国科学：信息科学,2010,40(增刊):173~183
陈莹莹.基于Noah模型的陆面模拟和陆面数据同化研究:[博士学位论文].北京:中国科学院研究生院,2008
代玉丽.应用环境星监测北部湾地区采伐和造林动态的研究.林业资源管理，2011,4:122~126
董晶晶,牛铮,沈艳,等.利用反射光谱信息提取叶片水分含量的方法比较.江西农业大学学报,2006,28(4):587~592
关丽,刘湘南.两种用于作物冠层叶绿素含量提取的改进光谱指数.地球科学进展,2009,24(5):91~97
国家统计局农村社会经济调查司.中国农村统计年鉴2010.北京:中国统计出版社,2010,137~152
郭建茂.基于遥感与作物生长模型的冬小麦生长模拟研究:[博士学位论文]．南京：南京信息工程大学,2007
郭琳,裴志远,张松龄,等.基于环境星CCD图像的甘蔗叶面积指数反演方法.农业工程学报,2010,26(10):201~205
郭文娟.基于MODIS和地物光谱的华北地区作物水分定量遥感研究:[硕士学位论文].北京：中国气象科学研究院,2006
何英彬.基于MODIS与TM的冷寒影响水稻产量评估研究:[博士学位论文]．北京：中国农业科学院,2007
何维.基于ASAR和生长模拟模型的水稻长势监测研究:[博士学位论文]．北京：中国林业科学研究院,2007
胡士强,敬忠良.粒子滤波算法综述.控制与决策,2005,20(4):361~365
黄春林,李新,卢玲.基于模拟退火算法的植被参数遥感反演.遥感技术与应用,2006,21(4):271~276
黄春林,李新.基于集合卡尔曼滤波的土壤水分同化试验.高原气象,2006,25(4):665~671
李存军,王纪华,王娴,等.遥感数据和作物生长模型集成方法与应用前景.农业工程学报,2008,24(11):295~301
李新,黄春林,车涛,等.中国陆面数据同化系统研究的进展与前瞻.自然科学进展,2007,17(2):163~173
林忠辉.河北平原夏玉米生长与水分利用效率的动态模拟:[博士学位论文].北京：中国科学院地理科学与资源研究所,2003
刘布春,王石立,马玉平.国外作物生长模型区域应用中升尺度问题的研究.中国生态农业学报,2003,11(4):89~91
刘荣高,刘纪远,庄大方.基于MODIS数据估算晴空陆地光合有效辐射.地理学报,2004,59(1):64~73
刘睿,孙九林,张金区,等.中国北方草地覆被的HJ星NDVI校正研究.草业学报,2011,20(1):189~198
马玉平,王石立,张黎.基于遥感信息的华北冬小麦区域生长模型及模拟研究.气象学报,2005a,63(2):204~215
马玉平,王石立,张黎,等.针对华北小麦越冬的WOFOST模型改进.中国农业气象,2005b,26(3):145~149
孟亚丽.基于过程的水稻生长模拟模型研究:[博士学位论文].南京：南京农业大学,2002
齐述华,王军邦,张庆员,等.利用MODIS遥感影像获取近地层气温的方法研究.遥感学报,2005,9(5):570~575
沈艳,牛铮,颜春燕.植被叶片及冠层层次含水量估算模型的建立.应用生态学报,2005,16(7):1218~1223
申双和,杨沈斌,李秉柏,等.基于ENVISATASAR数据的水稻估产方案.中国科学D辑：地球科学,2009,39(6):763~773
石春林.水稻形态建成模型及虚拟生长研究:[博士学位论文].南京:南京农业大学,2006
孙建华,齐琳琳,朱江,等.三维变分系统在我国夏季降雨预测中的应用试验.气候与环境研究,2007,12(3):413~425
谭正,刘湘南,张晓倩,等.作物生长模型同化SAR数据模拟作物生物量时域变化特征.中国农学通报,2011,27(27):161~167
王纯枝,宇振荣,辛景峰,等.基于遥感和作物生长模型的作物产量差估测.农业工程学报,2005,21(7):84~89
王航.基于遥感信息与模型耦合的水稻生长预测技术研究:[硕士学位论文].南京:南京农业大学,2011
王人潮,黄敬峰.水稻遥感估产.北京:中国农业出版社,2002.
王涛,吕昌河，于伯华.基于WOFOST模型的京津冀地区冬小麦生产潜力评估.自然资源学报,2010,25(3):475~487.
邬定荣,欧阳竹,赵小敏,等.作物生长模型WOFOST在华北平原的适用性研究.植物生态学报,2003,27(5):594~602
武志海,赵国臣,徐克章,等.吉林省过去47年来水稻品种遗传改良过程中叶片光合指标的变化.中国水稻科学,2009,23(2):165~171
吴伶,刘湘南,周博天,等.利用PROSPECT+SAIL模型反演植被生化参数的植被指数优化模拟分析.应用生态学报,2012a,23(12):3250~3256
吴伶,刘湘南,周博天,等.多源遥感与作物生长模型同化模拟作物生长参数时空域连续变化.遥感学报,2012b,16(6):1173~1191
谢文霞,严力蛟,王光火.运用WOFOST模型对浙江水稻潜在生长过程的模拟与验证.中国水稻科学,2006,20(3):319~323
辛景峰.基于3S技术与生长模型的作物长势监测与估产方法研究:[博士学位论文].北京：中国农业大学,2001
颜春燕,刘强,牛铮,等.植被生化组分的遥感反演方法研究.遥感学报,2004,8(4):300-308
闫岩,柳钦火,刘强,等.基于遥感数据与作物生长模型同化的冬小麦长势监测与估产方法研究.遥感学报,2006,10(5):804~811
杨鹏,吴文斌,周清波,等.基于作物生长模型与叶面积指数遥感影像同化的区域单产估测研究.农业工程学报,2007,23:130~136
杨曦光,范文义,于颖.基于PROSPECT+SAIL模型的森林冠层叶绿素含量反演.光谱学与光谱分析,2010,30(11):3022~3026
尹芳,江东,刘磊.基于环境星HIS影像的草地叶面积指数反演.遥感技术与应用，2011,26(3):360~364
宇振荣, Driessen P M.基于遥感反演作物冠层温度的作物生长模拟和预报.中国农业大学学报,2003,8(增刊):71~75
袁金国,牛铮.基于Hyperion高光谱图像的氮和叶绿素制图.农业工程学报,2007,23(4):172~179
张竞成,顾晓鹤,王纪华,等.基于HJ-CCD与TM影像的水稻LAI估测一致性分析.农业工程学报,2010,26(7):186~193
张建平,王春乙,赵艳霞,等.基于作物生长模型的低温冷寒对我国东北三省玉米产量影响评估.生态学报,2012,32(13):4132~4138
张华,薛纪善,庄世宇,等. GRAPES三维变分同化系统的立项试验.气象学报,2004,62(1):31~41
赵艳霞.遥感信息与作物生长模型结合方法研究及初步应用:[博士学位论文].北京：北京大学,2005
钟仕全,莫建飞,陈燕丽,等.基于HJ-1B卫星遥感数据的水稻识别技术研究.遥感技术与应用,2010,25(4):464~468
朱元励,朱艳,黄彦,等.应用粒子群算法的遥感信息与水稻生长模型同化技术.遥感学报,2010,14(6):1226~1240
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |