柑橘果园四项数字化信息的模型构建
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摘要
近年来,伴随着全球气候变化,极端气候频频发生,环境条件对柑橘产业所引起的负面影响日益严重。我国柑橘生产基地大多位于丘陵山地,气候和土壤条件变化大,设计状况复杂,监控和产业信息收集不易,管理调控措施困难。随着当前果农年龄老化程度不断提高,人们对未来柑橘产业监控、管理的信息化技术需求日益增强。本文对相柑橘园几项数字监控技术进行了研究,主要结果如下：
1.采用逐步回归法进行电导率和光谱反射率关系反演模拟,用原始光谱进行全波段相关性分析,然后对可见光和近红外两个波段的原始光谱分别以最小二乘法进行逐步回归,建立电导率为因变量的多元线性回归方程,并根据回归系数的显著性测验对模型进行检验。将日标波段锁定到近红外波段,进一步用移动平均法和标准归一化对近红外光谱进行校正分别建立逐步回归模型,经比较最终确定了模型为近红外波段的标准归一化的回归模型,得到较理想的电导率-光谱反射率模型：Y=0.465-6.193×K1528+9.059×K1891+7.161×K1682-32.388×K1837+22.27×K1830.
2、对不同生长时期的柑橘叶片(叶绿素含量不同)进行光谱扫描,采用了逐步回归法、红边参数法和光谱指数法分析了叶片光谱反射率和叶绿素含量之间的关系,构建了柑橘叶片叶绿素光谱反射模型。模型表明：柑橘叶片叶绿素含量与反射光谱之间有较强的相关性,模型预测值与实测值的相对误差都小于10%,说明模型具有良好的预测效果；选择波段的逐步回归法比后者的精度更高,但从建模参数的物理意义和逻辑性方面考虑,推荐光谱指数法建模；叶绿素光谱模型如下：Y=73.491+36.718×GNDVI-14.933×r。
3、对不同水分状况的柑橘叶片进行光谱扫描,采用光谱逐步回归分析和构造光谱指数两种方法分析了叶片光谱(380—2500nm)反射率与含水量之间的关系。构建了柑橘叶片含水量光谱反射率模型。研究结果表明：近红外光谱对叶片水分的模拟效果最好；光谱逐步回归分析所得模型精度高于构造光谱指数法；从物理含义和逻辑性方而考虑,推荐光谱指数法构建的模型：Y=-2.741×SR-5.979×NDVI+0.62×WI2+2.122
With the global climate change,extreme climate occurred frequently. The changes of environment conditions bring a worse impact to Citrus industry in recent years, it is difficult to devise, monitor and manage the orchard, built in hills. As the aging level of orchardists accelerates, informationize. Here is the result:
1^ The SPSS soft software is used for modeling using stepwise regression me thod which is base of the least square method. At first we make a correlation ana lysis of whole spectral reflectance using the original spectral reflectance, finding th at there are some modeling factors both in the visible spectra and the near-infrare d spectra. Then we make two stepwise regression for the visible spectra and the n ear-infrared spectra respectively, finding that he near-infrared spectra is better for t his model. At last we get the model using several spectral pretreatments, Y=0.465-6.193×K 1528.61+9.059×K1891.31+7.161×K 1682.74-32.388xK 1837.38+22.27xK 1830.64.
2、The paper studied the relationship between leaf chlorophyll content and lea f reflectance, and established models of leaves chlorophyll content based on spectr a with stepwise regression method and constructing spectral indexes method respec tively. The results showed there was good relevance between SPAD and reflection spectrum of Citrus leaves. The relative errors between predictive and observed mo dels with two methods were all less than 10%. It is clear that the two models obt ained nice forecasting results, and the model established with stepwise regression method had higher precision than the model with another method. Considering the physical meaning and logic, the model established with constructing spectral inde xes method was recommended. Y=73.491+36.718×gndvi-14.933×r.
3、The paper studied the relationship between leaf water content and leaf refl cctance, and established models of leaves water content based on spectra with step wise regression method and constructing spectral indexes method respectively. The results showed there was good relevance between water content and reflection spec trum of Citrus leaves. The relative errors between predictive and observed models with two methods were all less than 10%. It made clear that models obtained bett er forecasting results, and the model established with stepwise regression method h ad higher precision than the model with another method. Considering the physical meaning and logic, the model established with constructing spectral indexes metho d was recommended. Y=-2.741 xSR-5.979×NDVI+0.62×WI2+2.122.
1.采用逐步回归法进行电导率和光谱反射率关系反演模拟,用原始光谱进行全波段相关性分析,然后对可见光和近红外两个波段的原始光谱分别以最小二乘法进行逐步回归,建立电导率为因变量的多元线性回归方程,并根据回归系数的显著性测验对模型进行检验。将日标波段锁定到近红外波段,进一步用移动平均法和标准归一化对近红外光谱进行校正分别建立逐步回归模型,经比较最终确定了模型为近红外波段的标准归一化的回归模型,得到较理想的电导率-光谱反射率模型：Y=0.465-6.193×K1528+9.059×K1891+7.161×K1682-32.388×K1837+22.27×K1830.
2、对不同生长时期的柑橘叶片(叶绿素含量不同)进行光谱扫描,采用了逐步回归法、红边参数法和光谱指数法分析了叶片光谱反射率和叶绿素含量之间的关系,构建了柑橘叶片叶绿素光谱反射模型。模型表明：柑橘叶片叶绿素含量与反射光谱之间有较强的相关性,模型预测值与实测值的相对误差都小于10%,说明模型具有良好的预测效果；选择波段的逐步回归法比后者的精度更高,但从建模参数的物理意义和逻辑性方面考虑,推荐光谱指数法建模；叶绿素光谱模型如下：Y=73.491+36.718×GNDVI-14.933×r。
3、对不同水分状况的柑橘叶片进行光谱扫描,采用光谱逐步回归分析和构造光谱指数两种方法分析了叶片光谱(380—2500nm)反射率与含水量之间的关系。构建了柑橘叶片含水量光谱反射率模型。研究结果表明：近红外光谱对叶片水分的模拟效果最好；光谱逐步回归分析所得模型精度高于构造光谱指数法；从物理含义和逻辑性方而考虑,推荐光谱指数法构建的模型：Y=-2.741×SR-5.979×NDVI+0.62×WI2+2.122
With the global climate change,extreme climate occurred frequently. The changes of environment conditions bring a worse impact to Citrus industry in recent years, it is difficult to devise, monitor and manage the orchard, built in hills. As the aging level of orchardists accelerates, informationize. Here is the result:
1^ The SPSS soft software is used for modeling using stepwise regression me thod which is base of the least square method. At first we make a correlation ana lysis of whole spectral reflectance using the original spectral reflectance, finding th at there are some modeling factors both in the visible spectra and the near-infrare d spectra. Then we make two stepwise regression for the visible spectra and the n ear-infrared spectra respectively, finding that he near-infrared spectra is better for t his model. At last we get the model using several spectral pretreatments, Y=0.465-6.193×K 1528.61+9.059×K1891.31+7.161×K 1682.74-32.388xK 1837.38+22.27xK 1830.64.
2、The paper studied the relationship between leaf chlorophyll content and lea f reflectance, and established models of leaves chlorophyll content based on spectr a with stepwise regression method and constructing spectral indexes method respec tively. The results showed there was good relevance between SPAD and reflection spectrum of Citrus leaves. The relative errors between predictive and observed mo dels with two methods were all less than 10%. It is clear that the two models obt ained nice forecasting results, and the model established with stepwise regression method had higher precision than the model with another method. Considering the physical meaning and logic, the model established with constructing spectral inde xes method was recommended. Y=73.491+36.718×gndvi-14.933×r.
3、The paper studied the relationship between leaf water content and leaf refl cctance, and established models of leaves water content based on spectra with step wise regression method and constructing spectral indexes method respectively. The results showed there was good relevance between water content and reflection spec trum of Citrus leaves. The relative errors between predictive and observed models with two methods were all less than 10%. It made clear that models obtained bett er forecasting results, and the model established with stepwise regression method h ad higher precision than the model with another method. Considering the physical meaning and logic, the model established with constructing spectral indexes metho d was recommended. Y=-2.741 xSR-5.979×NDVI+0.62×WI2+2.122.
引文
1.包应时,近红外光谱分析技术在农业与食品无损检测上的应用.农业化研究,2006,10(10)：167-172.
2.陈建.以农业信息推动农业现代化.山东农机,2004,(12)：7-9
3.陈在新,刘海蓉.柑橘品种抗寒性生理指标的比较研究,安徽农业科学,2005,33(7)：1204-1206
4.程一松,胡春胜,郝二波.氮素胁迫下的冬小麦高光潜特征提取与分析.资源科学,2003,25(1)：86-93.
5.褚小立,袁洪福.近红外分析中光谱预处理及波长选择方法进展与应用.化学进展,2004,16(4)：528-542.
6.邓烈,何绍兰,易时来.三峡库区秋季柑橘园主要地物叶片的光谱特征研究.光谱学与光谱分析,2008.10：135-136
7.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)：587-591
8.范凤翠.国外主要国家农业信息化发展现状及特点的比较,农业图书情报学刊,2006,18(6)：175-177
9.胡愉华,潘涛.甘蔗初压汁锤度近红外光谱分析的波段优选,光谱实验室,2009,26(1)：90-95
10.黄东,丁维明.逐步回归法辨识线性系统模型.南京理工大学学报.2005.29(S1)：129-131.
11.黄敬峰,王渊,王福民.油菜红边特征及其叶面积指数的高光谱估算模型.农业工程学报,2006,22(8)：22-26.
12.吉海彦,王鹏新.冬小麦活体叶片叶绿素和水分含量与反射光谱的模型建立,光谱学与光谱分析,2007,27(3)：514-516.
13.李道亮.中国农村信息化发展报告.中国信息世界,2009,(ZI)：72-83
14.李云梅、倪绍祥,黄敬峰.高光谱数据探讨水稻叶片叶绿素含量对叶片及冠层光谱反射特性的影响,遥感技术与应用,2003,18(1)：1-4.
15.李云梅,倪赵祥.高光谱数据探索水稻叶片叶绿素含量对叶片及冠层光谱反射率特性的影响.遥感技术与应用,2003,18(1)：1-4.
16.刘金爱.我国农业信息化发展的现状、问题与对策,现代情报,2009,29(1)：61-63
17.毛罕平,张晓东,李雪.基于光谱反射特征葡萄叶片含水率模型的建立.江苏大学学报,2008,29(5)：369-372.
18.梅方权.中国农业信息产业化的发展分析.农业图书情报学刊,1999,(1)：23-28
19.孟枫平.日本农业信息化进程的主要特点.世界农业,2003(4)：38-47
20.沈艳,牛铮,王汶.基于导数光谱变量叶片含水量模型的建立.地理与地理信息科学,2005,21(4)：16-19.
21.沈瑛.国外农业信息化发展.世界农业,2001,(1)：43-45
22.宋开山,张柏,李方,等.高光谱反射率与大豆叶面积及地上鲜生物量的相关分析.农业工程学报,2005,21(1)：36-40.
23.宋开山,张柏,王宗明.大豆叶绿素含量高光谱反演模型研究,农业工程学报,2006,22(8)：16-21.
24.田庆久,宫鹏,赵春江用光谱反射率诊断小麦水分状况的可行性分析,科学通报,2000,45(24)：2645-2650.
25.田庆久,宫鹏,赵春江.用光谱反射率诊断小麦水分状况的可行性分析,科学通报,2000,45(4)：2645-2650
26.田永超.小麦冠层反射光谱与植株水分状况的关系,应用生态学报,2004,15(11)：2072-2076
27.王多加,周向阳.近红外光谱检测技术在农业和视频分析上的应用,光谱学与光谱分析,2004,24(4)：447-450.
28.王芳.陈达小波变换和偏最小二乘法在烟草常规成分预测中的应用.烟草科技,2004,3：31-34.
29.王桂芳,李广和.偏最小二乘法测定复方乙酰杨酸片中的有效成分.化学分析计量,2005,14(1)：40-42.
30.王国庆,王芳.近红外光谱技术用于复杂植物样品中无机离子测定的新方法,光谱学与光谱分析,24(12)：1540-1542
31.王纪华,赵春江,郭晓维.利用遥感方法诊断小麦叶片含水量的研究,华北农学报,2001.15(4)：68-72.
32.王纪华,赵春江,郭晓维.用光谱反射率诊断小麦叶片水分关况的研究,中国农业科学,2001,34(1)：104-107.
33.王纪华,赵春江.利用遥感方法诊断小麦叶片含水量的研究,华北农学报,2000,15(4)：68-72.
34.王秀珍,王人潮,李云梅.不同氮素营养水平的水稻冠层光谱红边参数及其应用研究.浙江大学学报,2001,27(3)：301-306.
35.武中臣,许晓轩.采用主成分分析结合相关系数法提高苯及其同系物含量预测结果的准确度,光谱学与光谱分析,2004,24(12)：1566-1570.
36.徐广通.现代近红外光谱技术及应用进展,光谱学与光谱分析,2000,20(2)：134-142
37.严衍禄,赵龙莲.近红外光谱分析基础与应用.北京：中国轻工业出版社,2005,7.
38.杨晓蓉.我国农业信息网站建设的现状与评价,计算机与农业,2003,(9)：18
39.杨勇,张冬强等.基于光谱反射特征的柑橘叶片含水量模型,中国农业通报,2011,27(02)：180-184
40.姚付启,张振华,杨润亚.基于红边参数的植被叶绿素含量高光谱估算模型,农业工程学报,2009,25(S2)：123-129
41.姚国才,杨勇,马鸿翔.不同氮肥运筹对弱筋小麦产量与品质的影响,中国农学通报.2009,25(08)：159-163
42.姚建松,杨海清,何勇.基于可见-近红外光谱技术的油菜叶片叶绿素含量无损检测研究,浙江大学报.2009.35.433-438
43.应进平.我国农业信息化的现状、问题和对策,安徽农业大学学报,2003,(5)：10-12
44.应进平.我国农业信息化发展的思路和对策,农机化研究,2007,(5)：21-24
45.于德浩,王艳红,邓正栋.基于实测光谱的叶绿素a估测模型,环境工程学报,2009.6.1147-1152
46.袁洪福.现代光谱分析中常用的化学计量方法,现代科学仪器,1998,5：6-9.
47.张永和,高庆玉等.幼龄苹果树抗旱能力早期鉴定方法,中国果蔬,1988,(1)：45-47
48.章文才.果树研究法,,中国农业出版社1986：149-158
49.赵静,王玉平.中国农村信息化研究述评.图书·情报·知识,2007,(11)：81-85
50.赵元凤.发达国家农业信息化发展报告.中国农村经济,2002,(ZI)：74-78
51.郑咏梅,张铁强.平滑、导数、基线校正对近红外光谱PLS定量分析的影响研究.光谱学与光谱分析,2004,24(12)：1546-1548.
52.朱海根,朱培仁.冬小麦抗冻性的季节变化及其与温度的关系,南京农学院报,1984,2：9-17
53. Aparicio N, Villegas D, Casadesus J, Araus JL. and Royo C. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal,2000,92,83-91.
54. Avery, S. Fundamentals of remote sensing and airphoto interpretation, New York: Macmillan 1992.5
55. Bauman B.Radiometric measurements and crop yield forecasting-some observations over Millet and Sorghum experimental plots Mali. International Journal of Remote Sensing,1992,9:1539-1552.
56. Blackburn GA. Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. International Journal of remote sensing, 1998,19:657-675
57. Blackmer TM, Sehepem JS. Light reflectance compared with other nitrogen stress measurements in corn leave. Agronomy Journal,1994,86:934-938.
58. Blackmer TM..schepers.Light reflectance compared with other nitrogen stress measure ements in cornlaves. Agronomy Journal.86:934-938
59. Boochs G. Shape of the red edge as vitality indicator for plants, International Journal of Remote Sensing,1990, vol 11:1741-1753
60. Brogea NH, Mortensen JV. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data, Remote Sensing of Environment,81,2002, pp.45-57.
61. Caraglio Y, Barthelemy D. Plant morphology and architecture:a dynamic, multilevel and comprehensive approach of plant form and ontogeny. Annals of Botany,2007, 99:375-407
62. Carter GA. Primary and secondary effects of water content of the spectral reflectance of leaves. American Journal of Botany,1996,78(7):916-924
63. Ceccato P, Flasse S, Tarantola B.Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment,2001(77):22-23.
64. Chang KW, Shen Y. Lo JC.Predicting Rice Yield Using Canopy Reflectance Measured at Booting Stage. Agronomy Journal,2005.3.872-878
65. Cournede PH, Kang MZ. Rostand-Mathieu A, Yan HP. Structural factorization of plants to compute their functional an architectural growth. Simulation, 2006,82:427-438
66. Curran PJ. Remote sensing of foliar chemistry. Remote Sensing ofEnvironment,! 989,30(3):271-278
67. Daniel AS, John AG. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance:a comparison of indices based on liquid water and chlorophyll absorption features, Remote Sensing of Environment,84,2003, pp.526-537.
68. Dart B. Visible/near infrared reflectance and chlorophyll content in eucalyptus leaves. Int Remote sensing,1999,20(14):2741-2459
69. De Reffye Ph, Blaise F. Calibration of a hydraulic architecture-based growth model of cotton plants. Agronomy Journarl,1999,19:265-280
70. Dobrowski S Z, Pushnik J C, Zarco-Tejada P J et al. Simple reflectance indices track heat and water stress induced changes in stead-state chlorophy Ⅱ fluorescence at the canopy scale. Remote Sensing of Environment,2005,97:403-414.
71. Dobrowski SZ. Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale. Remote Sensing of Environment,2005,97:403-414.
72. Dong QX, Wang YM. Tomato growth modeling based on interaction of its structure-function. Plant Growth Modeling and Applications:2003, Beijing:250-262.
73. Filella D, Pen-uelas J. The red edge positon and shape as indicators of plant chlorophyll content, biomass and hydric status. internal Journal of remote sensing,1994,15(7):1459-1470
74. Fitzgerald G J, Rodriguez D. Spectral and thermal sensing for nitrogen and water status in rained and irrigated wheat environments.Precision Agriculture,2006,7(4):233-248.
75. Gao B C. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment,1996, 58(3):257-266.
76. Gitelson A.Use of a green channel in remote sensing of glabal vegetation from EOSMDIS. Remote Sens.Environ.1996,58:289-298.
77. Godin C. Costes E. A method for describing plant architecture which integrates topology and geometry. Annals of Botany,84:343-357
78. Guo Y, Ma YT, Zhan ZG. Parameters optimization and field validation of the functional-structural model Greenlab for maize. Annals of Botany,97:217-230
79. Halle F, Oldeman RAA, Tomlinson PB. Tropical Trees and Forests:an Architectural Analysis, Springer-Verlag, Berlin,1978
80. Hardisky MA, Klemas V, Smart RM. The influence of soil salinity, growth form and leaf moisture on the spectral reflectance of Spartina alternifora canopies. Photogramm Eng Remote Sensing,1983,49(1):77-83.
81. Hong S, Rim S. Remote sensing for estimating chlorophyll amount in rice. Canopies Int. Proc Geoseience and Remote Sensing Symposium.Singapore,1997.
82. Hu BG, de reffye Ph. GreenLab:a new methodology towards plant functional-structure model-strctural aspect. Plant Growth Modeling and Application: 2003'International Symposium on Plant Growth Modeling, Simulation, Visualization and Their Applications. Tsinghua University Press-Springer, Beijing,21-35
83. Hunt ER, Rock BN. Detection of changes in leaf water content using near and middle infrared reflectances. Remote Sensing of Environment,1989,30(1):43-45.
84. Inoue Y, Morinaga S, Shibayama M. Non-destructive estimation of water status of intact crop leaves based on spectral reflectance measurements. Japanese Journal of Crop Science,1993,62(4):462-469.
85. Jordan CF. Derivation of leaf area index from quality of light on the forest floor. Ecology,1969,50:663-666
86. Kang MZ. Functional and Structural Stochastic Plant Modeling Based on Substructures. PhD dissertation, Institute of Automation, Chinese Academy of Sciences, Beijing.2003,21
87. Knipling EB. Physical and physiological basis for the reflectance of visible and niar infrared radiation from vegetation. Remote sensing of Environment,1970,1:155-159.
88. Le Dize S, Cruiziat P. A model for simulating structre-function relationships in walnut tree growth processes. Silva Fennica,31:313-328
89. Lei XD, Chang M. Areview on growth modeling and visualization for virtual trees. Scientia Silvae Sinicae,42(11):123-131
90. Ma YT, Guo Y. Evaluation of the plant growth model 1 GREENLAB-Maize. Acta Agronomica Sinica,32:956-963
91. MeMurtrey JE, Chappelle EW,Kim M S. Distinguishing nitrogen fertilization levels in field earl with actively induced fluorescence and passive Reflectance measurements. Remote Sensing of Environ,1994,47:36-44.
92. Orin J, Poldaru R. The impact of new information and communication technologies on agriculture in Estonia Agricultral University. Easti Pollumajandusulikool, 2005,2,221:169-178
93. Penuelas J, Filella I, Biel C, et al. The reflectance at the 950-970nm region as an indicator of plant water status. Internal Journal of Remote Sensing, 1993,14(10):1887-1905
94. Penuelas J, Filella I, Sweeano L. Cell wall elastivity and water index (R970/R900) in wheat under different nitrogen availabilities. International Journal of Remote Sensing,1996,17(3):373-382.
95. Perttunen J, Nikinmaa E. Application of the functional-structural tree model LIGNUM to sugar maple saplings growing in forest gaps. Annals of Botany, 88:471-481
96. Perttunen J, Sieanen R, Nikinmaa E. LIGNUM:a model combining the structure and the functioning of trees. Ecological Modelling,108:189-198
97. Perttunen J, Sieanen R. Incorporating Lindenmayer systems for architectural development in a functional-structural tree model. Ecological Modelling, 181:479-491
98. Perttunen J. LIGNUM:a tree model based on simple structural units. Annals of Botany,77:97-98
99. Pu R, GE S, Kell NM, et al. Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves. International Journal of Remote Sensing, 2003,24(9):1799-1810.
100.Rauscher HM, Isebrands JG. ECOPHYS:an ecophsiological growth process model for juvenile poplar. Tree Physiology,7:255-281
101.Room PM, iVlaillette I.. Module and metamer dynamics and virtual plants. Advances in Ecological Research,25:105-157
102.Schepers JS, Blackmer. Transmittance and reflectance measurements of corn leaves from plants differing in nitrogen an water supply. Plant Physiol.1996,148:523-529.
103.Sieanen R, Nikinmaa E, Nygren P. Components of functional-structural tree models. Annals of Forest Science,57:399-412
104.Su Wei'Ai, Mi Rong qin. The study on the quantification of plant resistance, Scientia Sinica(Series B),1988:59-68
105.Thomas J R, Gausman H W. leaf reflectance vs.leaf chlorophyll and corticoid concentration for eight crops, Agronomy,1977,69:799-802.
106.Thomas J R, Namken L N, Oerther G F, et al. Estimating leaf water content by reflectance measurement. Agronomy Journal,1971,63:845-847.
107.Tomas AS, Caula AB. Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency, advances in space research,35, No 2,2005:305-317.
108.Tsai F, Philpot W. Derivative analysis of hyperspectral data. Remote Sens Environ, 1998,66:41-51.
109.Tucker C. Red and photographic infrared linear combinations form monitoring vegetation.Remot Sensing Environ.1979,8:127-150
110.Yang D, Yang XQ. Studies on biomss and productivity of Pinus tabulaeformis plantation in the Wufengshang of Wudu, Gansu Province. Journal of Northwest Normal University.40:70-73
111.Zhan ZG,de Reffye Ph. Morphological architecturebased growth model of winter wheat. Transactions of the Chinese Society of Agricultural Engineering,2001,17:6-10
112.Zhan ZG, de Reffye Ph. Fitting a structural functional model with plant architectural data. In:Hu BG, Jaeger M eds. Plant Growth Modeling and Applications:2003 International Symposium on Plant Growth Modeling, Simulation, Visualization and Their Applications. Tsinghua University PressSpringer, Beijing,2003:236-249
2.陈建.以农业信息推动农业现代化.山东农机,2004,(12)：7-9
3.陈在新,刘海蓉.柑橘品种抗寒性生理指标的比较研究,安徽农业科学,2005,33(7)：1204-1206
4.程一松,胡春胜,郝二波.氮素胁迫下的冬小麦高光潜特征提取与分析.资源科学,2003,25(1)：86-93.
5.褚小立,袁洪福.近红外分析中光谱预处理及波长选择方法进展与应用.化学进展,2004,16(4)：528-542.
6.邓烈,何绍兰,易时来.三峡库区秋季柑橘园主要地物叶片的光谱特征研究.光谱学与光谱分析,2008.10：135-136
7.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)：587-591
8.范凤翠.国外主要国家农业信息化发展现状及特点的比较,农业图书情报学刊,2006,18(6)：175-177
9.胡愉华,潘涛.甘蔗初压汁锤度近红外光谱分析的波段优选,光谱实验室,2009,26(1)：90-95
10.黄东,丁维明.逐步回归法辨识线性系统模型.南京理工大学学报.2005.29(S1)：129-131.
11.黄敬峰,王渊,王福民.油菜红边特征及其叶面积指数的高光谱估算模型.农业工程学报,2006,22(8)：22-26.
12.吉海彦,王鹏新.冬小麦活体叶片叶绿素和水分含量与反射光谱的模型建立,光谱学与光谱分析,2007,27(3)：514-516.
13.李道亮.中国农村信息化发展报告.中国信息世界,2009,(ZI)：72-83
14.李云梅、倪绍祥,黄敬峰.高光谱数据探讨水稻叶片叶绿素含量对叶片及冠层光谱反射特性的影响,遥感技术与应用,2003,18(1)：1-4.
15.李云梅,倪赵祥.高光谱数据探索水稻叶片叶绿素含量对叶片及冠层光谱反射率特性的影响.遥感技术与应用,2003,18(1)：1-4.
16.刘金爱.我国农业信息化发展的现状、问题与对策,现代情报,2009,29(1)：61-63
17.毛罕平,张晓东,李雪.基于光谱反射特征葡萄叶片含水率模型的建立.江苏大学学报,2008,29(5)：369-372.
18.梅方权.中国农业信息产业化的发展分析.农业图书情报学刊,1999,(1)：23-28
19.孟枫平.日本农业信息化进程的主要特点.世界农业,2003(4)：38-47
20.沈艳,牛铮,王汶.基于导数光谱变量叶片含水量模型的建立.地理与地理信息科学,2005,21(4)：16-19.
21.沈瑛.国外农业信息化发展.世界农业,2001,(1)：43-45
22.宋开山,张柏,李方,等.高光谱反射率与大豆叶面积及地上鲜生物量的相关分析.农业工程学报,2005,21(1)：36-40.
23.宋开山,张柏,王宗明.大豆叶绿素含量高光谱反演模型研究,农业工程学报,2006,22(8)：16-21.
24.田庆久,宫鹏,赵春江用光谱反射率诊断小麦水分状况的可行性分析,科学通报,2000,45(24)：2645-2650.
25.田庆久,宫鹏,赵春江.用光谱反射率诊断小麦水分状况的可行性分析,科学通报,2000,45(4)：2645-2650
26.田永超.小麦冠层反射光谱与植株水分状况的关系,应用生态学报,2004,15(11)：2072-2076
27.王多加,周向阳.近红外光谱检测技术在农业和视频分析上的应用,光谱学与光谱分析,2004,24(4)：447-450.
28.王芳.陈达小波变换和偏最小二乘法在烟草常规成分预测中的应用.烟草科技,2004,3：31-34.
29.王桂芳,李广和.偏最小二乘法测定复方乙酰杨酸片中的有效成分.化学分析计量,2005,14(1)：40-42.
30.王国庆,王芳.近红外光谱技术用于复杂植物样品中无机离子测定的新方法,光谱学与光谱分析,24(12)：1540-1542
31.王纪华,赵春江,郭晓维.利用遥感方法诊断小麦叶片含水量的研究,华北农学报,2001.15(4)：68-72.
32.王纪华,赵春江,郭晓维.用光谱反射率诊断小麦叶片水分关况的研究,中国农业科学,2001,34(1)：104-107.
33.王纪华,赵春江.利用遥感方法诊断小麦叶片含水量的研究,华北农学报,2000,15(4)：68-72.
34.王秀珍,王人潮,李云梅.不同氮素营养水平的水稻冠层光谱红边参数及其应用研究.浙江大学学报,2001,27(3)：301-306.
35.武中臣,许晓轩.采用主成分分析结合相关系数法提高苯及其同系物含量预测结果的准确度,光谱学与光谱分析,2004,24(12)：1566-1570.
36.徐广通.现代近红外光谱技术及应用进展,光谱学与光谱分析,2000,20(2)：134-142
37.严衍禄,赵龙莲.近红外光谱分析基础与应用.北京：中国轻工业出版社,2005,7.
38.杨晓蓉.我国农业信息网站建设的现状与评价,计算机与农业,2003,(9)：18
39.杨勇,张冬强等.基于光谱反射特征的柑橘叶片含水量模型,中国农业通报,2011,27(02)：180-184
40.姚付启,张振华,杨润亚.基于红边参数的植被叶绿素含量高光谱估算模型,农业工程学报,2009,25(S2)：123-129
41.姚国才,杨勇,马鸿翔.不同氮肥运筹对弱筋小麦产量与品质的影响,中国农学通报.2009,25(08)：159-163
42.姚建松,杨海清,何勇.基于可见-近红外光谱技术的油菜叶片叶绿素含量无损检测研究,浙江大学报.2009.35.433-438
43.应进平.我国农业信息化的现状、问题和对策,安徽农业大学学报,2003,(5)：10-12
44.应进平.我国农业信息化发展的思路和对策,农机化研究,2007,(5)：21-24
45.于德浩,王艳红,邓正栋.基于实测光谱的叶绿素a估测模型,环境工程学报,2009.6.1147-1152
46.袁洪福.现代光谱分析中常用的化学计量方法,现代科学仪器,1998,5：6-9.
47.张永和,高庆玉等.幼龄苹果树抗旱能力早期鉴定方法,中国果蔬,1988,(1)：45-47
48.章文才.果树研究法,,中国农业出版社1986：149-158
49.赵静,王玉平.中国农村信息化研究述评.图书·情报·知识,2007,(11)：81-85
50.赵元凤.发达国家农业信息化发展报告.中国农村经济,2002,(ZI)：74-78
51.郑咏梅,张铁强.平滑、导数、基线校正对近红外光谱PLS定量分析的影响研究.光谱学与光谱分析,2004,24(12)：1546-1548.
52.朱海根,朱培仁.冬小麦抗冻性的季节变化及其与温度的关系,南京农学院报,1984,2：9-17
53. Aparicio N, Villegas D, Casadesus J, Araus JL. and Royo C. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal,2000,92,83-91.
54. Avery, S. Fundamentals of remote sensing and airphoto interpretation, New York: Macmillan 1992.5
55. Bauman B.Radiometric measurements and crop yield forecasting-some observations over Millet and Sorghum experimental plots Mali. International Journal of Remote Sensing,1992,9:1539-1552.
56. Blackburn GA. Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. International Journal of remote sensing, 1998,19:657-675
57. Blackmer TM, Sehepem JS. Light reflectance compared with other nitrogen stress measurements in corn leave. Agronomy Journal,1994,86:934-938.
58. Blackmer TM..schepers.Light reflectance compared with other nitrogen stress measure ements in cornlaves. Agronomy Journal.86:934-938
59. Boochs G. Shape of the red edge as vitality indicator for plants, International Journal of Remote Sensing,1990, vol 11:1741-1753
60. Brogea NH, Mortensen JV. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data, Remote Sensing of Environment,81,2002, pp.45-57.
61. Caraglio Y, Barthelemy D. Plant morphology and architecture:a dynamic, multilevel and comprehensive approach of plant form and ontogeny. Annals of Botany,2007, 99:375-407
62. Carter GA. Primary and secondary effects of water content of the spectral reflectance of leaves. American Journal of Botany,1996,78(7):916-924
63. Ceccato P, Flasse S, Tarantola B.Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment,2001(77):22-23.
64. Chang KW, Shen Y. Lo JC.Predicting Rice Yield Using Canopy Reflectance Measured at Booting Stage. Agronomy Journal,2005.3.872-878
65. Cournede PH, Kang MZ. Rostand-Mathieu A, Yan HP. Structural factorization of plants to compute their functional an architectural growth. Simulation, 2006,82:427-438
66. Curran PJ. Remote sensing of foliar chemistry. Remote Sensing ofEnvironment,! 989,30(3):271-278
67. Daniel AS, John AG. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance:a comparison of indices based on liquid water and chlorophyll absorption features, Remote Sensing of Environment,84,2003, pp.526-537.
68. Dart B. Visible/near infrared reflectance and chlorophyll content in eucalyptus leaves. Int Remote sensing,1999,20(14):2741-2459
69. De Reffye Ph, Blaise F. Calibration of a hydraulic architecture-based growth model of cotton plants. Agronomy Journarl,1999,19:265-280
70. Dobrowski S Z, Pushnik J C, Zarco-Tejada P J et al. Simple reflectance indices track heat and water stress induced changes in stead-state chlorophy Ⅱ fluorescence at the canopy scale. Remote Sensing of Environment,2005,97:403-414.
71. Dobrowski SZ. Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale. Remote Sensing of Environment,2005,97:403-414.
72. Dong QX, Wang YM. Tomato growth modeling based on interaction of its structure-function. Plant Growth Modeling and Applications:2003, Beijing:250-262.
73. Filella D, Pen-uelas J. The red edge positon and shape as indicators of plant chlorophyll content, biomass and hydric status. internal Journal of remote sensing,1994,15(7):1459-1470
74. Fitzgerald G J, Rodriguez D. Spectral and thermal sensing for nitrogen and water status in rained and irrigated wheat environments.Precision Agriculture,2006,7(4):233-248.
75. Gao B C. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment,1996, 58(3):257-266.
76. Gitelson A.Use of a green channel in remote sensing of glabal vegetation from EOSMDIS. Remote Sens.Environ.1996,58:289-298.
77. Godin C. Costes E. A method for describing plant architecture which integrates topology and geometry. Annals of Botany,84:343-357
78. Guo Y, Ma YT, Zhan ZG. Parameters optimization and field validation of the functional-structural model Greenlab for maize. Annals of Botany,97:217-230
79. Halle F, Oldeman RAA, Tomlinson PB. Tropical Trees and Forests:an Architectural Analysis, Springer-Verlag, Berlin,1978
80. Hardisky MA, Klemas V, Smart RM. The influence of soil salinity, growth form and leaf moisture on the spectral reflectance of Spartina alternifora canopies. Photogramm Eng Remote Sensing,1983,49(1):77-83.
81. Hong S, Rim S. Remote sensing for estimating chlorophyll amount in rice. Canopies Int. Proc Geoseience and Remote Sensing Symposium.Singapore,1997.
82. Hu BG, de reffye Ph. GreenLab:a new methodology towards plant functional-structure model-strctural aspect. Plant Growth Modeling and Application: 2003'International Symposium on Plant Growth Modeling, Simulation, Visualization and Their Applications. Tsinghua University Press-Springer, Beijing,21-35
83. Hunt ER, Rock BN. Detection of changes in leaf water content using near and middle infrared reflectances. Remote Sensing of Environment,1989,30(1):43-45.
84. Inoue Y, Morinaga S, Shibayama M. Non-destructive estimation of water status of intact crop leaves based on spectral reflectance measurements. Japanese Journal of Crop Science,1993,62(4):462-469.
85. Jordan CF. Derivation of leaf area index from quality of light on the forest floor. Ecology,1969,50:663-666
86. Kang MZ. Functional and Structural Stochastic Plant Modeling Based on Substructures. PhD dissertation, Institute of Automation, Chinese Academy of Sciences, Beijing.2003,21
87. Knipling EB. Physical and physiological basis for the reflectance of visible and niar infrared radiation from vegetation. Remote sensing of Environment,1970,1:155-159.
88. Le Dize S, Cruiziat P. A model for simulating structre-function relationships in walnut tree growth processes. Silva Fennica,31:313-328
89. Lei XD, Chang M. Areview on growth modeling and visualization for virtual trees. Scientia Silvae Sinicae,42(11):123-131
90. Ma YT, Guo Y. Evaluation of the plant growth model 1 GREENLAB-Maize. Acta Agronomica Sinica,32:956-963
91. MeMurtrey JE, Chappelle EW,Kim M S. Distinguishing nitrogen fertilization levels in field earl with actively induced fluorescence and passive Reflectance measurements. Remote Sensing of Environ,1994,47:36-44.
92. Orin J, Poldaru R. The impact of new information and communication technologies on agriculture in Estonia Agricultral University. Easti Pollumajandusulikool, 2005,2,221:169-178
93. Penuelas J, Filella I, Biel C, et al. The reflectance at the 950-970nm region as an indicator of plant water status. Internal Journal of Remote Sensing, 1993,14(10):1887-1905
94. Penuelas J, Filella I, Sweeano L. Cell wall elastivity and water index (R970/R900) in wheat under different nitrogen availabilities. International Journal of Remote Sensing,1996,17(3):373-382.
95. Perttunen J, Nikinmaa E. Application of the functional-structural tree model LIGNUM to sugar maple saplings growing in forest gaps. Annals of Botany, 88:471-481
96. Perttunen J, Sieanen R, Nikinmaa E. LIGNUM:a model combining the structure and the functioning of trees. Ecological Modelling,108:189-198
97. Perttunen J, Sieanen R. Incorporating Lindenmayer systems for architectural development in a functional-structural tree model. Ecological Modelling, 181:479-491
98. Perttunen J. LIGNUM:a tree model based on simple structural units. Annals of Botany,77:97-98
99. Pu R, GE S, Kell NM, et al. Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves. International Journal of Remote Sensing, 2003,24(9):1799-1810.
100.Rauscher HM, Isebrands JG. ECOPHYS:an ecophsiological growth process model for juvenile poplar. Tree Physiology,7:255-281
101.Room PM, iVlaillette I.. Module and metamer dynamics and virtual plants. Advances in Ecological Research,25:105-157
102.Schepers JS, Blackmer. Transmittance and reflectance measurements of corn leaves from plants differing in nitrogen an water supply. Plant Physiol.1996,148:523-529.
103.Sieanen R, Nikinmaa E, Nygren P. Components of functional-structural tree models. Annals of Forest Science,57:399-412
104.Su Wei'Ai, Mi Rong qin. The study on the quantification of plant resistance, Scientia Sinica(Series B),1988:59-68
105.Thomas J R, Gausman H W. leaf reflectance vs.leaf chlorophyll and corticoid concentration for eight crops, Agronomy,1977,69:799-802.
106.Thomas J R, Namken L N, Oerther G F, et al. Estimating leaf water content by reflectance measurement. Agronomy Journal,1971,63:845-847.
107.Tomas AS, Caula AB. Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency, advances in space research,35, No 2,2005:305-317.
108.Tsai F, Philpot W. Derivative analysis of hyperspectral data. Remote Sens Environ, 1998,66:41-51.
109.Tucker C. Red and photographic infrared linear combinations form monitoring vegetation.Remot Sensing Environ.1979,8:127-150
110.Yang D, Yang XQ. Studies on biomss and productivity of Pinus tabulaeformis plantation in the Wufengshang of Wudu, Gansu Province. Journal of Northwest Normal University.40:70-73
111.Zhan ZG,de Reffye Ph. Morphological architecturebased growth model of winter wheat. Transactions of the Chinese Society of Agricultural Engineering,2001,17:6-10
112.Zhan ZG, de Reffye Ph. Fitting a structural functional model with plant architectural data. In:Hu BG, Jaeger M eds. Plant Growth Modeling and Applications:2003 International Symposium on Plant Growth Modeling, Simulation, Visualization and Their Applications. Tsinghua University PressSpringer, Beijing,2003:236-249