不同氮素水平下玉米叶片的高光谱响应及其诊断
|
摘要
玉米是广泛种植的粮食作物，其产量的高低对世界粮食安全具有重要的影响。利用高光谱技术实时、快速监测玉米叶片氮素、叶绿素营养状况，能够合理指导其田间氮素管理，促使玉米的高产优质，体现现代农业的发展要求。本论文以两年玉米田间氮素梯度试验(N0～N6)为基础，以玉米叶片为基本研究对象，从不同角度(层次性、肥料用量、单株叶片光谱反射率平均值和SPAD值)探讨了叶片的光谱响应特性，明确了叶片光谱响应的敏感区域；通过筛选多种光谱参数，构建了基于单个和完整生育期的叶片氮素含量、SPAD值高光谱预测模型。全文的主要结论如下：
(1)通过研究各个生育期(拔节期、大喇叭口期、开花吐丝期、灌浆期和蜡熟期)玉米叶片同一施肥水平不同层次、同一层次不同施肥水平的光谱反射率，明确了叶片光谱响应的敏感区域。低氮处理、植株下层次的玉米叶片，对光谱响应都较敏感。在350～760nm可见光波段(VB)和761～1300nm近红外波段(NIB)，叶片光谱响应的差别比较明显，VB区域内叶片光谱反射率随施肥量的增大而逐渐变小，NIB区域内叶片光谱反射率的变化规律较为复杂；在1301～2500nm短波近红外波段(SIB)，叶片光谱反射率间的变化较小。
同一施肥水平下，玉米单株叶片光谱反射率平均值(LRM)在五个生育期的差别都很明显，主要位于VB和NIB区域；随着施肥量的增加，这种差别有逐渐减小的趋势。在VB区域，五个生育期的叶片光谱反射率都随着SPAD区间值的增大而逐渐变小；相同SPAD区间值内，叶片光谱反射率在各个时期的表现趋势也有所不同。
(2)以单个生育期(拔节期、大喇叭口期、开花吐丝期、灌浆期和蜡熟期)数据构建玉米叶片氮素含量(LNC)、SPAD值高光谱预测模型时，LNC、SPAD值与叶片光谱反射率呈极显著负相关的波段都位于VB、SIB的部分区域；以差值参数(DSI)为基础构建的预测模型，能有效预测LNC、叶片SPAD值。
(3)利用完整生育期(2011、2012、两年)数据构建玉米叶片氮素含量(LNC)、SPAD值高光谱预测模型时，叶片光谱反射率和一阶导数平均值曲线、LNC与光谱反射率相关系数曲线、SPAD值与光谱反射率及其一阶导数的相关系数曲线基本相同，LNC与一阶导数相关系数曲线的差别较大。以一阶导数为基础构建的预测模型，其预测效果不稳定；以DSI(R550附近，R680附近)、DSI(R680附近，R710附近)构建的预测模型，能有效预测LNC；以LCI、DSI(R550附近，R680附近)、DSI(R680附近，R710附近)构建的预测模型，能准确预测叶片SPAD值。
(4)玉米穗位叶光谱响应的敏感区域位于可见光波段和近红外波段，叶片SPAD值与光谱反射率呈极显著负相关的波段位于VB、SIB的部分区域，以LCI、DSI(678，717)、DSI(549，678)为基础的预测模型，具备良好的评价效果和稳定性，能准确预测叶片SPAD值。
(5)总体来讲，氮素营养下玉米叶片光谱反射率的差别明显区域位于可见光波段和近红外波段；叶片氮素含量(LNC)、SPAD值与光谱反射率呈极显著负相关的区域位于可见光波段，且两个相关系数最大值也位于该区域内；差值参数DSI(R550附近，R680附近)、DSI(R680附近，R710附近)都参与了最佳预测模型的构建。因此，利用高光谱遥感技术监测作物氮素、叶绿素状况时，应加强对该区域的重点研究，为研制低成本、高精度的便携式光谱诊断仪器提供数据支持。
Maize is widely planted in the world, and its yield has important influence on food security.Nitrogen nutrition, chlorophyll status of maize leaves can be timely, rapidly monitored usinghyperspectral technology, which could be used to guide maize field nitrogen management, improvemaize yield and quality, embody development requirements of modern agriculture. In this thesis, a2-year maize field experiment of nitrogen gradient (N0~N6) was conducted. As the basic researchobjects, spectral response characteristics of maize leaves under many angles (levels, fertilizer dose, leafreflectance mean of per plant, SPAD value) were discussed, and spectral response sensitive areas ofmaize leaves were found. Various spectral parameters were selected to construct prediction models formaize leaf nitrogen contents, SPAD values based on single or the whole growth periods. The mainconclusions of this thesis were as follows:
(1) Sensitive areas of maize leaves were found through studies on spectral response of maizeleaves under same fertilizer dose different levels, same levels different fertilizer dose at different growthstages (jointing stage, booting stage, anthesis-silking stage, filling stage and ripening stage). Maizeleaves of lower level or under low nitrogen fertilizer dose are more sensitive to spectral response. Thesensitive areas of maize leaves were in350~760nm visible band (VB) and761~1300nm near infraredband (NIB). Leaf spectral reflectance (LSR) in VB region decreased with the increase of fertilizer dose,and changes of LSR in NIB region were complex, while changes of LSR in1301~2500nm shortwaveinfrared band (SIB) were minor.
Under the same fertilizer level, differences among leaf reflectance means (LRM) of per plant atfive growth stages were all obvious, sensitive areas were in VB, NIB regions, and the differencesdecreased with the increase of fertilizer dose. In VB region, LSR decreased with increase of SPADinterval values at five growth stages. Under the same SPAD interval value, changes of LSR at fivegrowth stages were different.
(2) Prediction models for maize leaf nitrogen contents (LNC), SPAD values were constructedbased on data of a single growth stage (jointing stage, booting stage, anthesis-silking stage, filling stageand ripening stage). LNC, SPAD values were extremely significantly negatively correlated with LSR inpart of VB and SIB regions. LNC, SPAD values could be effectively predicted using prediction modelsbased on dissimilarity spectral index (DSI).
(3) Prediction models for maize leaf nitrogen contents (LNC), SPAD values were constructedbased on data of the whole growth stages (2011,2012, two-year). The mean curves of LSR or its firstderivative, the correlation coefficient curves of LNC and LSR, SPAD values and LSR or its firstderivative were basically the same, while the correlation coefficient curves of LNC and first derivativewere different. Prediction models built with spectral parameters based on first derivative were unstable,LNC could be effectively predicted using models built with DSI (R550around, R680around) and DSI (R680around,R710around), maize leaf SPAD values could be accurately predicted using model built with LCI, DSI (R550around, R680around) and DSI (R680around, R710around).
(4) Sensitive areas of maize ear leaves were in VB and NIB regions, and leaf SPAD values wereextremely significantly negatively correlated with LSR in part of VB and SIB regions. Evaluation andstability of prediction models built with LCI, DSI (678,717) and DSI (549,678) were good, thereforeleaf SPAD values could be accurately predicted.
(5) Overall, spectral response sensitive areas of maize leaves were in VB and NIB regions underdifferent nitrogen fertilizer dose. LNC, SPAD values were extremely significantly negatively correlatedwith LSR in VB region, and the two maxima of correlation coefficients were in this region. The bestprediction models were all built with spectral parameters DSI (R550around, R680around) and DSI (R680around,R710around). Therefore, the VB region should be mainly studied when crop nitrogen, chlorophyll statuswere monitored using hyperspectral remote sensing technology, and which can provide experiment datafor developing low-cost, high-precision portable spectroscopy diagnosis instruments.
(1)通过研究各个生育期(拔节期、大喇叭口期、开花吐丝期、灌浆期和蜡熟期)玉米叶片同一施肥水平不同层次、同一层次不同施肥水平的光谱反射率，明确了叶片光谱响应的敏感区域。低氮处理、植株下层次的玉米叶片，对光谱响应都较敏感。在350～760nm可见光波段(VB)和761～1300nm近红外波段(NIB)，叶片光谱响应的差别比较明显，VB区域内叶片光谱反射率随施肥量的增大而逐渐变小，NIB区域内叶片光谱反射率的变化规律较为复杂；在1301～2500nm短波近红外波段(SIB)，叶片光谱反射率间的变化较小。
同一施肥水平下，玉米单株叶片光谱反射率平均值(LRM)在五个生育期的差别都很明显，主要位于VB和NIB区域；随着施肥量的增加，这种差别有逐渐减小的趋势。在VB区域，五个生育期的叶片光谱反射率都随着SPAD区间值的增大而逐渐变小；相同SPAD区间值内，叶片光谱反射率在各个时期的表现趋势也有所不同。
(2)以单个生育期(拔节期、大喇叭口期、开花吐丝期、灌浆期和蜡熟期)数据构建玉米叶片氮素含量(LNC)、SPAD值高光谱预测模型时，LNC、SPAD值与叶片光谱反射率呈极显著负相关的波段都位于VB、SIB的部分区域；以差值参数(DSI)为基础构建的预测模型，能有效预测LNC、叶片SPAD值。
(3)利用完整生育期(2011、2012、两年)数据构建玉米叶片氮素含量(LNC)、SPAD值高光谱预测模型时，叶片光谱反射率和一阶导数平均值曲线、LNC与光谱反射率相关系数曲线、SPAD值与光谱反射率及其一阶导数的相关系数曲线基本相同，LNC与一阶导数相关系数曲线的差别较大。以一阶导数为基础构建的预测模型，其预测效果不稳定；以DSI(R550附近，R680附近)、DSI(R680附近，R710附近)构建的预测模型，能有效预测LNC；以LCI、DSI(R550附近，R680附近)、DSI(R680附近，R710附近)构建的预测模型，能准确预测叶片SPAD值。
(4)玉米穗位叶光谱响应的敏感区域位于可见光波段和近红外波段，叶片SPAD值与光谱反射率呈极显著负相关的波段位于VB、SIB的部分区域，以LCI、DSI(678，717)、DSI(549，678)为基础的预测模型，具备良好的评价效果和稳定性，能准确预测叶片SPAD值。
(5)总体来讲，氮素营养下玉米叶片光谱反射率的差别明显区域位于可见光波段和近红外波段；叶片氮素含量(LNC)、SPAD值与光谱反射率呈极显著负相关的区域位于可见光波段，且两个相关系数最大值也位于该区域内；差值参数DSI(R550附近，R680附近)、DSI(R680附近，R710附近)都参与了最佳预测模型的构建。因此，利用高光谱遥感技术监测作物氮素、叶绿素状况时，应加强对该区域的重点研究，为研制低成本、高精度的便携式光谱诊断仪器提供数据支持。
Maize is widely planted in the world, and its yield has important influence on food security.Nitrogen nutrition, chlorophyll status of maize leaves can be timely, rapidly monitored usinghyperspectral technology, which could be used to guide maize field nitrogen management, improvemaize yield and quality, embody development requirements of modern agriculture. In this thesis, a2-year maize field experiment of nitrogen gradient (N0~N6) was conducted. As the basic researchobjects, spectral response characteristics of maize leaves under many angles (levels, fertilizer dose, leafreflectance mean of per plant, SPAD value) were discussed, and spectral response sensitive areas ofmaize leaves were found. Various spectral parameters were selected to construct prediction models formaize leaf nitrogen contents, SPAD values based on single or the whole growth periods. The mainconclusions of this thesis were as follows:
(1) Sensitive areas of maize leaves were found through studies on spectral response of maizeleaves under same fertilizer dose different levels, same levels different fertilizer dose at different growthstages (jointing stage, booting stage, anthesis-silking stage, filling stage and ripening stage). Maizeleaves of lower level or under low nitrogen fertilizer dose are more sensitive to spectral response. Thesensitive areas of maize leaves were in350~760nm visible band (VB) and761~1300nm near infraredband (NIB). Leaf spectral reflectance (LSR) in VB region decreased with the increase of fertilizer dose,and changes of LSR in NIB region were complex, while changes of LSR in1301~2500nm shortwaveinfrared band (SIB) were minor.
Under the same fertilizer level, differences among leaf reflectance means (LRM) of per plant atfive growth stages were all obvious, sensitive areas were in VB, NIB regions, and the differencesdecreased with the increase of fertilizer dose. In VB region, LSR decreased with increase of SPADinterval values at five growth stages. Under the same SPAD interval value, changes of LSR at fivegrowth stages were different.
(2) Prediction models for maize leaf nitrogen contents (LNC), SPAD values were constructedbased on data of a single growth stage (jointing stage, booting stage, anthesis-silking stage, filling stageand ripening stage). LNC, SPAD values were extremely significantly negatively correlated with LSR inpart of VB and SIB regions. LNC, SPAD values could be effectively predicted using prediction modelsbased on dissimilarity spectral index (DSI).
(3) Prediction models for maize leaf nitrogen contents (LNC), SPAD values were constructedbased on data of the whole growth stages (2011,2012, two-year). The mean curves of LSR or its firstderivative, the correlation coefficient curves of LNC and LSR, SPAD values and LSR or its firstderivative were basically the same, while the correlation coefficient curves of LNC and first derivativewere different. Prediction models built with spectral parameters based on first derivative were unstable,LNC could be effectively predicted using models built with DSI (R550around, R680around) and DSI (R680around,R710around), maize leaf SPAD values could be accurately predicted using model built with LCI, DSI (R550around, R680around) and DSI (R680around, R710around).
(4) Sensitive areas of maize ear leaves were in VB and NIB regions, and leaf SPAD values wereextremely significantly negatively correlated with LSR in part of VB and SIB regions. Evaluation andstability of prediction models built with LCI, DSI (678,717) and DSI (549,678) were good, thereforeleaf SPAD values could be accurately predicted.
(5) Overall, spectral response sensitive areas of maize leaves were in VB and NIB regions underdifferent nitrogen fertilizer dose. LNC, SPAD values were extremely significantly negatively correlatedwith LSR in VB region, and the two maxima of correlation coefficients were in this region. The bestprediction models were all built with spectral parameters DSI (R550around, R680around) and DSI (R680around,R710around). Therefore, the VB region should be mainly studied when crop nitrogen, chlorophyll statuswere monitored using hyperspectral remote sensing technology, and which can provide experiment datafor developing low-cost, high-precision portable spectroscopy diagnosis instruments.
引文
1.白由路,杨俐苹,王磊,等.农业低空遥感技术及其应用前景[J].农业网络信息,2010,(1):5-7.
2.蔡红光,米国华,陈范骏,等.玉米叶片SPAD值、全氮及硝态氮含量的品种间变异[J].植物营养与肥料学报,2010,16(4):866-873.
3.曹玉军,刘春光,王晓慧,等.施钾量对甜玉米穗位叶蔗糖合成的影响[J].玉米科学,2010,18(5):72-75.
4.陈红艳,赵庚星,李希灿,等.小波分析用于土壤速效钾含量高光谱估测研究[J].中国农业科学,2012,45(2):1425-1431.
5.陈君颖,田庆久.高分辨率遥感植被分类研究[J].遥感学报,2007,11(2):221-227.
6.陈顺平.氮素在旱作系统作物中的吸收与转化[陈顺平硕士学位论文].北京:首都师范大学,2006.
7.陈杨,樊明寿,李斐,等.氮素营养诊断技术的发展及其在马铃薯生产中的应用[J].中国农学通报,2009,25(3):66-71.
8.程一松,胡春胜,郝二波,等.氮素胁迫下的冬小麦高光谱特征提取与分析[J].资源科学,2003,25(1):86-93.
9.崔贝,黄文江,杨武德,等.不同施肥决策对冬小麦生长影响的高光谱监测及对比分析[J].植物营养与肥料学报,2013,19(1):11-19.
10.代辉,胡春胜,程一松,等.不同氮水平下冬小麦农学参数与光谱植被指数的相关性[J].干旱地区农业研究,2005,23(4):16-21.
11.党蕊娟,李世清,穆晓慧,等.施氮对半湿润农田夏玉米冠层氮素及叶绿素相对值(SPAD值)垂直分布的影响[J].中国生态农业学报,2009,17(1):54-59.
12.邓劲松,李君,王珂.基于多时相PCA光谱增强和多源光谱分类器的SPOT影像土地利用变化检测[J].光谱学与光谱分析,2009,29(6):1627-1631.
13.杜华强,葛宏立,范文义,等.马尾松针叶光谱特征与其叶绿素含量间关系研究[J].光谱学与光谱分析,2009,29(11):3033-3037.
14.段巍巍,李慧玲,肖凯,等.氮肥对玉米穗位叶光合作用及其生理生化特性的影响[J].华北农学报,2007,22(1):26-29.
15.方慧,宋海燕,曹芳,等.油菜叶片的光谱特征与叶绿素含量之间的关系研究[J].光谱学与光谱分析,2007,27(9):1731-1734.
16.方昭希,王明录,彭代平,等.硝酸还原酶彗星与氮素营养的关系[J].植物生理学报,1979,5(2):123-128.
17.冯伟,郭天财,谢迎新,等.作物光谱分析技术及其在生长监测中的应用[J].中国农学通报,2009,25(23):182-188.
18.高飞,肖靖,谷运红,等.利用小麦叶片SPAD值预测成熟期籽粒蛋白质含量的研究[J].光谱学与光谱分析,2012,32(5):1350-1354.
19.郭书亚,张新,张前进,等.秸秆覆盖深松对夏玉米花后穗位叶衰老和产量的影响[J].玉米科学,2012,20(1):104-107.
20.韩晓日,姜琳琳,王帅,等.不同施肥处理对春玉米穗位叶光合指标的影响[J].沈阳农业大学学报,2009,40(4):444-448.
21.郝玉兰,潘金豹,张秋芝,等.玉米穗位叶蛋白质含量等生理性状的变化研究[J].玉米科学,2002,10(4):32-34.
22.胡昊,白由路,杨俐苹,等.不同氮营养冬小麦冠层光谱红边特征分析[J].植物营养与肥料学报,2009,15(6):1317-1323.
23.胡玉福,邓良基,张世熔,等.基于RS和GIS的西昌市土地利用及景观格局变化[J].农业工程学报,2011,27(10):322-327.
24.黄迪,张佳宝,张丛志,等.不同大气湿度与氮肥水平对夏玉米苗期水分利用效率的影响[J].玉米科学,2012,20(1):123-127.
25.吉海彦,王鹏新,严泰来,等.冬小麦活体叶片叶绿素和水分含量及反射光谱的模型建立[J].光谱学与光谱分析,2007,27(3):514-516.
26.贾方方,马新明,李春明,等.不同水分处理对烟草叶片高光谱及红边特征的影响[J].中国生态农业学报,2011,19(6):1330-1335.
27.贾良良,寿丽娜,李斐,等.遥感技术在植物氮营养诊断和推荐施肥中的应用之研究进展[J].中国农学通报,2007,23(12):396-401.
28.姜继萍,杨京平,杨正超,等.不同氮素水平下水稻叶片及其相邻叶位SPAD值变化特征[J].浙江大学学报,2012,38(2):166-174.
29.蒋金豹,陈云浩,黄文江,等.条锈病胁迫下冬小麦冠层叶片氮素含量的高光谱估测模型[J].农业工程学报,2008,24(1):35-39.
30.金继运,白由路,杨俐苹,等.高效土壤养分测试技术与设备[M].北京:中国农业出版社,2006:148-149.
31.金伟,葛宏立,杜华强,等.无人机遥感发展与应用概况[J].遥感信息,2009,(1):88-92.
32.隽英华,汪仁,刑月华,等.基于可见光光谱扫描的春玉米氮素营养诊断[J].玉米科学,2012,20(5):126-130.
33.李潮海,刘奎,周苏玫,等.不同施肥条件下夏玉米光合对生理生态因子的响应[J].作物学报,2002,28(2):265-269.
34.李耕,高辉远,刘鹏,等.氮素对玉米灌浆期叶片光合性能的影响[J].植物营养与肥料学报,2010,16(3):536-542.
35.李辉,白丹,张卓,等.羊草叶片SPAD值与叶绿素含量的相关分析[J].中国农学通报,2012,28(2):27-30.
36.李金文.基于水稻叶片生理生态学特征的氮营养诊断[李金文博士学位论文].杭州:浙江大学,2010.
37.李少昆,谭海珍,王克如,等.小麦籽粒蛋白质含量遥感监测研究进展[J].农业工程学报,2009,25(2):302-307.
38.李新海,高根来,梁晓玲,等.我估主要玉米自交系开花期耐干旱差异及改良[J].作物学报,2002,28(5):595-600.
39.李占录,翟世宏,李爱军,等.玉米抽雄期叶面喷洒DA-6增产效果的研究[J].玉米科学,2006,14(增刊):84-85.
40.李志宏,刘宏斌,张福锁.应用叶绿素仪诊断冬小麦氮营养状况的研究[J].植物营养与肥料学报,2003,9(4):401-405.
41.梁爽,赵庚星,朱西存.苹果树叶片叶绿素含量高光谱估测模型研究[J].光谱学与光谱分析,2012,32(5):1367-1370.
42.刘冰峰,李军,赵刚峰,等.夏玉米叶片全氮高光谱遥感估算模型研究[J].植物营养与肥料学报,2012,18(4):813-824.
43.刘宏斌,张云贵,李志宏,等.光谱技术在冬小麦氮素营养诊断中的应用研究[J].中国农业科学,2004,37(11):1743-1748.
44.刘良云,王纪华,黄文江,等.利用新型光谱指数改善冬小麦估产精度[J].农业工程学报,2004,20(1):172-175.
45.刘萍,陆大雷,孙建勇,等.拔节期追氮对春播和秋播糯玉米淀粉胶凝和回生特性的影响[J].植物营养与肥料学报,2010,16(3):543-551.
46.刘炜,常庆瑞,郭曼,等.冬小麦导数光谱特征提取与缺磷胁迫神经网络诊断[J].光谱学与光谱分析,2011,31(4):1092-1096.
47.刘贤德,李新海,张世煌.玉米开花期耐旱相关性状的遗传及育种策略[J].玉米科学,2002,10(3):13-18.
48.刘小军,田永超,姚霞,等.基于高光谱的水稻叶片含水量监测研究[J].中国农业科学,2012,45(3):435-442.
49.刘艳春,樊明寿.应用叶绿素仪SPAD-502进行马铃薯氮素营养诊断的可行性[J].中国马铃薯,2012,26(1):45-48.
50.卢艳丽,白由路,杨俐苹,等.东北平原不同类型土壤有机质含量高光谱反演模型同质性研究[J].植物营养与肥料学报,2011,17(2):456-463.
51.陆大雷,景立权,王德成,等.拔节期追氮对不同季节糯玉米淀粉糊化特性的影响[J].生态学报,2010,30(2):549-555.
52.罗庆锋.叶绿素的研究进展及叶绿素铜纳多开发利用[J].林产化工通讯,1995,(1):32-33.
53.马晓霞,王莲莲,黎青慧,等.长期施肥对玉米生育期土壤微生物碳氮及酶活性的影响[J].生态学报,2012,32(17):5502-5511.
54.牛铮,陈永华,随洪智,等.叶片化学组分成像光谱压迫感探测机理分析[J].遥感学报,2000,4(2):125-130.
55.潘静,曹兵,万仲武.两种果树叶片SPAD值与叶绿素含量相关性分析[J].北方园艺,2012,(5):9-12.
56.蒲瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社,2000:2.
57.裘正军,宋海燕,何勇,等.应用SPAD和光谱技术研究油菜生长期间的氮素变化规律[J].农业工程学报,2007,23(7):150-156.
58.邵国庆,李增嘉,宁堂原,等.灌溉与尿素类型对玉米花后穗位叶衰老、产量和效益的影响[J].中国农业科学,2009,42(10):3459-3466.
59.史典义,刘忠香,金危危.植物叶绿素合成、分解代谢及信号调控[J].遗传,2009,31(7):698-704.
60.宋海星,李生秀.玉米生长量、养分吸收量及氮肥利用率的动态变化[J].中国农业科学,2003,36(1):71-76.
61.孙红,李民赞,赵勇,等.冬小麦生长期光谱变化特征与叶绿素含量监测研究[J].光谱学与光谱分析,2010,30(1):192-196.
62.孙钦平,贾良良,芮玉奎,等.应用可见光光谱进行夏玉米氮营养诊断[J].光谱学与光谱分析,2009,29(2):432-435.
63.谭昌伟,郭文善,朱新开,等.不同条件下夏玉米冠层反射光谱响应特性研究[J].农业工程学报,2008,24(9):131-136.
64.谭昌伟,王纪华,黄文江,等.夏玉米叶片全氮、叶绿素及叶面积指数的光谱响应研究[J].西北植物学报,2004,24(6):1041-1046.
65.唐海涛,张彪,谭君,等.玉米杂交种产量性状与穗位叶光合性状关联度分析[J].中国农学通报,2011,27(1):69-73.
66.唐强,李少昆,王克如,等.基于高光谱反射率的冬小麦生长后期氮素丰度监测研究[J].光谱学与光谱分析,2010,30(11):3061-3066.
67.唐延林,黄敬峰,王秀珍,等.水稻、玉米、棉花的高光谱及其红边特征比较[J].中国农业科学,2004,37(1):29-35.
68.田国良,杨希华,郑柯.冬小麦旱情遥感监测模型研究[J].环境遥感,1992,7(2):83-90.
69.田永超,朱艳,姚霞,等.基于光谱信息的作物氮素营养无损监测技术[J].生态学杂志,2007,26(9):1454-1463.
70.童淑媛,宋凤斌. SPAD值在玉米氮素营养诊断及推荐施肥中的应用[J].农业系统科学与综合研究,2009,25(2):233-238.
71.汪仁,孙文涛,邢月华,等.春玉米穗位叶叶绿体超微结构对农田管理措施的响应[J].光谱学与光谱分析,2011,31(3):793-797.
72.汪璇,徐小洪,吕家恪,等.基于GIS和模糊神经网络的西南山地烤烟生态适宜性评价[J].中国生态农业学报,2012,20(10):1366-1374.
73.王春虎,杨文平.不同施肥方式对夏玉米植株及产量性状的影响[J].中国农学通报,2011,27(9):305-308.
74.王纪华,黄文江,劳彩莲,等.运用PLS算法由小麦冠层反射光谱反演氮素垂直分布[J].光谱学与光谱分析,2007,27(7):1319-1322.
75.王克如,潘文超,李少昆,等.不同施肥量棉花冠层高光谱特征研究[J].光谱学与光谱分析,2011,31(7):1868-1872.
76.王磊,白由路,卢艳丽,等.不同形式的光谱参量对春玉米氮素营养诊断的比较[J].农业工程学报,2010,26(2):218-223.
77.王囡囡,于忠和,贾会彬,等.蚜虫胁迫下两种栽培模式大豆生理指标及光谱特征分析[J].中国油料作物学报,2011,33(1):48-51.
78.王启现,王璞,王伟东,等.吐丝期施氮对夏玉米粒重和籽粒粗蛋白的影响[J].中国农业大学学报,中国农业大学学报,2001,7(1):59-64.
79.王强,易秋香,包安明,等.基于高光谱反射率的棉花冠层叶绿素密度估算[J].农业工程学报,2012,28(15):125-132.
80.王绍华,曹卫星,王强盛,等.水稻叶色分布特点与氮素营养诊断[J].中国农业科学,2002,35(12):1461-1466.
81.王为.高光谱遥感技术的发展及其在农业上的应用[J].江西农业学报,2009,21(5):23-26.
82.王宪泽,张树芹.不同蛋白质含量小麦品种叶片NRA与氮素积累关系的研究[J].西北植物学报,1999,19(3):315-320.
83.王渊,黄敬峰,王福民,等.油菜叶片和冠层水平氮素含量的高光谱反射率估算模型[J].光谱学与光谱分析,2008,28(2):273-277.
84.王圆圆,陈云浩,李京,等.指示冬小麦条锈病严重度的两个新的红边参数[J].遥感学报,2007,11(6):875-881.
85.王正瑞,芮玉奎,申建波,等.氮肥施用量和形态对玉米苗期叶绿素含量的影响[J].光谱学与光谱分析,2009,29(2):410-412.
86.王之杰,王纪华,马智宏,等.冬小麦冠层氮素及硝酸还原酶活性的垂直分布[J].麦类作物学报,2004,24(1):31-34.
87.吴迪,黄凌霞,何勇,等.作物和杂草叶片的可见-近红外光谱特性[J].光学学报,2008,28(8):1618-1622.
88.吴华兵,朱艳,田永超,等.棉花冠层高光谱参数与叶片氮素含量的定量关系[J].植物生态学报,2007,31(5):903-909.
89.吴素霞,毛任钊,李红军,等.冬小麦叶片绿度时空变异特征研究[J].中国生态农业学报,2005,13(4):82-85.
90.夏天,吴文斌,周清波,等.基于高光谱的冬小麦叶面积指数估算方法[J].中国农业科学,2012,45(10):2085-2092.
91.解振兴,董志强,薛金涛.供氮量及化学调控对玉米苗期生长及氮素吸收分配特征的影响[J].玉米科学,2012,20(2):128-133,137.
92.徐世昌,戴俊英,沈秀英,等.水分胁迫对玉米光合性能及产量的影响[J].作物学报,1990,21(3):356-363.
93.徐新刚,吴炳方,蒙继华,等.农作物单产遥感估算模型研究进展[J].农业工程学报,2008,24(2):290-298.
94.薛利红,曹卫星,罗卫红,等.基于冠层反射光谱的水稻群体叶片氮素状况监测[J].中国农业科学,2003,36(7):807-812.
95.杨飞,张柏,宋开山,等.大豆叶面积指数的高光谱估算方法比较[J].光谱学与光谱分析,2008,28(12):2951-2955.
96.杨峰,范亚民,李建龙,等.高光谱数据估测稻麦叶面积指数和叶绿素密度[J].农业工程学报,2010,26(2):237-245.
97.杨国鹏,余旭初,冯伍法,等.高光谱遥感技术的发展与应用现状[J].测绘通报,2008,(10):1-5.
98.杨红卫,童小华.中高分辨率遥感影像在农业中的应用现状[J].农业工程学报,2012,28(24):138-149.
99.杨杰,田永超,姚霞,等.水稻上部叶片叶绿素含量的高光谱估算模型[J].生态学报,2009,29(12):6561-6571.
100.杨武,李运起,李建国,等.大喇叭口期不同施肥组合对青贮玉米产量的影响研究[J].中国农学通报,2012,28(14):32-35.
101.姚付启,张振华,杨润亚,等.基于红边参数的植被叶绿素含量高光谱估算模型[J].农业工程学报,2009,25(S2):123-129.
102.姚霞,朱艳,田永超,等.小麦叶片氮含量估测的最佳高光谱参数研究[J].中国农业科学,2009,42(8):2716-2725.
103.姚云军,秦其明,张自力,等.高光谱技术在农业遥感中的应用进展研究[J].农业工程学报,2008,24(7):301-306.
104.易时来,邓烈,何绍兰,等.奥林达夏橙叶片锌含量可见近红外光谱监测模型研究[J].光谱学与光谱分析,2010,30(11):2927-2931.
105.于亚利,贾文凯,王春宏,等.春玉米叶片SPAD值与氮含量及产量的相关性研究[J].玉米科学,2011,19(4):89-92,97.
106.袁佐清,张建勇.水分胁迫对玉米大喇叭口期生长的影响[J].干旱地区农业研究,2007,25(4):235-239.
107.岳文俊,张富仓,李志军,等.不同氮磷营养下苗期水分亏缺对玉米生长及水分利用的影响[J].干旱地区农业研究,2011,29(6):1-6.
108.张东彦,宋晓宇,马智宏,等.扫描成像光谱仪和地物光谱仪在单叶尺度上的对比研究[J].中国农业科学,2010,43(11):2239-2245.
109.张丰,熊桢,寇宁.高光谱遥感数据用于水稻精细分类研究[J].武汉理工大学学报,2002,24(10):36-39,46.
110.张金恒,王珂,王人潮,等.高光谱评价植被叶绿素含量的研究进展[J].上海交通大学学报,2003,21(1):73-82.
111.张瑞美,彭世彰,徐俊增.光谱技术在农业领域的应用与展望[J].果树学报,2005,22(1):9-11.
112.张维娜.基于遥感技术的运城盆地干旱监测应用研究[J].测绘通报,2010,(7):23-27.
113.张宪政.植物叶绿素含量测定-丙酮乙醇混合液法[J].辽宁农业科学,2006,(3):2063.
114.张彦军,薛吉全,张仁和,等.前期干旱对玉米吐丝期光合特性的影响[J].西北农业学报,2008,17(3):135-138.
115.张正杨,马新明,贾方方,等.烟草叶面积指数的高光谱估算模型[J].生态学报,2012,32(1):168-175.
116.张智猛,戴良香,胡昌浩,等.玉米灌浆期水分差异供应对籽粒淀粉积累及其酶活性的影响[J].植物生态学报,2005,29(4):636-643.
117.赵福刚.玉米冠层光谱氮营养诊断追肥模型的研究[赵福刚硕士学位论文].长春:吉林农业大学,2007.
118.赵刚峰,李军,刘冰峰,等.关中冬小麦叶片氮素含量高光谱遥感监测模型[J].麦类作物学报,2012,32(3):530-536.
119.赵杰文,王开亮,欧阳琴,等.高光谱技术分析茶树叶片中叶绿素含量及分布[J].光谱学与光谱分析,2011,31(2):512-515.
120.赵士诚,何萍,仇少君,等.相对SPAD值用于不同品种夏玉米氮肥管理的研究[J].植物营养与肥料学报,2011,17(5):1091-1098.
121.郑根昌,刑彭龄,武月莲.玉米抽雄期干旱胁迫对产量构成因素的影响[J].中国农学通报,2001,17(5):24-26.
122.郑光辉,周生路,吴绍华.土壤砷含量高光谱估算模型研究[J].光谱学与光谱分析,2011,31(1):173-176.
123.仲晓春,戴其根,何理,等.不同浓度镉胁迫下水稻冠层光谱特征及其预测评价[J].农业环境科学学报,2012,31(3):448-454.
124.周冬琴,田永超,姚霞,等.水稻叶片全氮浓度与冠层反射光谱的定量关系[J].应用生态学报,2008,19(2):337-344.
125.周丽丽,冯汉宇,阎忠敏,等.玉米叶片氮含量的高光谱估算及其品种差异[J].农业工程学报,2010,26(8):195-199.
126.周新国,韩会玲,李彩霞,等.玉米灌浆期渍水对产量及氮磷淋失量的影响[J].农业工程学报,2012,28(14):99-104.
127.朱蕾,徐俊锋,黄敬峰,等.作物植被覆盖度的高光谱遥感估算模型[J].光谱学与光谱分析,2008,28(8):1827-1831.
128.朱文超,成芳.转基因水稻及其亲本叶片的可见-近红外光谱分析[J].光谱学与光谱分析,2012,32(2):370-373.
129.朱西存,赵庚星,王凌,等.基于高光谱的苹果花氮素含量预测模型研究[J].光谱学与光谱分析,2010,30(2):416-420.
130.邹红玉,丁丽霞.基于反射光谱数据的茶树叶片SPAD值估算模型研究[J].遥感信息,2011,(117):71-75.
131. Al-Abbas A H, Barr R, Hall J D, et al. Spectra of normal and nutrient-deficient maize leaves[J].Laboratory for Applications of Remote Sensing, Technical Reports,1972,128:1-20.
132. Asai H, Samson B K, Stephan H M, et al. Biochar amendment techniques for upland riceproduction in Northern Laos[J]. Field Crops Reasearch,2009,111:81-84.
133. Blackburn G A. Quantifying chlorophyll and caroteniods at leaf and canopy scales: an evaluationof some hyperspectral approaches[J]. Remote Sensing of Enviro nment,1998,66:273-285.
134. Blackmer T M, Schepers J S, Varvel G E. Light reflectance compared with other nitrogen stressmeasurements in corn leaves[J]. Agronomy Journal,1994,86:934-938.
135. Bojinski S, Schaepaman M, Schlapfer D, et al. SPECCHIO: Aspectrum datebase for remotesensing applications[J]. Computers and Geosciences,2003,29:27-38.
136. Broge N H, Lebianc E. Comparing prediction power and stability of broadband and hyperspectralvegetation indices for estimation of green leaf area index and canopy cholorophyll density[J].Remote Sensing of Enviro nment,2000,76:156-172.
137. Broge N H, Mortensen J V. Deriving green crop area index and canopy chlorophyll density ofwinter wheat rom spectral reflectance data[J]. Remote Sensing of Enviro nment,202,81:45-57.
138. Cartelat A, Cerovic Z G, Goulas Y, et al. Optically assessed contents of leaf polyphenolics andchlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.)[J]. Field CropsReasearch,2005,91:35-49.
139. Castro-Esau K L, Sanchez-Azofeifa G A, Rivard B. Comparison of spectral indices obtained usingmultiple spectroradiometers[J]. Remote Sensing of Enviro nment,2006,103:276-288.
140. Cavaglieri L, Orlando J, Etcheverry M. Rhizosphere microbial community structure at differentmaize plant growth stages and root locations[J]. Microbiological Research,2009,164:391-399.
141. Ceccato P, Flasse S, Tarantola, et al. Detecting vegetation leaf water content using reflectance inthe optical domain[J]. Remote Sensing of Enviro nment,2001,77:22-33.
142. Chappelle E W, Kim M S, Mcmurtrey J E. Ration analysis of reflectance spectra (RARS): analgorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B and thecarotenoids in soybean leaves[J]. Remote Sensing of Enviro nment,1992,39:239-247.
143. Chen P F, Habouudane D, Tremblay N, et al. New spectral indicator assessing the efficiency ofcrop nitrogen treatment in corn and wheat[J]. Remote Sensing of Enviro nment,2010,114:1987-1997.
144. Cheng T, Rivard B, Sanchez-Azofeifa. Spectroscopic determination of leaf water content usingcontinuous wavelet analysis[J]. Remote Sensing of Enviro nment,2011,115:659-670.
145. Cherif J, Derbel N, Nakkach M, et al. Analysis of in vivo chlorophyll fluorescence spectra tomonitor physiological state of tomato plants growing under zinc stress[J]. Journal ofPhotochemistry and Photobiology,2010,101:332-339.
146. Chiu H Y, Collins W E. A spectroradiometer for airnorne remote sensing[J].PhotogrammetricEngineering and Remote Sensing,1978,44:507-107.
147. Ciganda V, Gitelson A, Schepers J. Non-destructive determination of maize leaf and canopychlorophyll content[J]. Journal of Plant Physiology,2009,166:157-167.
148. Clevers J G P W, Gitelson A A. Remote estimation of crop and grass chlorophyll and nitrogencontent using red-edge bands on Sentinel-2and-3[J]. International Journal of Applied EarthObservation and Geoinformation,2013,23:344-351.
149. Clevers J G P W, Kooistra L, Schaepman M E. Using spectral information from the NIR waterabsorption features for the retrieval of canopy water content[J]. International Journal of AppliedEarth Observation and Geoinformation,2008,10:388-397.
150. Damm A, Erler A, Hillen W, et al. Modeling the impact of spectral sensor configurations on theFLD retrieval accuracy of sun-induced chlorophyll fluorescence[J]. Remote Sensing of Environment,2011,115:1882-1892.
151. Daughtry C S T, Walthall C L, Kim M S, et al. Estimating corn leaf chlorophyll concentrationfrom leaf and canopy reflectance[J]. Remote Sensing of Enviro nment,2000,74:229-239.
152. Dunagan S C, Gilmore M S, Varekamp J C. Effects of mercury on visible/near-infrared reflectancespectra of mustard spinach plants (Brassica rapa P.)[J]. Enviro nmental Pollution,2007,148:301-311.
153. Ecarnot M, Compan F, Roumet P. Assessing leaf nitrogen content and leaf mass per unit area ofwheat in the field throughout plant cycle with a portable spectrometer[J]. Field Crops Research,2013,140:44-50.
154. Eitel J U H, Vierling L A, Long D S, et al. Early season remote sensing of wheat nitrogen statususing a green scanning laser[J]. Agricultural and Forest Meteorology,2011,151:1338-1345.
155. Erdel K, Mistele B, Schmidhalter U. Spectral high-throughput assessments of phenotypicdifferences in biomass and nitrogen partitioning during grain filling of wheat under high yieldingWestern European conditions[J]. Field Crops Research,2013,141:16-26.
156. Errecart P M, Agnusdei M G, Lattanzi F A, et al. Leaf nitrogen concentration and chlorophyllmeter readings as predictors of tall fescue nitrogen nutrition status[J]. Field Crops Research,2012,129:46-58.
157. Farruggia A, Gastal F, Scholefield D. Assessment of the nitrogen status of grassland[J]. Grass andForage Science,59:113-120.
158. Feng M C, Yang W D, Cao L L, et al. Monitoring winter wheat freeze injury using muli-temporalMODIS data[J]. Agricultural Sciences in China,2009,8(9):1053-1062.
159. Ferrio J P, Villegas D, Zarco J, et al. Assessment of durum wheat yield using visible andnear-infrared reflectance spectra of canopies[J]. Field Crops Research,2005,94:126-148.
160. Fiore A D, Reverberi M, Ricelli A, et al. Early detection of toxigenic fungi on maize byhyperspectral imaging analysis[J]. International Journal of Food Microbiology,2010,144:64-71.
161. Fitzgerald G, Rodriguez D, Leary G. Measuring and predicting canopy nitrogen nutrition in wheatusing a spectral index-the canopy chlorophyll content index (CCCI)[J]. Field Crops Research,2010,116:318-324.
162. Foley S, Rivard B, Sanchez-Azofeifa, et al. Foliar spectral properties following leaf clipping andimplications for handling techniques[J]. Remote Sensing of Enviro nment,2006,103:265-275.
163. Gianquinto G, Orsini F, Fecondini M, et al. A methodological approach for defining spectralindices for assessing tomato nitrogen status and yield[J]. European Journal of Agronomy,2011,35:135-143.
164. Giardino C, Brivio P A. The application of a dedicated device to acquire birdirectional reflectancefactors over natural surfaces[J]. International Journal of Remoting Sensing,2003,24:2989-2995.
165. Gitelson A A, Merzlyak M N. Signature analysis of leaf reflectance spectra: Algorithmdevelopment for remote sensing[J]. Journal of plant physiology,1996,148:493-500.
166. Goetz A F H. Portable field reflectance spectrometer[J]. Pasadena, California Jet PropulsionLaboratory, JPL Technical Report,1997:183-188.
167. Graeff S, Claupein W. Quantifying nitrogen status of corn (Zea mays L.) in the field by reflectancemeasurements[J]. European Journal of Agronomy,2003,19:611-618.
168. Haboudane D, Miller J R, Tremblay N, et al. Integrated narrowed-band vegetation indices forprediction of crop chlorophyll content for application to precision agriculture[J]. Remote Sensingof Enviro nment,2002,81:416-426.
169. Hawkins T S, Gardiner E S, Comer G S. Modeling the relationship between extractablechlorophyll and SPAD-502readings for endangered plant species research[J]. Journal for NatureConservation,2009,17:123-127.
170. Houborg R, Boegh E. Mapping leaf chlorophyll and leaf area index using inverse and forwardcanopy reflectance modeling and SPOT reflectance data[J]. Remote Sensing of Enviro nment,2008,112:186-202.
171. Houles V, Guerif M, Mary B. Elaboration of a nitrogen nutrition indicator for winter wheat basedon leaf area index and chlorophyll content for making nitrogen recommendations[J]. EuropeanJournal of Agronomy,2007,27:1-11.
172. Idso S B, Glawson K L. Foliage temperature: Effects of enviro nmental factors with implication forplant water stress assessment and the CO2/climate connection[J]. Water Resource Research,1986,22(12):1702-1716.
173. Inoue Y, Sakaiya E, Zhu Y, et al. Diagnostic mapping of canopy nitrogen content in rice based onhyperspectral measurements[J]. Remote Sensing of Enviro nment,2012,126:210-221.
174. Jain N, Ray S S, Singh J P, et al. Use of hyperspectral data to assess the effects of different nitrogenapplications on a potato crop[J]. Precision Agriculture,2007,8(4):225-239.
175. Jin X L, Wang K R, Xiao C H, et al. Comparison of two methods for estimation of leaf totalchlorophyll content using remote sensing in wheat[J]. Field Crop Research,2012,135:24-29.
176. Kavzoglu T, Colkesen I. AA kernel functions analysis for support vector machines for land coverclassification[J]. International Journal of Applied Earth Observation and Geoinformation,2009,11(5):352-359.
177. Khabba S, Ledent J F, Lahrouni A. Development and validation of model for estimatingtemperature within maize ear[J]. Agricultural and Forest Meteorology,2001,106:131-146.
178. Khabba S, Ledent J F, Lahrouni A. Maize ear temperature[J]. European Journal of Agronomy,2001,14:197-208.
179. Kokaly R F, Clark R N. Spectroscopic determination of leaf biochemistry using band-depthanalysis of absorption features and stepwise multiple linear regression[J]. Remote Sensing ofEnviro nment,1999,67:267-287.
180. Leidi E O, Silberbush M, Soares M M, et al. Salinity and nitrogen nutrition studies on peanut andcotton plants[J]. Journal of Plant Nutrition,1992,15:591-604.
181. Lemaire G, Gastal F, Plenet D. Dynamics of N uptake and N distribution in plant canopies. Use ofcrop N status index in crop modeling[J]. Diagnostic Procedures for Crop N Management,1995,11:22-23.
182. Leuning R, Hughes D, Daniel P, et al. A multi-angle spectrometer for automatic measurement ofplant canopy reflectance spectra[J]. Remote Sensing of Enviro nment,2006,103:236-245.
183. Li F, Gnyp M L, Jia L L, et al. Estimating N status of winter wheat using a handheld spectrometerin the North China Plain[J]. Field Crop Research,2008,106:77-85.
184. Liu J G, Pattey E, Miller J R, et al. Estimating crop stresses, aboveground dry biomass and yield ofcorn using multi-temporal optical data combined with a radiation use efficiency model[J]. RemoteSensing of Enviro nment,2010,114:1167-1177.
185. Ma B L, Dwyer E R, Cober E R, et al. Early prediction of soybean yield from canopy reflectancemeasurements[J]. Agronomy Journal,2001,93:1227-1234.
186. Maderia A C, Mendonca A, Ferreira M E, et al. Relationship between spectroradiometric andchlorophyll measurements in green beans communication[J]. Soil Science and Plant Analysis,2000,31(5-6):631-643.
187. Maiorano A, Mancini M C. Water relationships and temperature interactions in maize grain duringmaturation[J]. Field Crop Research,2010,119:304-307.
188. Martin M E, Plourde L C, Ollinger S V, et al. A generalizable method for remote sensing ofcanopy nitrogen across a wide range of forest ecosystems[J]. Remote Sensing of Enviro nment,2008,112:3511-3519.
189. Mass S J, Dunlap J R. Reflectance, transmittance, and absorbance of light by normal etiolated, andalbino corn leaves[J]. Agronomy Journal,1989,81:105-110.
190. Matsukura K, Matsumura M. Cultural control of leafhopper-induced maize wallaby ear symptomin forage maize via early planting dates[J]. Crop Protection,2010,29:1401-1405.
191. Meng Z D, Zhang F J, Ding Z H, et al. Inheritance of ear tip-barrenness trait in maize[J].Agricultural Sciences in China,2007,6(5):628-633.
192. Merzlyak M N, Solovchenko A E, Gitelson A A. Reflectance spectral features and non-destructiveestimation of chlorophyll, carotenoid and anthocyanin content in apple fruit[J]. PostharvestBiology and Technology,2003,27:197-211.
193. Middleton W E, Mungall A G. The luminous directional reflectance of snow[J]. Journal of theOptical Society of America,1952,42:572-579.
194. Milap B, Benbi D K, Azad A S. Modifying soil test based fertilizer P recommendations fortargeted yield of rice on Typic Haplustalf[J]. Journal Indian Society of Soil Science,2004,52:258-261.
195. Milton E J, Schaepman M E, Anderson K, et al. Progress in field spectroscopy[J]. Remote Sensingof Enviro nment,2009,113: S92-S109.
196. Min M, Lee W S, Burks T F, et al. Design of a hyperspectral nitrogen sensing system for orangeleaves[J]. Computers and Electronics in Agriculture,2008,63:215-226.
197. Mirschel W, Wenkel K O, Schultz A, et al. Dynamic phonological model foe winter rye and winterbarley[J]. European Journal of Agronomy,2005,23:123-135.
198. Mistele B, Schmidhalter U. Estimating the nitrogen nutrition index using spectral canopyreflectance measurements[J]. European Journal of Agronomy,2008,29:184-190.
199. Mitchell J J, Glenn N F, Sankey T T, et al. Remote sensing of sagebrush canopy nitrogen[J].Remote Sensing of Enviro nment,2012,124:217-223.
200. Monasterio I O, Lobell D B. Remote sensing assessment of regional yield losses due tosub-optimal planting dates and fallow period weed management[J]. Field Crops Research,2007,101(1):80-87.
201. Namrata J, Shibendu S R, Singh J P, et al. Use of hyperspectral data to acess the effects of differentnitrogen applications on a potato crop[J]. Precision Agriculture,2007,8:225-239.
202. Naumann J C, Young D R, Anderson J E, et al. Leaf chlorophyll fluorescence, reflectance, andphysiological response to freshwater and saltwater flooding in the evergreen shrub, Muricacerifera[J]. Enviro nmental and Experimental Botany,2008,63:402-409.
203. Noh H, Zhang Q, Shin B, et al. A neural network model of maize crop nitrogen stress assessmentfor a multi-spectral imaging sensor[J]. Biosystems Engineering,2006,94(4):477-485.
204. Osborne S L, Schepers J S, Francis D D, et al. Detection of phosphorus and nitrogen deficienciesin corn using spectral radiance measurements[J]. Agronomy Journal,2002,94:1215-1221.
205. Otegui M E, Bonhomme R. Grain yield components in maize I. Ear growth and kernel set[J]. FieldCrops Reasearch,1998,56:247-256.
206. Pagano E, Cela S, Maddonni G A, et al. Intra-specific competition in maize: Ear development,flowering dynamics and kernel set of early-established plant hierarchies[J]. Field Crops Reasearch,2007,102:198-209.
207. Pagola M, Ortiz R, Irigoyen I, et al. New method to assess barley nitrogen nutrition status based onimage color analysis comparison with SPAD-502[J]. Computers and Electronics in Agriculture,2009,65:213-218.
208. Perry E M, Davenport J R. Spectral and spatial differences in response of vegetation indices tonitrogen treatments on apple[J]. Computers and Electronics in Agriculture,2007,59:56-65.
209. Pettersson C G, Eckersten H. Prediction of grain protein in spring malting barley grown in northernEurope[J]. Europ Journal of Agronomy,2007,27(2):205-214.
210. Pimstein A, Karnieli A, Bansal S K, et al. Exploring remotely sensed technologies for monitoringwheat potassium and phosphorus using field spectroscopy[J]. Field Crops Research,2011,121:125-135.
211. Pinar A, Curran P J. Grass chlorophyll and the reflectance red edge[J]. International Journal ofRemote Sensing,1996,17:351-357.
212. Raymond F K. Investigating a physical basis for spectroscopic estimates of leaf nitrogenconcentration[J]. Remote Sensing of Enviro nment,2001,2(75):153-161.
213. Reum D, Zhang Q. Wavelet based multi-spectral image analysis of maize leaf chlorophyllcontent[J]. Computers and Electronics in Agriculture,2007,56:60-71.
214. Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices[J]. RemoteSensing of Enviro nment,1996,58:1-12.
215. Rossini M A, Maddonni G A, Otegui M E. Inter-plant competition for resources in maize cropsgrown under contrasting nitrogen supply and density: Variability in plant and ear growth[J]. FieldCrops Research,2011,121:373-380.
216. Rotbart N, Schmilovitch Z, Cohen Y, et al. Estimating olive leaf nitrogen concentration usingvisible and near-infrared spectral reflectance[J]. Biosystems Engineering,2013,114:426-434.
217. Ryu C, Suguri M, Umeda M. Multivariate analysis of nitrogen content for rice at the heading stageusing reflectance of airborne hyperspectral remote sensing[J]. Field Crops Research,2011,122:214-224.
218. Schaepman M E. Spectrodirectional remote sensing: from pixels to processes[J] InternationalJournal of Applied Earth Observation and Geoinformation,2007,9(2):204-223.
219. Scharf P C, Lory J A. Calibrating corn color from aerial photographs to predict sidedress nitrogenneed[J]. Agronomy Journal,2002,94:397-404.
220. Schepers J S, Blackmer T M, Wilhelm W W, et al. Transmittance and reflectance measurements ofcorn leaves from plants with different nitrogen and water supply[J]. Journal of Plant Physiology,1996,148(5):523-529.
221. Serrano L, Filella I, Penuelas J. Remote sensing of biomass and yield of winter wheat underdifferent nitrogen supplies[J]. Crop Science,2000,40(3):723-731.
222. Serrano L, Flor C G, Gorchs G. Assessment of grape yield and composition using the reflectancebased Water Index in Mediterranean rainfed vineyards[J]. Remote Sensing of Enviro nment,2012,118:249-258.
223. Shi J Y, Zou X B, Zhao J W, et al. Nondestructive diagnostics of nitrogen deficiency by cucumberleaf chlorophyll distribution map based on near infrared hyperspectral imaging[J]. ScientiaHorticulturae,2012,138:190-197.
224. Shibayama M, Takahashi W, Morinaga S, et al. Canopy water deficit detection in paddy rice usinga high resolution field spectroradiometer[J]. Remote Sensing of Enviro nment,1993,45(2):117-126.
225. Shrestha S, Brueck H, Asch F. Chlorophyll index, photochemical reflectance index and chlorophyllfluorescence measurements of rice leaves supplied with different N levels[J]. Journal ofPhotochemistry and Photobiology B: Biology,2012,113:7-13.
226. Silva T R B D, Reis A C D S, Maciel C D D G. Relationship between chlorophyll meter readingsand total N in crambe leaves as affected by nitrogen topdressing[J]. Industrial Crops and Products,2012,39:135-138.
227. Sims D A, Gamon J A. Estimation of vegetation water content and photosynthetic tissue area fromspectral reflectance: a comparison of indices based on liquid water and chlorophyll absorptionfeatures[J]. Remote Sensing of Enviro nment,2003,84:526-537.
228. Slaton M R, Haut E R, Smith W K. Estimating near-infrared leaf reflectance from leaf structuralcharacteristics[J]. American Journal of Botany,2001,88(2):278-284.
229. Strachan I B, Pattey E, Boisvert J B, et al. Impact of nitrogen and enviro nmental conditions oncorn as detected by hyperspectral reflectance[J]. Remote Sensing of Enviro nment,2002,80:213-224.
230. Stroppiana D, Boschetti M, Brivio P A, et al. Plant nitrogen concentration in paddy rice from fieldcanopy hyperspectral Radiometry[J]. Field Crop Research,2009,111:119-129.
231. Stuckens J, Dzikiti S, Verstraeten W W, et al. Physiological interpretation of a hyperspectral timeseries in a citrus orchard[J]. Agricultural and Forest Meteorology,2011,151:1002-1015.
232. Swiader J M, Moore A. SPAD-chlorophyll response to nitrogen fertilization and evaluation ofnitrogen status in dry land and irrigated pumpkins[J]. Journal of Plant Nutrition,2002,25:1089-1100.
233. Szeles A V, Megyes A, Nagy J. Irrigation and nitrogen effects on the leaf chlorophyll content andgrain yield of maize in different crop years[J]. Agricultural Water Management,2012,107:133-144.
234. Takebe M, Yoneyama T. Measurement of leaf color scores and its implication to nitrogen nutritionof rice plants[J]. Japanese Agriculture Research Journal,1990,23:96-93.
235. Tarpley L, Raja R K, Sassenrath G F. Reflectance indices with precision and accuracy in predictioncotton leaf nitrogen concentration[J]. Crop Science,2000,40:1814-1819.
236. Thenkabail P S, Smith R B, Pauw E D. Evaluation of narrowband and broadband vegetationindices for determining optimal hyperspectral wavebands for agricultural crop characterization[J].Photogrammetric Engineering and Remote Sensing,2002,68:607-627.
237. Thomas J R, Oerther G F. Estimating nitrogen content of sweet peeper leaves by reflectancemeasurements[J]. Agronomy Journal,1972,64:11-13.
238. Uddling J, Alfredsson J G, Piikki K, et al. Evaluating the relationship between leaf chlorophyllconcentration and SPAD-502chlorophyll meter readings[J]. Photosynthesis Research,2007,91:37-46.
239. Ulloa S M, Datta A, Bruening C, et al. Maize response to broadcast flaming at different growthstages: Effects on growth, yield and yield components[J]. European Journal of Agronomy,2011,34:10-19.
240. Vigneau N, Ecarnot M, Rabatel G, et al. Potential of field hyperspectral imaging as a nondestructive method to assess leaf nitrogen content in Wheat[J]. Field Crop Research,2011,122:25-31.
241. Walter E R, Blackmer T M. Leaf reflectance spectra of cereal aphid-damaged wheat[J]. CropSciences,1999,39:1835-1840.
242. Wang F M, Huang J F, Tang Y L, et al. New vegetation index and its application in estimating leafarea index of rice[J]. Rice Science,2007,14(3):195-203.
243. Wang W, Yao X, Yao X F, et al. Estimating leaf nitrogen concentration with three-band vegetationindices in rice and wheat[J]. Field Crops Research,2012,129:90-98.
244. Wiesler F, Bauer M, Kamh M, et al. The crop as indicator for sidedress nitrogen demand in sugarbeet production–limitations and perspectives[J]. Journal of Plant Nutrition and Soil Science,2002,165:93-99.
245. Wright D L, Rasmussen V P, Ramsey R D, et al. Canopy reflectance estimation of wheat nitrogenfor grain protein management[J]. GIScience and Remote Sensing,2004,41:287-300.
246. Wu C Y, Niu Z, Gao S. The potential of the satellite derived green chlorophyll index for estimatingmidday light use efficiency in maize, coniferous forest and grassland[J]. Ecological Indicators,2012,14:66-73.
247. Yang F, Li J L, Gan X Y, et al. Assessing nutritional status of Festuca arundinacea by monitoringphotosynthetic pigments from hyperspectral data[J]. Computers and Electronics in Agriculture,2010,70:52-59.
248. Yang K, Chen Y H, Guo D Z, et al. Spectral information detection and extraction of wheat striperust based on hyperspectral image[J]. Acta Photonica Sinica,2008,31(1):145-150.
249. Ye X J, Sakai K S, Garciano L O, et al. Estimation of citrus yield from airborne hyperspectralimages using a neural network model[J]. Ecological Modelling,2006,198:426-432.
250. Yi P, Gitelson A A. Application of chlorophyll-related vegetation indices for remote estimation ofmaize productivity[J]. Agricultural and Forest Meteorology,2011,151:1267-1276.
251. Yi P, Gitelson A A, Keydan G, et al. Remote estimation of gross primary production in maize andsupport for a new paradigm based on total crop chlorophyll content[J]. Remote Sensing of Environment,2011,115:978-989.
252. Yu Z H, Cao Z G, Wu X, et al. Automatic image-based detection technology for two critical growthstages of maize: Emergence and three-leaf stage[J]. Agricultural and Forest Meteorology,2013,174:65-84.
253. Zhang H, Zhu L F, Hu H, et al. Monitoring leaf chlorophyll fluorescence with spectral reflectancein rice (Oryza sativa L.)[J]. Procedia Engineering,2011,15:4403-4408.
254. Zhang J H, Wang K. New vegetation index for estimating nitrogen concentration using fresh leafspectral reflectance[J]. Transactions of the CASE,2008,24(3):158-161.
255. Zhao D L, Reddy K R, Kakani V G, et al. Canopy reflectance in cotton for growth assessment andlint yield prediction[J]. European Journal of Agronomy,2007,26:335-334.
256. Zhao D L, Reddy K R, Kakani V G, et al. Selection of optimum reflectance ratios for estimatingleaf nitrogen and chlorophyll concentrations of field-grown cotton[J]. Agronomy Journal,2005,97:89-98.
257. Zhao F, Gu X F, Verhoef W, et al. A spectral directional reflectance model of row crops[J].Remote Sensing of Enviro nment,2010,114:265-285.
258. Zhou Q F, Wang J H. Comparison of upper leaf and lower leaf of rice plants in response tosupplemental nitrogen levels[J]. Journal of Plant Nutrition,2003,26(3):607-617.
259. Zhu Y, Yao X, Tian Y C, et al. Analysis of common canopy vegetation indices for indicating leafnitrogen accumulations in wheat and rice[J]. International Journal of Applied EarthObservationand Geoinformation,2008,10:1-10.
260. Zou X B, Shi J Y, Hao L M, et al. In vivo noninvasive detection of chlorophyll distribution incucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging[J]. AnalyticaChimica Acta,2011,706:105-112.
2.蔡红光,米国华,陈范骏,等.玉米叶片SPAD值、全氮及硝态氮含量的品种间变异[J].植物营养与肥料学报,2010,16(4):866-873.
3.曹玉军,刘春光,王晓慧,等.施钾量对甜玉米穗位叶蔗糖合成的影响[J].玉米科学,2010,18(5):72-75.
4.陈红艳,赵庚星,李希灿,等.小波分析用于土壤速效钾含量高光谱估测研究[J].中国农业科学,2012,45(2):1425-1431.
5.陈君颖,田庆久.高分辨率遥感植被分类研究[J].遥感学报,2007,11(2):221-227.
6.陈顺平.氮素在旱作系统作物中的吸收与转化[陈顺平硕士学位论文].北京:首都师范大学,2006.
7.陈杨,樊明寿,李斐,等.氮素营养诊断技术的发展及其在马铃薯生产中的应用[J].中国农学通报,2009,25(3):66-71.
8.程一松,胡春胜,郝二波,等.氮素胁迫下的冬小麦高光谱特征提取与分析[J].资源科学,2003,25(1):86-93.
9.崔贝,黄文江,杨武德,等.不同施肥决策对冬小麦生长影响的高光谱监测及对比分析[J].植物营养与肥料学报,2013,19(1):11-19.
10.代辉,胡春胜,程一松,等.不同氮水平下冬小麦农学参数与光谱植被指数的相关性[J].干旱地区农业研究,2005,23(4):16-21.
11.党蕊娟,李世清,穆晓慧,等.施氮对半湿润农田夏玉米冠层氮素及叶绿素相对值(SPAD值)垂直分布的影响[J].中国生态农业学报,2009,17(1):54-59.
12.邓劲松,李君,王珂.基于多时相PCA光谱增强和多源光谱分类器的SPOT影像土地利用变化检测[J].光谱学与光谱分析,2009,29(6):1627-1631.
13.杜华强,葛宏立,范文义,等.马尾松针叶光谱特征与其叶绿素含量间关系研究[J].光谱学与光谱分析,2009,29(11):3033-3037.
14.段巍巍,李慧玲,肖凯,等.氮肥对玉米穗位叶光合作用及其生理生化特性的影响[J].华北农学报,2007,22(1):26-29.
15.方慧,宋海燕,曹芳,等.油菜叶片的光谱特征与叶绿素含量之间的关系研究[J].光谱学与光谱分析,2007,27(9):1731-1734.
16.方昭希,王明录,彭代平,等.硝酸还原酶彗星与氮素营养的关系[J].植物生理学报,1979,5(2):123-128.
17.冯伟,郭天财,谢迎新,等.作物光谱分析技术及其在生长监测中的应用[J].中国农学通报,2009,25(23):182-188.
18.高飞,肖靖,谷运红,等.利用小麦叶片SPAD值预测成熟期籽粒蛋白质含量的研究[J].光谱学与光谱分析,2012,32(5):1350-1354.
19.郭书亚,张新,张前进,等.秸秆覆盖深松对夏玉米花后穗位叶衰老和产量的影响[J].玉米科学,2012,20(1):104-107.
20.韩晓日,姜琳琳,王帅,等.不同施肥处理对春玉米穗位叶光合指标的影响[J].沈阳农业大学学报,2009,40(4):444-448.
21.郝玉兰,潘金豹,张秋芝,等.玉米穗位叶蛋白质含量等生理性状的变化研究[J].玉米科学,2002,10(4):32-34.
22.胡昊,白由路,杨俐苹,等.不同氮营养冬小麦冠层光谱红边特征分析[J].植物营养与肥料学报,2009,15(6):1317-1323.
23.胡玉福,邓良基,张世熔,等.基于RS和GIS的西昌市土地利用及景观格局变化[J].农业工程学报,2011,27(10):322-327.
24.黄迪,张佳宝,张丛志,等.不同大气湿度与氮肥水平对夏玉米苗期水分利用效率的影响[J].玉米科学,2012,20(1):123-127.
25.吉海彦,王鹏新,严泰来,等.冬小麦活体叶片叶绿素和水分含量及反射光谱的模型建立[J].光谱学与光谱分析,2007,27(3):514-516.
26.贾方方,马新明,李春明,等.不同水分处理对烟草叶片高光谱及红边特征的影响[J].中国生态农业学报,2011,19(6):1330-1335.
27.贾良良,寿丽娜,李斐,等.遥感技术在植物氮营养诊断和推荐施肥中的应用之研究进展[J].中国农学通报,2007,23(12):396-401.
28.姜继萍,杨京平,杨正超,等.不同氮素水平下水稻叶片及其相邻叶位SPAD值变化特征[J].浙江大学学报,2012,38(2):166-174.
29.蒋金豹,陈云浩,黄文江,等.条锈病胁迫下冬小麦冠层叶片氮素含量的高光谱估测模型[J].农业工程学报,2008,24(1):35-39.
30.金继运,白由路,杨俐苹,等.高效土壤养分测试技术与设备[M].北京:中国农业出版社,2006:148-149.
31.金伟,葛宏立,杜华强,等.无人机遥感发展与应用概况[J].遥感信息,2009,(1):88-92.
32.隽英华,汪仁,刑月华,等.基于可见光光谱扫描的春玉米氮素营养诊断[J].玉米科学,2012,20(5):126-130.
33.李潮海,刘奎,周苏玫,等.不同施肥条件下夏玉米光合对生理生态因子的响应[J].作物学报,2002,28(2):265-269.
34.李耕,高辉远,刘鹏,等.氮素对玉米灌浆期叶片光合性能的影响[J].植物营养与肥料学报,2010,16(3):536-542.
35.李辉,白丹,张卓,等.羊草叶片SPAD值与叶绿素含量的相关分析[J].中国农学通报,2012,28(2):27-30.
36.李金文.基于水稻叶片生理生态学特征的氮营养诊断[李金文博士学位论文].杭州:浙江大学,2010.
37.李少昆,谭海珍,王克如,等.小麦籽粒蛋白质含量遥感监测研究进展[J].农业工程学报,2009,25(2):302-307.
38.李新海,高根来,梁晓玲,等.我估主要玉米自交系开花期耐干旱差异及改良[J].作物学报,2002,28(5):595-600.
39.李占录,翟世宏,李爱军,等.玉米抽雄期叶面喷洒DA-6增产效果的研究[J].玉米科学,2006,14(增刊):84-85.
40.李志宏,刘宏斌,张福锁.应用叶绿素仪诊断冬小麦氮营养状况的研究[J].植物营养与肥料学报,2003,9(4):401-405.
41.梁爽,赵庚星,朱西存.苹果树叶片叶绿素含量高光谱估测模型研究[J].光谱学与光谱分析,2012,32(5):1367-1370.
42.刘冰峰,李军,赵刚峰,等.夏玉米叶片全氮高光谱遥感估算模型研究[J].植物营养与肥料学报,2012,18(4):813-824.
43.刘宏斌,张云贵,李志宏,等.光谱技术在冬小麦氮素营养诊断中的应用研究[J].中国农业科学,2004,37(11):1743-1748.
44.刘良云,王纪华,黄文江,等.利用新型光谱指数改善冬小麦估产精度[J].农业工程学报,2004,20(1):172-175.
45.刘萍,陆大雷,孙建勇,等.拔节期追氮对春播和秋播糯玉米淀粉胶凝和回生特性的影响[J].植物营养与肥料学报,2010,16(3):543-551.
46.刘炜,常庆瑞,郭曼,等.冬小麦导数光谱特征提取与缺磷胁迫神经网络诊断[J].光谱学与光谱分析,2011,31(4):1092-1096.
47.刘贤德,李新海,张世煌.玉米开花期耐旱相关性状的遗传及育种策略[J].玉米科学,2002,10(3):13-18.
48.刘小军,田永超,姚霞,等.基于高光谱的水稻叶片含水量监测研究[J].中国农业科学,2012,45(3):435-442.
49.刘艳春,樊明寿.应用叶绿素仪SPAD-502进行马铃薯氮素营养诊断的可行性[J].中国马铃薯,2012,26(1):45-48.
50.卢艳丽,白由路,杨俐苹,等.东北平原不同类型土壤有机质含量高光谱反演模型同质性研究[J].植物营养与肥料学报,2011,17(2):456-463.
51.陆大雷,景立权,王德成,等.拔节期追氮对不同季节糯玉米淀粉糊化特性的影响[J].生态学报,2010,30(2):549-555.
52.罗庆锋.叶绿素的研究进展及叶绿素铜纳多开发利用[J].林产化工通讯,1995,(1):32-33.
53.马晓霞,王莲莲,黎青慧,等.长期施肥对玉米生育期土壤微生物碳氮及酶活性的影响[J].生态学报,2012,32(17):5502-5511.
54.牛铮,陈永华,随洪智,等.叶片化学组分成像光谱压迫感探测机理分析[J].遥感学报,2000,4(2):125-130.
55.潘静,曹兵,万仲武.两种果树叶片SPAD值与叶绿素含量相关性分析[J].北方园艺,2012,(5):9-12.
56.蒲瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社,2000:2.
57.裘正军,宋海燕,何勇,等.应用SPAD和光谱技术研究油菜生长期间的氮素变化规律[J].农业工程学报,2007,23(7):150-156.
58.邵国庆,李增嘉,宁堂原,等.灌溉与尿素类型对玉米花后穗位叶衰老、产量和效益的影响[J].中国农业科学,2009,42(10):3459-3466.
59.史典义,刘忠香,金危危.植物叶绿素合成、分解代谢及信号调控[J].遗传,2009,31(7):698-704.
60.宋海星,李生秀.玉米生长量、养分吸收量及氮肥利用率的动态变化[J].中国农业科学,2003,36(1):71-76.
61.孙红,李民赞,赵勇,等.冬小麦生长期光谱变化特征与叶绿素含量监测研究[J].光谱学与光谱分析,2010,30(1):192-196.
62.孙钦平,贾良良,芮玉奎,等.应用可见光光谱进行夏玉米氮营养诊断[J].光谱学与光谱分析,2009,29(2):432-435.
63.谭昌伟,郭文善,朱新开,等.不同条件下夏玉米冠层反射光谱响应特性研究[J].农业工程学报,2008,24(9):131-136.
64.谭昌伟,王纪华,黄文江,等.夏玉米叶片全氮、叶绿素及叶面积指数的光谱响应研究[J].西北植物学报,2004,24(6):1041-1046.
65.唐海涛,张彪,谭君,等.玉米杂交种产量性状与穗位叶光合性状关联度分析[J].中国农学通报,2011,27(1):69-73.
66.唐强,李少昆,王克如,等.基于高光谱反射率的冬小麦生长后期氮素丰度监测研究[J].光谱学与光谱分析,2010,30(11):3061-3066.
67.唐延林,黄敬峰,王秀珍,等.水稻、玉米、棉花的高光谱及其红边特征比较[J].中国农业科学,2004,37(1):29-35.
68.田国良,杨希华,郑柯.冬小麦旱情遥感监测模型研究[J].环境遥感,1992,7(2):83-90.
69.田永超,朱艳,姚霞,等.基于光谱信息的作物氮素营养无损监测技术[J].生态学杂志,2007,26(9):1454-1463.
70.童淑媛,宋凤斌. SPAD值在玉米氮素营养诊断及推荐施肥中的应用[J].农业系统科学与综合研究,2009,25(2):233-238.
71.汪仁,孙文涛,邢月华,等.春玉米穗位叶叶绿体超微结构对农田管理措施的响应[J].光谱学与光谱分析,2011,31(3):793-797.
72.汪璇,徐小洪,吕家恪,等.基于GIS和模糊神经网络的西南山地烤烟生态适宜性评价[J].中国生态农业学报,2012,20(10):1366-1374.
73.王春虎,杨文平.不同施肥方式对夏玉米植株及产量性状的影响[J].中国农学通报,2011,27(9):305-308.
74.王纪华,黄文江,劳彩莲,等.运用PLS算法由小麦冠层反射光谱反演氮素垂直分布[J].光谱学与光谱分析,2007,27(7):1319-1322.
75.王克如,潘文超,李少昆,等.不同施肥量棉花冠层高光谱特征研究[J].光谱学与光谱分析,2011,31(7):1868-1872.
76.王磊,白由路,卢艳丽,等.不同形式的光谱参量对春玉米氮素营养诊断的比较[J].农业工程学报,2010,26(2):218-223.
77.王囡囡,于忠和,贾会彬,等.蚜虫胁迫下两种栽培模式大豆生理指标及光谱特征分析[J].中国油料作物学报,2011,33(1):48-51.
78.王启现,王璞,王伟东,等.吐丝期施氮对夏玉米粒重和籽粒粗蛋白的影响[J].中国农业大学学报,中国农业大学学报,2001,7(1):59-64.
79.王强,易秋香,包安明,等.基于高光谱反射率的棉花冠层叶绿素密度估算[J].农业工程学报,2012,28(15):125-132.
80.王绍华,曹卫星,王强盛,等.水稻叶色分布特点与氮素营养诊断[J].中国农业科学,2002,35(12):1461-1466.
81.王为.高光谱遥感技术的发展及其在农业上的应用[J].江西农业学报,2009,21(5):23-26.
82.王宪泽,张树芹.不同蛋白质含量小麦品种叶片NRA与氮素积累关系的研究[J].西北植物学报,1999,19(3):315-320.
83.王渊,黄敬峰,王福民,等.油菜叶片和冠层水平氮素含量的高光谱反射率估算模型[J].光谱学与光谱分析,2008,28(2):273-277.
84.王圆圆,陈云浩,李京,等.指示冬小麦条锈病严重度的两个新的红边参数[J].遥感学报,2007,11(6):875-881.
85.王正瑞,芮玉奎,申建波,等.氮肥施用量和形态对玉米苗期叶绿素含量的影响[J].光谱学与光谱分析,2009,29(2):410-412.
86.王之杰,王纪华,马智宏,等.冬小麦冠层氮素及硝酸还原酶活性的垂直分布[J].麦类作物学报,2004,24(1):31-34.
87.吴迪,黄凌霞,何勇,等.作物和杂草叶片的可见-近红外光谱特性[J].光学学报,2008,28(8):1618-1622.
88.吴华兵,朱艳,田永超,等.棉花冠层高光谱参数与叶片氮素含量的定量关系[J].植物生态学报,2007,31(5):903-909.
89.吴素霞,毛任钊,李红军,等.冬小麦叶片绿度时空变异特征研究[J].中国生态农业学报,2005,13(4):82-85.
90.夏天,吴文斌,周清波,等.基于高光谱的冬小麦叶面积指数估算方法[J].中国农业科学,2012,45(10):2085-2092.
91.解振兴,董志强,薛金涛.供氮量及化学调控对玉米苗期生长及氮素吸收分配特征的影响[J].玉米科学,2012,20(2):128-133,137.
92.徐世昌,戴俊英,沈秀英,等.水分胁迫对玉米光合性能及产量的影响[J].作物学报,1990,21(3):356-363.
93.徐新刚,吴炳方,蒙继华,等.农作物单产遥感估算模型研究进展[J].农业工程学报,2008,24(2):290-298.
94.薛利红,曹卫星,罗卫红,等.基于冠层反射光谱的水稻群体叶片氮素状况监测[J].中国农业科学,2003,36(7):807-812.
95.杨飞,张柏,宋开山,等.大豆叶面积指数的高光谱估算方法比较[J].光谱学与光谱分析,2008,28(12):2951-2955.
96.杨峰,范亚民,李建龙,等.高光谱数据估测稻麦叶面积指数和叶绿素密度[J].农业工程学报,2010,26(2):237-245.
97.杨国鹏,余旭初,冯伍法,等.高光谱遥感技术的发展与应用现状[J].测绘通报,2008,(10):1-5.
98.杨红卫,童小华.中高分辨率遥感影像在农业中的应用现状[J].农业工程学报,2012,28(24):138-149.
99.杨杰,田永超,姚霞,等.水稻上部叶片叶绿素含量的高光谱估算模型[J].生态学报,2009,29(12):6561-6571.
100.杨武,李运起,李建国,等.大喇叭口期不同施肥组合对青贮玉米产量的影响研究[J].中国农学通报,2012,28(14):32-35.
101.姚付启,张振华,杨润亚,等.基于红边参数的植被叶绿素含量高光谱估算模型[J].农业工程学报,2009,25(S2):123-129.
102.姚霞,朱艳,田永超,等.小麦叶片氮含量估测的最佳高光谱参数研究[J].中国农业科学,2009,42(8):2716-2725.
103.姚云军,秦其明,张自力,等.高光谱技术在农业遥感中的应用进展研究[J].农业工程学报,2008,24(7):301-306.
104.易时来,邓烈,何绍兰,等.奥林达夏橙叶片锌含量可见近红外光谱监测模型研究[J].光谱学与光谱分析,2010,30(11):2927-2931.
105.于亚利,贾文凯,王春宏,等.春玉米叶片SPAD值与氮含量及产量的相关性研究[J].玉米科学,2011,19(4):89-92,97.
106.袁佐清,张建勇.水分胁迫对玉米大喇叭口期生长的影响[J].干旱地区农业研究,2007,25(4):235-239.
107.岳文俊,张富仓,李志军,等.不同氮磷营养下苗期水分亏缺对玉米生长及水分利用的影响[J].干旱地区农业研究,2011,29(6):1-6.
108.张东彦,宋晓宇,马智宏,等.扫描成像光谱仪和地物光谱仪在单叶尺度上的对比研究[J].中国农业科学,2010,43(11):2239-2245.
109.张丰,熊桢,寇宁.高光谱遥感数据用于水稻精细分类研究[J].武汉理工大学学报,2002,24(10):36-39,46.
110.张金恒,王珂,王人潮,等.高光谱评价植被叶绿素含量的研究进展[J].上海交通大学学报,2003,21(1):73-82.
111.张瑞美,彭世彰,徐俊增.光谱技术在农业领域的应用与展望[J].果树学报,2005,22(1):9-11.
112.张维娜.基于遥感技术的运城盆地干旱监测应用研究[J].测绘通报,2010,(7):23-27.
113.张宪政.植物叶绿素含量测定-丙酮乙醇混合液法[J].辽宁农业科学,2006,(3):2063.
114.张彦军,薛吉全,张仁和,等.前期干旱对玉米吐丝期光合特性的影响[J].西北农业学报,2008,17(3):135-138.
115.张正杨,马新明,贾方方,等.烟草叶面积指数的高光谱估算模型[J].生态学报,2012,32(1):168-175.
116.张智猛,戴良香,胡昌浩,等.玉米灌浆期水分差异供应对籽粒淀粉积累及其酶活性的影响[J].植物生态学报,2005,29(4):636-643.
117.赵福刚.玉米冠层光谱氮营养诊断追肥模型的研究[赵福刚硕士学位论文].长春:吉林农业大学,2007.
118.赵刚峰,李军,刘冰峰,等.关中冬小麦叶片氮素含量高光谱遥感监测模型[J].麦类作物学报,2012,32(3):530-536.
119.赵杰文,王开亮,欧阳琴,等.高光谱技术分析茶树叶片中叶绿素含量及分布[J].光谱学与光谱分析,2011,31(2):512-515.
120.赵士诚,何萍,仇少君,等.相对SPAD值用于不同品种夏玉米氮肥管理的研究[J].植物营养与肥料学报,2011,17(5):1091-1098.
121.郑根昌,刑彭龄,武月莲.玉米抽雄期干旱胁迫对产量构成因素的影响[J].中国农学通报,2001,17(5):24-26.
122.郑光辉,周生路,吴绍华.土壤砷含量高光谱估算模型研究[J].光谱学与光谱分析,2011,31(1):173-176.
123.仲晓春,戴其根,何理,等.不同浓度镉胁迫下水稻冠层光谱特征及其预测评价[J].农业环境科学学报,2012,31(3):448-454.
124.周冬琴,田永超,姚霞,等.水稻叶片全氮浓度与冠层反射光谱的定量关系[J].应用生态学报,2008,19(2):337-344.
125.周丽丽,冯汉宇,阎忠敏,等.玉米叶片氮含量的高光谱估算及其品种差异[J].农业工程学报,2010,26(8):195-199.
126.周新国,韩会玲,李彩霞,等.玉米灌浆期渍水对产量及氮磷淋失量的影响[J].农业工程学报,2012,28(14):99-104.
127.朱蕾,徐俊锋,黄敬峰,等.作物植被覆盖度的高光谱遥感估算模型[J].光谱学与光谱分析,2008,28(8):1827-1831.
128.朱文超,成芳.转基因水稻及其亲本叶片的可见-近红外光谱分析[J].光谱学与光谱分析,2012,32(2):370-373.
129.朱西存,赵庚星,王凌,等.基于高光谱的苹果花氮素含量预测模型研究[J].光谱学与光谱分析,2010,30(2):416-420.
130.邹红玉,丁丽霞.基于反射光谱数据的茶树叶片SPAD值估算模型研究[J].遥感信息,2011,(117):71-75.
131. Al-Abbas A H, Barr R, Hall J D, et al. Spectra of normal and nutrient-deficient maize leaves[J].Laboratory for Applications of Remote Sensing, Technical Reports,1972,128:1-20.
132. Asai H, Samson B K, Stephan H M, et al. Biochar amendment techniques for upland riceproduction in Northern Laos[J]. Field Crops Reasearch,2009,111:81-84.
133. Blackburn G A. Quantifying chlorophyll and caroteniods at leaf and canopy scales: an evaluationof some hyperspectral approaches[J]. Remote Sensing of Enviro nment,1998,66:273-285.
134. Blackmer T M, Schepers J S, Varvel G E. Light reflectance compared with other nitrogen stressmeasurements in corn leaves[J]. Agronomy Journal,1994,86:934-938.
135. Bojinski S, Schaepaman M, Schlapfer D, et al. SPECCHIO: Aspectrum datebase for remotesensing applications[J]. Computers and Geosciences,2003,29:27-38.
136. Broge N H, Lebianc E. Comparing prediction power and stability of broadband and hyperspectralvegetation indices for estimation of green leaf area index and canopy cholorophyll density[J].Remote Sensing of Enviro nment,2000,76:156-172.
137. Broge N H, Mortensen J V. Deriving green crop area index and canopy chlorophyll density ofwinter wheat rom spectral reflectance data[J]. Remote Sensing of Enviro nment,202,81:45-57.
138. Cartelat A, Cerovic Z G, Goulas Y, et al. Optically assessed contents of leaf polyphenolics andchlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.)[J]. Field CropsReasearch,2005,91:35-49.
139. Castro-Esau K L, Sanchez-Azofeifa G A, Rivard B. Comparison of spectral indices obtained usingmultiple spectroradiometers[J]. Remote Sensing of Enviro nment,2006,103:276-288.
140. Cavaglieri L, Orlando J, Etcheverry M. Rhizosphere microbial community structure at differentmaize plant growth stages and root locations[J]. Microbiological Research,2009,164:391-399.
141. Ceccato P, Flasse S, Tarantola, et al. Detecting vegetation leaf water content using reflectance inthe optical domain[J]. Remote Sensing of Enviro nment,2001,77:22-33.
142. Chappelle E W, Kim M S, Mcmurtrey J E. Ration analysis of reflectance spectra (RARS): analgorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B and thecarotenoids in soybean leaves[J]. Remote Sensing of Enviro nment,1992,39:239-247.
143. Chen P F, Habouudane D, Tremblay N, et al. New spectral indicator assessing the efficiency ofcrop nitrogen treatment in corn and wheat[J]. Remote Sensing of Enviro nment,2010,114:1987-1997.
144. Cheng T, Rivard B, Sanchez-Azofeifa. Spectroscopic determination of leaf water content usingcontinuous wavelet analysis[J]. Remote Sensing of Enviro nment,2011,115:659-670.
145. Cherif J, Derbel N, Nakkach M, et al. Analysis of in vivo chlorophyll fluorescence spectra tomonitor physiological state of tomato plants growing under zinc stress[J]. Journal ofPhotochemistry and Photobiology,2010,101:332-339.
146. Chiu H Y, Collins W E. A spectroradiometer for airnorne remote sensing[J].PhotogrammetricEngineering and Remote Sensing,1978,44:507-107.
147. Ciganda V, Gitelson A, Schepers J. Non-destructive determination of maize leaf and canopychlorophyll content[J]. Journal of Plant Physiology,2009,166:157-167.
148. Clevers J G P W, Gitelson A A. Remote estimation of crop and grass chlorophyll and nitrogencontent using red-edge bands on Sentinel-2and-3[J]. International Journal of Applied EarthObservation and Geoinformation,2013,23:344-351.
149. Clevers J G P W, Kooistra L, Schaepman M E. Using spectral information from the NIR waterabsorption features for the retrieval of canopy water content[J]. International Journal of AppliedEarth Observation and Geoinformation,2008,10:388-397.
150. Damm A, Erler A, Hillen W, et al. Modeling the impact of spectral sensor configurations on theFLD retrieval accuracy of sun-induced chlorophyll fluorescence[J]. Remote Sensing of Environment,2011,115:1882-1892.
151. Daughtry C S T, Walthall C L, Kim M S, et al. Estimating corn leaf chlorophyll concentrationfrom leaf and canopy reflectance[J]. Remote Sensing of Enviro nment,2000,74:229-239.
152. Dunagan S C, Gilmore M S, Varekamp J C. Effects of mercury on visible/near-infrared reflectancespectra of mustard spinach plants (Brassica rapa P.)[J]. Enviro nmental Pollution,2007,148:301-311.
153. Ecarnot M, Compan F, Roumet P. Assessing leaf nitrogen content and leaf mass per unit area ofwheat in the field throughout plant cycle with a portable spectrometer[J]. Field Crops Research,2013,140:44-50.
154. Eitel J U H, Vierling L A, Long D S, et al. Early season remote sensing of wheat nitrogen statususing a green scanning laser[J]. Agricultural and Forest Meteorology,2011,151:1338-1345.
155. Erdel K, Mistele B, Schmidhalter U. Spectral high-throughput assessments of phenotypicdifferences in biomass and nitrogen partitioning during grain filling of wheat under high yieldingWestern European conditions[J]. Field Crops Research,2013,141:16-26.
156. Errecart P M, Agnusdei M G, Lattanzi F A, et al. Leaf nitrogen concentration and chlorophyllmeter readings as predictors of tall fescue nitrogen nutrition status[J]. Field Crops Research,2012,129:46-58.
157. Farruggia A, Gastal F, Scholefield D. Assessment of the nitrogen status of grassland[J]. Grass andForage Science,59:113-120.
158. Feng M C, Yang W D, Cao L L, et al. Monitoring winter wheat freeze injury using muli-temporalMODIS data[J]. Agricultural Sciences in China,2009,8(9):1053-1062.
159. Ferrio J P, Villegas D, Zarco J, et al. Assessment of durum wheat yield using visible andnear-infrared reflectance spectra of canopies[J]. Field Crops Research,2005,94:126-148.
160. Fiore A D, Reverberi M, Ricelli A, et al. Early detection of toxigenic fungi on maize byhyperspectral imaging analysis[J]. International Journal of Food Microbiology,2010,144:64-71.
161. Fitzgerald G, Rodriguez D, Leary G. Measuring and predicting canopy nitrogen nutrition in wheatusing a spectral index-the canopy chlorophyll content index (CCCI)[J]. Field Crops Research,2010,116:318-324.
162. Foley S, Rivard B, Sanchez-Azofeifa, et al. Foliar spectral properties following leaf clipping andimplications for handling techniques[J]. Remote Sensing of Enviro nment,2006,103:265-275.
163. Gianquinto G, Orsini F, Fecondini M, et al. A methodological approach for defining spectralindices for assessing tomato nitrogen status and yield[J]. European Journal of Agronomy,2011,35:135-143.
164. Giardino C, Brivio P A. The application of a dedicated device to acquire birdirectional reflectancefactors over natural surfaces[J]. International Journal of Remoting Sensing,2003,24:2989-2995.
165. Gitelson A A, Merzlyak M N. Signature analysis of leaf reflectance spectra: Algorithmdevelopment for remote sensing[J]. Journal of plant physiology,1996,148:493-500.
166. Goetz A F H. Portable field reflectance spectrometer[J]. Pasadena, California Jet PropulsionLaboratory, JPL Technical Report,1997:183-188.
167. Graeff S, Claupein W. Quantifying nitrogen status of corn (Zea mays L.) in the field by reflectancemeasurements[J]. European Journal of Agronomy,2003,19:611-618.
168. Haboudane D, Miller J R, Tremblay N, et al. Integrated narrowed-band vegetation indices forprediction of crop chlorophyll content for application to precision agriculture[J]. Remote Sensingof Enviro nment,2002,81:416-426.
169. Hawkins T S, Gardiner E S, Comer G S. Modeling the relationship between extractablechlorophyll and SPAD-502readings for endangered plant species research[J]. Journal for NatureConservation,2009,17:123-127.
170. Houborg R, Boegh E. Mapping leaf chlorophyll and leaf area index using inverse and forwardcanopy reflectance modeling and SPOT reflectance data[J]. Remote Sensing of Enviro nment,2008,112:186-202.
171. Houles V, Guerif M, Mary B. Elaboration of a nitrogen nutrition indicator for winter wheat basedon leaf area index and chlorophyll content for making nitrogen recommendations[J]. EuropeanJournal of Agronomy,2007,27:1-11.
172. Idso S B, Glawson K L. Foliage temperature: Effects of enviro nmental factors with implication forplant water stress assessment and the CO2/climate connection[J]. Water Resource Research,1986,22(12):1702-1716.
173. Inoue Y, Sakaiya E, Zhu Y, et al. Diagnostic mapping of canopy nitrogen content in rice based onhyperspectral measurements[J]. Remote Sensing of Enviro nment,2012,126:210-221.
174. Jain N, Ray S S, Singh J P, et al. Use of hyperspectral data to assess the effects of different nitrogenapplications on a potato crop[J]. Precision Agriculture,2007,8(4):225-239.
175. Jin X L, Wang K R, Xiao C H, et al. Comparison of two methods for estimation of leaf totalchlorophyll content using remote sensing in wheat[J]. Field Crop Research,2012,135:24-29.
176. Kavzoglu T, Colkesen I. AA kernel functions analysis for support vector machines for land coverclassification[J]. International Journal of Applied Earth Observation and Geoinformation,2009,11(5):352-359.
177. Khabba S, Ledent J F, Lahrouni A. Development and validation of model for estimatingtemperature within maize ear[J]. Agricultural and Forest Meteorology,2001,106:131-146.
178. Khabba S, Ledent J F, Lahrouni A. Maize ear temperature[J]. European Journal of Agronomy,2001,14:197-208.
179. Kokaly R F, Clark R N. Spectroscopic determination of leaf biochemistry using band-depthanalysis of absorption features and stepwise multiple linear regression[J]. Remote Sensing ofEnviro nment,1999,67:267-287.
180. Leidi E O, Silberbush M, Soares M M, et al. Salinity and nitrogen nutrition studies on peanut andcotton plants[J]. Journal of Plant Nutrition,1992,15:591-604.
181. Lemaire G, Gastal F, Plenet D. Dynamics of N uptake and N distribution in plant canopies. Use ofcrop N status index in crop modeling[J]. Diagnostic Procedures for Crop N Management,1995,11:22-23.
182. Leuning R, Hughes D, Daniel P, et al. A multi-angle spectrometer for automatic measurement ofplant canopy reflectance spectra[J]. Remote Sensing of Enviro nment,2006,103:236-245.
183. Li F, Gnyp M L, Jia L L, et al. Estimating N status of winter wheat using a handheld spectrometerin the North China Plain[J]. Field Crop Research,2008,106:77-85.
184. Liu J G, Pattey E, Miller J R, et al. Estimating crop stresses, aboveground dry biomass and yield ofcorn using multi-temporal optical data combined with a radiation use efficiency model[J]. RemoteSensing of Enviro nment,2010,114:1167-1177.
185. Ma B L, Dwyer E R, Cober E R, et al. Early prediction of soybean yield from canopy reflectancemeasurements[J]. Agronomy Journal,2001,93:1227-1234.
186. Maderia A C, Mendonca A, Ferreira M E, et al. Relationship between spectroradiometric andchlorophyll measurements in green beans communication[J]. Soil Science and Plant Analysis,2000,31(5-6):631-643.
187. Maiorano A, Mancini M C. Water relationships and temperature interactions in maize grain duringmaturation[J]. Field Crop Research,2010,119:304-307.
188. Martin M E, Plourde L C, Ollinger S V, et al. A generalizable method for remote sensing ofcanopy nitrogen across a wide range of forest ecosystems[J]. Remote Sensing of Enviro nment,2008,112:3511-3519.
189. Mass S J, Dunlap J R. Reflectance, transmittance, and absorbance of light by normal etiolated, andalbino corn leaves[J]. Agronomy Journal,1989,81:105-110.
190. Matsukura K, Matsumura M. Cultural control of leafhopper-induced maize wallaby ear symptomin forage maize via early planting dates[J]. Crop Protection,2010,29:1401-1405.
191. Meng Z D, Zhang F J, Ding Z H, et al. Inheritance of ear tip-barrenness trait in maize[J].Agricultural Sciences in China,2007,6(5):628-633.
192. Merzlyak M N, Solovchenko A E, Gitelson A A. Reflectance spectral features and non-destructiveestimation of chlorophyll, carotenoid and anthocyanin content in apple fruit[J]. PostharvestBiology and Technology,2003,27:197-211.
193. Middleton W E, Mungall A G. The luminous directional reflectance of snow[J]. Journal of theOptical Society of America,1952,42:572-579.
194. Milap B, Benbi D K, Azad A S. Modifying soil test based fertilizer P recommendations fortargeted yield of rice on Typic Haplustalf[J]. Journal Indian Society of Soil Science,2004,52:258-261.
195. Milton E J, Schaepman M E, Anderson K, et al. Progress in field spectroscopy[J]. Remote Sensingof Enviro nment,2009,113: S92-S109.
196. Min M, Lee W S, Burks T F, et al. Design of a hyperspectral nitrogen sensing system for orangeleaves[J]. Computers and Electronics in Agriculture,2008,63:215-226.
197. Mirschel W, Wenkel K O, Schultz A, et al. Dynamic phonological model foe winter rye and winterbarley[J]. European Journal of Agronomy,2005,23:123-135.
198. Mistele B, Schmidhalter U. Estimating the nitrogen nutrition index using spectral canopyreflectance measurements[J]. European Journal of Agronomy,2008,29:184-190.
199. Mitchell J J, Glenn N F, Sankey T T, et al. Remote sensing of sagebrush canopy nitrogen[J].Remote Sensing of Enviro nment,2012,124:217-223.
200. Monasterio I O, Lobell D B. Remote sensing assessment of regional yield losses due tosub-optimal planting dates and fallow period weed management[J]. Field Crops Research,2007,101(1):80-87.
201. Namrata J, Shibendu S R, Singh J P, et al. Use of hyperspectral data to acess the effects of differentnitrogen applications on a potato crop[J]. Precision Agriculture,2007,8:225-239.
202. Naumann J C, Young D R, Anderson J E, et al. Leaf chlorophyll fluorescence, reflectance, andphysiological response to freshwater and saltwater flooding in the evergreen shrub, Muricacerifera[J]. Enviro nmental and Experimental Botany,2008,63:402-409.
203. Noh H, Zhang Q, Shin B, et al. A neural network model of maize crop nitrogen stress assessmentfor a multi-spectral imaging sensor[J]. Biosystems Engineering,2006,94(4):477-485.
204. Osborne S L, Schepers J S, Francis D D, et al. Detection of phosphorus and nitrogen deficienciesin corn using spectral radiance measurements[J]. Agronomy Journal,2002,94:1215-1221.
205. Otegui M E, Bonhomme R. Grain yield components in maize I. Ear growth and kernel set[J]. FieldCrops Reasearch,1998,56:247-256.
206. Pagano E, Cela S, Maddonni G A, et al. Intra-specific competition in maize: Ear development,flowering dynamics and kernel set of early-established plant hierarchies[J]. Field Crops Reasearch,2007,102:198-209.
207. Pagola M, Ortiz R, Irigoyen I, et al. New method to assess barley nitrogen nutrition status based onimage color analysis comparison with SPAD-502[J]. Computers and Electronics in Agriculture,2009,65:213-218.
208. Perry E M, Davenport J R. Spectral and spatial differences in response of vegetation indices tonitrogen treatments on apple[J]. Computers and Electronics in Agriculture,2007,59:56-65.
209. Pettersson C G, Eckersten H. Prediction of grain protein in spring malting barley grown in northernEurope[J]. Europ Journal of Agronomy,2007,27(2):205-214.
210. Pimstein A, Karnieli A, Bansal S K, et al. Exploring remotely sensed technologies for monitoringwheat potassium and phosphorus using field spectroscopy[J]. Field Crops Research,2011,121:125-135.
211. Pinar A, Curran P J. Grass chlorophyll and the reflectance red edge[J]. International Journal ofRemote Sensing,1996,17:351-357.
212. Raymond F K. Investigating a physical basis for spectroscopic estimates of leaf nitrogenconcentration[J]. Remote Sensing of Enviro nment,2001,2(75):153-161.
213. Reum D, Zhang Q. Wavelet based multi-spectral image analysis of maize leaf chlorophyllcontent[J]. Computers and Electronics in Agriculture,2007,56:60-71.
214. Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices[J]. RemoteSensing of Enviro nment,1996,58:1-12.
215. Rossini M A, Maddonni G A, Otegui M E. Inter-plant competition for resources in maize cropsgrown under contrasting nitrogen supply and density: Variability in plant and ear growth[J]. FieldCrops Research,2011,121:373-380.
216. Rotbart N, Schmilovitch Z, Cohen Y, et al. Estimating olive leaf nitrogen concentration usingvisible and near-infrared spectral reflectance[J]. Biosystems Engineering,2013,114:426-434.
217. Ryu C, Suguri M, Umeda M. Multivariate analysis of nitrogen content for rice at the heading stageusing reflectance of airborne hyperspectral remote sensing[J]. Field Crops Research,2011,122:214-224.
218. Schaepman M E. Spectrodirectional remote sensing: from pixels to processes[J] InternationalJournal of Applied Earth Observation and Geoinformation,2007,9(2):204-223.
219. Scharf P C, Lory J A. Calibrating corn color from aerial photographs to predict sidedress nitrogenneed[J]. Agronomy Journal,2002,94:397-404.
220. Schepers J S, Blackmer T M, Wilhelm W W, et al. Transmittance and reflectance measurements ofcorn leaves from plants with different nitrogen and water supply[J]. Journal of Plant Physiology,1996,148(5):523-529.
221. Serrano L, Filella I, Penuelas J. Remote sensing of biomass and yield of winter wheat underdifferent nitrogen supplies[J]. Crop Science,2000,40(3):723-731.
222. Serrano L, Flor C G, Gorchs G. Assessment of grape yield and composition using the reflectancebased Water Index in Mediterranean rainfed vineyards[J]. Remote Sensing of Enviro nment,2012,118:249-258.
223. Shi J Y, Zou X B, Zhao J W, et al. Nondestructive diagnostics of nitrogen deficiency by cucumberleaf chlorophyll distribution map based on near infrared hyperspectral imaging[J]. ScientiaHorticulturae,2012,138:190-197.
224. Shibayama M, Takahashi W, Morinaga S, et al. Canopy water deficit detection in paddy rice usinga high resolution field spectroradiometer[J]. Remote Sensing of Enviro nment,1993,45(2):117-126.
225. Shrestha S, Brueck H, Asch F. Chlorophyll index, photochemical reflectance index and chlorophyllfluorescence measurements of rice leaves supplied with different N levels[J]. Journal ofPhotochemistry and Photobiology B: Biology,2012,113:7-13.
226. Silva T R B D, Reis A C D S, Maciel C D D G. Relationship between chlorophyll meter readingsand total N in crambe leaves as affected by nitrogen topdressing[J]. Industrial Crops and Products,2012,39:135-138.
227. Sims D A, Gamon J A. Estimation of vegetation water content and photosynthetic tissue area fromspectral reflectance: a comparison of indices based on liquid water and chlorophyll absorptionfeatures[J]. Remote Sensing of Enviro nment,2003,84:526-537.
228. Slaton M R, Haut E R, Smith W K. Estimating near-infrared leaf reflectance from leaf structuralcharacteristics[J]. American Journal of Botany,2001,88(2):278-284.
229. Strachan I B, Pattey E, Boisvert J B, et al. Impact of nitrogen and enviro nmental conditions oncorn as detected by hyperspectral reflectance[J]. Remote Sensing of Enviro nment,2002,80:213-224.
230. Stroppiana D, Boschetti M, Brivio P A, et al. Plant nitrogen concentration in paddy rice from fieldcanopy hyperspectral Radiometry[J]. Field Crop Research,2009,111:119-129.
231. Stuckens J, Dzikiti S, Verstraeten W W, et al. Physiological interpretation of a hyperspectral timeseries in a citrus orchard[J]. Agricultural and Forest Meteorology,2011,151:1002-1015.
232. Swiader J M, Moore A. SPAD-chlorophyll response to nitrogen fertilization and evaluation ofnitrogen status in dry land and irrigated pumpkins[J]. Journal of Plant Nutrition,2002,25:1089-1100.
233. Szeles A V, Megyes A, Nagy J. Irrigation and nitrogen effects on the leaf chlorophyll content andgrain yield of maize in different crop years[J]. Agricultural Water Management,2012,107:133-144.
234. Takebe M, Yoneyama T. Measurement of leaf color scores and its implication to nitrogen nutritionof rice plants[J]. Japanese Agriculture Research Journal,1990,23:96-93.
235. Tarpley L, Raja R K, Sassenrath G F. Reflectance indices with precision and accuracy in predictioncotton leaf nitrogen concentration[J]. Crop Science,2000,40:1814-1819.
236. Thenkabail P S, Smith R B, Pauw E D. Evaluation of narrowband and broadband vegetationindices for determining optimal hyperspectral wavebands for agricultural crop characterization[J].Photogrammetric Engineering and Remote Sensing,2002,68:607-627.
237. Thomas J R, Oerther G F. Estimating nitrogen content of sweet peeper leaves by reflectancemeasurements[J]. Agronomy Journal,1972,64:11-13.
238. Uddling J, Alfredsson J G, Piikki K, et al. Evaluating the relationship between leaf chlorophyllconcentration and SPAD-502chlorophyll meter readings[J]. Photosynthesis Research,2007,91:37-46.
239. Ulloa S M, Datta A, Bruening C, et al. Maize response to broadcast flaming at different growthstages: Effects on growth, yield and yield components[J]. European Journal of Agronomy,2011,34:10-19.
240. Vigneau N, Ecarnot M, Rabatel G, et al. Potential of field hyperspectral imaging as a nondestructive method to assess leaf nitrogen content in Wheat[J]. Field Crop Research,2011,122:25-31.
241. Walter E R, Blackmer T M. Leaf reflectance spectra of cereal aphid-damaged wheat[J]. CropSciences,1999,39:1835-1840.
242. Wang F M, Huang J F, Tang Y L, et al. New vegetation index and its application in estimating leafarea index of rice[J]. Rice Science,2007,14(3):195-203.
243. Wang W, Yao X, Yao X F, et al. Estimating leaf nitrogen concentration with three-band vegetationindices in rice and wheat[J]. Field Crops Research,2012,129:90-98.
244. Wiesler F, Bauer M, Kamh M, et al. The crop as indicator for sidedress nitrogen demand in sugarbeet production–limitations and perspectives[J]. Journal of Plant Nutrition and Soil Science,2002,165:93-99.
245. Wright D L, Rasmussen V P, Ramsey R D, et al. Canopy reflectance estimation of wheat nitrogenfor grain protein management[J]. GIScience and Remote Sensing,2004,41:287-300.
246. Wu C Y, Niu Z, Gao S. The potential of the satellite derived green chlorophyll index for estimatingmidday light use efficiency in maize, coniferous forest and grassland[J]. Ecological Indicators,2012,14:66-73.
247. Yang F, Li J L, Gan X Y, et al. Assessing nutritional status of Festuca arundinacea by monitoringphotosynthetic pigments from hyperspectral data[J]. Computers and Electronics in Agriculture,2010,70:52-59.
248. Yang K, Chen Y H, Guo D Z, et al. Spectral information detection and extraction of wheat striperust based on hyperspectral image[J]. Acta Photonica Sinica,2008,31(1):145-150.
249. Ye X J, Sakai K S, Garciano L O, et al. Estimation of citrus yield from airborne hyperspectralimages using a neural network model[J]. Ecological Modelling,2006,198:426-432.
250. Yi P, Gitelson A A. Application of chlorophyll-related vegetation indices for remote estimation ofmaize productivity[J]. Agricultural and Forest Meteorology,2011,151:1267-1276.
251. Yi P, Gitelson A A, Keydan G, et al. Remote estimation of gross primary production in maize andsupport for a new paradigm based on total crop chlorophyll content[J]. Remote Sensing of Environment,2011,115:978-989.
252. Yu Z H, Cao Z G, Wu X, et al. Automatic image-based detection technology for two critical growthstages of maize: Emergence and three-leaf stage[J]. Agricultural and Forest Meteorology,2013,174:65-84.
253. Zhang H, Zhu L F, Hu H, et al. Monitoring leaf chlorophyll fluorescence with spectral reflectancein rice (Oryza sativa L.)[J]. Procedia Engineering,2011,15:4403-4408.
254. Zhang J H, Wang K. New vegetation index for estimating nitrogen concentration using fresh leafspectral reflectance[J]. Transactions of the CASE,2008,24(3):158-161.
255. Zhao D L, Reddy K R, Kakani V G, et al. Canopy reflectance in cotton for growth assessment andlint yield prediction[J]. European Journal of Agronomy,2007,26:335-334.
256. Zhao D L, Reddy K R, Kakani V G, et al. Selection of optimum reflectance ratios for estimatingleaf nitrogen and chlorophyll concentrations of field-grown cotton[J]. Agronomy Journal,2005,97:89-98.
257. Zhao F, Gu X F, Verhoef W, et al. A spectral directional reflectance model of row crops[J].Remote Sensing of Enviro nment,2010,114:265-285.
258. Zhou Q F, Wang J H. Comparison of upper leaf and lower leaf of rice plants in response tosupplemental nitrogen levels[J]. Journal of Plant Nutrition,2003,26(3):607-617.
259. Zhu Y, Yao X, Tian Y C, et al. Analysis of common canopy vegetation indices for indicating leafnitrogen accumulations in wheat and rice[J]. International Journal of Applied EarthObservationand Geoinformation,2008,10:1-10.
260. Zou X B, Shi J Y, Hao L M, et al. In vivo noninvasive detection of chlorophyll distribution incucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging[J]. AnalyticaChimica Acta,2011,706:105-112.
© 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 |