西北农林科技大学 资源环境学院, 陕西 杨凌,712100
纸质出版:2016
移动端阅览
解飞, 齐雁冰, 常庆瑞. 关中地区夏玉米抽穗期叶绿素含量的高光谱估算[J]. 水土保持通报, 2016,36(2):176-180.
XIE Fei, QI Yanbing, CHANG Qingrui. Hyperspectral Estimation of Canopy Chlorophyll Content in Summer Corn in Guanzhong Area[J]. Bulletin of Soiland Water Conservation, 2016, 36(2): 176-180.
解飞, 齐雁冰, 常庆瑞. 关中地区夏玉米抽穗期叶绿素含量的高光谱估算[J]. 水土保持通报, 2016,36(2):176-180. DOI: 10.13961/j.cnki.stbctb.2016.02.034.
XIE Fei, QI Yanbing, CHANG Qingrui. Hyperspectral Estimation of Canopy Chlorophyll Content in Summer Corn in Guanzhong Area[J]. Bulletin of Soiland Water Conservation, 2016, 36(2): 176-180. DOI: 10.13961/j.cnki.stbctb.2016.02.034.
[目的
]
利用高光谱数据进行叶绿素估算
为快速获取作物的生长信息、生长诊断及精确管理提供依据。[方法
]
基于陕西省关中地区抽穗期夏玉米冠层光谱特征及叶绿素含量的测定
运用线性及非线性分析方法建立了基于原始光谱敏感波段和一阶微分光谱敏感波段叶绿素估算模型。[结果
]
夏玉米抽穗期反射光谱在可见光及中远红外区域
叶绿素含量越高
光谱曲线越向下偏移;在红边区域
叶绿素含量对光谱曲线影响不显著;在近红外波段
叶绿素含量越高
光谱曲线越向上偏移。基于一阶微分光谱敏感波段的夏玉米叶绿素含量估算模型拟合精度要优于基于原始光谱敏感波段估算模型
决定系数R
2
分别为0.81和0.60
均方根误差(RMSE)分别为2.39
4.41。[结论
]
基于一阶微分光谱敏感波段建模分析是估测抽穗期夏玉米冠层叶绿素含量的重要方法
对指导西北地区夏玉米种植与生产具有积极的借鉴意义。
[Objective] Hyperspectral estimation of canopy chlorophyll content was expected to provide a clue for obtaining growth information rapidly
diagnosing growth situation and precision management of crops. [Methods] Based on the measurement of canopy spectral characteristics and chlorophyll content of summer corn in heading stage in Guanzhong area of Shaanxi Province
a model with regard to the canopy chlorophyll content estimation was established by relating the original spectrum and the first derivative spectral reflectance of sensitive band. [Results] For the reflection spectral of corn in the heading stage
the spectral curve showed downward trend with the increase of chlorophyll content in the visible and infrared band; influence of chlorophyll content on spectral curve in the red edge band was not obvious
and the spectral curve showed upward trend with the increase of chlorophyll content in the near infrared band. Fitting precision of the estimation model with the sensitive band of the first derivative spectral reflectance were superior to the original spectral reflectance. The coefficients of determination R2 of the model based on the first derivative spectral and the original spectral reflectance were 0.81 and 0.60
the root mean square errors(RMSE) were 2.39 and 4.41 respectively. [Conclusion] The sensitive bands of first derivative spectral reflectance was an important indexes for canopy chlorophyll content estimation of summer corn in heading stage. The models have positive significance to guide the plantation and the production for summer corn in the northwest of China.
Gausman H W, Allen W A, Cardenas R, et al. Relation of light reflectance to histological and physical evaluations of cotton leaf maturity[J]. Applied Optics, 1970,9(3):545-552.
姚付启,张振华,杨亚润,等.基于主成分分析和BP神经网络的法国梧桐叶绿素含量高光谱反演研究[J].测绘科学,2010,35(1):109-112.
Blachburn G A. Quantifying chlorophylls and carotenoids at leaf and canopy scales:An evaluation of some hyperspectaral approaches[J]. Remote Sensing of Environment, 1998, 66(3):273-285.
Penuelas J, Baret F, Filella I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance[J]. Photosynthetica, 1995, 31(2):221-230.
Knipling E B. Physical and physiological basis for the reflectance of visible and near in-frared radiation from vegetation[J]. Remote Sensing of Environment, 1970, 1(3):155-159.
Thomas J R, Gausman H W. Leaf reflectance vs. leaf chlorophyll and carotenoid concentration for eight crops[J]. Agronomy Journal, 1977, 69(5):799-802.
Blackmer T M, Schepers J S, Varvel G E. Light reflectance compared with other nitrogen stress measurements in corn leaves[J]. Agronomy Journal, 1994, 86(6):934-938.
McMurtrey J E, Chappelle E W, Kim M S, et al. Distinguishing nitrogen fertilization levels in field corn with actively induced fluorescence and passive reflectance measurements[J]. Remote Sensing of Environment, 1994, 47(1):36-44.
Hong S, Rim S, Lee J, et al. Remote sensing for estimating chlorophyll amount in rice canopies[C]//Singapore:Proc Geoscience and Remote Sensing Symposium. IEEE, 1997.
吴长山,童庆禧,郑兰芬.水稻、玉米的光谱数据与叶绿素的相关分析[J].应用基础与工程科学学报,2008,8(1):31-37.
刘伟东,项月琴,郑兰芬,等.高光谱数据与水稻叶面积指数及叶绿素密度的相关分析[J].遥感学报,2000,4(4):279-283.
张晓华,常庆瑞,章曼,等.基于高光谱植被指数的西北玉米不同时期叶绿素含量估测[J].中国农业大学学报,2015,20(4):75-81.
章曼,常庆瑞,张晓华,等.不同施肥条件下水稻冠层光谱特征与叶绿素含量的相关性[J].西北农业学报,2015,24(11):49-56.
Gitelson A A, Merzlyak M N. Signature analysis of leaf reflectance spectra:Algorithm development for remote sensing of chlorophyll[J]. Journal of Plant Physical, 1996(3/4):494-500.
方圣辉,乐源,杨光.基于HyperScan成像光谱数据的植被叶绿素反演[J].国土资源遥感,2013,25(4):40-47.
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.
王纪华,赵春江,黄文江,等.农业定量遥感基础与应用[M].北京:科学出版社,2008.
张凯,郭铌,王润元,等.甘肃省两种主要草地类型的光谱反射特征比较[J].农业工程学报,2009,25(2):142-147.
李民赞.光谱分析技术及其应用[M].北京:北京科学出版社,2006.
武倩雯,熊黑钢,王凯龙,等.干旱区玉米抽雄期叶绿素含量高光谱最佳模型选择[J].干旱地区农业研究,2015,33(2):81-86.
陈志强,王磊,白由路,等.整个生育期玉米叶片SPAD高光谱预测模型研究[J].光谱学与光谱分析,2013,33(10):2838-2842.
易秋香,黄敬峰,王秀珍,等.玉米叶绿素高光谱遥感估算模型研究[J].科技通报,2007,23(1):83-87.
0
浏览量
933
下载量
3
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621