|Table of Contents|

Predicting Model of Dry Matter Accumulation of Dwarf Soybean Hybrid 98-1667 by Leaf Reflectance Spectra(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2010年03期
Page:
429-432
Research Field:
Publishing date:

Info

Title:
Predicting Model of Dry Matter Accumulation of Dwarf Soybean Hybrid 98-1667 by Leaf Reflectance Spectra
Author(s):
SONG Ying-bo12 JIA Li-jun2 DU Yong-sheng2 WANG Nan-nan2 DENG Ji-hua3 CHEN Qing-shan1HU Guo-hua4
1.Agriculture College, Northest Agricultural University,Harbin 150030,Heilongjiang;
2.Jiamusi Branch of Heilongjiang Academy of Agricultural Sciences,Jiamusi 154007,Heilongjiang;
3.Hejiang Forestry Institute of Heilongjiang Academy of Forestry Sciences,Jiamusi 154002,Heilongjiang;
4.The Crop Research and Breeding Center of land-Reclamation,Harbin 150090,Heilongjiang,China
Keywords:
Vegetation indexSpectrum reflectanceDry matter accumulationEstimation model
PACS:
S565.1
DOI:
10.11861/j.issn.1000-9841.2010.03.0429
Abstract:
The sensitive wavebands were determined by analyzing relationship with spectra reflectance in different wavebands and the dry matter accumulation in above-ground part of soybean, and the prediction model was established. The results showed that there were highly significant correlations between spectra reflectance of 510 and 680 nm which selected from among visible light and the dry matter accumulation in the above-ground part of hybrid 98-1667, spectra reflectance of 800 and 900 nm in the range of near infrared light were highly significant correlated, and spectra reflectance of 1005 nm was significantly correlated with the above-ground weight. After compared with those four vegetation indices, the RVI has the best relativity. The corresponding prediction model established by vegetation indices of RVI was Y= 4.0216×RVI2(900,680)-99.106×RVI(900,680) + 625.36, and it could be accurate to predict the dry matter accumulation in above-ground part of soybean .

References:

[1]蒋焕煜, 应义斌, 谢丽娟. 光谱分析技术在作物生长信息检测中的应用研究进展[J]. 光谱学与光谱分析, 2008, 28(6): 1300-1304.( Jiang H Y, Ying Y B, Xin L J. Application of Spectroscopy Technique to Obtain Plant Growth Information[J]. Spectroscopy and Spectral Analysis, 2008, 28(6): 1300-1304.)

[2]肖春华, 李少昆, 卢艳丽, .冬小麦冠层叶片氮素营养方向反射光谱的预测[J]. 石河子大学学报(自然科学版), 2008,26(3):280-285.(Xiao C H, Li S K, Lu Y L, et al. Prediction of leave nitrogen content of winter wheat canopy based on direction reflection spectrum [J]. Journal of Shihezi University(Natural Science), 2008,26(3):280-285.)

[3]刘占宇,黄敬峰,王福民,. 估算水稻叶面积指数的调节型归一化植被指数[J].中国农业科学,2008,41(10):3350-3356.(Liu Z J, Huang J F, Wang F M ,et al. Adjusted-normalized difference vegetation index for estimating leaf area index of rice [J].Scientia Agricultura Sinica , 2008,41(10):3350-3356.)

[4]宋开山,张柏,王宗明,. 大豆叶绿素含量高光谱反演模型研究[J]. 农业工程学报, 2006,22(8):16-21.(Song K S,Zhang B,Wang Z M, et al. Inverse model for estimating soybean chlorophyll concentration using in-situ collected canopy hyperspectral data[J]. Transactions of the Chinese Society of Agricultural Engineering,2006,22(8):16-21.)

[5]吴华兵,朱艳,田永超,. 棉花冠层高光谱指数与叶片氮积累量的定量关系[J]. 作物学报, 2007,33(3):518-522.(Wu H B,Zhu Y ,Tian Y C, et al. Relationship between canopy hyperspectral index and leaf nitrogen accumulation in cotton[J]. Acta Agronomica Sinica, 2007, 33(3):518-522.)

[6]冯伟 朱艳, 姚霞, . 基于高光谱遥感的小麦叶干重和叶面积指数监测[J]. 植物生态学报, 2009,33(1):34-44.(Feng W, Zhu Y, Yao X ,et al. Monitoring leaf dry weight and leaf area index in wheat with Hyperspectral remote sensing [J]. Chinese Journal of Plant Ecology, 2009,33(1):34-44.)

[7]孙君明, 韩粉霞, 闫淑荣,.傅里叶近红外反射光谱法快速测定大豆脂肪酸含量[J].光谱学与光谱分析, 2008,28(6):1290-1294. (Sun J M, Han F X, Yan S R ,et al. Rapid determination of fatty acids in soybeans [Glycine max?(L.) Merr. ]by FT-near-infrared reflectance spectroscopy[J]. Spectroscopy and Spectral Analysis, 2008,28(6):1290-1294.)

[8]Luther J E, Carroll A L. Development of an index of balsam fir vigor by foliar spectral reflectance[J]. Remote sensing of Environment,1999, 69(3):241-252.

[9]宋开山,张柏,王宗明,. 小波分析在大豆叶绿素含量高光谱反演中的应用[J]. 中国农学通报, 2006,22(9):101-108. (Song K S,Zhang B,Wang Z M, et al. Application of wavelet transform ation in in-situ measured hyperspectra data for soybean LAI estimation[J]. Chinese Agricultural Science Bulletin, 2006,22(9):101-108.)

[10]Serrano L ,Filella L , Pe Nuelas J .Remote sensing of biomass and yield of winter wheat under different nitrogen supplies [J]. Crop Science,2000,40 :723-731.

[11]宋开山,张柏,王宗明,. 基于人工神经网络的大豆叶面积高光谱反演研究[J]. 中国农业科学, 2006, 39(6): 11381145. (Song K S, Zhang B, Wang Z M , et al. Soybean LAI estimation with in-situ collected hyperspectral data based on BP. neurai networks [J]. Scientia Agricultura Sinica, 2006, 39(6): 1138-l145.)

[12]孟卓强,胡春胜,程一松. 高光谱数据与冬小麦叶绿素密度的相关性研究[J]. 干旱地区农业研究, 2007,25(6):74-79. (Meng Z Q, Hu C S,Cheng Y S, Study on correlation between chlorophyll density of winter wheat and hyperspectral data[J]. Agricultural Research in the Arid Areas,2007,25(6):74-79.)

[13]宋开山,张柏,李方,. 高光谱反射率与大豆叶面积及地上鲜生物量的相关分析[J]. 农业工程学报, 2005, 21(1):1-4.(Song K S, Zhang B, Li F, et al. Correlative analyses of hyperspectral reflectance,soybean LAI and aboveground biomass[J]. Tansactions of the Chinese Society of Agricultural Engineering,2005,21(1):1-4.)


Memo

Memo:
-
Last Update: 2014-09-13