Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2016, Vol. 28 Issue (2) : 67-71     DOI: 10.6046/gtzyyg.2016.02.11
Technology and Methodology |
Study of hyperspectral detection for nitrogen content of apple leaves
AN Jing1, YAO Guoqing1, ZHU Xicun2
1. School of Information Engineering, China University of Geosciences(Beijing), Beijing 100083, China;
2. College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China
Download: PDF(2074 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Nitrogen(N)content of apple leaves is an important indicator for estimating growth status of apple tree. Quantitative inversion of the nitrogen content of apple leaves using high spectral technology can provide the theoretical basis for information management of apple tree. In this paper, the hyperspectral reflectance and nitrogen content of apple leaf samples were measured by using ASD FieldSpec 3 spectrometer. The authors constructed multiple regression analysis of the relationships between nitrogen content of apple tree leaves and the original spectrum, the first-order derivative and the transformation forms, selected four wavebands which are more sensitive to the nitrogen change, and constructed the retrieval model for nitrogen content of apple leaves using back propagation (BP) artificial neural network (ANN) algorithm. Finally, the model was optimized and tested. The results show that the model is an effective means to improve capability of predicting apple tree nitrogen content based on BP artificial neural network algorithm.

Keywords Enteromorpha prolifera      FAI index      sea surface temperature(SST)      precipitation      Rizhao offshore     
:  TP751.1  
Issue Date: 14 April 2016
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
SUN Hui
XIE Xiaoping
Cite this article:   
SUN Hui,XIE Xiaoping. Study of hyperspectral detection for nitrogen content of apple leaves[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 67-71.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.02.11     OR     https://www.gtzyyg.com/EN/Y2016/V28/I2/67

[1] 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- 154. Qiu Z J,Song H Y,He Y,et al.Variation rules of the nitrogen content of the oil seed rape at growth stage using SPAD and visible-NIR[J].Transactions of the CSAE,2007,23(7):150-154.

[2] Broge N H,Mortensen J V.Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data[J].Remote Sensing Environment,2002,81(1):45-57.

[3] Wang Y,Huang J F,Wang F M,et al.Predicting nitrogen concentrations from hyperspectral reflectance at leaf and canopy for Rape[J].Spectroscopy and Spectral Analysis,2008,28(2):273-277.

[4] Thomas J R,Oerther G F.Estimating nitrogen content of sweet pepper leaves by reflectance measurements[J].Agronomy Journal,1971,64(1):11-13.

[5] 朱艳,李映雪,周冬琴,等.稻麦叶片氮含量与冠层反射光谱的定量关系[J].生态学报,2006,26(10):3463-3469. Zhu Y,Li Y X,Zhou D Q,et al.Quantitative relationship between leaf nitrogen concentration and canopy reflectance spectra in rice and wheat[J].Acta Ecologica Sinica,2006,26(10):3463-3469.

[6] 邢东兴,常庆瑞.基于光谱分析的果树叶片微量元素含量估测研究——以红富士苹果为例[J].西北农林科技大学学报:自然科学版,2008,36(11):143-150. Xing D X,Chang Q R.Research on predicting the Fe,Mn,Cu,Zn contents in fruit trees' fresh leaves by spectral analysis:Red Fuji apple tree as an example[J].Journal of Northwest A & F Uni-versity:Natural Science Edition,2008,36(11):143-150.

[7] 宋开山,张柏,王宗明,等.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 soy bean chlorophyll con centration using in-situ collected canopy hyperspectral data[J].Transactions of the CSAE,2006,22(8):16- 21.

[8] 汤旭光,刘殿伟,宋开山,等.东北主要绿化树种叶面积指数(LAI)高光谱估算模型研究[J].遥感技术与应用,2010,25(3):334-341. Tang X G,Liu D W,Song K S,et al.A study for estimating the main tree species leaf area index in Northeast based on hyperspectral data[J].Remote Sensing Technology and Application,2010,25(3):334-341.

[9] 姚付启,张振华,杨润亚,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. Yao F Q,Zhang Z H,Yang R Y,et al.Research on Platanus orientalis L.chlorophyll concentration estimation with hyperspectral data based on BP-artificial neural network and principal component analysis[J].Science of Surveying and Mapping,2010,35(1):109- 112.

[10] Lubac B,Loisel H.Variability and classification of remote sensing reflectance spectra in the eastern English Channel and southern North Sea[J].Remote Sensing of Environment,2007,110(1):45- 58.

[11] 申广荣,王人潮.基于神经网络的水稻双向反射模型研究[J].遥感学报,2002,6(4):252-258. Shen G R,Wang R C.Study on bi-directional reflectance model of rice using a artificial neural network[J].Journal of Remote Sensing,2002,6(4):252-258.

[12] 王平,刘湘南,黄方.受污染胁迫玉米叶绿素含量微小变化的高光谱反演模型[J].光谱学与光谱分析, 2010,30(1):197- 201. Wang P,Liu X N,Huang F.Retrieval model for subtle variation of contamination stressed maize chlorophyll using hyperspectral data[J].Spectroscopy and Spectral Analysis,2010,30(1):197-201.

[13] 朱西存,赵庚星,王瑞燕,等.苹果叶片的高光谱特征及其色素含量监测[J].中国农业科学,2010,43(6):1189-1197. Zhu X C,Zhao G X,Wang R Y,et al.Hyperspectral characteristics of apple leaves and their pigment contents monitoring[J].Scientia Agricultura Sinica,2010,43(6):1189-1197.

[14] 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. Zhu X C,Zhao G X,Wang L,et al.Hyperspectrum based prediction model for Nitrogen content of apple flowers[J].Spectroscopy and Spectral Analysis,2010,30(2):416-420.

[1] WANG Ping, MAO Kebiao, MENG Fei, YUAN Zijin. Spatiotemporal evolution of sea surface temperature in the East China Sea[J]. Remote Sensing for Land & Resources, 2020, 32(4): 227-235.
[2] Yuanyuan LI, Shaowei NING, Wei DING, Juliang JIN, Zheng ZHANG. The evaluation of latest GPM-Era precipitation data in Yellow River Basin[J]. Remote Sensing for Land & Resources, 2019, 31(1): 164-170.
[3] Binren XU, Yuanyuan WEI. Spatial statistics of TRMM precipitation in the Tibetan Plateau using random forest algorithm[J]. Remote Sensing for Land & Resources, 2018, 30(3): 181-188.
[4] ZHANG Zhaoying, LU Yicen, WU Guozhou, WANG Yongli. Retrieval of precipitation for grassland based on the multi-temporal Sentinel-1 SAR data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 156-160.
[5] CAO Ying, GUO Zhaocheng, WANG Qiangqiang, JIAO Runcheng. Research on spatial differentiation of precipitation infiltration recharge condition based on remote sensing technology[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 91-95.
[6] LU Xinyu, WEI Ming, WANG Xiuqin, XIANG Fen. Applicability research on TRMM-3B43 precipitation over Xinjiang[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 166-173.
[7] SUN Hui, XIE Xiaoping. Monitoring of Enteromorpha prolifera and analysis of impact factors based on MODIS data in Rizhao offshore[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 144-151.
[8] LIANG Shouzhen, YU Dingfeng, WANG Meng, SHI Ping. Application of remote sensing time-series data to investigate the relationship between vegetation change and climatic factors: A case study of Circum-Bohai Sea area[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 114-121.
[9] LI Shuang, SONG Xiaoning, WANG Yawei, WANG Ruixin. Research on microwave remote sensing of soil moisture index in China based on AMSR-E[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(1): 68-74.
[10] Ma Xiangping, Xian Mailong, Lu Lushi, Sun Shunxin, Zhang Dunhu, Wang Hui . REMOTE SENSING MONITOR STUDY ON POLLUTED VEGETATION STATUS FROM ACID PRECIPITATION POLLUTION IN CHONGQING CITY[J]. REMOTE SENSING FOR LAND & RESOURCES, 1997, 9(4): 14-20.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech