基于无人机遥感的不同施氮水稻光谱与植被指数分析

裴信彪; 吴和龙; 马萍; 严永峰; 彭程; 郝亮; 白越

doi:10.3788/CO.20181105.0832

Volume 11 Issue 5

Oct. 2018

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Chinese Optics > 2018 > 11(5): 832-840.

PEI Xin-biao, WU He-long, MA Ping, YAN Yong-feng, PENG Cheng, HAO Liang, BAI Yue. Analysis of the spectrum and vegetation index of rice under different nitrogen levels based on unmanned aerial vehicle remote sensing[J]. Chinese Optics, 2018, 11(5): 832-840. doi: 10.3788/CO.20181105.0832

Citation:

PEI Xin-biao, WU He-long, MA Ping, YAN Yong-feng, PENG Cheng, HAO Liang, BAI Yue. Analysis of the spectrum and vegetation index of rice under different nitrogen levels based on unmanned aerial vehicle remote sensing[J]. Chinese Optics, 2018, 11(5): 832-840. doi: 10.3788/CO.20181105.0832

Citation:

PEI Xin-biao, WU He-long, MA Ping, YAN Yong-feng, PENG Cheng, HAO Liang, BAI Yue. Analysis of the spectrum and vegetation index of rice under different nitrogen levels based on unmanned aerial vehicle remote sensing[J]. Chinese Optics, 2018, 11(5): 832-840. doi: 10.3788/CO.20181105.0832

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Analysis of the spectrum and vegetation index of rice under different nitrogen levels based on unmanned aerial vehicle remote sensing

doi: 10.3788/CO.20181105.0832

PEI Xin-biao^{1, 2
,},
WU He-long^{1, 2},
MA Ping^{1, 2},
YAN Yong-feng³,
PENG Cheng¹,
HAO Liang^{1
,
,},
BAI Yue^{1
,
,}

1.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2.
University of Chinese Academy of Sciences, Beijing 100039, China
3.
Institute of Rice, Jilin Academy of Agriculture, Gongzhuling 136100, China

Funds:

the National Natural Science Foundation of China 11372309

the National Natural Science Foundation of China 61304017

Key Technology Development Project of Jilin Province 20150204074GX

Key Technology Development Project of Jilin Province 20160204010NY

the Provincial Special Funds Project of Science and Technology Cooperation 2017SYHZ0024

Youth Innovation Promotion Association 2014192

More Information

Corresponding author: HAO Liang; BAI Yue, E-mail: baiy@ciomp.ac.cn
Received Date: 14 Dec 2017
Rev Recd Date: 02 Mar 2018
Publish Date: 01 Oct 2018

Abstract

Abstract

Satellite remote sensing has low spatial resolution and is susceptible to the atmosphere, cloud layer, rain, and snow and so on. In this paper, the coaxial remote sensing system is constructed by using a coaxial 12-rotor unmanned aerial vehicle with spectrometer. Firstly, the self-designed UAV structure and flight control system are introduced, and a multi-link data backup UAV remote sensing data acquisition system is built around the flight platform, control system and remote sensing load. Then, the change of spectral index of four rices with different nitrogen levels is tested. Finally, by analyzing the experimental data, it can be obtained that the spectral reflectance of rice canopy decreases with the increase of nitrogen level in the visible region, and the spectral reflectance increases with the increase of nitrogen level in the near-infrared region. However, when the nitrogen level is increased to a certain extent, the increase of nitrogen will cause the reflectivity to decrease. Under the four nitrogen levels, the RVI and NDVI increased from tillering stage to jointing stage, then decreased gradually in heading stage, and the values of RVI and NDVI at heading stage are lower than those of RVI and NDVI in tillering stage. The test shows that the multi-rotor UAV platform equipped with a spectrometer composed of agricultural remote sensing monitoring system is feasible in the inversion of crop vegetation index. The UAV remote sensing data acquisition system designed in this paper can obtain remote sensing information effectively and in real time. The real time information of farmland with high spatial resolution and spectral resolution can provide necessary data support for crop growth analysis and health monitoring.
- unmanned aerial vehicle,
- multi link data backup,
- data acquisition,
- rice vegetation index,
- nitrogen

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References(19)

References

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Analysis of the spectrum and vegetation index of rice under different nitrogen levels based on unmanned aerial vehicle remote sensing

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