基于无人机遥感的不同施氮水稻光谱与植被指数分析

卫星遥感空间分辨率低且易受大气、云层、雨雪等因素的影响。本文使用共轴十二旋翼无人机搭载光谱仪构成农情遥感系统。首先, 给出自主设计的无人机结构和飞行控制系统, 围绕飞行平台、控制系统、遥感载荷构建了多环节数据备份的无人机遥感数据采集系统; 然后, 试验测试4种施氮水平水稻的光谱指数变化规律; 最后, 通过试验数据分析可得: 在可见光区水稻冠层光谱反射率随氮素水平增加而减小, 在近红外区, 光谱反射率一开始随氮素水平增加而增大, 但氮素水平增大到一定程度后再增加氮素导致反射率降低。在4种氮素水平下, 水稻植被指数 RVI和NDVI由分蘖期到拔节期先增大, 然后至抽穗期又逐渐减小, 且抽穗期RVI和NDVI值小于其分蘖期RVI和NDVI值。试验表明以多旋翼无人机为平台搭载光谱仪器构成农情遥感监测系统用于反演作物植被指数方面是可行的。本文设计的无人机遥感数据采集系统能够有效、实时获取遥感信息, 其获取的高空间分辨率和光谱分辨率的农田实时信息能够为作物长势的分析、健康状况的监测提供必要的数据支持。

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.

参考文献

[1] 殷春渊, 张庆, 魏海燕,等.不同产量类型水稻基因型氮素吸收、利用效率的差异[J].中国农业科学, 2010,43(1): 39-50.

YIN CH Y,ZHANG Q,WEI H Y,et al.. Difference in nitrogen absorption and use efficiency in rice genotypes with different yield performance[J]. Scientia Agricultura Sinica,2010,43(1): 39-50.(in Chinese)

[2] 谢芳, 韩晓日, 杨劲峰,等.不同施氮处理对水稻氮素吸收及产量的影响[J].中国土壤与肥料, 2010(4): 24-26,45.

XIE F,HAN X R,YANG J F,et al.. Effect of N fertilizer application on nitrogen absorption and yield of rice[J]. Soil and Fertilizer Sciences in China,2010(4): 24-26,45.(in Chinese)

[3] 齐冰洁,刘金国,张博研,等.高分辨率遥感图像SIFT和SURF算法匹配性能研究[J].中国光学,2017,10(3): 331-339.

QI B J,LIU J G,ZHANG B Y,et al.. Research on matching performance of SIFT and SURF algorithms for high resolution remote sensing image[J]. Chinese Optics,2017,10(3): 331-339.(in Chinese)

[4] 巩盾.空间遥感测绘光学系统研究综述[J].中国光学,2015,8(5): 714-724.

GONG D. Review on mapping space remote sensor optical system[J]. Chinese Optics,2015,8(5): 714-724.(in Chinese)

[5] 吴银花,胡炳樑,高晓惠,等.利用区域增长技术的自适应高光谱图像分类[J].光学精密工程,2018,26(2): 426-434.

WU Y H,HU B L,GAO X H,et al.. Adaptive hyperspectral image classification using region-growing techniques[J]. Opt. Precision Eng.,2018,26(2): 426-434.(in Chinese)

[6] 侯榜焕,姚敏立,贾维敏,等.面向高光谱图像分类的空谱判别分析[J].光学精密工程,2018,26(2): 450-460.

HOU BA H,YAO M L,JIA W M,et al.. Spatial-spectral discriminant analysis for hyperspectral image classification[J]. Opt. Precision Eng.,2018,26(2): 450-460.(in Chinese)

[7] 陈明,汪正坤,辛鑫,等.基于高光谱的微藻生物膜生长特性研究[J].光学精密工程,2017,25(10s): 39-45.

CHEN M,WANG ZH K,XIN X,et al.. Study on growth characteristics of microalgae biofilm based on hyperspectral imaging[J]. Opt. Precision Eng.,2017,25(10s): 39-45.(in Chiese)

[8] 吴龙国,王松磊,何建国,等.基于高光谱成像技术的土壤水分机理研究及模型建立[J].发光学报,2017,38(10): 1366-1376.

WU L G,WANG S L,HE J G,et al.. Soil moisture mechanism and establishment of model based on hyperspectral imaging technique[J]. Chinese Journal of Luminescence,2017,38(10): 1366-1376.(in Chinese)

[9] 贾瑞栋,夏志伟,王玉鹏,等.太阳光谱辐照度绝对测量及其定标单色仪[J].光学学报,2017,38(8): 1097-1101.

JIA R D,XIA ZH W,WANG Y P,et al.. Absolute solar spectral irradiance measurement and its calibration monochromator[J]. Acta Optica Sinica,2017,38(8): 1097-1101.(in Chinese)

[10] 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.. Hyper spectrum based prediction model for nitrogen content of apple flowers[J]. Spectroscopy and Spectral Analysis,2010,30(2): 416-420.(in Chinese)

[11] 冯书谊, 张宁, 沈霁,等.基于反射率特性的高光谱遥感图像云检测方法研究[J].中国光学, 2015,8(2): 198-203.

FENG SH Y,ZHANG N,SHEN J,et al.. Method of cloud detection with hyperspectral remote sensing image based on the reflective characteristics[J]. Chinese Optics,2015,8(2): 198-203.(in Chinese)

[12] 刘轲, 周清波, 吴文斌,等.基于多光谱与高光谱遥感数据的冬小麦叶面积指数反演比较[J].农业工程学报, 2016,32(3): 155-162.

LIU K,ZHOU Q B,WU W B,et al.. Comparison between multispectral and hyperspectral remote sensing for LAI estimation[J]. Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(3): 155-162.(in Chinese)

[13] 周明辉, 廖春艳, 任兆玉,等.表面增强拉曼光谱生物成像技术及其应用[J].中国光学, 2013,6(5): 633-641.

ZHOU M H,LIAO CH Y,REN ZH Y,et al.. Bioimaging technologies based on surface-enhanced raman spectroscopy and their applications[J]. Chinese Optics,2013,6(5): 633-641.(in Chinese)

[14] THENKABAIL P S,WARD A D,LYON J G. Landsat-5 Thematic Mapper models of soybean and corn crop characteristics[J]. International Journal of Remote Sensing,1994,15(1): 49-61.

[15] BUNNIK N J J. The multispectral reflectance of shortwave radiation by agricultural crops in relation with their morphological and optical properties[D]. Wageningen: Meded. Landbouwhoge School, 1978,1: 167-175..

[16] WALTHALL C,DULANEY W,ANDERSON M,et al.. A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+imagery[J]. Remote Sensing of Environment,2004,92(4): 465-474.

[17] 史舟,梁宗正,杨媛媛,等.农业遥感研究现状与展望[J].农业机械学报, 2015,46(2): 247-260.

SHI ZH,LIANG Z ZH,YANG Y Y,et al.. Status and prospect of agricultural remote sensing[J]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(2): 247-260.(in Chinese)

[18] 刘峰,刘素红,向阳.园地植被覆盖度的无人机遥感监测研究[J].农业机械学报,2014,45(11): 250-257.

LIU F,LIU S H,XIANG Y. Study on monitoring fractional vegetation cover of garden plots by unmanned aerial vehicles[J]. Transactions of the Chinese Society for Agriculural Machinery,2014,45(11): 250-257.(in Chinese)

[19] 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.

DAI H,HU CH SH,CHENG Y S,et al.. Correlation between agronomic parameters and spectral vegetation index in winter wheat under different nitrogen levels[J]. Agricultural Research in the Arid Areas,2005,23(4): 16-21.(in Chinese)

裴信彪, 吴和龙, 马萍, 严永峰, 彭程, 郝亮, 白越. 基于无人机遥感的不同施氮水稻光谱与植被指数分析[J]. 中国光学, 2018, 11(5): 832. 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.

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