Analysis of Spectral Change of Cotton During Growth Period Based on Endmember Extraction of UAV Hyperspectral Image

doi:10.6048/j.issn.1001-4330.2021.07.004

Abstract

Abstract: 【Objective】 Studying the trend of reflection spectrum in the time series of cotton growth cycle is of great significance for cotton growth monitoring.【Method】 In order to obtain the spectrum curve of cotton at different growth stages and its changing rule. In this paper, a multi-rotor unmanned aerial vehicle(UAV) M600 PRO carrying a Rikola hyperspectral imager was used to collect a total of five periods of cotton hyperspectral data from flowering period to later period. Moguhu village, Shawan county, Tacheng district, Xinjiang (85°51'49.44' E, 44°25'26.61' N) was selected as the study area. We collected hyperspectral data between 12 and 14 noon. UAV flied 3 sorties per period, flying at altitudes of 60, 80 and 100 m respectively.And the flight time of each sortie was about 13 minutes. Meanwhile, SR-3500 portable spectrometer was used to collect cotton canopy spectral data on the ground for verification.The acquired hyperspectral data of UAV were preprocessed by lens halo correction, dark current correction, band registration, image Mosaic, and reflectivity correction, etc. Finally we got usable hyperspectral images.The pixel purity index(PPI) endmember extraction algorithm and the N-FINDR endmember extraction algorithm were used to extract the cotton spectrum curve from the UAV hyperspectral images. Then, the spectral curve collected by the SR-3500 spectrometer on the ground was taken as the standard. Spectral angle mapping(SAM) was used as the evaluation index to verify the endmember extraction effect.【Result】 The results showed that The spectral angles of the spectral curves which extracted by the N-FINDR algorithm in the hyperspectral images at 60, 80 and 100 m altitude were 0.065,8, 0.065,9 and 0.067,7. It was closer to the SR-3500 spectrometer data than the results of the PPI algorithm. N-FINDR algorithm could better extract small sample endmember. The spatial resolution change caused by the variation of altitude below 100m has little impact on the reflection spectrum, with the result difference was less than 2%. Analyzeing to the spectrum curve of cotton in different growth stages, the absorption valley and red edge reached the peak value on July 18.The maximum values of NDVI and RVI reached 0.841,7 and 11.630,5 on July 11, while the maximum values of EVI, DVI and OSAVI reached 0.818,9, 0.501,3 and 0.501,2 on July 18.【Conclusion】 The N-FINDR endmember extraction algorithm can extract the cotton spectral curve from the cotton hyperspectral image.100 m is the better altitude of UAV data acquisition.The photosynthesis of cotton at its peak in july. The strong absorption of red light and the strong reflection of near infrared wave band are the most obvious during peak flowering period and bell period. This paper can provide reference for crop growth monitoring based on UAV remote sensing.

Key words: endmember extraction; unmanned aerial vehicle; imaging hyperspectral; spectral Analysis; cotton

摘要： 【目的】棉花在不同生长期的波谱曲线变化具有规律性,研究其时间序列上的反射光谱变化趋势与规律并监测长势,为基于无人机多光谱、高光谱遥感的作物长势监测提供借鉴和参考。【方法】使用多旋翼无人机搭载Rikola高光谱成像仪,获取棉花从花期到后期之间的高光谱影像。使用纯净像元指数算法和最大单形体体积算法进行端元提取,并以SR-3500光谱仪采集的地面光谱曲线为标准,光谱角度为评价指标,依次从端元提取算法效果、不同航高数据对比、光谱相关性、多期光谱曲线变化趋势等分析。【结果】最大单形体体积算法在60、80、100 m航高下波谱角度结果分别为0.065 8、0.065 9、0.067 7,相较于纯净像元指数算法结果更接近地面光谱仪数据,具有较优的相关性(R²均在0.99以上),且能更好地提取小样本端元。航高对端元提取的影响较小,同种算法在不同航高下提取结果差异均在2%以下。不同生长期棉花波谱曲线变化呈规律性,吸收谷与红边值在7月中旬到达峰值。标准植被指数与比值植被指数在7月上中旬达到最大值(0.841 7、11.630 5),增强型植被指数、差值植被指数、优化土壤调节植被指数在7月中下旬达到最大值(0.818 9、0.501 3、0.501 2)。【结论】最大单形体体积算法可较好的从棉花高光谱影像中提取出棉花波谱曲线,且100 m为较优的无人机数据采集航高。棉花在7月光合作用最大,对红光的强吸收、近红外波段强反射现象最为明显。

关键词: 端元提取, 无人机, 成像高光谱, 光谱分析, 棉花

CLC Number:

S252
TP79

LONG Xiang, ZHAO Qingzhan, WANG Xuewen, MA Yongjian, JIANG Ping. Analysis of Spectral Change of Cotton During Growth Period Based on Endmember Extraction of UAV Hyperspectral Image[J]. Xinjiang Agricultural Sciences, 2021, 58(7): 1207-1216.

龙翔, 赵庆展, 王学文, 马永建, 江萍. 基于机载高光谱端元提取分析棉花生长期光谱变化[J]. 新疆农业科学, 2021, 58(7): 1207-1216.

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