生物与土壤

基于特征波段选择和机器学习的陆地棉叶片水分估算

  • 崔锦涛 ,
  • 买买提·沙吾提
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  • 1.新疆大学地理与遥感科学学院,新疆 乌鲁木齐 830046
    2.新疆绿洲生态重点实验室,新疆 乌鲁木齐 830046
    3.智慧城市与环境建模自治区普通高校重点实验室,新疆 乌鲁木齐 830046
崔锦涛(1997-),男,硕士研究生,主要从事干旱区资源环境及农业遥感应用方面的研究. E-mail: 107552101099@stu.xju.edu.cn

收稿日期: 2022-12-15

  修回日期: 2023-01-17

  网络出版日期: 2023-12-05

基金资助

新疆自然科学计划(自然科学基金)联合基金项目(2021D01C055)

Estimation of leaf water content in upland cotton based on feature band selection and machine learning

  • Jintao CUI ,
  • SAWUT Mamat
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  • 1. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, Xinjiang, China
    2. Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, Xinjiang, China
    3. Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, Xinjiang, China

Received date: 2022-12-15

  Revised date: 2023-01-17

  Online published: 2023-12-05

摘要

棉花叶片含水量的及时准确监测对于评价棉花生长状态具有重要作用。为了精准估算棉花叶片含水量,以新疆渭干河-库车河三角洲绿洲田间尺度上棉花叶片的高光谱数据和叶片水分数据为基础,采用分数阶微分对原始光谱进行处理,通过相关系数分析法、竞争性自适应重加权采样算法(Competitive adaptive reweighted sampling,CARS)、连续投影算法(Successive projections algorithm,SPA)、遗传算法(Genetic algorithm,GA)、蒙特卡罗无信息变量消除算法(Monte Carlo uninformative variables elimination,MC-UVE)以及将CARS与SPA耦合等方法筛选特征波段,采用基于鲸鱼优化算法(Whale optimization algorithm,WOA)改进随机森林回归(Random forest regression,RFR)建立全波段和特征波段的叶片水分含量反演模型,并使用独立样本进行验证分析。结果表明:(1) 不同特征波段筛选方法得到的波段数量与位置不同,其中MC-UVE所得特征波段数量为8个,CARS所得特征波段数量为38个。SPA、GA与CARS-SPA方法中特征波段位置较为一致,基本集中在近红外的950~1050 nm范围内。(2) CARS-SPA-WOA-RFR模型反演效果最好,模型预测值决定系数(R2)=0.93,均方根误差(Root mean square error,RMSE)=0.032。最终构建的模型可为准确快速地监测棉花旱情以及精准灌溉提供决策依据。

本文引用格式

崔锦涛 , 买买提·沙吾提 . 基于特征波段选择和机器学习的陆地棉叶片水分估算[J]. 干旱区地理, 2023 , 46(11) : 1836 -1847 . DOI: 10.12118/j.issn.1000-6060.2022.667

Abstract

It is critical to ensure timely and accurate monitoring of leaf water content (LWC) when assessing the growth status of cotton. To accurately estimate cotton LWC, hyperspectral data, and leaf water data from cotton leaves in the oasis of the Ugan River-Kuqa River Delta, Xinjiang, China, were selected and processed using fractional differentiation of raw spectra. The sample were analyzed through correlation coefficient analysis, competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), genetic algorithm (GA), Monte Carlo uninformative variables elimination (MC-UVE), and a combination of CARS and SPA to filter the feature bands. The modeling of the LWC inversion was executed through random forest regression (RFR) based on the whale optimization algorithm (WOA), and independent samples were used for validation analysis. The results show that: (1) The disparities in the number and positions of the feature bands obtained using the different feature band screening methods are different, where the number of feature bands obtained through MC-UVE is 8 while CARS produced 38. The positions of the characteristic bands identified through the SPA, GA, and CARS-SPA methods are considerably consistent and fundamentally concentrated in the near-infrared range of 950-1050 nm. (2) The CARS-SPA-WOA-RFR model has the best inversion with an R2 of 0.93 and a root mean square error of 0.032. This model can provide a decision basis for accurate and rapid monitoring of cotton drought and precision irrigation.

参考文献

[1] 刘文静, 范永胜, 董彦琪, 等. 我国棉花生产现状分析及建议[J]. 中国种业, 2022(1): 21-25.
[1] [ Liu Wenjing, Fan Yongsheng, Dong Yan Qi, et al. Analysis and suggestions on the current situation of cotton production in China[J]. China Seed Industry, 2022(1): 21-25. ]
[2] 马春玥, 买买提·沙吾提, 依尔夏提·阿不来提, 等. 新疆棉花种植业地理集聚特征及影响因素研究[J]. 作物学报, 2019, 45(12): 1859-1867.
[2] [ Ma Chunyue, Sawut Mamat, Ablet Ershat, et al. Characteristics and influencing factors of geographical agglomeration of cotton plantation in Xinjiang[J]. Acta Agronomica Sinica, 2019, 45(12): 1859-1867. ]
[3] 孙俊, 丛孙丽, 毛罕平, 等. 基于高光谱的油麦菜叶片水分CARS-ABC-SVR预测模型[J]. 农业工程学报, 2017, 33(5): 178-184.
[3] [ Sun Jun, Cong Sunli, Mao Hanping, et al. CARS-ABC-SVR model for predicting leaf moisture of leaf-used lettuce based on hyperspectral[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(5): 178-184. ]
[4] Junttila S, H?ltt? T, Saarinen N, et al. Close-range hyperspectral spectroscopy reveals leaf water content dynamics[J]. Remote Sensing of Environment, 2022, 277: 113071, doi: 10.20944/preprints 202108.0497.v1.
[5] Li L, Ustin S L, Riano D. Retrieval of fresh leaf fuel moisture content using genetic algorithm partial least squares (GA-PLS) modeling[J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(2): 216-220.
[6] Sun J, Zhou X, Hu Y G, et al. Visualizing distribution of moisture content in tea leaves using optimization algorithms and NIR hyperspectral imaging[J]. Computers and Electronics in Agriculture, 2019, 160: 153-159.
[7] Li X L, Wei Z X, Peng F F, et al. Estimating the distribution of chlorophyll content in CYVCV-infected lemon leaf using hyperspectral imaging[J]. Computers and Electronics in Agriculture, 2022, 198: 107036, doi: 10.1016/j.compag.2022.107036.
[8] 杨宝华, 陈建林, 陈林海, 等. 基于敏感波段的小麦冠层氮含量估测模型[J]. 农业工程学报, 2015, 31(22): 176-182.
[8] [ Yang Baohua, Chen Jianlin, Chen Linhai, et al. Estimation model of wheat canopy nitrogen content based on sensitive bands[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(22): 176-182. ]
[9] 张文旭, 佟炫梦, 周天航, 等. 基于高光谱成像的棉花叶片氮素含量遥感估测[J]. 沈阳农业大学学报, 2021, 52(5): 586-596.
[9] [ Zhang Wenxu, Tong Xuanmeng, Zhou Tianhang, et al. Remote sensing estimation of cotton leaf nitrogen content based on hyperspectral imaging[J]. Journal of Shenyang Agricultural University, 2021, 52(5): 586-596. ]
[10] 易翔, 张立福, 吕新, 等. 基于无人机高光谱融合连续投影算法估算棉花地上部生物量[J]. 棉花学报, 2021, 33(3): 224-234.
[10] [ Yi Xiang, Zhang Lifu, Lü Xin, et al. Estimation of cotton above-ground biomass based on unmanned aerial vehicle hyperspectral and successive projections algorithm[J]. Cotton Science, 2021, 33(3): 224-234. ]
[11] 陈鹏. 基于无人机多源遥感的马铃薯叶绿素含量反演机理及模型构建[D]. 焦作: 河南理工大学, 2019.
[11] [ Chen Peng. Retrieval mechanism and model construction of chlorophyll content in potato based on multi-source remote sensing of unmanned aerial vehicle[D]. Jiaozuo: Henan Polytechnic University, 2019. ]
[12] 于雷, 洪永胜, 周勇, 等. 高光谱估算土壤有机质含量的波长变量筛选方法[J]. 农业工程学报, 2016, 32(13): 95-102.
[12] [ Yu Lei, Hong Yongsheng, Zhou Yong, et al. Wavelength variable selection methods for estimation of soil organic matter content using hyperspectral technique[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(13): 95-102. ]
[13] 王佳文, 彭杰, 纪文君, 等. 基于电磁感应数据的南疆棉田土壤pH反演研究[J]. 干旱区研究, 2022, 39(4): 1293-1302.
[13] [ Wang Jiawen, Peng Jie, Ji Wenjun, et al. Soil pH inversion based on electromagnetic induction data in cotton field of southern Xinjiang[J]. Arid Zone Research, 2022, 39(4): 1293-1302. ]
[14] Li L L, Sun J, Tseng M L, et al. Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation[J]. Expert Systems with Applications, 2019, 127: 58-67.
[15] Zhou J, Zhu S Li, Qiu Y G, et al. Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm[J]. Acta Geotechnica, 2022, 17: 1343-1366.
[16] Zhao F, Li W D. A combined model based on feature selection and WOA for PM2.5 concentration forecasting[J]. Atmosphere, 2019, 10(4): 223, doi: 10.3390/atmos10040223.
[17] 苏毅, 王克如, 李少昆, 等. 棉花植株水分含量的高光谱监测模型研究[J]. 棉花学报, 2010, 22(6): 554-560.
[17] [ Su Yi, Wang Keru, Li Shaokun, et al. Monitoring models of the plant water content based on cotton canopy hyperspectral reflectance[J]. Cotton Science, 2010, 22(6): 554-560. ]
[18] 王强, 易秋香, 包安明, 等. 棉花冠层水分含量估算的高光谱指数研究[J]. 光谱学与光谱分析, 2013, 33(2): 507-512.
[18] [ Wang Qiang, Yi Qiuxiang, Bao Anming, et al. Discussion on hyperspectral index for the estimation of cotton canopy water content[J]. Spectroscopy and Spectral Analysis, 2013, 33(2): 507-512. ]
[19] 赵巧珍, 丁建丽, 韩礼敬, 等. MODIS和Landsat时空融合影像在土壤盐渍化监测中的适用性研究——以渭干河—库车河三角洲绿洲为例[J]. 干旱区地理, 2022, 45(4): 1155-1164.
[19] [ Zhao Qiaozhen, Ding Jianli, Han Lijing, et al. Exploring the application of MODIS and Landsat spatiotemporal fusion images in soil salinization: A case of Ugan River-Kuqa River Delta Oasis[J]. Arid Land Geography, 2022, 45(4): 1155-1164. ]
[20] 玉苏甫·买买提, 吐尔逊·艾山, 买合皮热提·吾拉木. 新疆渭-库绿洲棉花种植面积遥感监测研究[J]. 农业现代化研究, 2014, 35(2): 240-243.
[20] [ Mamat Yusup, Hasan Tursun, Gulam Magpirat. Remote sensing of cotton plantation areas monitoring in delta oasis of Ugan-Kucha River, Xinjiang[J]. Research of Agricultural Modernization, 2014, 35(2): 240-243. ]
[21] 刘帆. 分数阶微分算法在医学超声弹性图像去噪中的应用研究[D]. 昆明: 昆明理工大学, 2018.
[21] [ Liu Fan. Application of fractional differential algorithm in medical ultrasonic elastic image denoising[D]. Kunming: Kunming University of Science and Technology, 2018. ]
[22] 李长春, 施锦锦, 马春艳, 等. 基于小波变换和分数阶微分的冬小麦叶绿素含量估算[J]. 农业机械学报, 2021, 52(8): 172-182.
[22] [ Li Changchun, Shi Jinjin, Ma Chunyan, et al. Estimation of chlorophyll content in winter wheat based on wavelet transform and fractional differential[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(8): 172-182. ]
[23] Li H D, Liang Y Z, Xu Q S, et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J]. Analytica Chimica Acta, 2009, 648(1): 77-84.
[24] Zhang J K, Rivard B, Rogge D M. The successive projection algorithm (SPA), an algorithm with a spatial constraint for the automatic search of endmembers in hyperspectral data[J]. Sensors, 2008, 8(2): 1321-1342.
[25] 程介虹, 陈争光, 衣淑娟. 最小相关系数的多元校正波长选择算法[J]. 光谱学与光谱分析, 2022, 42(3): 719-725.
[25] [ Cheng Jiehong, Chen Zhengguang, Yi Shujuan. Wavelength selection algorithm based on minimum correlation coefficient for multivariate calibration[J]. Spectroscopy and Spectral Analysis, 2022, 42(3): 719-725. ]
[26] 宋相中. 近红外光谱定量分析中三种新型波长选择方法研究[D]. 北京: 中国农业大学, 2017.
[26] [ Song Xiangzhong. Research of three new wavelength selection methods in near infrared spectroscopy quantitative analysis area[D]. Beijing: China Agricultural University, 2017. ]
[27] Mirjalili S, Lewis A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.
[28] 李畸勇, 张伟斌, 赵新哲, 等. 改进鲸鱼算法优化支持向量回归的光伏最大功率点跟踪[J]. 电工技术学报, 2021, 36(9): 1771-1781.
[28] [ Li Qiyong, Zhang Weibin, Zhao Xinzhe, et al. Global maximum power point tracking for PV array based on support vector regression optimized by improved whale algorithm[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1771-1781. ]
[29] 吾木提·艾山江, 买买提·沙吾提, 马春玥. 基于分数阶微分和连续投影算法-反向传播神经网络的小麦叶片含水量高光谱估算[J]. 激光与光电子学进展, 2019, 56(15): 251-259.
[29] [ Hasan Umut, Sawut Mamat, Ma Chunyue. Hyperspectral estimation of wheat leaf water content using fractional differentials and successive projection algorithm-back propagation neural network[J]. Laser & Optoelectronics Progress, 2019, 56(15): 251-259. ]
[30] Zhang M J, Zhang S Z, Iqbal J. Key wavelengths selection from near infrared spectra using Monte Carlo sampling-recursive partial least squares[J]. Chemometrics and Intelligent Laboratory Systems, 2013, 128: 17-24.
[31] Han Z Z, Deng L M. Application driven key wavelengths mining method for aflatoxin detection using hyperspectral data[J]. Computers and Electronics in Agriculture, 2018, 153: 248-255.
[32] Jia M, Li W, Wang K K, et al. A newly developed method to extract the optimal hyperspectral feature for monitoring leaf biomass in wheat[J]. Computers and Electronics in Agriculture, 2019, 165: 104942, doi: 10.1016/j.compag.2019.104942.
[33] Mohammadi B, Mehdizadeh S. Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm[J]. Agricultural Water Management, 2020, 237: 106145, doi: 10.1016/j.agwat.2020.106145.
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