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

doi:10.11963/1002-7807.yxzlf.20210428

棉花学报 ›› 2021, Vol. 33 ›› Issue (3): 224-234.doi: 10.11963/1002-7807.yxzlf.20210428

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

易翔¹(),张立福^1,^3,^*(),吕新¹,张泽¹,田敏²,印彩霞¹,马怡茹¹,范向龙¹

1.石河子大学农学院/新疆生产建设兵团绿洲生态农业重点实验室,新疆石河子 832003
2.石河子大学机械电气工程学院,新疆石河子 832003
3.中国科学院空天信息创新研究院/遥感科学国家重点实验室,北京100094

收稿日期:2020-12-28 出版日期:2021-05-15 发布日期:2021-06-02
通讯作者: ^*张立福 E-mail:20192012003@stu.shzu.edu.cn;zhanglf@radi.ac.cn
作者简介:易翔（1996―）,男,硕士研究生, 20192012003@stu.shzu.edu.cn。
基金资助:
国家自然科学基金(61962053);新疆生产建设兵团棉花生产大数据关键技术及农业大数据平台研发应用(2018AA004)

Estimation of cotton above-ground biomass based on unmanned aerial vehicle hyperspectral and successive projections algorithm

Yi Xiang¹(),Zhang Lifu^1,^3,^*(),Lü Xin¹,Zhang Ze¹,Tian Min²,Yin Caixia¹,Ma Yiru¹,Fan Xianglong¹

1. Agriculture College of Shihezi University/The Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Group, Shihezi, Xinjiang 832003, China
2. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, Xinjiang 832003, China
3. Aerospace Information Research Institute, Chinese Academy of Sciences/State Key Laboratory of Remote Sensing Science, Beijing 100094, China

Received:2020-12-28 Online:2021-05-15 Published:2021-06-02
Contact: ^*Zhang Lifu E-mail:20192012003@stu.shzu.edu.cn;zhanglf@radi.ac.cn

摘要/Abstract

摘要：

【目的】地上部生物量是表征植物生命活动的重要参数。探索不同的光谱预处理方法和建模方法,实现对棉花地上部生物量快速、无损、准确的估算,对棉花长势监测和大田精准管理具有重要意义。【方法】以新陆早53号、新陆早45号为研究对象,设置不同施氮处理,于出苗后不同阶段获取棉花地上部生物量和无人机高光谱数据,通过连续投影算法（Successive projections algorithm,SPA）筛选不同预处理[一阶导数、二阶导数、Savitzky-Golay（SG平滑）、多元散射校正]后的特征波长,基于筛选出的不同波长组合使用偏最小二乘法回归（Partial least square regression,PLSR）和随机森林回归（Random forest regression,RFR）分别构建棉花地上部生物量估算模型,比较不同预处理后建立模型的精度,确定最优估算模型。【结果】（1）利用SPA算法对不同预处理后的光谱信息筛选出特征波长9~26个,可实现光谱信息降维。（2）基于SG平滑-SPA处理及PLSR方法建立的模型最佳,R²达到了0.63,均方根误差（Root mean square error,RMSE）为0.42,验证集的R²为0.67,RMSE为0.44。（3）一阶导数-SPA处理后,采用RFR构建的模型最佳,R²达到0.87,RMSE为0.45,验证集R²为0.81,RMSE为0.37。【结论】采用一阶导数预处理结合SPA筛选特征波长,经RFR构建的估算模型结果和验证效果均最佳,可用于棉花地上部生物量定量估算。

关键词: 棉花; 地上部生物量; 光谱预处理; 特征波长; 偏最小二乘法回归; 随机森林回归

Abstract:

[Objective] Above-ground biomass is an important parameter of plant life activity. Exploring different spectral pretreatment methods and modeling methods to achieve rapid, nondestructive and accurate estimation of cotton above-ground biomass is of great significance for cotton growth monitoring and field precision management. [Method] Xinluzao 53 and Xinluzao 45 were selected as the research objects, and different nitrogen application treatments were set up to obtain cotton above-ground biomass and UAV hyperspectral data at different stages after emergence. The successive projections algorithm (SPA) was used to select the characteristic wavelengths after different pretreatments [first derivative, second derivative, Savitzky-Golay (SG smoothing), multiple scatter correction]. Based on the selected wavelengths, partial least squares regression (PLSR) and random forest regression (RFR) were used to construct cotton above-ground biomass (dry matter) estimation models, respectively, and the optimal estimation model was determined by comparison of the model estimation accuracy. [Result] (1) The SPA algorithm can effectively remove the redundant bands from the spectra, and the number of above-ground biomass-sensitive characteristic wavelengths after different pretreatments ranges from 9 to 26, which could be used to reduce the dimension of spectral information. (2) Based on SG smoothing-SPA processing, the model established by PLSR is the best, with a coefficient of determination (R²) of 0.63, root mean square error (RMSE) of 0.42, and validation set R², RMSE of 0.67, 0.44. (3) After the first derivative-SPA processing, the model constructed by RFR is the best, with R² of 0.87, RMSE of 0.45, validation set R² and RMSE of 0.81 and 0.37. [Conclusion] Using the first derivative pretreatment combined with SPA to select biomass-sensitive wavelengths, the RFR model has the best results and validation performance, which can be used for quantitative estimation of cotton above-ground biomass.

Key words: cotton; above-ground biomass; spectral pretreatment; sensitive bands; partial least squares; random forest regression

易翔, 张立福, 吕新, 张泽, 田敏, 印彩霞, 马怡茹, 范向龙. 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.

Yi Xiang, Zhang Lifu, Lü Xin, Zhang Ze, Tian Min, Yin Caixia, Ma Yiru, Fan Xianglong. Estimation of cotton above-ground biomass based on unmanned aerial vehicle hyperspectral and successive projections algorithm[J]. Cotton Science, 2021, 33(3): 224-234.

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: http://journal.cricaas.com.cn/Jweb_mhxb/CN/10.11963/1002-7807.yxzlf.20210428

http://journal.cricaas.com.cn/Jweb_mhxb/CN/Y2021/V33/I3/224

图/表 11

图1

图2

表1

图3

图4

图5

表2

表3

图6

图7

表4

参考文献 30

[1]	李继福, 何俊峰, 陈佛文,等. 中国棉花生产格局与施肥研究现状——基于CNKI数据计量分析[J/OL]. 中国棉花, 2019, 46(4): 17-24, 28 [2020-12-25]. https://doi.org/10.11963/1000-632X.ljfljf.20190402. doi: https://doi.org/10.11963/1000-632X.ljfljf.20190402
	Li Jifu, He Junfeng, Chen Fowen, et al. Status of cotton planting and fertilization research in China——Based on CNKI data analysis[J/OL]. China Cotton, 2019, 46(4): 17-24, 28[2020-12-25]. https://doi.org/10.11963/1000-632X.ljfljf.20190402. doi: https://doi.org/10.11963/1000-632X.ljfljf.20190402
[2]	卫小华, 张立杰. 农业供给侧改革背景下新疆棉花产量变动及其驱动因素分析[J/OL]. 江苏农业科学, 2019, 47(14): 338-342 [2020-12-25]. https://doi.org/10.15889/j.issn.1002-1302.2019.14.076. doi: https://doi.org/10.15889/j.issn.1002-1302.2019.14.076
	Wei Xiaohua, Zhang Lijie. Analysis of cotton yield change in Xinjiang area and its driving factors under agricultural supply-side reform[J/OL]. Jiangsu Agricultural Sciences, 2019, 47(14): 338-342 [2020-12-25]. https://doi.org/10.15889/j.issn.1002-1302.2019.14.076. doi: https://doi.org/10.15889/j.issn.1002-1302.2019.14.076
[3]	刘畅, 杨贵军, 李振海,等. 融合无人机光谱信息与纹理信息的冬小麦生物量估测[J/OL]. 中国农业科学, 2018, 51(16): 3060-3073 [2020-12-25]. https://doi.org/10.3864/j.issn.0578-1752.2018.16.003A. doi: https://doi.org/10.3864/j.issn.0578-1752.2018.16.003A
	Liu Chang, Yang Guijun, Li Zhenhai, et al. Biomass estimation in winter wheat by UAV spectral information and texture information fusion[J/OL]. Scientia Agricultura Sinica, 2018, 51(16): 3060-73[2020-12-25]. https://doi.org/10.3864/j.issn.0578-1752.2018.16.003. doi: https://doi.org/10.3864/j.issn.0578-1752.2018.16.003A
[4]	范云豹, 宫兆宁, 赵文吉,等. 基于高光谱遥感的植被生物量反演方法研究[J/OL]. 河北师范大学学报(自然科学版), 2016, 40(3): 267-271[2020-12-25]. https://doi.org/10.13763/j.cnki.jhebnu.nse.2016.03.015. doi: https://doi.org/10.13763/j.cnki.jhebnu.nse.2016.03.015
	Fan Yunbao, Gong Zhaoning, Zhao Wenji, et al. study on vegetation biomass inversion method based on hyperspectral remote sensing[J/OL]. Journal of Hebei Normal University (Natural Science), 2016, 40(3): 267-271 [2020-12-25]. https://doi.org/10.13763/j.cnki.jhebnu.nse.2016.03.015. doi: https://doi.org/10.13763/j.cnki.jhebnu.nse.2016.03.015
[5]	王利民, 刘佳, 杨玲波,等. 基于无人机影像的农情遥感监测应用[J/OL]. 农业工程学报, 2013, 29(18): 136-145 [2020-12-25]. https://doi.org/10.3969/j.issn.1002-6819.2013.18.017. doi: https://doi.org/10.3969/j.issn.1002-6819.2013.18.017
	Wang Liming, Liu Jia, Yang Lingbo, et al. Applications of unmanned aerial vehicle images on agricultural remote sensing monitoring[J/OL]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(18): 136-145 [2020-12-25]. https://doi.org/10.3969/j.issn.1002-6819.2013.18.017. doi: https://doi.org/10.3969/j.issn.1002-6819.2013.18.017
[6]	Yue J B, Feng H K, Yang G J, et al. A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy[J/OL]. Remote Sensing, 2018, 10(2): 66. https://doi.org/10.3390/rs10010066. doi: https://doi.org/10.3390/rs10010066
[7]	李冰, 刘镕源, 刘素红,等. 基于低空无人机遥感的冬小麦覆盖度变化监测[J/OL]. 农业工程学报, 2012, 28(13): 160-165 [2020-12-25]. https://doi.org/1002-6819 (2012)-13-0160-06. doi: https://doi.org/1002-6819 (2012)-13-0160-06
	Li Bing, Liu Rongyuan, Liu Suhong, et al. Monitoring vegetation coverage variation of winter wheat by low-altitude UAV remote sensing system[J/OL]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(13): 160-165[2020-12-25]. https://doi.org/1002-6819(2012)-13-0160-06. doi: https://doi.org/1002-6819 (2012)-13-0160-06
[8]	颜安, 郭涛, 陈全家,等. 基于无人机影像的棉花株高预测[J/OL]. 新疆农业科学, 2020, 57(8): 1493-1502[2020-12-25]. https://doi.org/10.6048/j.issn.1001-4330.2020.08.014. doi: https://doi.org/10.6048/j.issn.1001-4330.2020.08.014
	An Yan, Guo Tao, Chen Quanjia, et al. Prediction of cotton plant height based on UAV image[J]. Xinjiang Agricultural Sciences, 2020, 57(8): 1493-1502[2020-12-25]. https://doi.org/10.6048/j.issn.1001-4330.2020.08.014. doi: https://doi.org/10.6048/j.issn.1001-4330.2020.08.014
[9]	陈鹏飞, 梁飞. 基于低空无人机影像光谱和纹理特征的棉花氮素营养诊断研究[J/OL]. 中国农业科学, 2019, 52(13): 2220-2229 [2020-12-25]. https://doi.org/10.3864/j.issn.0578-1752.2019.13.003. doi: https://doi.org/10.3864/j.issn.0578-1752.2019.13.003
	Cheng Pengfei, Liang Fei. Cotton nitrogen nutrition diagnosis based on spectrum and texture feature of images from low altitude unmanned aerial vehicle[J/OL]. Scientia Agricultura Sinica, 2019, 52(13): 2220-2229[2020-12-25]. https://doi.org/10.3864/j.issn.0578-1752.2019.13.003. doi: https://doi.org/10.3864/j.issn.0578-1752.2019.13.003
[10]	崔美娜. 基于无人机遥感的棉花螨害动态监测研究[D]. 石河子: 石河子大学, 2019.
	Cui Meina. Study on dynamic monitoring of cotton spider mites based on remote sensing of UAV[D]. Shihezi: Shihezi University, 2019.
[11]	Tao H, Feng H, Xu L, et al. Estimation of crop growth parameters using UAV-based hyperspectral remote sensing data[J/OL]. Sensors, 2020, 20(5): 1296[2020-12-25]. https://doi.org/10.3390/s20051296. doi: https://doi.org/10.3390/s20051296
[12]	Hansen P M, Schjoerring J K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression[J/OL]. Remote Sensing of Environment, 2003, 86(4): 542-553 [2020-12-25]. https://doi.org/10.1016/S0034-4257(03)00131-7. doi: https://doi.org/10.1016/S0034-4257(03)00131-7
[13]	邓江, 谷海斌, 王泽,等. 基于无人机遥感的棉花主要生育时期地上生物量估算及验证[J/OL]. 干旱地区农业研究, 2019, 37(5): 55-61, 69[2020-12-25]. https://doi.org/10.7606/j.issn.1000-7601.2019.05.09. doi: https://doi.org/10.7606/j.issn.1000-7601.2019.05.09
	Deng Jiang, Gu Haibin, Wang Ze, et al. Estimation and validation of above-ground biomass of cotton during main growth period using unmanned aerial vehicle (UAV)[J/OL]. Agricultural Research in the Arid Areas, 2019, 37(5): 55-61, 69 [2020-12-25]. https://doi.org/10.7606/j.issn.1000-7601.2019.05.09. doi: https://doi.org/10.7606/j.issn.1000-7601.2019.05.09
[14]	刘明博, 唐延林, 李晓利,等. 水稻叶片氮含量光谱监测中使用连续投影算法的可行性[J/OL]. 红外与激光工程, 2014, 43(4): 1265-1271.
	Liu Mingbo, Tang Yanlin, Li Xiaoli, et al. Feasibility of using successive projections algorithm in spectral monitoring of rice leaves nitrogen contents[J/OL]. Infrared and Laser Engineering, 2014, 43(4): 1265-1271.
[15]	Lei T, Lin X H, Sun D W. Rapid classification of commercial Cheddar cheeses from different brands using PLSDA, LDA and SPA-LDA models built by hyperspectral data[J/OL]. Journal of Food Measurement and Characterization, 2019, 13(4): 3119-3129 [2020-12-25]. https://doi.org/10.1007/s11694-019-00234-0. doi: https://doi.org/10.1007/s11694-019-00234-0
[16]	Geladi P, Kowalski B R. Partial least-squares regression: a tutorial[J]. Analytica Chimica Acta, 1986, 185: 1-17 doi: 10.1016/0003-2670(86)80028-9
[17]	Gleason C J, IM J. Forest biomass estimation from airborne LiDAR data using machine learning approaches[J/OL]. Remote Sensing of Environment, 2012, 125: 80-91[2020-12-25]. https://doi.org/10.1016/j.rse.2012.07.006. doi: https://doi.org/10.1016/j.rse.2012.07.006
[18]	Van Huffel S. Proceedings of the second international workshop on Recent advances in total least squares techniques and errors-in-variables modeling[C]//International Workshop on Recent Advances in Total Least Squares Techniques & Errors-in-variables Modeling. Society for Industrial and Applied Mathematics, 1997.
[19]	陶惠林, 徐良骥, 冯海宽,等. 基于无人机数码影像的冬小麦株高和生物量估算[J/OL]. 农业工程学报, 2019, 35(19): 107-116 [2020-12-25]. https://doi.org/10.11975/j.issn.1002-6819.2019.19.013. doi: https://doi.org/10.11975/j.issn.1002-6819.2019.19.013
	Tao Huilin, Xu Liangji, Feng Haikuan, et al. Estimation of plant height and biomass of winter wheat based on UAV digital image[J/OL]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(19): 107-116[2020-12-25]. https://doi.org/10.11975/j.issn.1002-6819.2019.19.013. doi: https://doi.org/10.11975/j.issn.1002-6819.2019.19.013
[20]	Yeniaysupsu O, Goktas A. A comparison of partial least squares regression with other prediction methods[J]. Hacettepe University Bulletin of Natural Sciences and Engineering Series B: Mathematics and Statistics, 2002, 31: 99-111.
[21]	吴芳, 李映雪, 张缘园,等. 基于机器学习算法的冬小麦不同生育时期生物量高光谱估算[J/OL]. 麦类作物学报, 2019, 39(2): 95-102 [2020-12-25]. https://doi.org/10.7606/j.issn.1009-1041.2019.02.13. doi: https://doi.org/10.7606/j.issn.1009-1041.2019.02.13
	Wu Fang, Li Yingxue, Zhang Yuanyuan, et al. Hyperspectral estimation of biomass of winter wheat at different growth stages based on machine learing algorithms[J/OL]. Journal of Triticeae Crops, 2019, 39(2): 95-102[2020-12-25]. https://doi.org/10.7606/j.issn.1009-1041.2019.02.13. doi: https://doi.org/10.7606/j.issn.1009-1041.2019.02.13
[22]	岳继博, 杨贵军, 冯海宽. 基于随机森林算法的冬小麦生物量遥感估算模型对比[J/OL]. 农业工程学报, 2016, 32(18): 175-182 [2020-12-25]. https://doi.org/10.11975/j.issn.1002-6819.2016.18.024. doi: https://doi.org/10.11975/j.issn.1002-6819.2016.18.024
	Yue Jibo, Yang Guijun, Feng Haikuan. Comparative of remote sensing estimation models of winter wheat biomass based on random forest algorithm[J/OL]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(18): 175-182 [2020-12-25]. https://doi.org/10.11975/j.issn.1002-6819.2016.18.024. doi: https://doi.org/10.11975/j.issn.1002-6819.2016.18.024
[23]	Belgiu M Dr?, gu? L. Random forest in remote sensing: A review of applications and future directions[J/OL]. Journal of Photogrammetry and Remote Sensing, 2016, 114: 24-31 [2020-12-25]. https://doi.org/10.1016/j.isprsjprs.2016.01.011. doi: https://doi.org/10.1016/j.isprsjprs.2016.01.011
[24]	第五鹏瑶, 卞希慧, 王姿方,等. 光谱预处理方法选择研究[J/OL]. 光谱学与光谱分析, 2019, 39(9): 2800-2806 [2020-12-25]. https://doi.org/10.3964/j.issn.1000-0593(2019)09-2800-07. doi: https://doi.org/10.3964/j.issn.1000-0593(2019)09-2800-07
	Diwu Pengyao, Bian Xihui, Wang Zifang, et al. Study on the selection of spectral preprocessing methods[J/OL]. Spectroscopy and Spectral Analysis, 2019, 39(9): 2800-2806 [2020-12-25]. https://doi.org/10.3964/j.issn.1000-0593(2019)09-2800-07. doi: https://doi.org/10.3964/j.issn.1000-0593(2019)09-2800-07
[25]	王玉娜, 李粉玲, 王伟东,等. 基于连续投影算法和光谱变换的冬小麦生物量高光谱遥感估算[J/OL]. 麦类作物学报, 2020, 40(11): 1389-1398 [2020-12-25]. https://doi.org/10.7606/j.issn.1009-1041.2020.11.14. doi: https://doi.org/10.7606/j.issn.1009-1041.2020.11.14
	Wang Yuna, Li Fenling, Wang Weidong, et al. Hyper-spectral remote sensing stimation of shoot biomass of winter wheat based on SPA and transformation spectra[J]. Journal of Triticeae Crops, 2020, 40(11): 1389-1398 [2020-12-25]. https://doi.org/10.7606/j.issn.1009-1041.2020.11.14. doi: https://doi.org/10.7606/j.issn.1009-1041.2020.11.14
[26]	马文君, 常庆瑞, 田明璐,等. 棉花全生育期叶片SPAD值的遥感估算模型[J/OL]. 干旱地区农业研究, 2017, 35(5): 42-48 [2020-12-25]. https://doi.org/10.7606/j.issn.1000-7601.2017.05.07. doi: https://doi.org/10.7606/j.issn.1000-7601.2017.05.07
	Ma Wenjun, Chang Qingrui, Tian Minglu, et al. Remote sensing estimation model of cotton leaf SPAD value at the whole growth period[J/OL]. Agricultural Research in the Arid Areas, 2017, 35(5): 42-48 [2020-12-25]. https://doi.org/10.7606/j.issn.1000-7601.2017.05.07. doi: https://doi.org/10.7606/j.issn.1000-7601.2017.05.07
[27]	Daniels A J, Poblete-Echeverría C, Opara U L, et al. Measuring internal maturity parameters contactless on intact table grape bunches using NIR spectroscopy[J/OL]. Frontiers in Plant Science, 2019, 10: 1517 [2020-12-25]. https://doi.org/10.3389/ fpls.2019.01517. doi: https://doi.org/10.3389/ fpls.2019.01517
[28]	Yang W, Yang C, Hao Z, et al. Diagnosis of plant cold damage based on hyperspectral imaging and convolutional neural network[J/OL]. IEEE Access, 2019(99): 1-1[2020-12-25]. https://doi.org/10.1109/ACCESS.2019.2936892. doi: https://doi.org/10.1109/ACCESS.2019.2936892
[29]	Han L, Yang G, Dai H, et al. Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data[J/OL]. Plant Methods, 2019, 15(1): 10 [2020-12-25]. https://doi.org/10.1186/s13007-019-0394-z. doi: https://doi.org/10.1186/s13007-019-0394-z
[30]	Lu Ning, Zhou Jie, Han Zixu, et al. Improved estimation of aboveground biomass in wheat from RGB imagery and pointcloud data acquired with a low-cost unmanned aerial vehicle system[J/OL]. Plant Methods, 2019, 15: 17 [2020-12-25]. https://doi.org/10.1186/s13007-019-0402-3. doi: https://doi.org/10.1186/s13007-019-0402-3

棉花品种 Cotton variety	处理 Treatment	样本数量 Sample number	最大值 Maximum	最小值 Minimum	平均值 Mean	标准差 Standard deviation
新陆早53号	N₀	15	2.73	0.32	1.30	0.74
Xinluzao 53	N1	15	2.87	0.34	1.38	0.78
	N₂	15	3.53	0.34	1.43	0.96
	NC	15	3.31	0.51	1.37	0.83
	N₃	15	2.83	0.45	1.37	0.81
	N₄	15	3.09	0.34	1.26	0.84
新陆早45号	N₀	15	0.43	0.28	0.36	0.05
Xinluzao 45	N₁	15	0.76	0.33	0.58	0.13
	N₂	15	1.12	0.56	0.82	0.13
	NC	15	1.41	0.73	0.97	0.16
	N₃	15	1.71	0.68	1.08	0.27
	N₄	15	2.19	0.87	1.35	0.41

预处理方法 Pretreatment	特征波长数量 Number of characteristic wavelengths	光谱样本提取的特征波长 Characteristic wavelength/nm
原始光谱Original spectrum	10	873、938、962、958、758、942、960、940、727、556
一阶导数First derivative	9	940、931、942、947、971、511、976、936、949
二阶导数 Second derivative	24	942、962、929、758、967、938、654、918、958、769、980、971、936、993、978、878、940、920、922、1 000、820、911、900、924
SG平滑 Savitzky-Golay smoothing	25	916、951、900、676、907、993、820、876、982、973、953、962、765、991、958、938、987、1 000、889、931、971、942、447、922、700
多元散射校正Multiplicative scatter correction	26	873、678、949、534、984、931、907、998、576、971、902、924、762、760、936、944、1 000、727、982、891、967、958、976、916、978、991

预处理 Pretreatment	偏最小二乘法回归（PLSR） Partial least squares regression		随机森林回归（RFR） Random forest regression
预处理 Pretreatment	R²	RMSE	R²	RMSE
原始光谱Original spectrum	0.53	0.49	0.84	0.47
一阶导数First derivative	0.48	0.47	0.87	0.45
二阶导数Second derivative	0.52	0.46	0.84	0.45
SG平滑Savitzky-Golay smoothing	0.63	0.42	0.65	0.46
多元散射校正Multiplicative scatter correction	0.32	0.54	0.79	0.50

预处理 Pretreatment	偏最小二乘法回归（PLSR） Partial least squares regression		随机森林回归（RFR） Random forest regression
预处理 Pretreatment	R²	RMSE	R²	RMSE
原始光谱Original spectrum	0.63	0.44	0.73	0.45
一阶导数First derivative	0.55	0.50	0.81	0.37
二阶导数Second derivative	0.57	0.49	0.75	0.40
SG平滑Savitzky-Golay smoothing	0.67	0.44	0.71	0.62
多元散射校正Multiplicative scatter correction	0.21	0.66	0.57	0.56

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

Estimation of cotton above-ground biomass based on unmanned aerial vehicle hyperspectral and successive projections algorithm

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 30

相关文章 15

Metrics

本文评价

推荐阅读 10

[1]	张岚,程琦,梁士辰,邓雨潇,潘玉欣. 棉花UGPase基因鉴定与生物信息学分析[J]. 棉花学报, 2021, 33(4): 337-346.
[2]	马怡茹,吕新,祁亚琴,张泽,易翔,陈翔宇,鄢天荥,侯彤瑜. 基于无人机数码图像的机采棉脱叶率监测模型构建[J]. 棉花学报, 2021, 33(4): 347-359.
[3]	苟浩琦,马常凯,张迁,范术丽,马启峰,张朝军. 棉花光敏雄性不育系psm5的培育及其育性转变规律[J]. 棉花学报, 2021, 33(4): 360-367.
[4]	王林, 张强, 马江锋, 朱玉永, 田英, 李红, 毕显杰, 宋敏, 王海标, 雷天翔, 李召虎, 田晓莉, 杜明伟, 张立祯, 赵冰梅. 新疆棉区植保无人机喷施棉花脱叶催熟剂效果研究[J]. 棉花学报, 2021, 33(3): 200-208.
[5]	王金刚, 姜艳, 田甜, 朱永琪, 杨振康, 周天航, 张文旭, 佟炫梦, 孙嘉祺, 王海江. 减氮配施生物刺激素对棉花产量及氮肥吸收利用的影响[J]. 棉花学报, 2021, 33(3): 209-223.
[6]	孙璘, 海艳, 唐晓雪, 祖丽皮亚·艾买, 焦瑞莲, 任毓忠, 李国英. 新疆棉花茎腐病的病原鉴定及其生物学特性研究[J]. 棉花学报, 2021, 33(3): 235-246.
[7]	党文芳, 刘萍, 管力慧, 杨红梅, 牛新湘, 李萍, 楚敏, 娄恺, 史应武. 土壤环境因子对棉花根际与内生拮抗细菌存活数量的影响[J]. 棉花学报, 2021, 33(3): 247-257.
[8]	杨可心, 陈秀叶, 刘畅, 鹿秀云, 郭庆港, 马平. 棉花枯萎病菌新生理型菌株毒素鉴定及其活性测定[J]. 棉花学报, 2021, 33(3): 258-268.
[9]	安杰, 韩迎春, 张正贵, 冯璐, 雷亚平, 杨北方, 王国平, 李小飞, 王占彪, 邢芳芳, 熊世武, 辛明华, 李亚兵. 不同熟性棉花品种冠层温度分布特点[J]. 棉花学报, 2021, 33(2): 134-143.
[10]	孟浩峰, 雷长英, 张旺锋, 张亚黎. 系统调控下棉花比叶重的变化机制[J]. 棉花学报, 2021, 33(2): 144-154.
[11]	张友昌, 黄晓莉, 胡爱兵, 李洪菊, 冯常辉, 李蔚, 张贤红, 罗艳萍, 杨国正. 长江流域麦/油后直播棉花播种时间下限研究[J]. 棉花学报, 2021, 33(2): 155-168.
[12]	王广恩, 郭丽, 钱玉源, 刘祎, 张曦. 不同咸水利用方式对棉花叶绿素荧光参数及土壤盐分的影响[J]. 棉花学报, 2021, 33(1): 13-21.
[13]	郁凯, 霍钰阳, 朱俊俊, 陈兵林, 汤秋香. 盐胁迫下施钾调节棉纤维断裂比强度的糖代谢机制[J]. 棉花学报, 2021, 33(1): 22-32.
[14]	汪苏洁, 贵会平, 董强, 张恒恒, 王香茹, 牛静, 张西岭, 宋美珍. 有机肥替代对棉花养分积累、产量及土壤肥力的影响[J]. 棉花学报, 2021, 33(1): 54-65.
[15]	李雨哲, 邢淋雪, 刘梦洁, 刘苏瑶, 鲍梦楠, 刘震. 棉花苯丙氨酸解氨酶基因家族的生物信息学分析[J]. 棉花学报, 2021, 33(1): 66-74.