基于高光谱的土壤有机质及全氮估测

doi:10.6048/j.issn.1001-4330.2024.10.017

新疆农业科学 ›› 2024, Vol. 61 ›› Issue (10): 2491-2499.DOI: 10.6048/j.issn.1001-4330.2024.10.017

• 植物保护·土壤肥料·节水灌溉·农业装备工程与机械化·草业 • 上一篇下一篇

基于高光谱的土壤有机质及全氮估测

李嘉琦¹(), 冯宇华¹, 陈署晃², 王子傲¹, 刘鹏¹, 梁智永¹, 孙法福¹, 陈荣¹, 耿庆龙²()

1.新疆农业大学资源与环境学院,乌鲁木齐 830052
2.新疆农业科学院土壤肥料与农业节水研究所/新疆农业科学院农业遥感中心,乌鲁木齐 830091

收稿日期:2024-04-15 出版日期:2024-10-20 发布日期:2024-11-07
通信作者: 耿庆龙(1982-),男,山东人,副研究员,硕士,研究方向为土壤肥料与农业信息技术应用,(E-mail)564819139@qq.com
作者简介:李嘉琦(1995-),男,河南鹤壁人,硕士研究生,研究方向为土壤肥料与农业信息技术应用,(E-mail)515815502@qq.com
基金资助:
农业科技创新稳定支持专项(xjnkyywdzc-2022002)

Estimation of soil organic matter and total nitrogen based on hyperspectral technology

LI Jiaqi¹(), FENG Yuhua¹, CHEN Shuhuang², WANG Ziao¹, LIU Peng¹, LIANG Zhiyong¹, SUN Fafu¹, CHEN Rong¹, GENG Qinglong²()

1. College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
2. Institute of Soil, Fertilizer and Agricultural Water Conservation, Xinjiang Academy of Agricultural Sciences/Agricultural Remote Sensing Center, Xinjang Academy of Agricultural Sciences, Urumqi 830091, China

Received:2024-04-15 Published:2024-10-20 Online:2024-11-07
Correspondence author: GENG Qinglong(1982-), male, from Shandong, associate researcher, research direction: research direction: soil fertilizer and application of intelligence agricultural information technology, (E-mail) 564819139@qq.com
Supported by:
Agricultural Science and Technology Innovation and Stability Support Special Project(xjnkyywdzc-2022002)

摘要/Abstract

摘要：

【目的】 研究土壤高光谱数据经不同形式变换后与不同建模方法构建土壤有机质与全氮估测模型的精度,建立快速、稳定的估测模型,为现代化农业生产的精准施肥提供科学依据。【方法】 以新疆博尔塔拉蒙古自治州(简称博州)耕地土壤为研究对象,在暗室中使用ASD Field4地物光谱仪测量处理后的土壤样品光谱。将原始光谱进行断点拟合与Savitzky-Golay(S-G)平滑滤波校正处理,对校正后光谱(R)进行一阶导数(First Derivative,FD)、对数的一阶导数(First derivative of logarithmic,(lgR)’)、倒数的一阶导数(First derivative of reciprocal,(1/R)’)、多元散射校正(Multipication scatter correction,MSC)4种变换,分析5种光谱数据与土壤有机质和全氮含量,筛选特征波段,基于特征波段运用偏最小二乘回归(PLSR)、BP神经网络(BP)和随机森林(RF)3种方法,分别建立土壤有机质、全氮的估测模型并评价模型的精度与稳定性。【结果】 光谱经不同变换后,与土壤有机质和全氮的相关系数有所提高,且特征波段更为明显,一阶导数与倒数的一阶导数变换优于其他变换,FD-PLSR模型预测有机质精度最高,Rv²、RPD分别为0.89、2.63;(1/R)’-PLSR模型预测土壤全氮精度最高,Rv²、RPD分别为0.83、2.42。【结论】 基于高光谱技术与机器学习模型可以估测博州耕地土壤的有机质与全氮含量。

关键词: 高光谱; 土壤有机质; 土壤全氮; 光谱估测; 偏最小二乘回归; 随机森林

Abstract:

【Objective】 To explore the accuracy of soil organic matter and total nitrogen estimation model with different modeling methods and to establish a fast and stable estimation model, so as to provide scientific basis for precision fertilization of modern agricultural production. 【Methods】 Taking the cultivated soil from Bortala Mongolia Autonomous Prefecture as the research object, the ASD Field4 ground object spectrometer was used to measure the spectrum of the treated soil samples in the dark room. The original spectrum was processed by breakpoint fitting and Savitzky-Golay (S-G) smoothing filtering correction. First derivative (FD), first derivative of logarithm ((lgR)'), first derivative of reciprocal ((1/R)') and multipication scatter correction (MSC) were performed on the corrected spectrum (R). The correlation analysis of the above five forms of spectra with soil organic matter and total nitrogen content was carried out to screen the characteristic bands. Based on the characteristic bands, partial least squares regression (PLSR), BP neural network (BP) and random forest (RF) were used to establish the estimation models of soil organic matter and total nitrogen, and the accuracy and stability of the models were evaluated. 【Results】 After different transformations, the correlation coefficients between the spectra and soil organic matter and total nitrogen increased, and the characteristic bands were more obvious. The first derivative transformation of the first derivative and the reciprocal was better than those of other transformations. The FD-PLSR model had the highest accuracy in predicting organic matter, with Rv² and RPD of 0.89 and 2.63, respectively. The (1/R)'-PLSR model had the highest accuracy in predicting soil total nitrogen, with Rv² and RPD of 0.83 and 2.42, respectively. 【Conclusion】 Based on hyperspectral technology and machine learning model, the estimation of soil organic matter and total nitrogen in cultivated land of Bozhou can be realized.

Key words: hyperspectral; soil organic matter; soil total nitrogen; spectral estimation; partial least squares regression; random forest

中图分类号:

李嘉琦, 冯宇华, 陈署晃, 王子傲, 刘鹏, 梁智永, 孙法福, 陈荣, 耿庆龙. 基于高光谱的土壤有机质及全氮估测[J]. 新疆农业科学, 2024, 61(10): 2491-2499.

LI Jiaqi, FENG Yuhua, CHEN Shuhuang, WANG Ziao, LIU Peng, LIANG Zhiyong, SUN Fafu, CHEN Rong, GENG Qinglong. Estimation of soil organic matter and total nitrogen based on hyperspectral technology[J]. Xinjiang Agricultural Sciences, 2024, 61(10): 2491-2499.

图/表 9

参考文献 30

[1]	沈润平, 丁国香, 魏国栓, 等. 基于人工神经网络的土壤有机质含量高光谱反演[J]. 土壤学报, 2009, 46(3): 391-397.
	SHEN Runping, DING Guoxiang, WEI Guoshuan, et al. Retrieval of soil organic matter content from hyper-spectrum based on ann[J]. Acta Pedologica Sinica, 2009, 46(3): 391-397.
[2]	叶勤, 姜雪芹, 李西灿, 等. 基于高光谱数据的土壤有机质含量反演模型比较[J]. 农业机械学报, 2017, 48(3): 164-172.
	YE Qin, JIANG Xueqin, LI Xican, et al. Comparison on inversion model of soil organic matter content based on hyperspectral data[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(3): 164-172.
[3]	张娟娟, 田永超, 朱艳, 等. 不同类型土壤的光谱特征及其有机质含量预测[J]. 中国农业科学, 2009, 42(9): 3154-3163. DOI
	ZHANG Juanjuan, TIAN Yongchao, ZHU Yan, et al. Spectral characteristics and estimation of organic matter contents of different soil types[J]. Scientia Agricultura Sinica, 2009, 42(9): 3154-3163.
[4]	洪永胜, 于雷, 耿雷, 等. 应用DS算法消除室内几何测试条件对土壤高光谱数据波动性的影响[J]. 华中师范大学学报(自然科学版), 2016, 50(2): 303-308.
	HONG Yongsheng, YU Lei, GENG Lei, et al. Using Direct Standardization algorithm to eliminate the effect of laboratory geometric parameters on soil hyperspectral data fluctuate characteristic[J]. Journal of Central China Normal University (Natural Sciences), 2016, 50(2): 303-308.
[5]	栾福明, 熊黑钢, 王芳, 等. 基于小波分析的土壤碱解氮含量高光谱反演[J]. 光谱学与光谱分析, 2013, 33(10): 2828.
	LUAN Fuming, XIONG Heigang, WANG Fang, et al. The inversion of soil alkaline hydrolysis nutrient content with hyperspectral reflectance based on wavelet analysis[J]. Spectroscopy and Spectral Analysis, 2013, 33(10): 2828. PMID
[6]	林楠, 刘海琪, 杨佳佳, 等. BA-Adaboost模型的黑土区土壤养分含量高光谱估测[J]. 光谱学与光谱分析, 2020, 40(12): 3825-3831.
	LIN Nan, LIU Haiqi, YANG Jiajia, et al. Hyperspectral estimation of soil nutrient content in the black soil region based on BA-adaboost[J]. Spectroscopy and Spectral Analysis, 2020, 40(12): 3825-3831.
[7]	陈奕云, 齐天赐, 黄颖菁, 等. 土壤有机质含量可见-近红外光谱反演模型校正集优选方法[J]. 农业工程学报, 2017, 33(6): 107-114.
	CHEN Yiyun, QI Tianci, HUANG Yingjing, et al. Optimization method of calibration dataset for VIS-NIR spectral inversion model of soil organ ic matter content[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(6): 107-114.
[8]	Liu Y L, Jiang Q H, Fei T, et al. Transferability of a visible and near-infrared model for soil organic matter estimation in riparian landscapes[J]. Remote Sensing, 2014, 6(5): 4305-4322.
[9]	王一丁, 赵铭钦, 刘鹏飞, 等. 基于高光谱分析的植烟土壤有机质和全氮含量预测研究[J]. 中国烟草学报, 2016, 22(3): 44-51.
	WANG Yiding, ZHAO Mingqin, LIU Pengfei, et al. Prediction of organic matter and total nitrogen contents in tobacco-growing soil based on hyper-spectral analysis[J]. Acta Tabacaria Sinica, 2016, 22(3): 44-51.
[10]	杜军, 古军伟, 邱士可, 等. 基于支持向量回归的土壤全氮含量高光谱估测研究[J]. 河南科学, 2020, 38(10): 1585-1590.
	DU Jun, GU Junwei, QIU Shike, et al. Prediction of total nitrogen by using hyperspectral data based on support vector regression[J]. Henan Science, 2020, 38(10): 1585-1590.
[11]	Chang C W, Laird D A, Mausbach M J, et al. Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties[J]. Soil Science Society of America Journal, 2001, 65(2): 480-490.
[12]	王海江, 刘凡, YUNGER John A, 等. 不同粒径处理的土壤全氮含量高光谱特征拟合模型[J]. 农业机械学报, 2019, 50(2): 195-204.
	WANG Haijiang, LIU Fan, YUNGER J A, et al. Fitting model of soil total nitrogen content in different soil particle sizes using hyperspectral analysis[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(2): 195-204.
[13]	南锋, 朱洪芬, 毕如田. 黄土高原煤矿区复垦农田土壤有机质含量的高光谱预测[J]. 中国农业科学, 2016, 49(11): 2126-2135. DOI
	NAN Feng, ZHU Hongfen, BI Rutian. Hyperspectral prediction of soil organic matter content in the reclamation cropland of coal mining areas in the Loess Plateau[J]. Scientia Agricultura Sinica, 2016, 49(11): 2126-2135. DOI
[14]	朱亚星, 于雷, 洪永胜, 等. 土壤有机质高光谱特征与波长变量优选方法[J]. 中国农业科学, 2017, 50(22): 4325-4337. DOI
	ZHU Yaxing, YU Lei, HONG Yongsheng, et al. Hyperspectral features and wavelength variables selection methods of soil organic matter[J]. Scientia Agricultura Sinica, 2017, 50(22): 4325-4337. DOI
[15]	杨玉军. 基于机器学习的时间序列模型研究及其应用[D]. 成都: 电子科技大学, 2018.
	YANG Yujun. Research and Application of Time Series Model Based on Machine Learning[D]. Chengdu: University of Electronic Science and Technology of China, 2018.
[16]	姚登举, 杨静, 詹晓娟. 基于随机森林的特征选择算法[J]. 吉林大学学报(工学版), 2014, 44(1): 137-141.
	YAO Dengju, YANG Jing, ZHAN Xiaojuan. Feature selection algorithm based on random forest[J]. Journal of Jilin University (Engineering and Technology Edition), 2014, 44(1): 137-141.
[17]	赵宁博, 秦凯, 赵英俊, 等. 土壤铬的航空高光谱间接反演模型及可迁移性研究[J]. 光谱学与光谱分析, 2021, 41(5): 1617-1624.
	ZHAO Ningbo, QIN Kai, ZHAO Yingjun, et al. Study on indirect inversion model and migration ability of chromium in soil by aerial hyperspectral method[J]. Spectroscopy and Spectral Analysis, 2021, 41(5): 1617-1624.
[18]	郭云开, 谢晓峰, 谢琼, 等. 基于小波变换的土壤重金属铬含量定量反演[J]. 测绘工程, 2021, 30(3): 66-71.
	GUO Yunkai, XIE Xiaofeng, XIE Qiong, et al. Quantitative inversion of heavy metal chromium content in soil based on wavelet transform[J]. Engineering of Surveying and Mapping, 2021, 30(3): 66-71.
[19]	Chang C W, Laird D A. Near-infrared reflectance spectroscopic analysis of soil C and N[J]. Soil Science, 2002, 167(2): 110-116.
[20]	侯化刚, 王丹阳, 马斯琦, 等. 黄河三角洲不同盐渍度土壤有机质含量的高光谱预测研究[J]. 中国农业科学, 2023, 56(10): 1905-1919. DOI
	HOU Huagang, WANG Danyang, MA Siqi, et al. Hyperspectral prediction of organic matter in soils of different salinity levels in the Yellow River Delta[J]. Scientia Agricultura Sinica, 2023, 56(10): 1905-1919. DOI
[21]	Viscarra Rossel R A, Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra[J]. Geoderma, 2010, 158(1/2): 46-54.
[22]	玉米提·买明, 王雪梅. 连续小波变换的土壤有机质含量高光谱估测[J]. 光谱学与光谱分析, 2022, 42(4): 1278-1284.
	Yumiti Maiming, WANG Xuemei. Hyperspectral estimation of soil organic matter content based on continuous wavelet transformation[J]. Spectroscopy and Spectral Analysis, 2022, 42(4): 1278-1284.
[23]	谢文, 赵小敏, 郭熙, 等. 基于RBF组合模型的山地红壤有机质含量光谱估测[J]. 林业科学, 2018, 54(6): 16-23.
	XIE Wen, ZHAO Xiaomin, GUO Xi, et al. Spectrum based estimation of the content of soil organic matters in mountain red soil using RBF combination model[J]. Scientia Silvae Sinicae, 2018, 54(6): 16-23.
[24]	王婷, 刘振华, 彭一平, 等. 华南地区土壤有机质含量高光谱反演[J]. 江苏农业学报, 2020, 36(2): 350-357.
	WANG Ting, LIU Zhenhua, PENG Yiping, et al. Predicting soil organic matter content in South China based on hyperspectral reflectance[J]. Jiangsu Journal of Agricultural Sciences, 2020, 36(2): 350-357.
[25]	张恒, 梁太波, 江鸿, 等. 基于高光谱成像的区域尺度植烟土壤有机质含量和pH值估测[J]. 烟草科技, 2023, 11(17): 1-15.
	ZHANG Heng, LIANG Taibo, Jiang Hong, et al. Estimation of organic matter content and pH of regional-scale tobacco-growing soils using hyperspectral imaging[J]. Tobacco Science & Technology, 2023, 11(17): 1-15.
[26]	张娟娟, 牛圳, 马新明, 等. 基于离散小波的土壤全氮高光谱特征提取与反演[J]. 光谱学与光谱分析, 2023, 43(10): 3223-3229.
	ZHANG Juanjuan, NIU Zhen, MA Xinming, et al. Hyperspectral feature extraction and estimation of soil total nitrogen based on discrete wavelet transform[J]. Spectroscopy and Spectral Analysis, 2023, 43(10): 3223-3229.
[27]	于锦涛, 李西灿, 曹双, 等. 土壤有机质高光谱自反馈灰色模糊估测模型[J]. 山东农业大学学报(自然科学版), 2023, 54(4): 495-499.
	YU Jintao, LI Xican, CAO Shuang, et al. Self-feedback grey fuzzy estimation model of soil organic matter using Hyper-spectral data[J]. Journal of Shandong Agricultural University (Natural Science Edition), 2023, 54(4): 495-499.
[28]	刘杰亚, 李西灿, 任文静, 等. 利用卷积神经网络的土壤有机质含量高光谱估测[J]. 河北农业大学学报, 2023, 46(4): 118-124.
	LIU Jieya, LI Xican, REN Wenjing, et al. Hyperspectral estimation of soil organic matter content using convolutional neural network[J]. Journal of Hebei Agricultural University, 2023, 46(4): 118-124.
[29]	赵明松, 谢毅, 陆龙妹, 等. 基于高光谱特征指数的土壤有机质含量建模[J]. 土壤学报, 2021, 58(1): 42-54.
	ZHAO Mingsong, XIE Yi, LU Longmei, et al. Modeling for soil organic matter content based on hyperspectral feature indices[J]. Acta Pedologica Sinica, 2021, 58(1): 42-54.
[30]	王动民, 纪俊敏, 高洪智. 多元散射校正预处理波段对近红外光谱定标模型的影响[J]. 光谱学与光谱分析, 2014, 34(9): 2387-2390.
	WANG Dongmin, JI Junmin, GAO Hongzhi. The effect of MSC spectral pretreatment regions on near infrared spectroscopy calibration results[J]. Spectroscopy and Spectral Analysis, 2014, 34(9): 2387-2390. PMID

编辑推荐 0

Metrics

阅读次数

全文

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	8	0	0	29

来源	本网站	其他网站

次数	18	19
比例	49%	51%

摘要

148

最新录用	在线预览	正式出版

0	0	148

来源	本网站	其他网站

次数	46	102
比例	31%	69%

土壤属性含量 Content of soil properties	均值 Mean	最小值 Minimum	最大值 Maximum	标准差 SD	变异系数 CV(%)
全氮含量Total nitrogen content(g/kg)	1	0.29	1.73	0.30	30%
有机质含量Organic matter content(g/kg)	18.09	5.66	31.70	5.23	28.91%

土壤属性含量 Content of soil properties	均值 Mean	最小值 Minimum	最大值 Maximum	标准差 SD	变异系数 CV(%)
全氮含量Total nitrogen content(g/kg)	1	0.29	1.73	0.30	30%
有机质含量Organic matter content(g/kg)	18.09	5.66	31.70	5.23	28.91%

土壤成分 Soil composition	光谱处理 Spectral processing	建模集 Calibration set		验证集 Validation set
土壤成分 Soil composition	光谱处理 Spectral processing	Rc²	RMSEc	Rv²	RMSEv	RPD
全氮 Total nitrogen	R	0.59	0.21	0.49	0.23	1.01
	FD	0.79	0.15	0.81	0.13	2.14
	(lgR)’	0.77	0.16	0.75	0.18	2.02
	(1/R)’	0.82	0.16	0.83	0.14	2.42
	MSC	0.34	0.29	0.44	0.23	1.17
有机质 Organic matter	R	0.67	3.55	0.64	3.36	1.35
	FD	0.89	2.01	0.89	1.92	2.63
	(lgR)’	0.86	2.16	0.85	2.08	2.15
	(1/R)’	0.87	2.12	0.87	1.98	2.22
	MSC	0.66	5.91	0.42	6.24	1.21

土壤成分 Soil composition	光谱处理 Spectral processing	建模集 Calibration set		验证集 Validation set
土壤成分 Soil composition	光谱处理 Spectral processing	Rc²	RMSEc	Rv²	RMSEv	RPD
全氮 Total nitrogen	R	0.59	0.21	0.49	0.23	1.01
	FD	0.79	0.15	0.81	0.13	2.14
	(lgR)’	0.77	0.16	0.75	0.18	2.02
	(1/R)’	0.82	0.16	0.83	0.14	2.42
	MSC	0.34	0.29	0.44	0.23	1.17
有机质 Organic matter	R	0.67	3.55	0.64	3.36	1.35
	FD	0.89	2.01	0.89	1.92	2.63
	(lgR)’	0.86	2.16	0.85	2.08	2.15
	(1/R)’	0.87	2.12	0.87	1.98	2.22
	MSC	0.66	5.91	0.42	6.24	1.21

土壤成分 Soil composition	光谱处理 Spectral processing	建模集 Calibration set		验证集 Validation set
土壤成分 Soil composition	光谱处理 Spectral processing	Rc²	RMSEc	Rv²	RMSEv	RPD
全氮 Total nitrogen	R	0.63	0.24	0.52	0.24	1.18
	FD	0.64	0.22	0.64	0.21	1.23
	(lgR)’	0.58	0.24	0.55	0.26	1.09
	(1/R)’	0.61	0.22	0.59	0.25	1.13
	MSC	0.64	0.31	0.42	0.34	1.11
有机质 Organic matter	R	0.59	3.45	0.51	3.34	1.24
	FD	0.65	3.22	0.61	3.12	1.51
	(lgR)’	0.61	2.98	0.59	3.33	1.41
	(1/R)’	0.63	3.32	0.62	3.07	1.43
	MSC	0.52	6.11	0.39	4.82	1.14

基于高光谱的土壤有机质及全氮估测

Estimation of soil organic matter and total nitrogen based on hyperspectral technology

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 30

相关文章 15

编辑推荐 0

Metrics

[1]	杨吉祥, 李新国. 湖滨绿洲土壤有机碳含量的支持向量机估算模型[J]. 新疆农业科学, 2024, 61(6): 1477-1486.
[2]	张磊, 姚梦瑶, 刘志刚, 李娟, 杨洋, 蔡大润, 陈果, 李波, 李晓荣, 陈勋基, 翟云龙. 基于无人机多光谱NDVI值估测玉米产量[J]. 新疆农业科学, 2024, 61(4): 845-851.
[3]	吕金城, 王振锡, 杨勇强, 曲延斌, 马琪瑶. 基于WorldView-2影像和随机森林算法的天山云杉蓄积量反演[J]. 新疆农业科学, 2022, 59(8): 1992-1998.
[4]	阿丽娅·阿力木, 丛小涵, 夏晓莹, 席丽, 王卫霞. 不同土地利用方式下土壤养分特征变化分析[J]. 新疆农业科学, 2022, 59(4): 925-933.
[5]	贠静, 郑逢令, 安沙舟, 阿斯娅·曼力克, 李超, 艾尼玩·艾麦尔, 田聪. 基于PROSAIL模型的山地草原叶面积指数高光谱反演[J]. 新疆农业科学, 2022, 59(2): 451-457.
[6]	艾孜提艾力·克依木, 李新国, 赵慧, 麦麦提吐尔逊·艾则孜. 基于地理加权回归模型的绿洲土壤表层有机质含量高光谱估算[J]. 新疆农业科学, 2022, 59(1): 223-230.
[7]	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.
[8]	谭京红, 吴启侠, 朱建强, 佘子浩, 柯鑫瑶, 马泓雨. 麦后移栽棉氮素营养诊断与追肥模型[J]. 新疆农业科学, 2021, 58(7): 1225-1235.
[9]	李志, 苏武峥, 李新国, 王银方, 毛东雷, 麦麦提吐尔逊·艾则孜. 基于高光谱特征参数优选的土壤盐分含量建模及其验证[J]. 新疆农业科学, 2021, 58(12): 2342-2352.
[10]	王卫霞, 杨光, 王振锡. 更新方式对天山云杉林土壤碳氮的影响[J]. 新疆农业科学, 2020, 57(8): 1474-1483.
[11]	武文丽, 袁也, 李园园, 楚光明, 张昊. 荒漠林高光谱与光学影像植被指数(VI)的比较[J]. 新疆农业科学, 2020, 57(1): 149-156.
[12]	张峰, 赵忠国, 李刚, 陈刚. 基于不同分类器的农用地分类提取[J]. 新疆农业科学, 2019, 56(8): 1560-1568.
[13]	葛元梅, 陈翔宇, 洪帅, 马露露, 吕新, 张泽. 基于红边参数不同品种的估算模型[J]. 新疆农业科学, 2019, 56(6): 1032-1040.
[14]	李天胜, 崔静, 王海江, 杨晋. 基于高光谱特征波长的冬小麦水分含量估测模型[J]. 新疆农业科学, 2019, 56(10): 1772-1782.
[15]	刘馨月, 王登伟, 黄春燕, 黄凯杰, 王永胜, 来瑞欣, 马如海, 韩勇超, 王露霞. 基于荧光参数的棉花盛铃期水分状况高光谱监测[J]. 新疆农业科学, 2018, 55(7): 1177-1185.