[1]彭晓伟,张爱军,杨晓楠.基于WOS的高光谱技术在农业方面应用的计量分析[J].江苏农业学报,2022,38(01):239-249.[doi:doi:10.3969/j.issn.1000-4440.2022.01.029]
　PENG Xiao-wei,ZHANG Ai-jun,YANG Xiao-nan.Quantitative analysis of hyperspectral technology application in agriculture based on Web of Science (WOS)[J].,2022,38(01):239-249.[doi:doi:10.3969/j.issn.1000-4440.2022.01.029]
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基于WOS的高光谱技术在农业方面应用的计量分析()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
38
期数:
2022年01期
页码:
239-249
栏目:
农业经济·农业信息
出版日期:
2022-02-28

文章信息/Info

Title:
Quantitative analysis of hyperspectral technology application in agriculture based on Web of Science (WOS)
作者:
彭晓伟1; 张爱军1; 2; 杨晓楠2
（1.河北农业大学资源与环境科学学院，河北保定071000；2.河北省山区农业技术创新中心，河北保定071001）
Author(s):
PENG Xiao-wei1; ZHANG Ai-jun1; 2; YANG Xiao-nan2
(1.College of Resources and Environment Science, Hebei Agricultural University, Baoding 071000, China;2.Agricultural Technology Innovation Center in Mountainous Area of Hebei Province, Baoding 071001, China)
关键词:
高光谱技术; 农业领域; 计量分析
Keywords:
hyperspectral technology; agricultural field; quantitative analysis
分类号:
S127
DOI:
doi:10.3969/j.issn.1000-4440.2022.01.029
文献标志码:
A
摘要:
利用高光谱技术可对作物在生长发育过程中出现的问题进行实时监控和处理，从而实现精准施肥、精准施药及实时管理。为了解高光谱技术在农业领域中的利用状况，利用Web of Science网站自带的数据分析功能以及VOSviewer可视化分析软件对Web of Science核心合集数据库中高光谱技术在农业领域中应用的发文数量与研究领域、主要发文国家（地区）、主要发文机构和研究学者、主要发文学术期刊、发文被引频次较多的文章、主要研究热点及其变化趋势等进行计量分析。结果表明，高光谱技术在农业领域的应用受到的关注越来越多，发文量呈现指数增长的趋势。发文量最高的20个国家中有9个是欧洲国家，中国、美国对该领域的贡献最大。高光谱技术在农业领域应用研究的影响力最高的期刊为Remote Sensing of Environment，发文量最多的学者为浙江大学的He Yong，影响力最高的文章是“Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review”。高光谱技术在农业领域应用的研究趋势主要涉及数据采集、分析方式的更新及作物对光谱的作用机制。
Abstract:
Hyperspectral technology can be used to monitor and deal with the problems in the process of crop growth and development in real time, so as to realize precise fertilization, precise application and real-time management. In order to understand the utilization of hyperspectral technology in the field of agriculture, the data analysis function of Web of Science system and VOSviewer visualization analysis software were used to quantitatively analyze the number and research field of hyperspectral technology in the core collection database of Web of Science, the main countries ( regions ), the main institutions and scholars, the main academic journals, the articles with more citations, the main research hotspots and their trends. The results showed that the application of hyperspectral technology in agriculture had attracted more and more attention, and the number of publications exhibited an exponential growth trend. Nine of the 20 countries with the highest number of publications were European countries, but China and the United States had the largest contribution to this field. The most influential journal of hyperspectral technology in agricultural applications was Remote Sensing of Environment, and HE Y of Zhejiang University was the scholar with the largest number of publications. The most influential article was ‘Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review’. The development trend of hyperspectral technology in agriculture mainly involves the updating of data collection and analysis methods and the action mechanism of crop on spectrum.

参考文献/References:

[1]郑兰芬,王晋年. 成像光谱遥感技术及其图像光谱信息提取的分析研究[J]. 环境遥感, 1992(1): 49-58,84.
[2]张学治. 基于冠层反射光谱的夏玉米氮素营养与生长监测研究[D]. 南京：南京农业大学, 2011.
[3]马勤建. 基于高光谱植被指数的棉花冠层结构参数的估算研究[D]. 石河子：石河子大学, 2008.
[4]陈硕博. 无人机多光谱遥感反演棉花光合参数与水分的模型研究[D]. 杨凌：西北农林科技大学, 2019.
[5]房华乐,任润东,苏飞,等. 高光谱遥感在农业中的应用[J]. 测绘通报, 2012（增刊）: 255-257.
[6]THENKABAIL P S, SMITH R B, PAUW E D. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics[J]. Remote Sensing of Environment, 2000, 71(2): 158-182.
[7]刘冰峰. 夏玉米不同生育时期生理生态参数的高光谱遥感监测模型[D]. 杨凌：西北农林科技大学, 2016.
[8]韩沁姗. 基于近红外高光谱的甘草种子鉴别系统研建[D]. 北京：北京林业大学, 2020.
[9]彭晓伟,张爱军,王楠,等. 高光谱成像技术在作物种子方面的应用[J]. 国土资源遥感, 2020, 32(4): 23-32.
[10]曾旭. 油菜三种叶片的成像高光谱特征与SPAD值估测建模[D]. 长沙：湖南农业大学, 2019.
[11]白青蒙,韩玉国,彭致功,等. 利用叶面积指数优化冬小麦高光谱水分预测模型[J]. 应用与环境生物学报, 2020,26(4): 943-950.
[12]张龙英. 不同土壤盐度下柠条锦鸡儿叶绿素荧光监测及种子性状研究[D]. 呼和浩特：内蒙古农业大学, 2020.
[13]贺婷,李建东,刘桂鹏,等. 基于高光谱遥感的玉米全氮含量估测模型[J]. 沈阳农业大学学报, 2016,47(3)： 257-265.
[14]齐双丽. 基于多角度高光谱遥感的小麦白粉病监测研究[D]. 郑州：河南农业大学, 2018.
[15]赵珊. 基于高光谱成像的玉米苗期氮素营养监测的研究[D]. 哈尔滨：东北农业大学, 2016.
[16]肖珍珍,李毅,冯浩. 西北盐碱土理化性质的高光谱建模及预测（英文）[J]. 光谱学与光谱分析, 2016, 36(5): 1615-1622.
[17]安琪琪. 土壤重金属污染检测方法的研究进展[J]. 现代农业科技, 2020(17): 166-168,173.
[18]包青岭,丁建丽,王敬哲,等. 基于随机森林算法的土壤有机质含量高光谱检测[J]. 干旱区地理, 2019,42(6): 1404-1414.
[19]刘秀英. 玉米生理参数及农田土壤信息高光谱监测模型研究[D]. 杨凌：西北农林科技大学, 2016.
[20]MAO G Z, SHI T T, ZHANG S, et al. Bibliometric analysis of insights into soil remediation[J]. Journal of Soil & Sediments, 2018, 18(7): 2520-2534.
[21]REDDY R L R, SHANKARAPPA T H, REDDY K S, et al. Review of trends in soil fertility research (2007-2016) using Scopus database[J]. Communications in Soil Science & Plant Analysis, 2019, 50(1): 1-18.
[22]胡远妹,周俊,刘海龙,等. 基于Web of Science对土壤重金属污染修复研究的计量分析[J]. 土壤学报, 2018, 55(3): 707-720.
[23]HIRSCH J E. An index to quantify an individual’s scientific research output[J]. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(46): 16569-16572.
[24]CURRAN P J. Remote sensing of foliar chemistry[J]. Remote Sensing of Environment, 1989, 30(3): 271-278.
[25]GITELSON A A, MERZLYAK M N, LICHTENTHALER H K. Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm[J]. J Plant Physiology, 1996, 148(3/4): 501-508.
[26]HUNT E R, DORAISWAMY P C, MCMURTREY J E, et al. A visible band index for remote sensing leaf chlorophyll content at the canopy scale[J]. International Journal of Applied Earth Observations & Geoinformation, 2013, 21(1): 103-112.
[27]LYU J, DENG F L, YAN Z G. Using PROSEPCT and SVM for the estimation of chlorophyll concentration[J]. Advanced Materials Research, 2014, 989/990/991/992/993/994: 2184-2187.
[28]MORIER T, CAMBOURIS A N, CHOKMANI K. In-season nitrogen status sssessment and yield estimation using hyperspectral vegetation indices in a potato crop[J]. Agronomy Journal, 2015, 107(4): 1295-1309.
[29]LI Z H, WANG J H, XU X G, et al. Assimilation of two variables derived from hyperspectral data into the DSSAT-CERES model for grain yield and quality estimation[J]. Remote Sensing, 2015, 7(9): 12400-12418.
[30]SIMKO I, JIMENEZ-BERNI J A, FURBANK R T. Detection of decay in fresh-cut lettuce using hyperspectral imaging and chlorophyll fluorescence imaging[J]. Postharvest Biology & Technology, 2015, 106: 44-52.
[31]ELMASRY G M, NAKAUCHI S. Image analysis operations applied to hyperspectral images for non-invasive sensing of food quality-a comprehensive review[J]. Biosystems Engineering, 2016, 142: 53-82.

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备注/Memo

备注/Memo:
收稿日期：2021-05-12基金项目：河北省重点研发计划项目（19226421D）作者简介：彭晓伟（1997-），男，河北石家庄人，硕士研究生，主要从事高光谱技术在农业方面的应用研究。(E-mail)1187846870@qq.com通讯作者：张爱军，(E-mail)xm70526@163.com
更新日期/Last Update: 2022-03-04