[1]张代维,马友华,吴雷,等.基于文献计量的农作物种植结构遥感提取发展态势分析[J].江苏农业学报,2023,(04):1026-1035.[doi:doi:10.3969/j.issn.1000-4440.2023.04.012]
 ZHANG Dai-wei,MA You-hua,WU Lei,et al.Development situation analysis of remote sensing extraction of crop planting structure based on bibliometrics[J].,2023,(04):1026-1035.[doi:doi:10.3969/j.issn.1000-4440.2023.04.012]
点击复制

基于文献计量的农作物种植结构遥感提取发展态势分析()
分享到:

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2023年04期
页码:
1026-1035
栏目:
农业信息工程
出版日期:
2023-08-30

文章信息/Info

Title:
Development situation analysis of remote sensing extraction of crop planting structure based on bibliometrics
作者:
张代维12马友华12吴雷12王强12王肖飞12
(1.安徽农业大学资源与环境学院,安徽合肥230036;2.安徽省北斗精准农业信息工程实验室,安徽合肥230036)
Author(s):
ZHANG Dai-wei12MA You-hua12WU Lei12WANG Qiang12WANG Xiao-fei12
(1.College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China;2.Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information,Hefei 230036, China)
关键词:
农作物种植结构遥感文献计量Vosviewer
Keywords:
cropplanting structureremote sensingbibliometricsVosviewer
分类号:
TP75
DOI:
doi:10.3969/j.issn.1000-4440.2023.04.012
文献标志码:
A
摘要:
及时获取农作物种植结构对粮食安全及农业可持续发展具有重要意义。为了解当前农作物种植结构遥感提取研究前沿和进展,利用文献计量法探究了该领域近20年的研究现状及热点。利用Vosviewer软件和Bibliometrix程序包,分析了Web of Science 核心合集和中国知网中2000-2022年农作物种植结构遥感提取领域的867篇文献,从发文量、发文国家、国内发文机构、载文期刊、关键词共现及关键词时序等角度进行可视化分析。农作物种植结构遥感提取研究领域整体发文量呈指数上升;英文发文量最多的国家是中国,为197篇,国际合作最多的也是中国,中文发文量较多的机构是中国科学院(89篇)、中国农业科学院(38篇);研究主要集中于如何选择遥感影像、提取方法以及特征;通过对热点关键词进行时序变化分析发现,当前和未来的研究热点数据是基于时间序列的多源数据融合,热门分类方法主要是以面向对象并结合随机森林、卷积神经网络进行提取,热门特征指数还是以归一化植被指数(NDVI)为主并结合其他特征选择最优特征组合。农作物种植结构遥感提取研究未来将聚焦在如何向低成本、高效率、高精度提取发展。
Abstract:
It is of great significance to acquire crop planting structure timely for food security and sustainable agricultural development. The research status and focuses in the field in recent 20 years were explored by bibliometric analysis to learn about the research frontier and progress on crop planting structure mapping by remote sensing extraction. Literatures with a number of 867 in the field of crop planting structure mapping by remote sensing extraction in the core collection of Web of Science and CNKI from 2000 to 2022 were analyzed. Visualized analysis was made by Vosviewer software and Bibliometrix program package, from the perspectives of published articles number, countries of publication, domestic institutions of publication, journals of publication, keywords co-occurrence and keywords timing sequence. The total publication number in research field of crop planting structure mapping by remote sensing extraction had increased exponentially. China was the country with the largest number of articles published in English, which was 197. Besides, China was the country with the greatest contribution to international cooperation. The institutions with large number of articles published in Chinese were Chinese Academy of Sciences and Chinese Academy of Agricultural Sciences, with articles number of 89 and 38, respectively. The articles mainly focused on selection of remote sensing images, extraction methods and features. It was found through analysis on time sequence changes of hot keywords that, the hot data of current and future researches were multi-source data fusion based on time series, the hot classification method was mainly led by the object-oriented mapping in combination with random forest and convolutional neural networks, and the hot feature indexes were still dominated by the normalized difference vegetation index (NDVI) combined with other features to select the optimal combination of the features. In conclusion, the research on the crop planting structure mapping by remote sensing extraction will focus on extraction with low-cost, high-efficiency and high-precision.

参考文献/References:

[1]CHANG Y L, TAN T H, CHEN T H, et al. Spatial-temporal neural network for rice field classification from SAR images[J]. Remote Sensing,2022,14(8): 1929.
[2]SONG X P, POTAPOV P V, KRYLOV A, et al. National-scale soybean mapping and area estimation in the United States using medium resolution atellite imagery and field survey[J]. Remote Sensing of Environment, 2017, 190: 383-395.
[3]LIU Y Q,WANG J Y. Revealing annual crop type distribution and spatiotemporal changes in Northeast China based on Google Earth Engine[J]. Remote Sensing,2022, 14(16): 4056.
[4]胡琼,吴文斌,宋茜,等. 农作物种植结构遥感提取研究进展[J]. 中国农业科学, 2015, 48(10): 1900-1914.
[5]ORYNBAIKYZY A, GESSNER U, CONRAD C. Crop type classification using a combination of optical and radar remote sensing data: a review[J]. International Journal of Remote Sensing, 2019, 40(17): 6553-6595.
[6]宋茜,胡琼,陆苗,等. 农作物空间分布遥感制图发展方向探讨[J]. 中国农业资源与区划, 2020, 41(6): 57-65.
[7]刘俊伟,陈鹏飞,张东彦,等 基于时序Sentinel-2影像的梨树县作物种植结构[J]. 江苏农业学报, 2020, 36(6): 1428-1436.
[8]张馨予,蔡志文,杨靖雅,等. 时序滤波对农作物遥感识别的影响[J]. 农业工程学报, 2022, 38(4): 215-224.
[9]刘吉凯,钟仕全,梁文海. 基于多时相Landsat8 OLI影像的作物种植结构提取[J]. 遥感技术与应用, 2015, 30(4): 775-783.
[10]陈诗扬,刘佳. 基于GF-6时序数据的农作物识别深度学习算法评估[J]. 农业工程学报, 2021, 37(15): 161-168.
[11]BLICKENSDRFER L, SCHWIEDER M, PFLUGMACHER D, et al. Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany[J]. Remote Sensing of Environment,2022, 269: 112831.
[12]XIAO P N, QIAN P, XU J, et al. A bibliometric analysis of the application of remote sensing in crop spatial patterns: current status, progress and future directions[J]. Sustainability, 2022, 14(7): 4104.
[13]林湘岷,沈宗专,李荣,等. 基于Web of Science的抑病型土壤文献计量分析[J]. 江苏农业学报, 2022, 38(3): 821-829.
[14]崔峰,尚久杨. 中国农业文化遗产研究的文献计量与知识图谱分析——基于中国知网(CNKI)和Web of Science数据库[J]. 中国生态农业学报, 2020, 28(9): 1294-1304.
[15]PAN L, XIA H M, ZHAO X Y, et al. Mapping winter crops using a phenology algorithm, time-series Sentinel-2 and Landsat-7/8 images, and google earth engine[J]. Remote Sensing, 2021, 13(13): 2510.
[16]ZENG L, WARDLOW B D, XIANG D, et al. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data[J]. Remote Sensing of Environment, 2020, 237: 111511.
[17]王镕,赵红莉,蒋云钟,等. 月尺度农作物提取中GF-1 WFV纹理特征的应用及分析[J]. 自然资源遥感, 2021, 33(3): 72-79.
[18]杨闫君,占玉林,田庆久,等. 基于GF-1/WFVNDVI时间序列数据的作物分类[J]. 农业工程学报, 2015, 31(24): 155-161.
[19]张良培,何江,杨倩倩,等. 数据驱动的多源遥感信息融合研究进展[J]. 测绘学报, 2022, 51(7): 1317-1337.
[20]张立福,彭明媛,孙雪剑,等. 遥感数据融合研究进展与文献定量分析(1992-2018)[J]. 遥感学报, 2019, 23(4): 603-619.
[21]牛乾坤,刘浏,黄冠华,等. 基于GEE和机器学习的河套灌区复杂种植结构识别[J]. 农业工程学报, 2022, 38(6): 165-174.
[22]朱梦豪,李国清,彭壮壮. 特征优选下的农作物遥感分类研究[J]. 测绘科学, 2022, 47(3): 122-128.
[23]刘闯,葛成辉. 美国对地观测系统(EOS)中分辨率成像光谱仪(MODIS)遥感数据的特点与应用[J]. 遥感信息, 2000, 15(3): 45-48.
[24]李晓慧,王宏,李晓兵,等. 基于多时相Landsat 8 OLI影像的农作物遥感分类研究[J]. 遥感技术与应用, 2019, 34(2): 389-397.
[25]李冰,梁燕华,李丹丹,等. 多时相GF-1卫星PMS影像提取农作物种植结构[J]. 中国农业资源与区划, 2017, 38(9): 56-62.
[26]杨闫君,占玉林,田庆久,等. 基于GF-1/WFVNDVI时间序列数据的作物分类[J]. 农业工程学报, 2015, 31(24): 155-161.
[27]刘昊. 基于Sentinel-2影像的河套灌区作物种植结构提取[J]. 干旱区资源与环境, 2021, 35(2): 88-95.
[28]钱丽沙,姜浩,陈水森,等. 基于时空滤波Sentinel-1时序数据的田块尺度岭南作物分布提取[J]. 农业工程学报, 2022, 38(5): 158-166.
[29]HALDAR D, PATNAIK C. Synergistic use of multi-temporal Radarsat SAR and AWiFS data for Rabi rice identification[J]. Journal of the Indian Society of Remote Sensing, 2010, 38(1): 153-160.
[30]王迪,周清波,陈仲新,等. 基于合成孔径雷达的农作物识别研究进展[J]. 农业工程学报, 2014, 30(16): 203-212.
[31]张影,赵小娟,王迪. 基于高光谱遥感的农作物分类研究进展[J]. 中国农业信息, 2019, 31(5): 1-12.
[32]郭交,李仪邦,董思意,等. 融合栈式自编码与CNN的高光谱影像作物分类方法[J]. 农业机械学报, 2021, 52(12): 225-232.
[33]ROY D P, KOVALSKYY V, ZHANG H K, et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity[J]. Remote Sensing of Environment, 2016, 185: 57-70.
[34]TATSUMI K, YAMASHIKI Y, TORRES M A C, et al. Crop classification of upland fields using random forest of time-series Landsat 7 ETM+ data[J]. Computers and Electronics in Agriculture, 2015, 115: 171-179.
[35]CHEN Y S, HOU J L, HUANG C L, et al. Mapping maize area in heterogeneous agricultural landscape with Multi-Temporal Sentinel-1 and Sentinel-2 images based on random forest[J]. Remote Sensing, 2021, 13(15): 2988.
[36]ZHONG L H, HU L N, ZHOU H. Deep learning based multi-temporal crop classification[J]. Remote Sensing of Environment, 2019, 221: 430-443.
[37]赵红伟,陈仲新,刘佳. 深度学习方法在作物遥感分类中的应用和挑战[J]. 中国农业资源与区划, 2020, 41(2): 35-49.
[38]杜保佳,张晶,王宗明,等. 应用Sentinel-2A NDVI时间序列和面向对象决策树方法的农作物分类[J]. 地球信息科学学报, 2019, 21(5): 740-751.
[39]王连喜,徐胜男,李琪,等. 基于决策树和混合像元分解的江苏省冬小麦种植面积提取[J]. 农业工程学报, 2016, 32(5): 182-187.
[40]单治彬,孔金玲,张永庭,等. 面向对象的特色农作物种植遥感调查方法研究[J]. 地球信息科学学报, 2018, 20(10): 1509-1519.
[41]黄健熙,侯矞焯,武洪峰,等. 基于时间序列MODIS的农作物类型空间制图方法[J]. 农业机械学报, 2017, 48(10): 142-147,285.

相似文献/References:

[1]舒田,岳延滨,李莉婕,等.基于高光谱遥感的农作物识别[J].江苏农业学报,2016,(06):1310.[doi:doi:10.3969/j.issn.1000-4440.2016.06.018]
 SHU Tian,YUE Yan-bin,LI Li-jie,et al.Crop indentification based on hyperspectral remote sensing[J].,2016,(04):1310.[doi:doi:10.3969/j.issn.1000-4440.2016.06.018]
[2]杨敏慎,刘晓雨,郭辉.气候变暖和CO2浓度升高对农作物的影响[J].江苏农业学报,2021,(01):246.[doi:doi:10.3969/j.issn.1000-4440.2021.01.032]
 YANG Min-shen,LIU Xiao-yu,GUO Hui.Effects of climate warming and elevated CO2 concentration on crops[J].,2021,(04):246.[doi:doi:10.3969/j.issn.1000-4440.2021.01.032]
[3]孟亮,郭小燕,杜佳举,等.一种轻量级CNN农作物病害图像识别模型[J].江苏农业学报,2021,(05):1143.[doi:doi:10.3969/j.issn.1000-4440.2021.05.008]
 MENG Liang,GUO Xiao-yan,DU Jia-ju,et al.A lightweight CNN model for image recognition of crop disease[J].,2021,(04):1143.[doi:doi:10.3969/j.issn.1000-4440.2021.05.008]
[4]魏宏宇,李怡,彭帅英,等.胞外多糖促进胁迫条件下农作物生长的研究与展望[J].江苏农业学报,2022,38(04):1123.[doi:doi:10.3969/j.issn.1000-4440.2022.04.032]
 WEI Hong-yu,LI Yi,PENG Shuai-ying,et al.Promoting crop growth under stress conditions by exopolysaccharides: review and perspective[J].,2022,38(04):1123.[doi:doi:10.3969/j.issn.1000-4440.2022.04.032]

备注/Memo

备注/Memo:
收稿日期:2022-10-26 基金项目:安徽省科技重大专项(202003a06020002);安徽农业大学稳定和引进人才科研项目(rc522013)作者简介:张代维(1998-),男,四川达州人,硕士研究生,研究方向为农业遥感。(E-mail)zhangdw129@qq.com 通讯作者:吴雷,(E-mail)wulei@ahau.edu.cn
更新日期/Last Update: 2023-09-12