作物遥感精细识别与自动制图研究进展与展望
|
摘要
作物识别与制图产品数据是作物长势、风险胁迫、产量等生产参量监测预测,种植结构调整与供需决策分析,以及耕地资源安全与生态效应评估等工作的基础数据,遥感数据成为作物类型识别与制图的最主要数据源,新兴数字技术则为遥感作物识别与制图提供了新的方法手段。本文通过综述近年基于遥感的作物识别与制图相关研究成果,探究当前技术趋势、关键问题,以及需求差距。分别从小尺度作物精细识别、大尺度作物自动化制图,以及作物识别与制图模式变化3个视角总结归纳面临的主要问题和主要研究工作。作物识别与制图产品在小尺度上需要更加精细、近实时和更高的识别精度,主要使用超高空间分辨率(如米级、亚米级)的影像数据,在提高作物识别精度(95%以上)进而提取满足应用需求的高精度作物表型等信息方面依旧面临巨大挑战。而在大尺度上需要更加自动化、满足可靠识别精度(90%左右),主要使用高时空分辨率(2～5 d,10～30 m)的影像数据,面临着如何处理海量数据的存储管理、分析计算,发展大范围上具有鲁棒性的分类识别方法,寻找科学高效的地面样本获取途径的难题。同时,作物识别与制图的模式也将从确认监测向提前预判和特定作物探测转变。最后从加强科学研究与加快应用落地2个角度提出展望,为发展满足智慧农业与国土监管不同需求的遥感作物识别与制图产品提供参考与借鉴。
Crop type identification and mapping products are required for the monitoring of crop growth,risk stress,crop yield and other parameters,as well as the planting structure adjustment,decision analysis of supply and demand,arable land resource security and ecological effect assessment. Remote sensing data have become the most important data source for crop type mapping,and the emerging digital technology also provides a series of new approaches. However,with the advent of smart agriculture era,new demands are placed on crop type mapping with higher spatial and temporal resolution,higher product accuracy and more automated. The object was to provide a review of technology trends,key issues and demand gaps of crop type mapping based on remote sensing. It was concentrated on the main problems and main research work from the three perspectives of small-scale crop type fine identification,large-scale crop type automated mapping and crop type mapping mode change. It was highlighted that crop type mapping products needed more precise,near real-time and higher accuracy on the small scale,mainly using super-high spatialresolution image data,such as one meter or less. Furthermore,it still facedsignificant challenges to improve crop type mapping accuracy,such as more than 95%,for extracting high accuracy crop phenotypes information to meet application needs. On the large-scale crop type mapping,it needed to be more automated and meet the reliable accuracy,such as around 90%. High spatial and temporal resolution image data were mainly used,such as 2 ～ 5 d and 10 ～ 30 m,and also the issues of how to deal with the storage management and analysis were faced when it came to big data,to develop the classification method in a robust manner over the large scale,and to fine a scientific and efficient ground true sample acquisition approach. It was also presented that the pattern of crop type mapping would also shift from confirming monitoring to early prediction and specific crop detection.Moreover,five prospects were proposed from the perspectives of strengthening scientific research and accelerating application,which provided some ideas for the development of remote sensing crop type identification and mapping products that met the different needs of smart agriculture and smart land.
Crop type identification and mapping products are required for the monitoring of crop growth,risk stress,crop yield and other parameters,as well as the planting structure adjustment,decision analysis of supply and demand,arable land resource security and ecological effect assessment. Remote sensing data have become the most important data source for crop type mapping,and the emerging digital technology also provides a series of new approaches. However,with the advent of smart agriculture era,new demands are placed on crop type mapping with higher spatial and temporal resolution,higher product accuracy and more automated. The object was to provide a review of technology trends,key issues and demand gaps of crop type mapping based on remote sensing. It was concentrated on the main problems and main research work from the three perspectives of small-scale crop type fine identification,large-scale crop type automated mapping and crop type mapping mode change. It was highlighted that crop type mapping products needed more precise,near real-time and higher accuracy on the small scale,mainly using super-high spatialresolution image data,such as one meter or less. Furthermore,it still facedsignificant challenges to improve crop type mapping accuracy,such as more than 95%,for extracting high accuracy crop phenotypes information to meet application needs. On the large-scale crop type mapping,it needed to be more automated and meet the reliable accuracy,such as around 90%. High spatial and temporal resolution image data were mainly used,such as 2 ～ 5 d and 10 ～ 30 m,and also the issues of how to deal with the storage management and analysis were faced when it came to big data,to develop the classification method in a robust manner over the large scale,and to fine a scientific and efficient ground true sample acquisition approach. It was also presented that the pattern of crop type mapping would also shift from confirming monitoring to early prediction and specific crop detection.Moreover,five prospects were proposed from the perspectives of strengthening scientific research and accelerating application,which provided some ideas for the development of remote sensing crop type identification and mapping products that met the different needs of smart agriculture and smart land.
引文
1 ABBASI A Z,ISLAM N,SHAIKH Z A,et al.A review of wireless sensors and networks'applications in agriculture.A review on the practice of big data analysis in agriculture[J].Computer Standards&Interfaces Computers and Electronics in Agriculture,2014,36(2):263-270.
2 BASSO B,RITCHIE J T,PIERCE F J,et al.Spatial validation of crop models for precision agriculture[J].Agricultural Systems,2001,68(2):97-112.
3 HUANG J,TIAN L,LIANG S,et al.Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model[J].Agricultural and Forest Meteorology,2015,204:106-121.
4 HUANG J,SEDANO F,HUANG Y,et al.Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation[J].Agricultural and Forest Meteorology,2016,216:188-202.
5 KAMILARIS A,KARTAKOULLIS A,PRENAFETA-BOLD U F X.A review on the practice of big data analysis in agriculture[J].Computers and Electronics in Agriculture,2017,143:23-37.
6唐华俊,吴文斌,余强毅,等.农业土地系统研究及其关键科学问题[J].中国农业科学,2015,48(5):900-910.TANG Huajun,WU Wenbin,YU Qiangyi,et al.Key research priorities for agricultural land system studies[J].Scientia Agricultura Sinica,2015,48(5):900-910.(in Chinese)
7陈仲新,任建强,唐华俊,等.农业遥感研究应用进展与展望[J].遥感学报,2016,20(5):748-767.CHEN Zhongxin,REN Jianqiang,TANG Huajun,et al.Progress and perspectives on agricultural remote sensing research and applications in China[J].Journal of Remote Sensing,2016,20(5):748-767.(in Chinese)
8 ATZBERGER C.Advances in remote sensing of agriculture:context description,existing operational monitoring systems and major information needs[J].Remote Sensing,2013,5(2):949-981.
9张喜旺,刘剑锋,秦奋,等.作物类型遥感识别研究进展[J].中国农学通报,2014,30(33):278-285.ZHANG Xiwang,LIU Jianfeng,QIN Fen,et al.A review of remote sensing application in crop type discrimination[J].Chinese Agricultural Science Bulletin,2014,30(33):278-285.(in Chinese)
10周清波.国内外农情遥感现状与发展趋势[J].中国农业资源与区划,2004,25(5):9-14.ZHOU Qingbo.Status and tendency for development in remote sensing of agriculture situation[J].Journal of China Agricultural Resources and Regional Planning,2004,25(5):9-14.(in Chinese)
11王乃斌.中国小麦遥感动态监测与估产[M].北京:中国科学技术出版社,1996.
12尤淑撑,张玮,严泰来,等.模糊分类技术在作物类型识别中的应用[J].国土资源遥感,2000(1):39-43.YOU Shucheng,ZHANG Wei,YAN Tailai,et al.The application of fuzzy classification to crop classification[J].Remote Sensing for Land&Resources,2000(1):39-43.(in Chinese)
13杨红卫,童小华.中高分辨率遥感影像在农业中的应用现状[J].农业工程学报,2012,28(24):138-149.YANG Hongwei,TONG Xiaohua.Application status of middle and high resolution remote sensing images in agriculture[J].Transactions of the CSAE,2012,28(24):138-149.(in Chinese)
14蒙继华.卫星遥感技术助力智慧农业[J].高科技与产业化,2018(5):54-59.MENG Jihua.Satellite remote sensing technology power wisdom agriculture[J].High-Technology&Industrialization,2018(5):54-59.(in Chinese)
15 ZHE L,ZHANG F,QIN M A,et al.Advances in crop phenotyping and multi-environment trials[J].Frontiers of Agricultural Science and Engineering,2015,2(1):28-37.
16 WU J,WANG D,BAUER M E.Assessing broadband vegetation indices and Quick Bird data in estimating leaf area index of corn and potato canopies[J].Field Crops Research,2007,102(1):33-42.
17 GRAY J,SONG C.Mapping leaf area index using spatial,spectral,and temporal information from multiple sensors[J].Remote Sensing of Environment,2012,119(8):173-183.
18 ZARCO-TEJADA P J,MILLER J R,MORALES A,et al.Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops[J].Remote Sensing of Environment,2004,90(4):463-476.
19竞霞,黄文江,琚存勇,等.基于PLS算法的棉花黄萎病高空间分辨率遥感监测[J].农业工程学报,2010,26(8):229-235.JING Xia,HUANG Wenjiang,JU Cunyong,et al.Remote sensing monitoring severity level of cotton verticillium wilt based on partial least squares[J].Transactions of the CSAE,2010,26(8):229-235.(in Chinese)
20孙刚,黄文江,陈鹏飞,等.轻小型无人机多光谱遥感技术应用进展[J/OL].农业机械学报,2018,49(3):1-17.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20180301&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.03.001.SUN Gang,HUANG Wenjiang,CHEN Pengfei,et al.Advances in UAV-based multispectral remote sensing applications[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(3):1-17.(in Chinese)
21 LIU T,LI R,ZHONG X,et al.Estimates of rice lodging using indices derived from UAV visible and thermal infrared images[J].Agricultural and Forest Meteorology,2018,252:144-154.
22 JIN X,LIU S,BARET F E D E,et al.Estimates of plant density of wheat crops at emergence from very low altitude UAVimagery[J].Remote Sensing of Environment,2017,198:105-114.
23刘建刚,赵春江,杨贵军,等.无人机遥感解析田间作物表型信息研究进展[J].农业工程学报,2016,32(24):98-106.LIU Jiangang,ZHAO Chunjiang,YANG Guijun,et al.Review of field-based phenotyping by unmanned aerial vehicle remote sensing platform[J].Transactions of the CSAE,2016,32(24):98-106.(in Chinese)
24 HOFFMEISTER D,BOLTEN A,CURDT C,et al.High-resolution crop surface models(CSM)and crop volume models(CVM)on field level by terrestrial laser scanning[Z].International Society for Optics and Photonics,201078400E.
25 BENDIG J,BOLTEN A,BENNERTZ S,et al.Estimating biomass of barley using crop surface models(CSMs)derived from UAV-based RGB imaging[J].Remote Sensing,2014,6(11):10395-10412.
26 LELONG C C,BURGER P,JUBELIN G,et al.Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots[J].Sensors,2008,8(5):3557-3585.
27高林,杨贵军,李红军,等.基于无人机数码影像的冬小麦叶面积指数探测研究[J].中国生态农业学报,2016,24(9):1254-1264.GAO Lin,YANG Guijun,LI Hongjun,et al.Winter wheat LAI estimation using unmanned aerial vehicle RGB-imaging[J].Chinese Journal of Eco-Agriculture,2016,24(9):1254-1264.(in Chinese)
28 AASEN H,BURKART A,BOLTEN A,et al.Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring:from camera calibration to quality assurance[J].ISPRS Journal of Photogrammetry and Remote Sensing,2015,108:245-259.
29闫峰,李茂松,王艳姣,等.遥感技术在农业灾害监测中的应用[J].自然灾害学报,2006,15(6):131-136.YAN Feng,LI Maosong,WANG Yanjiao,et al.Application of remote sensing technique to monitor agricultural disasters[J].Journal of Natural Disasters,2006,15(6):131-136.(in Chinese)
30 LI Z,CHEN Z,WANG L,et al.Area extraction of maize lodging based on remote sensing by small unmanned aerial vehicle[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(19):207-213.
31 YANG M,HUANG K,KUO Y,et al.Spatial and spectral hybrid image classification for rice lodging assessment through UAVimagery[J].Remote Sensing,2017,9(6):583.
32 HAN L,YANG G,FENG H,et al.Quantitative identification of maize lodging-causing feature factors using unmanned aerial vehicle images and a nomogram computation[J].Remote Sensing,2018,10(10):1528.
33 DORAISWAMY P C,STERN A J,AKHMEDOV B.Crop classification in the US corn belt using MODIS imagery[C].Geoscience and Remote Senceing Symposium,IGARSS 2007.IEEE International,2007:809-812.
34 ARVOR D,JONATHAN M,MEIRELLES M S O E,et al.Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso,Brazil[J].International Journal of Remote Sensing,2011,32(22):7847-7871.
35陈思宁,赵艳霞,申双和.基于波谱分析技术的遥感作物分类方法[J].农业工程学报,2012,28(5):154-160.CHEN Sining,ZHAO Yanxia,SHEN Shuanghe.Crop classification by remote sensing based on spectral analysis[J].Transactions of the CSAE,2012,28(5):154-160.(in Chinese)
36张霞,焦全军,张兵,等.利用MODIS_EVI图像时间序列提取作物种植模式初探[J].农业工程学报,2008,24(5):161-165.ZHANG Xia,JIAO Quanjun,ZHANG Bing,et al.Preliminary study on cropping pattern mapping using MODIS_EVI image time series[J].Transactions of the CSAE,2008,24(5):161-165.(in Chinese)
37陈健,刘云慧,宇振荣.基于时序MODIS-EVI数据的冬小麦种植信息提取[J].中国农学通报,2011,27(1):446-450.CHEN Jian,LIU Yunhui,YU Zhenrong.Planting information extraction of winter wheat based on the time-series MODIS-EVI[J].Chinese Agricultural Science Bulletin,2011,27(1):446-450.(in Chinese)
38 GOMEZ C,WHITE J C,WULDER M A.Optical remotely sensed time series data for land cover classification:a review[J].ISPRS Journal of Photogrammetry and Remote Sensing,2016,116:55-72.
39 MOORE R T,HANSEN M C.Google earth engine:a new cloud-computing platform for global-scale earth observation data and analysis[Z].https:∥earthengine.google.com,2011.
40 GORELICK N.Google earth engine[C].EGU General Assembly Conference Abstracts,2013,15:11997.
41 GORELICK N,HANCHER M,DIXON M,et al.Google Earth Engine:Planetary-scale geospatial analysis for everyone[J].Remote Sensing of Environment,2017,202:18-27.
42 PATEL N N,ANGIULI E,GAMBA P,et al.Multitemporal settlement and population mapping from Landsat using Google Earth Engine[J].International Journal of Applied Earth Observation and Geoinformation,2015,35:199-208.
43 JOHANSEN K,PHINN S,TAYLOR M.Mapping woody vegetation clearing in Queensland,Australia from Landsat imagery using the Google Earth Engine[J].Remote Sensing Applications:Society and Environment,2015(1):36-49.
44 LEE J S H,WICH S,WIDAYATI A,et al.Detecting industrial oil palm plantations on Landsat images with Google Earth Engine[J].Remote Sensing Applications:Society and Environment,2016(4):219-224.
45 SHELESTOV A,LAVRENIUK M,KUSSUL N,et al.Exploring Google Earth Engine platform for big data processing:classification of multi-temporal satellite imagery for crop mapping[J].Frontiers in Earth Science,2017(5):17.
46 SHELESTOV A,LAVRENIUK M,KUSSUL N,et al.Large scale crop classification using Google Earth Engine platform[Z].IEEE,2017:3696-3699.
47 DONG J,XIAO X,MENARGUEZ M A,et al.Mapping paddy rice planting area in northeastern Asia with Landsat 8 images,phenology-based algorithm and Google Earth Engine[J].Remote Sensing of Environment,2016,185(SI):142-154.
48 XIONG J,THENKABAIL P S,GUMMA M K,et al.Automated cropland mapping of continental Africa using Google Earth Engine cloud computing[J].ISPRS Journal of Photogrammetry and Remote Sensing,2017,126:225-244.
49 LEWIS A,OLIVER S,LYMBURNER L,et al.The Australian geoscience data cube-foundations and lessons learned[J].Remote Sensing of Environment,2017,202:276-292.
50戚将辉,张丰,杜震洪,等.基于内存数据库的矢量数据存储与空间索引研究[J].浙江大学学报(理学版),2015,42(3):365-370.QI Jianghui,ZHANG Feng,DU Zhenhong,et al.Research of the landuse vector data storage and spatial index based on the main memory database[J].Journal of Zhejiang University(Science Edition),2015,42(3):365-370.(in Chinese)
51 YE S,LIU D,YAO X,et al.RDCRMG:a raster dataset clean&reconstitution multi-grid architecture for remote sensing monitoring of vegetation dryness[J].Remote Sensing,2018,10(9):1376.
52 YAO X,MOKBEL M F,ALARABI L,et al.Spatial coding-based approach for partitioning big spatial data in Hadoop[J].Computers&Geosciences,2017,106:60-67.
53 YAO X,MOKBEL M,YE S,et al.Land Qv2:a MapReduce-based system for processing arable land quality big data[J].ISPRSInternational Journal of Geo-Information,2018,7(7):271.
54郑利娟.基于高分一/六号卫星影像特征的农作物分类研究[D].北京:中国科学院大学,2017.ZHENG Lijuan.Crop classification using multi-features of Chinese Gaofen-1/6 Sateliite remote sensing images[D].Beijing:University of Chinese Academy of Sciences,2017.(in Chinese)
55 FORKUOR G,CONRAD C,THIEL M,et al.Integration of optical and synthetic aperture radar imagery for improving crop mapping in northwestern Benin,West Africa[J].Remote Sensing,2014,6(7):6472-6499.
56 UPADHYAY P,GHOSH S K,KUMAR A,et al.Effect on specific crop mapping using World View-2 multispectral add-on bands:soft classification approach[J].Journal of Applied Remote Sensing,2012,6(1):63524.
57吕雅慧,张超,郧文聚,等.高分辨率遥感影像农田林网自动识别[J/OL].农业机械学报,2018,49(1):157-163.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20180120&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.01.020.LYahui,ZHANG Chao,YUN Wenju,et al.Automatic recognition of farmland shelterbelts in high spatial resolution remote sensing data[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(1):157-163.(in Chinese)
58张超,金虹杉,刘哲,等.基于GF遥感数据纹理分析识别制种玉米[J].农业工程学报,2016,32(21):183-188.ZHANG Chao,JIN Hongshan,LIU Zhe,et al.Seed maize identification based on texture analysis of GF remote sensing data[J].Transactions of the CSAE,2016,32(21):183-188.(in Chinese)
59 CHUANG Y M,SHIU Y.A comparative analysis of machine learning with World View-2 pan-sharpened imagery for tea crop mapping[J].Sensors,2016,16(5):594.
60黄健熙,侯矞焯,苏伟,等.基于GF-1 WFV数据的玉米与大豆种植面积提取方法[J].农业工程学报,2017,33(7):164-170.HUANG Jianxi,HOU Yuzhuo,SU Wei,et al.Mapping corn and soybean cropped area with GF-1 WFV data[J].Transactions of the CSAE,2017,33(7):164-170.(in Chinese)
61贺鹏,徐新刚,张宝雷,等.基于多时相GF-1遥感影像的作物分类提取[J].河南农业科学,2016,45(1):152-159.HE Peng,XU Xingang,ZHANG Baolei,et al.Crop classification extraction based on multi-temporal GF-1 remote sensing image[J].Journal of Henan Agricultural Sciences,2016,45(1):152-159.(in Chinese)
62 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.
63 INGLADA J,VINCENT A,ARIAS M,et al.Improved early crop type identification by joint use of high temporal resolution SARand optical image time series[J].Remote Sensing,2016,8(5):362.
64 HASPERU E W.The master algorithm:how the quest for the ultimate learning machine will remake our world[J].Journal of Computer Science and Technology,2015,15(2):157-158.
65马丽,徐新刚,刘良云,等.基于多时相NDVI及特征波段的作物分类研究[J].遥感技术与应用,2008,23(5):520-524.MA Li,XU Xin'gang,LIU Liangyun,et al.Study on crops classification based on multi-temporal NDVI and characteristic bands[J].Remote Sensing Technology and Application,2008,23(5):520-524.(in Chinese)
66 WARDLOW B D,EGBERT S L.Large-area crop mapping using time-series MODIS 250 m NDVI data:an assessment for the USCentral Great Plains[J].Remote Sensing of Environment,2008,112(3):1096-1116.
67黄青,李丹丹,陈仲新,等.基于MODIS数据的冬小麦种植面积快速提取与长势监测[J/OL].农业机械学报,2012,43(7):163-167.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20120730&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2012.07.030.HUANG Qing,LI Dandan,CHEN Zhongxin,et al.Monitoring of planting area and growth condition of winter wheat in China based on MODIS data[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2012,43(7):163-167.(in Chinese)
68 De RAINVILLE F C C O,DURAND A,FORTIN F E L,et al.Bayesian classification and unsupervised learning for isolating weeds in row crops[J].Pattern Analysis and Applications,2014,17(2):401-414.
69 MURTHY C S,RAJU P V,BADRINATH K.Classification of wheat crop with multi-temporal images:performance of maximum likelihood and artificial neural networks[J].International Journal of Remote Sensing,2003,24(23):4871-4890.
70熊勤学,黄敬峰.利用NDVI指数时序特征监测秋收作物种植面积[J].农业工程学报,2009,25(1):144-148.XIONG Qinxue,HUANG Jingfeng.Estimation of autumn harvest crop planting area based on NDVI sequential characteristics[J].Transactions of the CSAE,2009,25(1):144-148.(in Chinese)
71 NITZE I,SCHULTHESS U,ASCHE H.Comparison of machine learning algorithms random forest,artificial neural network and support vector machine to maximum likelihood for supervised crop type classification[C]∥Proc.of the 4th GEOBIA,2012:7-9.
72 HAO P,NIU Z,WANG L.Crop classification using multi-temporal HJ satellite images:case study in Kashgar,Xinjiang[C]∥Land Surface Remote SenceingⅡ.International Socity for Optics and Photonics,2014,9260:926005.
73 LI L,KONG Q,WANG P,et al.Precise identification of maize in the North China Plain based on Sentinel-1A SAR time series data[J].International Journal of Remote Sensing,2018:1-18.
74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/OL].农业机械学报,2017,48(10):142-147,285.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20171017&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.10.017.HUANG Jianxi,HOU Yuzhuo,WU Hongfeng,et al.Crop type mapping method based on time-series MODIS data in Heilongjiang Province[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(10):142-147,285.(in Chinese)
75 KUSSUL N,LAVRENIUK M,SKAKUN S,et al.Deep learning classification of land cover and crop types using remote sensing data[J].IEEE Geoscience and Remote Sensing Letters,2017,14(5):778-782.
76 JI S,ZHANG C,XU A,et al.3D convolutional neural networks for crop classification with multi-temporal remote sensing images[J].Remote Sensing,2018,10(1):75.
77 PETITJEAN F,INGLADA J,GANCCARSKI P.Satellite image time series analysis under time warping[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(8):3081-3095.
78 LAVRENIUK M S,SKAKUN S V,SHELESTOV A J,et al.Large-scale classification of land cover using retrospective satellite data[J].Cybernetics and Systems Analysis,2016,52(1):127-138.
79 YOU J,LI X,LOW M,et al.Deep gaussian process for crop yield prediction based on remote sensing data[C].20174559-4566.
80 WANG A X,TRAN C,DESAI N,et al.Deep transfer learning for crop yield prediction with remote sensing data[C].ACM,201850.
81姜成晟,王劲峰,曹志冬,等.地理空间抽样理论研究综述[J].地理学报,2009,64(3):368-380.JIANG Chengsheng,WANG Jinfeng,CAO Zhidong,et al.A review of geo-spatial sampling theory[J].Acta Geographica Sinica,2009,64(3):368-380.(in Chinese)
82刘海启.欧盟MARS计划简介与我国农业遥感应用思路[J].中国农业资源与区划,1999,20(3):55-57.LIU Haiqi.The introduction of MARS plan of European commission and Chinese agriculture remote sensing application[J].Journal of China Agricultural Resources and Regional Planning,1999,20(3):55-57.(in Chinese)
83吴炳方,李强子.基于两个独立抽样框架的农作物种植面积遥感估算方法[J].遥感学报,2004,8(6):551-569.WUBingfang,LI Qiangzi.Rop acreage estimation using two individual sampling frameworks with stratification[J].Journal of Remote Sensing,2004,8(6):551-569.(in Chinese)
84张焕雪,李强子,文宁,等.农作物种植面积遥感抽样调查的误差影响因素分析[J].农业工程学报,2014,30(13):176-184.ZHANG Huanxue,LI Qiangzi,WEN Ning,et al.Analysis on estimation accuracy of crop area caused by spatial sampling factors based on remote sensing data[J].Transactions of the CSAE,2014,30(13):176-184.(in Chinese)
85王迪,周清波,陈仲新,等.空间抽样方法估算冬小麦播种面积[J].农业工程学报,2012,28(10):177-184.WANG Di,ZHOU Qingbo,CHEN Zhongxin,et al.Spatial sampling method for estimating winter wheat sown area[J].Transactions of the CSAE,2012,28(10):177-184.(in Chinese)
86张焕雪,曹新,李强子,等.基于多时相环境星NDVI时间序列的农作物分类研究[J].遥感技术与应用,2015(2):304-311.ZHANG Huanxue,CAO Xin,LI Qiangzi,et al.Research on crop identification using multi-temporal NDVI HJ images[J].Remote Sensing Technology and Application,2015(2):304-311.(in Chinese)
87杨闫君,田庆久,王磊,等.基于GF-1/WFV NDVI时间序列数据的作物分类[J].农业工程学报,2015,31(24):155-161.YANG Yanjun,TIAN Qingjiu,WANG Lei,et al.Crop classification based on GF-1/WFV NDVI time series[J].Transactions of the CSAE,2015,31(24):155-161.(in Chinese)
88 GONG P,WANG J,YU L,et al.Finer resolution observation and monitoring of global land cover:first mapping results with Landsat TM and ETM+data[J].International Journal of Remote Sensing,2013,34(7):2607-2654.
89宫鹏,张伟,俞乐,等.全球地表覆盖制图研究新范式[J].遥感学报,2016,20(5):1002-1016.GONG Peng,ZHANG Wei,YU Le,et al.New research paradigm for global land cover mapping[J].Journal of Remote Sensing,2016,20(5):1002-1016.(in Chinese)
90 MASSEY R,SANKEY T T,CONGALTON R G,et al.MODIS phenology-derived,multi-year distribution of conterminous UScrop types[J].Remote Sensing of Environment,2017,198:490-503.
91 HAO P,WANG L,ZHAN Y,et al.Using moderate-resolution temporal NDVI profiles for high-resolution crop mapping in years of absent ground reference data:a case study of bole and manas counties in Xinjiang,China[J].Isprs International Journal of GEO-Information,2016,5(5):67.
92 MUHAMMAD S,ZHAN Y,WANG L,et al.Major crops classification using time series MODIS EVI with adjacent years of ground reference data in the US state of Kansas[J].OPTIK,2016,127(3):1071-1077.
93 HAO P,WANG L,ZHAN Y,et al.Crop classification using crop knowledge of the previous-year:case study in Southwest Kansas,USA[J].European Journal of Remote Sensing,2016,49:1061-1077.
94马超红,翁小清.时间序列早期分类综述[J].微型机与应用,2016,35(16):13-15.MA Chaohong,WENG Xiaoqing.Review of early classification on time series[J].Microcomputer&Its Applications,2016,35(16):13-15.(in Chinese)
95郝鹏宇,唐华俊,陈仲新,等.基于历史增强型植被指数时序的农作物类型早期识别[J].农业工程学报,2018,34(13):179-186.HAO Pengyu,TANG Huajun,CHEN Zhongxin,et al.Early season crop type recognition based on historical EVI time series[J].Transactions of the CSAE,2018,34(13):179-186.(in Chinese)
96 GALLEGO J,CRAIG M,MICHAELSEN J,et al.Best practices for crop area estimation with remote sensing[R].Ispra:Joint Research Center,2008.
97 DONG J,XIAO X,KOU W,et al.Tracking the dynamics of paddy rice planting area in 1986-2010 through time series Landsat images and phenology-based algorithms[J].Remote Sensing of Environment,2015,160(160):99-113.
98 MUHAMMAD S,NIU Z,WANG L,et al.Crop classification based on time series MODIS EVI and ground observation for three adjoining years in Xinjiang[J].Spectroscopy and Spectral Analysis,2015,35(5):1345-1350.
99陆永帅,李元祥,彭希帅.深度置信网络模型的机载多光谱数据罂粟识别[J].遥感信息,2017,32(4):98-103.LU Yongshuai,LI Yuanxiang,PENG Xishuai.Poppy detection in airborne multispectral data based on deep belief network[J].Remote Sensing Information,2017,32(4):98-103.(in Chinese)
100刘哲,李智晓,张延宽,等.基于时序EVI决策树分类与高分纹理的制种玉米识别[J/OL].农业机械学报,2015,46(10):321-327.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20151043&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2015.10.043.LIU Zhe,LI Zhixiao,ZHANG Yankuan,et al.Seed maize identification based on time-series EVI decision tree classification and high resolution remote sensing texture analysis[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2015,46(10):321-327.(in Chinese)
101张超,乔敏,刘哲,等.基于无人机和卫星遥感影像的制种玉米田识别纹理特征尺度优选[J].农业工程学报,2017,33(17):98-104.ZHANG Chao,QIAO Min,LIU Zhe,et al.Texture scale analysis and identification of seed maize fields based on UAV and satellite remote sensing images[J].Transactions of the CSAE,2017,33(17):98-104.(in Chinese)
102张超,乔敏,刘哲,等.基于时序光谱和高分纹理分析的制种玉米田遥感识别[J/OL].农业机械学报,2018,49(5):218-225.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20180525&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.05.025.ZHANG Chao,QIAO Min,LIU Zhe,et al.Seed maize field identification based on analysis of remote sensing timing spectrum and high resolution texture[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):218-225.(in Chinese)
2 BASSO B,RITCHIE J T,PIERCE F J,et al.Spatial validation of crop models for precision agriculture[J].Agricultural Systems,2001,68(2):97-112.
3 HUANG J,TIAN L,LIANG S,et al.Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model[J].Agricultural and Forest Meteorology,2015,204:106-121.
4 HUANG J,SEDANO F,HUANG Y,et al.Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation[J].Agricultural and Forest Meteorology,2016,216:188-202.
5 KAMILARIS A,KARTAKOULLIS A,PRENAFETA-BOLD U F X.A review on the practice of big data analysis in agriculture[J].Computers and Electronics in Agriculture,2017,143:23-37.
6唐华俊,吴文斌,余强毅,等.农业土地系统研究及其关键科学问题[J].中国农业科学,2015,48(5):900-910.TANG Huajun,WU Wenbin,YU Qiangyi,et al.Key research priorities for agricultural land system studies[J].Scientia Agricultura Sinica,2015,48(5):900-910.(in Chinese)
7陈仲新,任建强,唐华俊,等.农业遥感研究应用进展与展望[J].遥感学报,2016,20(5):748-767.CHEN Zhongxin,REN Jianqiang,TANG Huajun,et al.Progress and perspectives on agricultural remote sensing research and applications in China[J].Journal of Remote Sensing,2016,20(5):748-767.(in Chinese)
8 ATZBERGER C.Advances in remote sensing of agriculture:context description,existing operational monitoring systems and major information needs[J].Remote Sensing,2013,5(2):949-981.
9张喜旺,刘剑锋,秦奋,等.作物类型遥感识别研究进展[J].中国农学通报,2014,30(33):278-285.ZHANG Xiwang,LIU Jianfeng,QIN Fen,et al.A review of remote sensing application in crop type discrimination[J].Chinese Agricultural Science Bulletin,2014,30(33):278-285.(in Chinese)
10周清波.国内外农情遥感现状与发展趋势[J].中国农业资源与区划,2004,25(5):9-14.ZHOU Qingbo.Status and tendency for development in remote sensing of agriculture situation[J].Journal of China Agricultural Resources and Regional Planning,2004,25(5):9-14.(in Chinese)
11王乃斌.中国小麦遥感动态监测与估产[M].北京:中国科学技术出版社,1996.
12尤淑撑,张玮,严泰来,等.模糊分类技术在作物类型识别中的应用[J].国土资源遥感,2000(1):39-43.YOU Shucheng,ZHANG Wei,YAN Tailai,et al.The application of fuzzy classification to crop classification[J].Remote Sensing for Land&Resources,2000(1):39-43.(in Chinese)
13杨红卫,童小华.中高分辨率遥感影像在农业中的应用现状[J].农业工程学报,2012,28(24):138-149.YANG Hongwei,TONG Xiaohua.Application status of middle and high resolution remote sensing images in agriculture[J].Transactions of the CSAE,2012,28(24):138-149.(in Chinese)
14蒙继华.卫星遥感技术助力智慧农业[J].高科技与产业化,2018(5):54-59.MENG Jihua.Satellite remote sensing technology power wisdom agriculture[J].High-Technology&Industrialization,2018(5):54-59.(in Chinese)
15 ZHE L,ZHANG F,QIN M A,et al.Advances in crop phenotyping and multi-environment trials[J].Frontiers of Agricultural Science and Engineering,2015,2(1):28-37.
16 WU J,WANG D,BAUER M E.Assessing broadband vegetation indices and Quick Bird data in estimating leaf area index of corn and potato canopies[J].Field Crops Research,2007,102(1):33-42.
17 GRAY J,SONG C.Mapping leaf area index using spatial,spectral,and temporal information from multiple sensors[J].Remote Sensing of Environment,2012,119(8):173-183.
18 ZARCO-TEJADA P J,MILLER J R,MORALES A,et al.Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops[J].Remote Sensing of Environment,2004,90(4):463-476.
19竞霞,黄文江,琚存勇,等.基于PLS算法的棉花黄萎病高空间分辨率遥感监测[J].农业工程学报,2010,26(8):229-235.JING Xia,HUANG Wenjiang,JU Cunyong,et al.Remote sensing monitoring severity level of cotton verticillium wilt based on partial least squares[J].Transactions of the CSAE,2010,26(8):229-235.(in Chinese)
20孙刚,黄文江,陈鹏飞,等.轻小型无人机多光谱遥感技术应用进展[J/OL].农业机械学报,2018,49(3):1-17.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20180301&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.03.001.SUN Gang,HUANG Wenjiang,CHEN Pengfei,et al.Advances in UAV-based multispectral remote sensing applications[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(3):1-17.(in Chinese)
21 LIU T,LI R,ZHONG X,et al.Estimates of rice lodging using indices derived from UAV visible and thermal infrared images[J].Agricultural and Forest Meteorology,2018,252:144-154.
22 JIN X,LIU S,BARET F E D E,et al.Estimates of plant density of wheat crops at emergence from very low altitude UAVimagery[J].Remote Sensing of Environment,2017,198:105-114.
23刘建刚,赵春江,杨贵军,等.无人机遥感解析田间作物表型信息研究进展[J].农业工程学报,2016,32(24):98-106.LIU Jiangang,ZHAO Chunjiang,YANG Guijun,et al.Review of field-based phenotyping by unmanned aerial vehicle remote sensing platform[J].Transactions of the CSAE,2016,32(24):98-106.(in Chinese)
24 HOFFMEISTER D,BOLTEN A,CURDT C,et al.High-resolution crop surface models(CSM)and crop volume models(CVM)on field level by terrestrial laser scanning[Z].International Society for Optics and Photonics,201078400E.
25 BENDIG J,BOLTEN A,BENNERTZ S,et al.Estimating biomass of barley using crop surface models(CSMs)derived from UAV-based RGB imaging[J].Remote Sensing,2014,6(11):10395-10412.
26 LELONG C C,BURGER P,JUBELIN G,et al.Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots[J].Sensors,2008,8(5):3557-3585.
27高林,杨贵军,李红军,等.基于无人机数码影像的冬小麦叶面积指数探测研究[J].中国生态农业学报,2016,24(9):1254-1264.GAO Lin,YANG Guijun,LI Hongjun,et al.Winter wheat LAI estimation using unmanned aerial vehicle RGB-imaging[J].Chinese Journal of Eco-Agriculture,2016,24(9):1254-1264.(in Chinese)
28 AASEN H,BURKART A,BOLTEN A,et al.Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring:from camera calibration to quality assurance[J].ISPRS Journal of Photogrammetry and Remote Sensing,2015,108:245-259.
29闫峰,李茂松,王艳姣,等.遥感技术在农业灾害监测中的应用[J].自然灾害学报,2006,15(6):131-136.YAN Feng,LI Maosong,WANG Yanjiao,et al.Application of remote sensing technique to monitor agricultural disasters[J].Journal of Natural Disasters,2006,15(6):131-136.(in Chinese)
30 LI Z,CHEN Z,WANG L,et al.Area extraction of maize lodging based on remote sensing by small unmanned aerial vehicle[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(19):207-213.
31 YANG M,HUANG K,KUO Y,et al.Spatial and spectral hybrid image classification for rice lodging assessment through UAVimagery[J].Remote Sensing,2017,9(6):583.
32 HAN L,YANG G,FENG H,et al.Quantitative identification of maize lodging-causing feature factors using unmanned aerial vehicle images and a nomogram computation[J].Remote Sensing,2018,10(10):1528.
33 DORAISWAMY P C,STERN A J,AKHMEDOV B.Crop classification in the US corn belt using MODIS imagery[C].Geoscience and Remote Senceing Symposium,IGARSS 2007.IEEE International,2007:809-812.
34 ARVOR D,JONATHAN M,MEIRELLES M S O E,et al.Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso,Brazil[J].International Journal of Remote Sensing,2011,32(22):7847-7871.
35陈思宁,赵艳霞,申双和.基于波谱分析技术的遥感作物分类方法[J].农业工程学报,2012,28(5):154-160.CHEN Sining,ZHAO Yanxia,SHEN Shuanghe.Crop classification by remote sensing based on spectral analysis[J].Transactions of the CSAE,2012,28(5):154-160.(in Chinese)
36张霞,焦全军,张兵,等.利用MODIS_EVI图像时间序列提取作物种植模式初探[J].农业工程学报,2008,24(5):161-165.ZHANG Xia,JIAO Quanjun,ZHANG Bing,et al.Preliminary study on cropping pattern mapping using MODIS_EVI image time series[J].Transactions of the CSAE,2008,24(5):161-165.(in Chinese)
37陈健,刘云慧,宇振荣.基于时序MODIS-EVI数据的冬小麦种植信息提取[J].中国农学通报,2011,27(1):446-450.CHEN Jian,LIU Yunhui,YU Zhenrong.Planting information extraction of winter wheat based on the time-series MODIS-EVI[J].Chinese Agricultural Science Bulletin,2011,27(1):446-450.(in Chinese)
38 GOMEZ C,WHITE J C,WULDER M A.Optical remotely sensed time series data for land cover classification:a review[J].ISPRS Journal of Photogrammetry and Remote Sensing,2016,116:55-72.
39 MOORE R T,HANSEN M C.Google earth engine:a new cloud-computing platform for global-scale earth observation data and analysis[Z].https:∥earthengine.google.com,2011.
40 GORELICK N.Google earth engine[C].EGU General Assembly Conference Abstracts,2013,15:11997.
41 GORELICK N,HANCHER M,DIXON M,et al.Google Earth Engine:Planetary-scale geospatial analysis for everyone[J].Remote Sensing of Environment,2017,202:18-27.
42 PATEL N N,ANGIULI E,GAMBA P,et al.Multitemporal settlement and population mapping from Landsat using Google Earth Engine[J].International Journal of Applied Earth Observation and Geoinformation,2015,35:199-208.
43 JOHANSEN K,PHINN S,TAYLOR M.Mapping woody vegetation clearing in Queensland,Australia from Landsat imagery using the Google Earth Engine[J].Remote Sensing Applications:Society and Environment,2015(1):36-49.
44 LEE J S H,WICH S,WIDAYATI A,et al.Detecting industrial oil palm plantations on Landsat images with Google Earth Engine[J].Remote Sensing Applications:Society and Environment,2016(4):219-224.
45 SHELESTOV A,LAVRENIUK M,KUSSUL N,et al.Exploring Google Earth Engine platform for big data processing:classification of multi-temporal satellite imagery for crop mapping[J].Frontiers in Earth Science,2017(5):17.
46 SHELESTOV A,LAVRENIUK M,KUSSUL N,et al.Large scale crop classification using Google Earth Engine platform[Z].IEEE,2017:3696-3699.
47 DONG J,XIAO X,MENARGUEZ M A,et al.Mapping paddy rice planting area in northeastern Asia with Landsat 8 images,phenology-based algorithm and Google Earth Engine[J].Remote Sensing of Environment,2016,185(SI):142-154.
48 XIONG J,THENKABAIL P S,GUMMA M K,et al.Automated cropland mapping of continental Africa using Google Earth Engine cloud computing[J].ISPRS Journal of Photogrammetry and Remote Sensing,2017,126:225-244.
49 LEWIS A,OLIVER S,LYMBURNER L,et al.The Australian geoscience data cube-foundations and lessons learned[J].Remote Sensing of Environment,2017,202:276-292.
50戚将辉,张丰,杜震洪,等.基于内存数据库的矢量数据存储与空间索引研究[J].浙江大学学报(理学版),2015,42(3):365-370.QI Jianghui,ZHANG Feng,DU Zhenhong,et al.Research of the landuse vector data storage and spatial index based on the main memory database[J].Journal of Zhejiang University(Science Edition),2015,42(3):365-370.(in Chinese)
51 YE S,LIU D,YAO X,et al.RDCRMG:a raster dataset clean&reconstitution multi-grid architecture for remote sensing monitoring of vegetation dryness[J].Remote Sensing,2018,10(9):1376.
52 YAO X,MOKBEL M F,ALARABI L,et al.Spatial coding-based approach for partitioning big spatial data in Hadoop[J].Computers&Geosciences,2017,106:60-67.
53 YAO X,MOKBEL M,YE S,et al.Land Qv2:a MapReduce-based system for processing arable land quality big data[J].ISPRSInternational Journal of Geo-Information,2018,7(7):271.
54郑利娟.基于高分一/六号卫星影像特征的农作物分类研究[D].北京:中国科学院大学,2017.ZHENG Lijuan.Crop classification using multi-features of Chinese Gaofen-1/6 Sateliite remote sensing images[D].Beijing:University of Chinese Academy of Sciences,2017.(in Chinese)
55 FORKUOR G,CONRAD C,THIEL M,et al.Integration of optical and synthetic aperture radar imagery for improving crop mapping in northwestern Benin,West Africa[J].Remote Sensing,2014,6(7):6472-6499.
56 UPADHYAY P,GHOSH S K,KUMAR A,et al.Effect on specific crop mapping using World View-2 multispectral add-on bands:soft classification approach[J].Journal of Applied Remote Sensing,2012,6(1):63524.
57吕雅慧,张超,郧文聚,等.高分辨率遥感影像农田林网自动识别[J/OL].农业机械学报,2018,49(1):157-163.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20180120&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.01.020.LYahui,ZHANG Chao,YUN Wenju,et al.Automatic recognition of farmland shelterbelts in high spatial resolution remote sensing data[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(1):157-163.(in Chinese)
58张超,金虹杉,刘哲,等.基于GF遥感数据纹理分析识别制种玉米[J].农业工程学报,2016,32(21):183-188.ZHANG Chao,JIN Hongshan,LIU Zhe,et al.Seed maize identification based on texture analysis of GF remote sensing data[J].Transactions of the CSAE,2016,32(21):183-188.(in Chinese)
59 CHUANG Y M,SHIU Y.A comparative analysis of machine learning with World View-2 pan-sharpened imagery for tea crop mapping[J].Sensors,2016,16(5):594.
60黄健熙,侯矞焯,苏伟,等.基于GF-1 WFV数据的玉米与大豆种植面积提取方法[J].农业工程学报,2017,33(7):164-170.HUANG Jianxi,HOU Yuzhuo,SU Wei,et al.Mapping corn and soybean cropped area with GF-1 WFV data[J].Transactions of the CSAE,2017,33(7):164-170.(in Chinese)
61贺鹏,徐新刚,张宝雷,等.基于多时相GF-1遥感影像的作物分类提取[J].河南农业科学,2016,45(1):152-159.HE Peng,XU Xingang,ZHANG Baolei,et al.Crop classification extraction based on multi-temporal GF-1 remote sensing image[J].Journal of Henan Agricultural Sciences,2016,45(1):152-159.(in Chinese)
62 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.
63 INGLADA J,VINCENT A,ARIAS M,et al.Improved early crop type identification by joint use of high temporal resolution SARand optical image time series[J].Remote Sensing,2016,8(5):362.
64 HASPERU E W.The master algorithm:how the quest for the ultimate learning machine will remake our world[J].Journal of Computer Science and Technology,2015,15(2):157-158.
65马丽,徐新刚,刘良云,等.基于多时相NDVI及特征波段的作物分类研究[J].遥感技术与应用,2008,23(5):520-524.MA Li,XU Xin'gang,LIU Liangyun,et al.Study on crops classification based on multi-temporal NDVI and characteristic bands[J].Remote Sensing Technology and Application,2008,23(5):520-524.(in Chinese)
66 WARDLOW B D,EGBERT S L.Large-area crop mapping using time-series MODIS 250 m NDVI data:an assessment for the USCentral Great Plains[J].Remote Sensing of Environment,2008,112(3):1096-1116.
67黄青,李丹丹,陈仲新,等.基于MODIS数据的冬小麦种植面积快速提取与长势监测[J/OL].农业机械学报,2012,43(7):163-167.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20120730&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2012.07.030.HUANG Qing,LI Dandan,CHEN Zhongxin,et al.Monitoring of planting area and growth condition of winter wheat in China based on MODIS data[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2012,43(7):163-167.(in Chinese)
68 De RAINVILLE F C C O,DURAND A,FORTIN F E L,et al.Bayesian classification and unsupervised learning for isolating weeds in row crops[J].Pattern Analysis and Applications,2014,17(2):401-414.
69 MURTHY C S,RAJU P V,BADRINATH K.Classification of wheat crop with multi-temporal images:performance of maximum likelihood and artificial neural networks[J].International Journal of Remote Sensing,2003,24(23):4871-4890.
70熊勤学,黄敬峰.利用NDVI指数时序特征监测秋收作物种植面积[J].农业工程学报,2009,25(1):144-148.XIONG Qinxue,HUANG Jingfeng.Estimation of autumn harvest crop planting area based on NDVI sequential characteristics[J].Transactions of the CSAE,2009,25(1):144-148.(in Chinese)
71 NITZE I,SCHULTHESS U,ASCHE H.Comparison of machine learning algorithms random forest,artificial neural network and support vector machine to maximum likelihood for supervised crop type classification[C]∥Proc.of the 4th GEOBIA,2012:7-9.
72 HAO P,NIU Z,WANG L.Crop classification using multi-temporal HJ satellite images:case study in Kashgar,Xinjiang[C]∥Land Surface Remote SenceingⅡ.International Socity for Optics and Photonics,2014,9260:926005.
73 LI L,KONG Q,WANG P,et al.Precise identification of maize in the North China Plain based on Sentinel-1A SAR time series data[J].International Journal of Remote Sensing,2018:1-18.
74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/OL].农业机械学报,2017,48(10):142-147,285.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20171017&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.10.017.HUANG Jianxi,HOU Yuzhuo,WU Hongfeng,et al.Crop type mapping method based on time-series MODIS data in Heilongjiang Province[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(10):142-147,285.(in Chinese)
75 KUSSUL N,LAVRENIUK M,SKAKUN S,et al.Deep learning classification of land cover and crop types using remote sensing data[J].IEEE Geoscience and Remote Sensing Letters,2017,14(5):778-782.
76 JI S,ZHANG C,XU A,et al.3D convolutional neural networks for crop classification with multi-temporal remote sensing images[J].Remote Sensing,2018,10(1):75.
77 PETITJEAN F,INGLADA J,GANCCARSKI P.Satellite image time series analysis under time warping[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(8):3081-3095.
78 LAVRENIUK M S,SKAKUN S V,SHELESTOV A J,et al.Large-scale classification of land cover using retrospective satellite data[J].Cybernetics and Systems Analysis,2016,52(1):127-138.
79 YOU J,LI X,LOW M,et al.Deep gaussian process for crop yield prediction based on remote sensing data[C].20174559-4566.
80 WANG A X,TRAN C,DESAI N,et al.Deep transfer learning for crop yield prediction with remote sensing data[C].ACM,201850.
81姜成晟,王劲峰,曹志冬,等.地理空间抽样理论研究综述[J].地理学报,2009,64(3):368-380.JIANG Chengsheng,WANG Jinfeng,CAO Zhidong,et al.A review of geo-spatial sampling theory[J].Acta Geographica Sinica,2009,64(3):368-380.(in Chinese)
82刘海启.欧盟MARS计划简介与我国农业遥感应用思路[J].中国农业资源与区划,1999,20(3):55-57.LIU Haiqi.The introduction of MARS plan of European commission and Chinese agriculture remote sensing application[J].Journal of China Agricultural Resources and Regional Planning,1999,20(3):55-57.(in Chinese)
83吴炳方,李强子.基于两个独立抽样框架的农作物种植面积遥感估算方法[J].遥感学报,2004,8(6):551-569.WUBingfang,LI Qiangzi.Rop acreage estimation using two individual sampling frameworks with stratification[J].Journal of Remote Sensing,2004,8(6):551-569.(in Chinese)
84张焕雪,李强子,文宁,等.农作物种植面积遥感抽样调查的误差影响因素分析[J].农业工程学报,2014,30(13):176-184.ZHANG Huanxue,LI Qiangzi,WEN Ning,et al.Analysis on estimation accuracy of crop area caused by spatial sampling factors based on remote sensing data[J].Transactions of the CSAE,2014,30(13):176-184.(in Chinese)
85王迪,周清波,陈仲新,等.空间抽样方法估算冬小麦播种面积[J].农业工程学报,2012,28(10):177-184.WANG Di,ZHOU Qingbo,CHEN Zhongxin,et al.Spatial sampling method for estimating winter wheat sown area[J].Transactions of the CSAE,2012,28(10):177-184.(in Chinese)
86张焕雪,曹新,李强子,等.基于多时相环境星NDVI时间序列的农作物分类研究[J].遥感技术与应用,2015(2):304-311.ZHANG Huanxue,CAO Xin,LI Qiangzi,et al.Research on crop identification using multi-temporal NDVI HJ images[J].Remote Sensing Technology and Application,2015(2):304-311.(in Chinese)
87杨闫君,田庆久,王磊,等.基于GF-1/WFV NDVI时间序列数据的作物分类[J].农业工程学报,2015,31(24):155-161.YANG Yanjun,TIAN Qingjiu,WANG Lei,et al.Crop classification based on GF-1/WFV NDVI time series[J].Transactions of the CSAE,2015,31(24):155-161.(in Chinese)
88 GONG P,WANG J,YU L,et al.Finer resolution observation and monitoring of global land cover:first mapping results with Landsat TM and ETM+data[J].International Journal of Remote Sensing,2013,34(7):2607-2654.
89宫鹏,张伟,俞乐,等.全球地表覆盖制图研究新范式[J].遥感学报,2016,20(5):1002-1016.GONG Peng,ZHANG Wei,YU Le,et al.New research paradigm for global land cover mapping[J].Journal of Remote Sensing,2016,20(5):1002-1016.(in Chinese)
90 MASSEY R,SANKEY T T,CONGALTON R G,et al.MODIS phenology-derived,multi-year distribution of conterminous UScrop types[J].Remote Sensing of Environment,2017,198:490-503.
91 HAO P,WANG L,ZHAN Y,et al.Using moderate-resolution temporal NDVI profiles for high-resolution crop mapping in years of absent ground reference data:a case study of bole and manas counties in Xinjiang,China[J].Isprs International Journal of GEO-Information,2016,5(5):67.
92 MUHAMMAD S,ZHAN Y,WANG L,et al.Major crops classification using time series MODIS EVI with adjacent years of ground reference data in the US state of Kansas[J].OPTIK,2016,127(3):1071-1077.
93 HAO P,WANG L,ZHAN Y,et al.Crop classification using crop knowledge of the previous-year:case study in Southwest Kansas,USA[J].European Journal of Remote Sensing,2016,49:1061-1077.
94马超红,翁小清.时间序列早期分类综述[J].微型机与应用,2016,35(16):13-15.MA Chaohong,WENG Xiaoqing.Review of early classification on time series[J].Microcomputer&Its Applications,2016,35(16):13-15.(in Chinese)
95郝鹏宇,唐华俊,陈仲新,等.基于历史增强型植被指数时序的农作物类型早期识别[J].农业工程学报,2018,34(13):179-186.HAO Pengyu,TANG Huajun,CHEN Zhongxin,et al.Early season crop type recognition based on historical EVI time series[J].Transactions of the CSAE,2018,34(13):179-186.(in Chinese)
96 GALLEGO J,CRAIG M,MICHAELSEN J,et al.Best practices for crop area estimation with remote sensing[R].Ispra:Joint Research Center,2008.
97 DONG J,XIAO X,KOU W,et al.Tracking the dynamics of paddy rice planting area in 1986-2010 through time series Landsat images and phenology-based algorithms[J].Remote Sensing of Environment,2015,160(160):99-113.
98 MUHAMMAD S,NIU Z,WANG L,et al.Crop classification based on time series MODIS EVI and ground observation for three adjoining years in Xinjiang[J].Spectroscopy and Spectral Analysis,2015,35(5):1345-1350.
99陆永帅,李元祥,彭希帅.深度置信网络模型的机载多光谱数据罂粟识别[J].遥感信息,2017,32(4):98-103.LU Yongshuai,LI Yuanxiang,PENG Xishuai.Poppy detection in airborne multispectral data based on deep belief network[J].Remote Sensing Information,2017,32(4):98-103.(in Chinese)
100刘哲,李智晓,张延宽,等.基于时序EVI决策树分类与高分纹理的制种玉米识别[J/OL].农业机械学报,2015,46(10):321-327.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20151043&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2015.10.043.LIU Zhe,LI Zhixiao,ZHANG Yankuan,et al.Seed maize identification based on time-series EVI decision tree classification and high resolution remote sensing texture analysis[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2015,46(10):321-327.(in Chinese)
101张超,乔敏,刘哲,等.基于无人机和卫星遥感影像的制种玉米田识别纹理特征尺度优选[J].农业工程学报,2017,33(17):98-104.ZHANG Chao,QIAO Min,LIU Zhe,et al.Texture scale analysis and identification of seed maize fields based on UAV and satellite remote sensing images[J].Transactions of the CSAE,2017,33(17):98-104.(in Chinese)
102张超,乔敏,刘哲,等.基于时序光谱和高分纹理分析的制种玉米田遥感识别[J/OL].农业机械学报,2018,49(5):218-225.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20180525&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.05.025.ZHANG Chao,QIAO Min,LIU Zhe,et al.Seed maize field identification based on analysis of remote sensing timing spectrum and high resolution texture[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):218-225.(in Chinese)