P. Thenkabail, P. Teluguntla, J. Xiong, A. Oliphant, R. Congalton, M. Ozdogan, M. Gumma, J. Tilton, C. Giri, C. Milesi, A. Phalke, R. Massey, Kamini Yadav, T. Sankey, Ying Zhong, I. Aneece, Daniel Foley
{"title":"利用谷歌地球引擎云上的多种机器学习算法,从2015年Landsat卫星时间序列数据中获得30米分辨率的全球耕地范围产品(GCEP30)","authors":"P. Thenkabail, P. Teluguntla, J. Xiong, A. Oliphant, R. Congalton, M. Ozdogan, M. Gumma, J. Tilton, C. Giri, C. Milesi, A. Phalke, R. Massey, Kamini Yadav, T. Sankey, Ying Zhong, I. Aneece, Daniel Foley","doi":"10.3133/pp1868","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":132462,"journal":{"name":"Professional Paper","volume":"130 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud\",\"authors\":\"P. Thenkabail, P. Teluguntla, J. Xiong, A. Oliphant, R. Congalton, M. Ozdogan, M. Gumma, J. Tilton, C. Giri, C. Milesi, A. Phalke, R. Massey, Kamini Yadav, T. Sankey, Ying Zhong, I. Aneece, Daniel Foley\",\"doi\":\"10.3133/pp1868\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":132462,\"journal\":{\"name\":\"Professional Paper\",\"volume\":\"130 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Professional Paper\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3133/pp1868\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Professional Paper","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3133/pp1868","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud