{"title":"利用哨兵-2 数据和机器学习算法绘制塞内加尔异质性土地利用/土地覆被和作物类型图","authors":"Murali Krishna Gumma, Pranay Panjala, Pardhasaradhi Teluguntla","doi":"10.1080/17538947.2024.2378815","DOIUrl":null,"url":null,"abstract":"In rainfed and dryland agricultural areas with smallholder farms (less than 2 ha), crop diversity is high due to farmers' decisions and local climatic conditions, leading to a complex spatial–tempo...","PeriodicalId":54962,"journal":{"name":"International Journal of Digital Earth","volume":"26 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mapping heterogeneous land use/land cover and crop types in Senegal using sentinel-2 data and machine learning algorithms\",\"authors\":\"Murali Krishna Gumma, Pranay Panjala, Pardhasaradhi Teluguntla\",\"doi\":\"10.1080/17538947.2024.2378815\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In rainfed and dryland agricultural areas with smallholder farms (less than 2 ha), crop diversity is high due to farmers' decisions and local climatic conditions, leading to a complex spatial–tempo...\",\"PeriodicalId\":54962,\"journal\":{\"name\":\"International Journal of Digital Earth\",\"volume\":\"26 1\",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Digital Earth\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1080/17538947.2024.2378815\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Digital Earth","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/17538947.2024.2378815","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
Mapping heterogeneous land use/land cover and crop types in Senegal using sentinel-2 data and machine learning algorithms
In rainfed and dryland agricultural areas with smallholder farms (less than 2 ha), crop diversity is high due to farmers' decisions and local climatic conditions, leading to a complex spatial–tempo...
期刊介绍：
The International Journal of Digital Earth is a response to this initiative. This peer-reviewed academic journal (SCI-E) focuses on the theories, technologies, applications, and societal implications of Digital Earth and those visionary concepts that will enable a modeled virtual world. The journal encourages papers that:
Progress visions for Digital Earth frameworks, policies, and standards;
Explore geographically referenced 3D, 4D, or 5D models to represent the real planet, and geo-data-intensive science and discovery;
Develop methods that turn all forms of geo-referenced data, from scientific to social, into useful information that can be analyzed, visualized, and shared;
Present innovative, operational applications and pilots of Digital Earth technologies at a local, national, regional, and global level;
Expand the role of Digital Earth in the fields of Earth science, including climate change, adaptation and health related issues,natural disasters, new energy sources, agricultural and food security, and urban planning;
Foster the use of web-based public-domain platforms, social networks, and location-based services for the sharing of digital data, models, and information about the virtual Earth; and
Explore the role of social media and citizen-provided data in generating geo-referenced information in the spatial sciences and technologies.
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