Agricultural remote sensing big data: Management and applications

• Published in: Journal of Integrative Agriculture Vol. 17; no. 9; pp. 1915 - 1931
• Main Authors: Huang, Yanbo, CHEN, Zhong-xin, YU, Tao, HUANG, Xiang-zhi, GU, Xing-fa
• Format: Journal Article
• Language: English
• Published: Crop Production Systems Research Unit,Agricultural Research Service,United States Department of Agriculture,MS 38776,USA%Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,P.R.China%Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100094,P.R.China 01.09.2018; KeAi Communications Co., Ltd
• Subjects: agricultural information; big data; farms; information processing; occupations; precision agriculture; remote sensing; satellites; spatial data; traditional farming; unmanned aerial vehicles; precision agriculture; remote sensing; agricultural information; big data
• ISSN: 2095-3119
• DOI: 10.1016/S2095-3119(17)61859-8

Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen-level (FLFL) satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level (FLTL) remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.