Agricultural remote sensing big data: Management and applications

Authors: Huang, Yanbo; CHEN, ZHONGXIN - Chinese Academy Of Agricultural Sciences; YU, TAO - Chinese Academy Of Sciences; HUANG, XIANGZHI - Chinese Academy Of Sciences; GU, XINGFA - Chinese Academy Of Sciences

Submitted to: Journal of Integrative Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/8/2017
Publication Date: 7/25/2018
Citation: Huang, Y., Chen, Z., Yu, T., Huang, X., Gu, X. 2018. Agricultural remote sensing big data: Management and applications. Journal of Integrative Agriculture. 17(9): 1915-1931.

Interpretive Summary: Big data is used more and more in the society. Remote sensing is a source of big data for earth observation and analysis to assist decision making in a lot of aspects of socioeconomic development. Scientists at USDA-ARS Crop Production Systems Research Unit, Stoneville, Mississippi; Chinese Academy of Agricultural Sciences, Beijing, China; and Chinese Academy of Sciences, Beijing, China collaboratively developed a new data management structure to facilitate processing and analysis of remote sensing big data for improved data quality and information extraction in precision agriculture. The new structure has been developed for processing the remote sensing data acquired by the sensors mounted on low-orbit satellites, aircrafts, unmanned aerial vehicles flown at low altitudes. This new structure has been achieved by expanding the structure developed by the Chinese Academy of Sciences for processing the remote sensing data acquired by the sensors mounted on high-orbit satellites. The new data structure is a suitable management and application framework of agricultural remote sensing big data for precision agriculture.

Technical Abstract: 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 everyday from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is the important application area of remote sensing science and technology. 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 is to overview the theory and practice of agricultural remote sensing big data management for data processing and applications. An established five-layer-fifteen-level satellite remote sensing data management structure is presented and evaluated. Based on the study, a more appropriate three-layer-nine-level remote sensing data management structure is proposed and evaluated 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.