Handling of Big Data in Agricultural Remote Sensing
农业遥感中的大数据处理
- 关键词：
- 来源：
- Encyclopedia of Smart Agriculture Technologies
- 类型：
- 学术文献
- 语种：
- 英语
- 原文发布日期：
- 2022-12-26
- 摘要：
- The acquisition, processing, storage, analysis, visualization, and application of agricultural remote sensing big data are critical to guide the success of precision agriculture. This entry overviews available data resources for agricultural remote sensing, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture.Remote sensing technology has been developed for earth observation (EO) with massive remotely sensed data for various research needs and practical applications. As of September 1, 2021, there are 4550 active man-made satellites in space orbiting earth and 22% of the satellites serve for EO remote sensing (DEWEsoft 2022). It can be predicted that this number will increase rapidly, especially for those satellites that will be launched into low orbits around the earth for near earth observation. For remote sensing, satellites are equipped with one or more sensors or instruments to collect various observation data of earth surface, including land, water, and atmosphere. These satellite sensors acquire images of earth surface unceasingly at different spatial, spectral, and temporal resolutions so that huge volume of remotely sensed images is available in many countries and international agencies with the data volume growing every day, every hour, and even every second.Sustainable development of agriculture is the goal for global food security. Modern agricultural development is the result from the development of precision agriculture. Precision agriculture began to renovate agricultural operations in the 1980s when the theory and practice were established based on agricultural mechanization through the integration of global positioning system (GPS), geographic information system (GIS), and remote sensing technologies (Zhang et al. 2002). Over the past four years, precision agriculture has evolved from strategic monitoring using satellite imagery for regional decision-making to tactical monitoring and control prescribed by the information from low-altitude remotely sensed data by unmanned aerial vehicles (UAVs) for field-scale site-specific management to deal with within-field variability of soil properties, weed distributions, crop stress of insects, diseases, nutrients and water, biomass, and yield. Now data science and big data technology are gradually merged into precision agricultural schemes so that the data can be analyzed rapidly in time for decision-making although research remains for how to manipulate big data and convert the big data to “small” data and actionable information for specific issues or fields for accurate precision agricultural operation. Agricultural remote sensing is a key technology that, with global positioning data, produces spatially varied data and information for agricultural planning and prescription for precision agricultural operations with GIS (Yao and Huang 2013). Agricultural remote sensing data appear in different forms and are acquired from different sensors and at different intervals and scales. Agricultural remote sensing data all have characteristics of big data in complexity and volume. The acquisition, processing, storage, analysis, and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. With the most recent and coming advances of information and electronics technologies and remote sensing big data support, precision agriculture will be developed into the stage of smart, intelligent agriculture.
- 所属专题：
- 62
