JIA-2018-09

1916 Yanbo Huang et al. Journal of Integrative Agriculture 2018, 17(9): 1915–1931 depending on the purpose. Various sensors are onboard to collect various observation data from earth surface, including land, water and atmosphere. These satellites with their sensors usually acquire images of earth surface unceasingly at different spatial and temporal resolutions. In this way, huge volume of remotely sensed images are available in many countries and international agencies and the volume grows every day, every hour and even every second (Rosenqvist et al. 2003; Anonymous 2015). Precision agriculture has revolutionized agricultural operations since the 1980s established on the basis of 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 thirty 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 for field-scale site-specific treatment. 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 (Bendre et al. 2015; Wolfert et al. 2017) although research remains for how to manipulate big data and convert the big data to “small” data for specific issues or fields for accurate precision agricultural operation (Sabarina and Priya 2015). 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. 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 smart, intelligent agriculture (Wolfert et al. 2017). The objective of this paper is to overview the theory and practice of agricultural remote sensing big data management for data processing and applications. From this study, a new scheme for management and applications of agricultural remote sensing big data is formulated for precision agriculture. 2. Agricultural remote sensing big data Remote sensing technology has been developed today for earth observation from different sensors and platforms. Sensors are mainly for imaging and non-imaging broad-band multispectral or narrow-band hyperspectral data acquisition. Platforms are space-borne for satellite-based sensors, airborne for sensors on manned and unmanned airplanes, and ground-based for field on-the-go and laboratory sensors. Objects on the earth continuously transmit, reflect and absorb electromagnetic waves. In principle, remote sensing technology differentiates the objects through determining the difference of the transmitted, reflected and absorbed electromagnetic waves. Remote sensing typically works on the bands of visible (0.4–0.7 mm), infrared (0.7–15 mm), and microwave (0.75–100 cm) in the electromagnetic spectrum. All the factors with geospatial distribution and data acquisition frequency result in remote sensing big data with huge volume and high complexity. Remote sensing technology has been developing with new, high-performance sensors with higher spatial, spectral and temporal resolutions. Agricultural remote sensing is a highly specialized field to generate images and spectral data in huge volume and extreme complexity to drive decisions for agricultural development. In the agricultural area, remote sensing is conducted for monitoring soil properties and crop stress for decision support in fertilization, irrigation and pest management for crop production. Typical agricultural remote sensing systems include visible-NIR (near infrared) (0.4–1.5 mm) sensors for plant vegetation studies, SWIR (short wavelength infrared) (1.5–3 mm) sensors for plant moisture studies, TI (thermal infrared) (3–15 mm) sensors for crop field surface or crop canopy temperature studies, and microwave sensors for soil moisture studies (Moran et al. 1997; Bastiaanssen et al. 2000; Pinter et al. 2003; Mulla 2013). LiDAR (Light Detection and Ranging) and SAR (Synthetic Aperture Radar) have been enabled to measure vegetation structure over agricultural lands (Zhang and Kovacs 2012; Mulla 2013). Remote sensing is the cornerstone of modern precision agriculture to realize site- specific crop field management to account for within-field variability of soil, plant stress and effect of treatments. With the rapid development of remote sensing technology, especially the use of new sensors with higher resolutions, the volume of remote sensing data will dramatically increase with a much higher complexity. Now, a major concern is determining how to effectively extract useful information from such big data for users to enhance analysis, answer questions, and solve problems. Remote sensing data are a form of big data (Ma et al. 2015). Storage, rapid processing, information extraction, information fusion, and applications of massive remote sensing data have become research hotspots at present (Rathore et al. 2015; Jagannathan 2016). Agricultural remote sensing big data have the same features as all remote sensing big data. The specialty of