JIA-2018-09

1922 Yanbo Huang et al. Journal of Integrative Agriculture 2018, 17(9): 1915–1931 image contains fifteen 10°×10° blocks at the third level of the first layer (Table 3). This figure also shows the naming convention of the FLFL scheme. The standardized image naming can help speed up image retrieval. In general, the FLFL structure can be used for different kinds of remote sensing images. The structure has been used for managing MODIS, Landsat and Gaofen (China National Space Administration) data. Through FLFL, data management, massive remote sensing data are stored in the distributed mode. Moreover, the data path can be directly determined through the method of data retrieval through direct access to data address. In this way, any new-generated massive remote sensing data can be blocked and sliced into image tiles on the surface of the earth sphere and the data of the image tiles can be stored and queried quickly. In such a way, the FLFL structure can provide powerful support for rapid storage, production, retrieval and services of remote sensing big data to the public. In 2014, a similar blocking/ tile remote sensing data analysis and access structure called Australian Geoscience Data Cube has been established by Geoscience Australia (GA), CSIRO (Commonwealth Scientific and Industrial Research Organization) and the NCI (National Computational Infrastructure) to analyze and publish Landsat data GA archived covering the Australian continent (http://www.datacube.org.au/ ). The Data Cube has made more than three decades of satellite imagery spanning Australia’s total land area at a resolution of 25 square meters become available, and provides over 240000 images showing howAustralia’s vegetation, land use, water movements and urban expansion have changed over the past 30 years (Lewis et al. 2016; Mueller et al. 2016). Open Geospatial Consortium (OGC) (Wayland, MA, USA) defined a Discrete Global Grid Systems (DGGS) as “A form of Earth reference that, unlike its established counterpart the coordinate reference system that represents the Earth as a continual lattice of points, represents the Earth with a tessellation of nested cells.” The DGGS is “A solution can only be achieved through the conversion of traditional data archives into standardized data architectures that support parallel processing in distributed and/or high performance computing environments. A common framework is required that will link very large multi-resolution and multi-domain datasets together and to enable the next generation of analytic processes to be applied. Asolution must be capable of handling multiple data streams rather than being explicitly linked to a sensor or data type. Success has been achieved using a framework called a discrete global grid system (DGGS).” (http://www.opengeospatial.org/projects/groups/ dggsdwg). Therefore, the GRID Cube based on the FLFL scheme agrees with the definition and purpose description of the OGC-DGGS. With the FLFL scheme, a new FLFLGRID Cube was created to become a general DGGS Software entity with unique interfaces, standardized integration of multiple-source, heterogeneous and massive spatial data, distributed storage, parallel computing, integrated data mining, diverse applications and high-efficiency network services while OGC only provides the definition and description of DGGS and OGC-DGGS Core Standard for defining the components of DGGS data models, methods of frame operation and interface parameters in general. The uniqueness of the FLFL structure is in that the resolution structure of the blocking tiles is 500m/250m/100m /50 m/25 m/10 m/5 m/2.5 m/1 m and this structure can be expanded up and down indefinitely. The layer/level are divided for one layer with three levels with the ratio of 5:2.5:1 and the ratioof layers is10:1. Thisdiving ratios canmatchupcommonly used map scales like 1:1 000 000, 1:500 000, 1:250 000, 1:100000, 1:15000, 1:25000, 1:10000, etc. Compared to the traditional ratio of m n , such as 2 3 /2 2 /2 1 /2 0 ,2.5 3 /2.5 2 /2.5 1 /2.5 0 , 3 3 /3 2 /3 1 /3 0 , 2 6 /3 4 /2 4 /2 0 and 5 3 /5 2 /5 1 /5 0 , 10/5/2.5/1 of FLFLoffers the uniformity of layer/level mesh size, better fit to application scales and reduced redundancies. The decimal system is the most popular numeric system people use everyday. The ratio of 10 between layers in FLFL is convenient for being memorized and conversed. 3.6. Remote sensing data management for precision agriculture The FLFL remote sensing data management structure has been developed for satellite imagery at different resolutions. Precision agriculture mostly deals with tactical variable- rate operations prescribed by the site-specific data and information extracted from remote sensing data in the scale of farm fields. In this way, the low-resolution (such as MODIS) and medium-resolution (such as Landsat OLI) data cannot play a role directly in precision agriculture. Agricultural remote sensing is conducted on airplanes at different altitudes for different resolutions. Low altitude remote sensing (LARS) is very effective for precision agriculture with manned airplanes at 300–1 000 m and UAVs at 10–300 m. Another platform of agricultural remote sensing is ground on-the-go with sensors mounted on tractors or other movable structures for proximal sensing over crop fields. Therefore, high-resolution satellite remote sensing (such as Worldview and Quickbird), airborne remote sensing and ground-based remote sensing are integral to agricultural remote sensing. Specific to precision agriculture LARS and ground on-the-go remote sensing are major data sources for prescription of variable-rate applications of seeds, fertilizers, pesticides and water. Based on the characteristics of remote sensing data for precision agriculture, a four-layer-twelve-level (FLTL) remote sensing data management structure can be built by