JIA-2018-09

1927 Yanbo Huang et al. Journal of Integrative Agriculture 2018, 17(9): 1915–1931 the data with such management. Fig. 6 shows the original mosaicked RGB image and blocked and resampled images of the two farms of USDA ARS Crop Production Systems Research Unit with the FLTL scheme in the blocks of 0.0005°×0.0005° at the fourth level in the second layer. The raw RGB images were acquired using a 10MP GoPro HERO3+ camera (GoPro Inc., San Mateo, California). The GoPro camera was used on UAVs for low-altitude small field imaging. In order to cover larger areas at the scale of research farms the camera was mounted and operated on an Air Tractor 402B airplane (Air Tractor Inc., Olney, TX, USA) with a 2.97 mm f/4.0 non-distortion lens at the altitude about 900 m for a spatial resolution at about 55 cm/pixel. Multiple images were acquired to be mosaicked to cover the farm areas and the resulting images were georectified and resampled to fit into the FLTL structure. 5. Comments and outlook Agricultural remote sensing big data will be developed and used for the studies at the global, regional and field scales. Agricultural studies face the challenge of uncertainties from the variations of weather conditions and management strategies. Remote sensing big data are the valuable resource for precision agriculture to potentially make robust distributions of agricultural variables, such as yield and other biotic and abiotic indicators of crops, to tackle the uncertainties from experiments and analysis from different sites and farms. The global and regional trends identified from the big data are definitely important for the studies of global and regional agriculture, but they are definitely not capable of addressing the issues of individual farms. In this way, local remote sensing data management is as important as large-scale remote sensing data management. Large- scale big data could tell the general trends while the local data provides specific features of the farm and fields with the weather information. For site-specific recommendations, large-scale data and local data have been coupled together to balance between big data and local conditions (Rubin 2016). The established FLFL structure provides the management and application framework for large-scale remote sensing big data while the proposed FLTL structure in this paper is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies. Science and technology of remote sensing are advancing with the advancement of information and communication technology. Remote sensing data industry has been boomed up and the supply chain of remote sensing data from raw data to products is going to establish with the explosion of data along the chain. Working with big data for extraction of useful information to support decision making is one of the competitive advantages for organizations today. Enterprises are balancing the analytical power to formulate the strategies in every aspects in the operations to reduce business risk (Biswas and Sen 2016). The developing market of remote sensing data requires the industry to define and establish the supply chain management for remote sensing big data. For this purpose, an agricultural remote Fig. 5 Polygons of the two research farms (A and B) of the USDA ARS Crop Production Systems Research Unit in Stoneville, MS, USA on GoogleEarth. 33.455° 33.450° 33.445° 33.440° 33.435° 1:5 000 –90.895° –90.890° –90.885° –90.880° –90.875° –90.870° –90.865° N A B Fig. 6 Original mosaicked RGB (red, green and blue) image and blocked and resampled images of the two farms (A and B) of USDA ARS Crop Production Systems Research Unit with the FLTL (four-layer-twelve-level) scheme in the blocks of 0.0005°×0.0005°.