JIA-2018-09

1924 Yanbo Huang et al. Journal of Integrative Agriculture 2018, 17(9): 1915–1931 ranges at longitude in (–90.872°, –90.867°) and latitude in (33.4425°, 33.450°). With the FLTL scheme, the image is divided into six 0.0025°×0.0025° blocks at the fifth level in the second layer (Table 4). After resampling, the entire image has a resolution of 0.28 m/pixel. Such processed and organized images can serve for decision support for local precision agriculture and at the same time the images can be shared and referred in the global perspective. The complexity and frequency of remote sensing monitoring for precision agriculture, especially LARS monitoring, have produced massive volume of data to process and analyze. This is the new horizon of big data from agricultural remote sensing. In the past, the management of the remote sensing data for precision agriculture was organized in file-based systems and separated from processing and analysis tools. With the accumulation of the data from all the dimensions in time (yearly, quarterly, monthly, daily, hourly and even minutely), spatial location and spectral range, the management of the remote sensing data for precision agriculture requires that the data are organized, processed and analyzed in a unified framework so that the stored, processed and analyzed data and products can be shared locally, nationally and even globally. To meet the requirements, the data can be fed into the FLTL framework and streamlined into the flow of data processing, analysis and management as shown in Fig. 3. In the flow image coverage clustering is the key for best use of the images to reduce redundancy and get rid of the coverage of non-agricultural areas. 4. Applications of agricultural remote sensing big data 4.1. China agricultural remote sensing monitoring service China Agriculture Remote Sensing Monitoring System (CHARMS) was originally developed by the Remote Sensing Application Center in the Ministry of Agriculture (MOA) of China (Chen et al. 2011). The system has been operational since 1998. It monitors crop acreage change, yield, production, growth, drought and other agro-information for 7 major crops (wheat, corn, rice, soybean, cotton, canola and sugarcane) in China. The system provides the monitoring information to MOA and related agriculture management sectors according to MOA’s Agriculture Information Dissemination Calendar with more than 100 reports per year. The CHARMS system is a comprehensive operational crop monitoring system in the Remote Sensing Application Center in MOA of China (Fig. 4). The system consists of a database system and six modules for crop acreage change monitoring, crop yield estimation, crop growth monitoring, soil moisture monitoring, disaster monitoring and information service, respectively. Now, the 7 crops as mentioned above are monitored. More crops are being added into this system progressively. The monitoring intervals of the system are every 10 days for crop growth and soil moisture monitoring, every 30 days for crop yield estimation, 20–30 days before harvest for sowing area and yield prediction, every 30 days for grass growth monitoring, and once a year for aquacultural area estimation. A space and ground integrated network system for agricultural data acquisition is operated to coordinate multiple sources of remotely sensed crop parameters from satellites and ground-based systems with WSNs (wireless sensor networks). UAVs have been utilized to capture agricultural data in very low altitudes to complement to the ground-based systems. With the use of the CHARMS system, a series of crop remote sensingmonitoring analysis have been accomplished. Remote sensing data analysis was conducted for the survey of planting areas of rice, wheat, and corn in China using high-resolution satellite imagery such as RapidEye imagery. Table 4 FLTL (four-layer-twelve-level) remote sensing data blocking for precision agriculture Layer Level Block size ( ° ) Sphere dimension (km) Pixel size (m) Scale 1 1 0.05 5.57 5.57 1:50 000 2 0.025 2.78 2.78 1:25 000 3 0.01 1.11 1.11 1:10 000 2 4 0.005 0.56 0.56 1:5 000 5 0.0025 0.28 0.28 1:2 500 6 0.001 0.11 0.11 1:1 000 3 7 0.0005 0.06 0.06 1:500 8 0.00025 0.03 0.03 1:250 9 0.0001 0.01 0.01 1:100 4 10 0.00005 0.006 0.006 1:50 11 0.000025 0.003 0.003 1:25 12 0.00001 0.001 0.001 1:5