JIA-2018-09

1917 Yanbo Huang et al. Journal of Integrative Agriculture 2018, 17(9): 1915–1931 agricultural remote sensing is that it not only helps manage planning and management of agricultural production strategically at regional, national and even global scales, but also provides control information tactically for precision agricultural operations at the scale of farm fields. Therefore, agricultural remote sensing may produce higher spatial and temporal resolution data. In recent years, unmanned aerial vehicle (UAV) has become a unique platform for agricultural remote sensing to provide coverage of crop fields with multiple images from very low altitude (Huang et al. 2013b). The images can, in turn, be converted into not only a two dimensional visualization of the field but also a three- dimensional surface representation of the field (Huang and Reddy 2015). So, UAV-based agricultural remote sensors are contributing significantly to agricultural remote sensing big data. UAV-based remote sensing is a special kind of airborne remote sensing with possible monitoring of crop fields at ultra-low altitude. How to rapidly and effectively process and apply the data acquired from UAV agricultural remote sensing platforms is being studied widely at present (Huang et al. 2013b; Suomalainen et al. 2014; Candiago et al. 2015). 3. Management of agricultural remote sensing big data 3.1. Remote sensing data processing and products Remote sensing data have to be processed before it can be used. In general, it is not suggested to use raw images directly acquired from remote sensors on satellites and aircraft because the data have to be corrected due to deformations from interactions between sensors, atmospheric conditions and terrain profiles. The corrections typically include radiometric and geometric corrections. A complete radiometric correction is related to the sensitivity of the remote sensor, topography and sun angle, and atmospheric scattering and absorption. The atmospheric correction is difficult for agronomists and agro-technicians, in general, because it requires the data and information of atmospheric conditions during image acquisition. However, the data and information typically vary with time and location. The geometric correction is aimed at correcting squeezing, twisting, stretching and shifting of remotely sensed image pixels relative to the actual position on the ground, which are caused by remote sensing platform’s angle, altitude and speed, sensitivity of the remote sensor, and earth surface topography and sun angle, atmospheric scattering and absorption, and rotation of the earth. The raw and corrected remote sensing images can be summarized into data products at different levels as explained in general in Table 1 (Di and Kobler 2000; Piwowar 2001). The programs of MODIS (moderate resolution imaging spectroradiometer) (National Aeronautics and Space Administration (NASA), Washington, D.C.), Landsat (NASA and United States Geological Survey (USGS), Reston, VA), European satellites such as SPOT (SPOT Image, Toulouse, France), and Chinese satellites such as Ziyuan (China Centre for Resources Satellite Data andApplication, Beijing, China) all provide products at different levels more or less depending on different applications. Table 2 shows the remote sensing data characteristics of the main medium- and high-resolution land satellites of different countries in the world. Besides there are ocean observation satellite, such as OrbView of United States, and meteorological satellites, such as AVHRR (Advanced Very High Resolution Radiometer), NOAA (National Oceanic and Atmospheric Administration, Washington, D.C.) of United States and FY-3A/B of China. Thus, as illustrated, one remote sensing image can have many products at different levels and the same image product could be resampled up or down scale to meet the practical requirements and transformed for different applications so that the volume and complexity of remote sensing data are rapidly increased as big data. Table 1 Product levels of satellite remote sensing data Level Product description 0 Reconstructed, unprocessed instrument and payload data at full resolution, with any and all communication artifacts (e.g., synchronization frames, communications headers, duplicate data) removed 1A Reconstructed, unprocessed instrument data at full resolution, time-referenced, and annotated with ancillary information, including radiometric and geometric calibration coefficients and georeferencing parameters (e.g., platform ephemeris) computed and appended but not applied to the Level 0 data (or if applied, in a manner that Level 0 is fully recoverable from Level 1A data) 1B Level 1A data that have been processed to sensor units (e.g., radar backscatter cross section, brightness temperature, optical, etc.); not all instruments have Level 1B data; Level 0 data are not recoverable from Level 1B data 2 Derived agro-geophysical variables (e.g., ocean wave height, ice concentration, soil moisture/temperature, canopy temperature, etc.) at the same resolution and location as Level 1A source data 3 Variables mapped on uniform spatial grid scales, usually with some completeness and consistency (e.g., missing points interpolated, complete regions mosaicked together from multiple orbits, etc.) 4 Model output or results from analyses of lower level data (i.e., variables that were not measured by the instruments but instead are derived from these measurements)