Implications of Sensor Inconsistencies and Remote Sensing Error in the Use of Small Unmanned Aerial Systems for Generation of Information Products for Agricultural Management
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
International Society for Optical Engineering
NASA, National Aeronautics and Space Administration NNX17AF51G
NASA, National Aeronautics and Space Administration
Small, unmanned aerial systems (sUAS) for remote sensing represent a relatively new and growing technology to support decisions for agricultural operations. The size and power limitations of these systems present challenges for the weight, size, and capability of the sensors that can be carried, as well as the geographical coverage that is possible. These factors, together with a lack of standards for sensor technology, its deployment, and data analysis, lead to uncertainties in data quality that can be difficult to detect or characterize. These, in turn, limit comparability between data from different sources and, more importantly, imply limits on the analyses that can be accomplished with the data that are acquired with sUAS. This paper offers a simple statistical examination of the implications toward information products of an array of sensor data uncertainty issues. The analysis relies upon high-resolution data collected in 2016 over a commercial vineyard, located near Lodi, California, for the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) Program. A Monte Carlo analysis is offered of how uncertainty in sensor spectral response and/or orthorectification accuracy can affect the estimation of information products of potential interest to growers, as illustrated in the form of common vegetation indices.
Mac McKee, Ayman Nassar, Alfonso Torres-Rua, Mahyar Aboutalebi, and William Kustas "Implications of sensor inconsistencies and remote sensing error in the use of small unmanned aerial systems for generation of information products for agricultural management", Proc. SPIE 10664, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, 1066402 (21 May 2018); http://dx.doi.org/10.1117/12.2305826
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