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Implications of sensor inconsistencies and remote sensing error in the use of small unmanned aerial systems for generation of information products for agricultural management

机译：在使用小型无人航空系统生产用于农业管理的信息产品时传感器不一致和遥感误差的影响

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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 USD A 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.
机译：用于遥感的小型无人航空系统（sUAS）代表了一种相对较新的，正在发展的技术，可以支持农业运营决策。这些系统的大小和功率限制给可携带的传感器的重量，大小和功能以及可能的地理覆盖范围带来了挑战。这些因素以及缺乏传感器技术，其部署和数据分析的标准，导致数据质量的不确定性，可能难以检测或表征。这些反过来限制了来自不同来源的数据之间的可比性，更重要的是，这意味着对可以使用sUAS采集的数据完成的分析的限制。本文提供了一系列传感器数据不确定性问题对信息产品的影响的简单统计检验。该分析基于2016年在加利福尼亚洛迪附近的商业葡萄园中收集的高分辨率数据，用于美国农业研究服务葡萄遥感大气廓线和蒸散实验（GRAPEX）计划。蒙特卡洛分析提供了传感器光谱响应和/或矫正精度的不确定性如何影响种植者潜在兴趣的信息产品的估计，如常见植被指数的形式所示。

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