A UAS-based RF testbed for water utilization in agroecosystems
A UAS-based RF testbed for water utilization in agroecosystems
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基于 UAS 的农业生态系统用水射频测试台
DOI：
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发表时间：
2021
期刊：
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通讯作者：
V. Senyurek
中科院分区：
文献类型：
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作者：
M. Kurum;A. Gurbuz;Spencer Barnes;D. Boyd;Matthew Duck;M. Farhad;Austin Flynt;Nathan Goyette;Preston Peranich;M. Scheider;V. Senyurek
农业生态系统构成了以农业为主的社会的大部分经济部门。水资源的供应和管理对农业生态系统的可持续性有着巨大的影响。土壤水分低是作物生长的主要制约因素，因为它在为作物提供充足的营养以供根系吸收方面起着至关重要的作用。目前的精确农业方法不足以直接检测土壤湿度，因为反射的短波太阳辐射和红外长波辐射只能提供关于地表特征的信息。虽然已知微波信号对植物和土壤中的水分高度敏感，但与短波/长波光学传感器的使用相比，小型无人机系统（UAS）平台的实现处于相对较低的技术准备水平。在本文中，我们总结了我们的努力，应用射频（RF）/微波遥感无人机在农业生态系统中的水利用。最近，我们开发了一个全面的基于无人机的射频测试平台，包括微波辐射计，散射计，宽带探地雷达系统以及机会信号（SoOp）接收器。这些仪器在无人机系统平台上运行，使用频谱的微波/无线电波部分。该测试平台通过自主无人地面车辆进行近端传感，这些车辆获取现场土壤水分和植被地球物理参数，为训练和测试物理感知的机器学习模型提供适当的数据集。在本文中，我们介绍了RF传感框架，可以通过基于UAS的有源/无源/ SoOp RF仪器在土壤的多个深度进行非侵入式高分辨率土壤水分估计。
Agroecosystems compose large economic sectors in dominantly agriculture-based societies. Availability and management of water resources have a huge influence on the sustainability of agroecosystems. Low soil moisture is a major constraint on crop growth due to its vital role in providing crops with sufficient nutrition for root uptake. Current methodologies in precision agriculture are insufficient for direct soil moisture sensing since reflected shortwave solar radiation and infrared long-wave emission can only provide information about surface characteristics. While microwave signals are known to be highly sensitive to water within plants and soil, its implementation from small Unmanned Aircraft Systems (UAS) platforms are at relatively low technological readiness level compared to the use of shortwave / longwave optical sensors. In this paper, we summarize our efforts to apply radio frequency (RF) / microwave remote sensing from UAS for water utilization in agroecosystems. Recently, we developed a comprehensive UAS-based RF testbed, including a microwave radiometer, a scatterometer, wideband ground penetrating radar system as well as Signals of Opportunity (SoOp) receivers. These instruments operate from UAS platforms and use the microwave / radio wave portions of the spectrum. The testbed is accompanied with proximal sensing via autonomous unmanned ground vehicles that acquire in- situ soil moisture and vegetation geophysical parameters to provide appropriate datasets for training and testing physics aware, machine learning-based models. In this paper, we introduce the RF sensing framework that can enable non-intrusive high-resolution soil moisture estimates at multiple depths of soil via UAS-based active / passive / SoOp RF instruments.