不同真菌病害压力下冬小麦和大麦作物蒸散动态的无人机评估

Obaid-ur-Rehman, Rana Ahmad Faraz Ishaq, Syed Roshaan Ali Shah, Y. Shabbir
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摘要

及早发现作物发病,可以更及时、更有效地进行管理。一旦临床症状可见于人眼,往往为时已晚。为此,使用在红外和热波段(在我们的可见范围之外)工作的传感器在疾病变得可见之前检测疾病是有很大潜力的。基于近红外反射响应的植物指数常用于此。然而,活力是对植物功能的延迟反应。蒸腾速率是植物功能和健康的一个更直接的指标。根据热卫星图像估算的日蒸散发率已在若干研究中得到证实。首次将基于一般过程的物理表面能量平衡(SEBS)方法应用于无人机(UAV)的热成像和RGB图像采集。现场COSMOS-UK气象站数据用于气象输入。采用ILWIS2地表能量平衡系统(SEBS)对日蒸散发进行建模。2015年5月初和6月的每日小麦和大麦ET测量值与从附近谷物田的Landsat图像获得的值相似,更接近Penman-Monteith在调查期间的计算结果。这表明从卫星图像到无人机图像的转换有望用于估算ET。在图像中可以看到不同的空间模式,对应于环境(土壤)、品种和处理(杀菌剂)的差异。大麦在季节的任何阶段都表现出很少的病害压力。在小麦的中后期(5月初),病害压力不明显,但易感品种有明显的后期锈病感染。5月份ET比NDVI更能鉴别非可见感染。此外,需要进一步研究来验证这一概念的证明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UAV Assessment of Crop Evapo-transpiration Dynamics in Winter Wheat and Barley under Varying Pressures of Fungal Diseases
Early detection of the onset of disease in crops allows for more timely and more effective management. Often it is too late to treat diseases once the clinical symptoms are visible to the human eye. To this end, there is great potential to use sensors operating in the infra-red and thermal bands (outside our visible range) to detect diseases before they become visible. Vegetative indices based on near-infrared reflectance responses are often used for this. However, vigor is a delayed response to plant function. The rate of transpiration is a more immediate indicator of plant function and health. Estimations of daily ET rates from thermal satellite imagery have been shown in several studies. The general process-based physical surface energy balance (SEBS) method used was adapted and applied for the first time to Unmanned Aerial Vehicle (UAV) collected thermal and RGB imagery using off-the-shelf low cost camera. On-site COSMOS-UK weather station data were used for meteorological inputs. The ILWIS2 Surface Energy Balance System (SEBS) was used for the daily ET modeling. The daily wheat and barley ET measurements for early May and June 2015 were similar to values obtained from Landsat imagery over nearby cereal fields and closer to the Penman-Monteith calculations for the survey days. This indicates promising transformation from satellite to UAV imagery for estimating ET. Varied spatial patterns were visible in the imagery corresponding to environmental (soil), variety and treatment (fungicide) differences. Barley exhibited little disease pressure at any stage of the season. Disease pressure was not visible at mid-late season (early May) in wheat, but susceptible varieties had visible late season rust infections. ET was a better discriminator of non-visible infections in May than NDVI. Further, studies are required to further validate this proof of concept.
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