Torres-Rua Alfonso, Aboutalebi Mahyar, Wright Timothy, Nassar Ayman, Guillevic Pierre, Hipps Lawrence, Gao Feng, Jim Kevin, Alsina Maria Mar, Coopmans Calvin, McKee Mac, Kustas William
Utah State University, Civil and Env Engineering, Old Main Hill, Logan, Utah 84322, USA.
Utah State University, College of Agriculture, Old Main Hill, Logan, Utah 84322, USA.
Proc SPIE Int Soc Opt Eng. 2019;11008. doi: 10.1117/12.2518958. Epub 2019 May 14.
Microbolometer thermal cameras in UAVs and manned aircraft allow for the acquisition of high-resolution temperature data, which, along with optical reflectance, contributes to monitoring and modeling of agricultural and natural environments. Furthermore, these temperature measurements have facilitated the development of advanced models of crop water stress and evapotranspiration in precision agriculture and heat fluxes exchanges in small river streams and corridors. Microbolometer cameras capture thermal information at blackbody or radiometric settings (narrowband emissivity equates to unity). While it is customary that the modeler uses assumed emissivity values (e.g. 0.99-0.96 for agricultural and environmental settings); some applications (e.g. Vegetation Health Index), and complex models such as energy balance-based models (e.g. evapotranspiration) could benefit from spatial estimates of surface emissivity for true or kinetic temperature mapping. In that regard, this work presents an analysis of the spectral characteristics of a microbolometer camera with regard to emissivity, along with a methodology to infer thermal emissivity spatially based on the spectral characteristics of the microbolometer camera. For this work, the MODIS UCBS Emissivity Library, NASA HyTES hyperspectral emissivity, Landsat, and Utah State University AggieAir UAV surface reflectance products are employed. The methodology is applied to a commercial vineyard agricultural setting located in Lodi, California, where HyTES, Landsat, and AggieAir UAV spatial data were collected in the 2014 growing season. Assessment of the microbolometer spectral response with regards to emissivity and emissivity modeling performance for the area of study are presented and discussed.
无人机和有人驾驶飞机上的微测辐射热计热成像相机能够获取高分辨率温度数据,这些数据与光学反射率一起,有助于对农业和自然环境进行监测与建模。此外,这些温度测量促进了精准农业中作物水分胁迫和蒸散的先进模型以及小河溪流和廊道中热通量交换模型的发展。微测辐射热计相机在黑体或辐射测量设置下捕捉热信息(窄带发射率等于1)。虽然建模人员通常使用假定的发射率值(例如农业和环境设置中为0.99 - 0.96);但一些应用(如植被健康指数)以及基于能量平衡的复杂模型(如蒸散模型)可能会受益于表面发射率的空间估计,以进行真实或动态温度映射。在这方面,本文分析了微测辐射热计相机在发射率方面的光谱特性,并提出了一种基于微测辐射热计相机光谱特性在空间上推断热发射率的方法。在这项工作中,使用了MODIS UCBS发射率库、美国国家航空航天局的HyTES高光谱发射率数据、陆地卫星数据以及犹他州立大学AggieAir无人机表面反射率产品。该方法应用于位于加利福尼亚州洛迪的一个商业葡萄园农业区域,在2014年生长季节收集了HyTES、陆地卫星和AggieAir无人机的空间数据。本文展示并讨论了针对研究区域的微测辐射热计光谱响应在发射率方面的评估以及发射率建模性能。