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Estimation of surface thermal emissivity in a vineyard for UAV microbolometer thermal cameras using NASA HyTES hyperspectral thermal, and landsat and AggieAir optical data
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Paper Abstract

Microbolometer thermal cameras in UAVs and manned aircraft allow for the acquisition of highresolution 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.

Paper Details

Date Published: 14 May 2019
PDF: 20 pages
Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 1100802 (14 May 2019); doi: 10.1117/12.2518958
Show Author Affiliations
Alfonso Torres-Rua, Utah State Univ. (United States)
Mahyar Aboutalebi, Utah State Univ. (United States)
Timothy Wright, Antorcha LLC (United States)
Ayman Nassar, Utah State Univ. (United States)
Pierre Guillevic, Univ. of Maryland, Baltimore County (United States)
Lawrence Hipps, Utah State Univ. (United States)
Feng Gao, Agricultural Research Service (United States)
Kevin Jim, Oceanit Labs., Inc. (United States)
Maria Mar Alsina, E&J Gallo Winery (United States)
Calvin Coopmans, Utah State Univ. (United States)
Mac McKee, Utah State Univ. (United States)
William Kustas, Agricultural Research Service (United States)


Published in SPIE Proceedings Vol. 11008:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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