Literature DB >> 31024191

Inter-comparison of thermal measurements using ground-based sensors, UAV thermal cameras, and eddy covariance radiometers.

Alfonso Torres-Rua1, Hector Nieto2, Christopher Parry3, Manal Elarab4, Wesley Collatz5, Calvin Coopmans1, Lynn McKee3, Mac McKee1, William Kustas3.   

Abstract

With the increasing availability of thermal proximity sensors, UAV-borne cameras, and eddy covariance radiometers there may be an assumption that information produced by these sensors is interchangeable or compatible. This assumption is often held for estimation of agricultural parameters such as canopy and soil temperature, energy balance components, and evapotranspiration. Nevertheless, environmental conditions, calibration, and ground settings may affect the relationship between measurements from each of these thermal sensors. This work presents a comparison between proximity infrared radiometer (IRT) sensors, microbolometer thermal cameras used in UAVs, and thermal radiometers used in eddy covariance towers in an agricultural setting. The information was collected in the 2015 and 2016 irrigation seasons at a commercial vineyard located in California for the USDA Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program. Information was captured at different times during diurnal cycles, and IRT and radiometer footprint areas were calculated for comparison with UAV thermal raster information. Issues such as sensor accuracy, the location of IRT sensors, diurnal temperature changes, and surface characterizations are presented.

Keywords:  UAV; high-resolution; proximity sensors; sensor comparison; surface temperature; thermal sensors

Year:  2018        PMID: 31024191      PMCID: PMC6476550          DOI: 10.1117/12.2305832

Source DB:  PubMed          Journal:  Proc SPIE Int Soc Opt Eng        ISSN: 0277-786X


  3 in total

1.  Estimation of surface thermal emissivity in a vineyard for UAV microbolometer thermal cameras using NASA HyTES hyperspectral thermal, Landsat and AggieAir optical data.

Authors:  Alfonso Torres-Rua; Mahyar Aboutalebi; Timothy Wright; Ayman Nassar; Pierre Guillevic; Lawrence Hipps; Feng Gao; Kevin Jim; Maria Mar Alsina; Calvin Coopmans; Mac McKee; William Kustas
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2019-05-14

2.  Estimation of Evapotranspiration and Energy Fluxes using a Deep-Learning based High-Resolution Emissivity Model and the Two-Source Energy Balance Model with sUAS information.

Authors:  Alfonso Torres-Rua; Andres M Ticlavilca; Mahyar Aboutalebi; Hector Nieto; Maria Mar Alsina; Alex White; John H Prueger; Joseph Alfieri; Lawrence Hipps; Lynn McKee; William Kustas; Calvin Coopmans; Nick Dokoozlian
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2020-05-14

3.  Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances.

Authors:  Jie Xue; Martha C Anderson; Feng Gao; Christopher Hain; Liang Sun; Yun Yang; Kyle R Knipper; William P Kustas; Alfonso Torres-Rua; Mitch Schull
Journal:  Remote Sens Environ       Date:  2020-09-08       Impact factor: 10.164

  3 in total

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