Laboratory calibration and field measurement of land surface temperature and emissivity using thermal infrared multiband radiometers

Coll, César; Niclòs, Raquel; Puchades, Jesús; García-Santos, Vicente; Galve, Joan M.; Pérez-Planells, Lluís; Valor, E.; Theocharous, E

doi:10.1016/j.jag.2019.02.002

Cited by 15 publications

(13 citation statements)

References 18 publications

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“…Moreover, both radiometers were calibrated against the National Physical Laboratory (NPL) ammonia heat-pipe reference blackbody during the Fiducial Reference Measurements for validation of surface temperature from satellites (FRM4STS) experiment in June 2016. It was obtained a root mean square error (RMSE) between 0.06 and 0.10 K for bands 1 to 3 and a RMSE between 0.13 and 0.23 K for bands 4 to 6 [33,37]. measurements of the sample for each band for the first period and 180 measurements for each band for the second period were used in this study.…”

Section: Cimel Electroniquece312-2mentioning

confidence: 99%

“…The TES method [31] was first devised and extensively applied to ASTER sensor [39][40][41], but because of the similarities between the ASTER and the CE312-2 spectral bands, it can also be applied to the latter using the same empirical equations for both instruments [33,42]. The TES algorithm starts with the normalized emissivity method (NEM) [43] using the at-surface and the downwelling radiance measurements taken by the five ASTER TIR channels.…”

Section: Tes Algorithmmentioning

confidence: 99%

“…Second, to compare and evaluate the four RTM-type algorithms described above using these in situ data, in order to investigate their predictive power in the assessment of canopy emissivity. All emissivity measurements were acquired using the temperature-emissivity separation (TES) algorithm [32] adapted to multiband ground radiometers CIMEL Electronique CE312-2 [33].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Six Directional Canopy Emissivity Models in the Thermal Infrared Using Emissivity Measurements

et al. 2019

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Land surface temperature (LST) is a fundamental physical quantity in a range of different studies, for example in climatological analyses and surface–atmosphere heat flux assessments, especially in heterogeneous and complex surfaces such as vegetated canopies. To obtain accurate LST values, it is important to measure accurately the land surface emissivity (LSE) in the thermal infrared spectrum. In the past decades, different directional emissivity canopy models have been proposed. This paper evaluates six radiative transfer models (FR97, Mod3, Rmod3, 4SAIL, REN15, and CE-P models) through a comparison with in situ emissivity measurements performed using the temperature-emissivity separation (TES) method. The evaluation is done using a single set of rose plants over two different soils with very different spectral behavior. First, using an organic soil, the measurements were done for seven different observation angles, from 0° to 60° in steps of 10°, and for six different values of leaf area index (LAI). Taking into account all LAIs, the bias (and root mean square error, RMSE) obtained were 0.003 (±0.006), −0.004 (±0.005), −0.009 (±0.011), 0.005 (±0.007), 0.004 (±0.007), and 0.005 (±0.007) for FR97, Mod3, Rmod3, 4SAIL, REN 15, and CE-P models, respectively. Second, using an inorganic soil, the measurements were done for six different LAIs but for two different observation angles: 0° and 55°. The bias (and RMSE) obtained were 0.012 (±0.014), 0.004 (±0.007), −0.020 (±0.035), 0.016 (±0.017), 0.013 (±0.015), 0.013 (±0.015) and for FR97, Mod3, Rmod3, 4SAIL, REN15, and CE-P models, respectively. Overall, the Mod3 model appears as the best model in comparison to the TES emissivity reference measurements.

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Section: Cimel Electroniquece312-2mentioning

confidence: 99%

Section: Tes Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Six Directional Canopy Emissivity Models in the Thermal Infrared Using Emissivity Measurements

et al. 2019

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“…With the rapid development of detective technology, infrared emissivity has become an important indicator [1], widely used in the military [2]. Infrared low-emissivity materials have become an important research topic [3], and the corresponding coating on the surface of such equipment has become a convenient and efficient choice [4], which can prevent the equipment from buckling, deformation, and being detected [5]. An infrared low-emissivity coating plays important roles in both civil and military fields; for instance, camouflaging military equipment or vehicles from infrared detection.…”

Section: Introductionmentioning

confidence: 99%

The Properties of an Aluminum/UV-Curable, Infrared, Low-Emissivity Coating Modified by Nano-Silica Slurry

Yan¹,

Chang²,

Qian³

2020

Coatings

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To improve the performances of UV-curable coatings, the effects of nano-silica slurry, aluminum and UV-curing time on the glossiness and infrared emissivity of UV-curable coatings were investigated by orthogonal experiments. The results showed that UV-curing time is a key factor affecting the performance of the coating. When the UV-curing time was increased from 30 to 360 s, the glossiness of the UV-curable coating slowly decreased from 11.1% to 9.0%. The L’ value decreased from 78.6 to 75.0. The infrared emissivity of the coating with UV-curing time of 180 s was 0.106, which was the lowest. The coating hardness with different curing time was 6H. The coating roughness was high when the UV-curing time was 30–120 s. When the UV-curing time was greater than 300 s, the coating adhesion was 0 and the coating had the best impact resistance of 500 N/cm. The overall performance of the aluminum/UV-curable coating was optimal when the UV-curing time was 180 s. This research is able to promote the industrial development of UV-curable, infrared, low-emissivity coatings.

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“…One of the most challenging aspects of these intercomparisons in the field is the difficulty to find naturally homogeneous sites compared to well-controlled laboratory-based comparisons. Recently, a few attempts have been made to assess the uncertainties in situ LST under laboratory and field conditions during fiducial reference measurements for validation of surface temperature from Satellites (FRM4STS) experiment in 2016 [ 31 , 32 ] and field inter-comparison experiment (FICE) in 2017 [ 33 ]. However, these experiments utilized only directional narrow angle IR radiometers for a short duration.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of In Situ Sensors for Ground-Based Land Surface Temperature Measurements

Krishnan

Meyers

Hook

et al. 2020

Sensors

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Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA’s Air Resources Laboratory (ARL)’s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

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Laboratory calibration and field measurement of land surface temperature and emissivity using thermal infrared multiband radiometers

Cited by 15 publications

References 18 publications

Evaluation of Six Directional Canopy Emissivity Models in the Thermal Infrared Using Emissivity Measurements

Evaluation of Six Directional Canopy Emissivity Models in the Thermal Infrared Using Emissivity Measurements

The Properties of an Aluminum/UV-Curable, Infrared, Low-Emissivity Coating Modified by Nano-Silica Slurry

Intercomparison of In Situ Sensors for Ground-Based Land Surface Temperature Measurements

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