In-situ Measurements of Surface Temperature Fields on Ring-Block Contact Surface under Friction Using an Infrared Thermography

2009 ◽  
pp. 744-747
Author(s):  
Tao You ◽  
Jianwei Yu ◽  
Xiaofen Yu
Keyword(s):  
Surface Temperature ◽  
Infrared Thermography ◽  
Contact Surface ◽  
In Situ Measurements ◽  
Temperature Fields

scholarly journals Validation of Collection 6 MODIS land surface temperature product using in situ measurements

2019 ◽  
Vol 225 ◽  
pp. 16-29 ◽  
Author(s):  
Si-Bo Duan ◽  
Zhao-Liang Li ◽  
Hua Li ◽  
Frank-M. Göttsche ◽  
Hua Wu ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
In Situ Measurements ◽  
Modis Land Surface Temperature

scholarly journals Comparison of Satellite-Based Sea Surface Temperature to In Situ Observations Surrounding Coral Reefs in La Parguera, Puerto Rico

2020 ◽  
Vol 8 (6) ◽  
pp. 453
Author(s):  
Andrea M. Gomez ◽  
Kyle C. McDonald ◽  
Karsten Shein ◽  
Stephanie DeVries ◽  
Roy A. Armstrong ◽  
...  
Keyword(s):  
Puerto Rico ◽  
Coral Reefs ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Thermal Stresses ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Strong Positive Correlation ◽  
La Parguera

Coral reefs are among the most biologically diverse ecosystems on Earth. In the last few decades, a combination of stressors has produced significant declines in reef expanse, with declining reef health attributed largely to thermal stresses. We investigated the correspondence between time-series satellite remote sensing-based sea surface temperature (SST) datasets and ocean temperature monitored in situ at depth in coral reefs near La Parguera, Puerto Rico. In situ temperature data were collected for Cayo Enrique and Cayo Mario, San Cristobal, and Margarita Reef. The three satellite-based SST datasets evaluated were NOAA’s Coral Reef Watch (CoralTemp), the UK Meteorological Office’s Operational SST and Sea Ice Analysis (OSTIA), and NASA’s Jet Propulsion Laboratory (G1SST). All three satellite-based SST datasets assessed displayed a strong positive correlation (>0.91) with the in situ temperature measurements. However, all SST datasets underestimated the temperature, compared with the in situ measurements. A linear regression model using the SST datasets as the predictor for the in situ measurements produced an overall offset of ~1 °C for all three SST datasets. These results support the use of all three SST datasets, after offset correction, to represent the temperature regime at the depth of the corals in La Parguera, Puerto Rico.

scholarly journals A Multivariate Regression Approach to Adjust AATSR Sea Surface Temperature to In Situ Measurements

2009 ◽  
Vol 6 (1) ◽  
pp. 8-12 ◽  
Author(s):  
Pierre Tandeo ◽  
Emmanuelle Autret ◽  
Jean FranÇois Piolle ◽  
Jean Tournadre ◽  
Pierre Ailliot
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Multivariate Regression ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Regression Approach

scholarly journals Reconstruction of super-resolution ocean <i>p</i>CO<sub>2</sub> and air–sea fluxes of CO<sub>2</sub> from satellite imagery in the southeastern Atlantic

2015 ◽  
Vol 12 (17) ◽  
pp. 5229-5245 ◽  
Author(s):  
I. Hernández-Carrasco ◽  
J. Sudre ◽  
V. Garçon ◽  
H. Yahia ◽  
C. Garbe ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Partial Pressure ◽  
Sea Surface Temperature ◽  
Ocean Color ◽  
Super Resolution ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Co2 Fluxes ◽  
Partial Pressure Of Co2

Abstract. An accurate quantification of the role of the ocean as source/sink of greenhouse gases (GHGs) requires to access the high-resolution of the GHG air–sea flux at the interface. In this paper we present a novel method to reconstruct maps of surface ocean partial pressure of CO2 ( pCO2) and air–sea CO2 fluxes at super resolution (4 km, i.e., 1/32° at these latitudes) using sea surface temperature (SST) and ocean color (OC) data at this resolution, and CarbonTracker CO2 fluxes data at low resolution (110 km). Inference of super-resolution pCO2 and air–sea CO2 fluxes is performed using novel nonlinear signal processing methodologies that prove efficient in the context of oceanography. The theoretical background comes from the microcanonical multifractal formalism which unlocks the geometrical determination of cascading properties of physical intensive variables. As a consequence, a multi-resolution analysis performed on the signal of the so-called singularity exponents allows for the correct and near optimal cross-scale inference of GHG fluxes, as the inference suits the geometric realization of the cascade. We apply such a methodology to the study offshore of the Benguela area. The inferred representation of oceanic partial pressure of CO2 improves and enhances the description provided by CarbonTracker, capturing the small-scale variability. We examine different combinations of ocean color and sea surface temperature products in order to increase the number of valid points and the quality of the inferred pCO2 field. The methodology is validated using in situ measurements by means of statistical errors. We find that mean absolute and relative errors in the inferred values of pCO2 with respect to in situ measurements are smaller than for CarbonTracker.

scholarly journals Sensitivity Analysis and Validation of Daytime and Nighttime Land Surface Temperature Retrievals from Landsat 8 Using Different Algorithms and Emissivity Models

2020 ◽  
Vol 12 (17) ◽  
pp. 2776 ◽  
Author(s):  
Aliihsan Sekertekin ◽  
Stefania Bonafoni
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
The United States ◽  
In Situ Measurements ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Sensitivity Analyses ◽  
Landsat 8

Land Surface Temperature (LST) is a substantial element indicating the relationship between the atmosphere and the land. This study aims to examine the efficiency of different LST algorithms, namely, Single Channel Algorithm (SCA), Mono Window Algorithm (MWA), and Radiative Transfer Equation (RTE), using both daytime and nighttime Landsat 8 data and in-situ measurements. Although many researchers conducted validation studies of daytime LST retrieved from Landsat 8 data, none of them considered nighttime LST retrieval and validation because of the lack of Land Surface Emissivity (LSE) data in the nighttime. Thus, in this paper, we propose using a daytime LSE image, whose acquisition is close to nighttime Thermal Infrared (TIR) data (the difference ranges from one day to four days), as an input in the algorithm for the nighttime LST retrieval. In addition to evaluating the three LST methods, we also investigated the effect of six Normalized Difference Vegetation Index (NDVI)-based LSE models in this study. Furthermore, sensitivity analyses were carried out for both in-situ measurements and LST methods for satellite data. Simultaneous ground-based LST measurements were collected from Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) stations, located at different rural environments of the United States. Concerning the in-situ sensitivity results, the effect on LST of the uncertainty of the downwelling and upwelling radiance was almost identical in daytime and nighttime. Instead, the uncertainty effect of the broadband emissivity in the nighttime was half of the daytime. Concerning the satellite observations, the sensitivity of the LST methods to LSE proved that the variation of the LST error was smaller than daytime. The accuracy of the LST retrieval methods for daytime Landsat 8 data varied between 2.17 K Root Mean Square Error (RMSE) and 5.47 K RMSE considering all LST methods and LSE models. MWA with two different LSE models presented the best results for the daytime. Concerning the nighttime accuracy of the LST retrieval, the RMSE value ranged from 0.94 K to 3.34 K. SCA showed the best results, but MWA and RTE also provided very high accuracy. Compared to daytime, all LST retrieval methods applied to nighttime data provided highly accurate results with the different LSE models and a lower bias with respect to in-situ measurements.

Surface temperature and radiation budget of snow-covered complex terrains

2021 ◽  
Author(s):  
Alvaro Robledano ◽  
Ghislain Picard ◽  
Laurent Arnaud ◽  
Fanny Larue ◽  
Inès Ollivier
Keyword(s):  
Surface Temperature ◽  
Single Channel ◽  
Spatial Variations ◽  
In Situ Measurements ◽  
Surface Energy Budget ◽  
Radiation Budget ◽  
Atmospheric Effects ◽  
Landsat 8 ◽  
The Impact

&lt;p&gt;The temporal evolution of the snowpack is controlled by the surface temperature, which plays a key role in physical processes such as snowmelt. It shows large spatial variations in mountainous areas, where the illumination conditions are variable and depend on the topography. The surface energy budget is affected by the particular processes that occur in these areas, such as the modulation of the illumination by local slope and the re-illumination of the surface from surrounding slopes. These topography effects are often neglected in models, considering the surface as flat and smooth. Here we aim at estimating the surface temperature and the radiation budget of snow-covered complex terrains, in order to evaluate the role of the different processes that control their spatial variations. For this, a modelling chain is implemented to derive surface temperature from in-situ measurements. The main component is the Rough Surface Ray-Tracing (RSRT) model, based on a photon transport algorithm to quantify the impact of surface roughness in snow-covered areas. It is coupled to a surface scheme in order to estimate the radiation budget. To validate the model, we use in-situ measurements and satellite thermal observations (TIRS sensor aboard Landsat-8) in the Col du Lautaret area, in the French Alps. The satellite images are corrected from atmospheric effects with a single-channel algorithm. The results of the simulations show (i) an agreement between the simulated and observed surface temperature for a diurnal cycle in winter; (ii) the spatial variations of surface temperature are on the order of 5 to 10&amp;#176;C between opposed slope orientations; (iii) the agreement with satellite observations is improved when considering topography effects. It is therefore necessary to account for these effects to estimate the spatial variations of the radiation budget and surface temperature over snow-covered complex terrain.&amp;#160;&lt;/p&gt;

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