Permanent Stations for Calibration/Validation of Thermal Sensors over Spain

Sobrino, José A.; Skokovic, D.

doi:10.3390/data1020010

Cited by 19 publications

(10 citation statements)

References 27 publications

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“…Therefore, there is no universal method that always provides accurate LSTs from all satellite TIR data, and it is not easy to say which algorithm is superior to others [7]. The accuracy of the radiometric measurements and emissivity is the primary uncertainty for ground-based LST retrievals [119][120][121][122][123]. Sobrino and Skoković [119,122] presented an example of an error budget for The Global Change Unit (GCU) sites at the University of Valencia, and Table 8 indicates the impact of the parameter uncertainty ranges on ground-based LST.…”

Section: Discussionmentioning

confidence: 99%

“…The accuracy of the radiometric measurements and emissivity is the primary uncertainty for ground-based LST retrievals [119][120][121][122][123]. Sobrino and Skoković [119,122] presented an example of an error budget for The Global Change Unit (GCU) sites at the University of Valencia, and Table 8 indicates the impact of the parameter uncertainty ranges on ground-based LST. It is interesting to discuss our results in comparison with those of other studies that utilized SURFRAD LST measurements and Landsat data for LST retrieval.…”

Section: Discussionmentioning

confidence: 99%

“…A literature example showing the impact of the parameter uncertainty ranges on ground-based LST, from[119,122].…”

mentioning

confidence: 99%

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Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

2020

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Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA) and Split Window Algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images; fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMSE) equals to 2.39 K and a lower bias error. In addition, the spatio-temporal and seasonal analyses indicated that RTE and SCA presented similar results regardless of the season, while MWA differed from RTE and SCA for all seasons, especially in summer. To efficiently perform this work, an ArcGIS toolbox, including all the methods and models analyzed here, was implemented and provided as a user facility for the LST retrieval from Landsat data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

2020

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“…While surface reflection of downwelling longwave radiation is ignored, Sun et al [32] discussed the effects of these two factors and found that the total effect may be a slight underestimation of the skin temperature. Generally, estimated impact of uncertainty in relevant parameters on in situ LST are as follows: radiometric calibration uncertainty of ±0.2 to 0.5 K can impact LST as much as 0.2 K; emissivity uncertainty of ±1% can impact LST by as much as 0.3 K; downwelling atmospheric radiance uncertainty of ±10% can impact LST by as much as 0.1 K [47].…”

Section: Mod11mentioning

confidence: 99%

Towards a Unified and Coherent Land Surface Temperature Earth System Data Record from Geostationary Satellites

Pinker

Chen

et al. 2019

Remote Sensing

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Our objective is to develop a framework for deriving long term, consistent Land Surface Temperatures (LSTs) from Geostationary (GEO) satellites that is able to account for satellite sensor updates. Specifically, we use the Radiative Transfer for TOVS (RTTOV) model driven with Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) information and Combined ASTER and MODIS Emissivity over Land (CAMEL) products. We discuss the results from our comparison of the Geostationary Operational Environmental Satellite East (GOES-E) with the MODIS Land Surface Temperature and Emissivity (MOD11) products, as well as several independent sources of ground observations, for daytime and nighttime independently. Based on a six-year record at instantaneous time scale (2004–2009), most LST estimates are within one std from the mean observed value and the bias is under 1% of the mean. It was also shown that at several ground sites, the diurnal cycle of LST, as averaged over six years, is consistent with a similar record generated from satellite observations. Since the evaluation of the GOES-E LST estimates occurred at every hour, day and night, the data are well suited to address outstanding issues related to the temporal variability of LST, specifically, the diurnal cycle and the amplitude of the diurnal cycle, which are not well represented in LST retrievals form Low Earth Orbit (LEO) satellites.

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“…The thermal products were obtained directly from the NASA, then they were georeferenced and multiplied by the MOD11 scaling factor of 0.02. Depending on the angle of image acquisition, the error was between 1.5-2 • C, as [48,49] described it.…”

Section: Remote-sensing Datamentioning

confidence: 99%

The Urban Heat Island Effect in the City of Valencia: A Case Study for Hot Summer Days

et al. 2017

Self Cite

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Extreme heat poses significant risks to the world's growing urban population, and the heat stress to human health is likely to escalate with the anthropogenically increased temperatures projected by climate models. Thus, the additional heat from the urban heat island (UHI) effect needs to be quantified, including the spatial pattern. This study focuses on the city of Valencia (Spain), investigating the intensity and spatial pattern of UHI during three consecutive hot summer days accompanying a heat record. For the analysis, long-term in situ measurements and remote sensing data were combined. The UHI effect was evaluated using two approaches: (a) based on air temperature (AT) time-series from two meteorological stations and (b) using land surface temperature (LST) images from MODIS products by NASA with 1 km resolution. The strongest nighttime UHI estimated from AT was 2.3 • C, while the most intense surface UHI calculated as the difference between the LST of urban and rural regions (defined by NDVI) was 2.6 • C-both measured during the night after the record hot day. To assess the human thermal comfort in the city the Discomfort Index was applied. With the increasing number of tropical nights, the mitigation of nighttime UHI is a pressing issue that should be taken into consideration in climate-resilient urban planning.

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Permanent Stations for Calibration/Validation of Thermal Sensors over Spain

Cited by 19 publications

References 27 publications

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

Towards a Unified and Coherent Land Surface Temperature Earth System Data Record from Geostationary Satellites

The Urban Heat Island Effect in the City of Valencia: A Case Study for Hot Summer Days

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