Uncrewed aerial vehicle (UAV) thermal infrared (TIR) remote sensing is playing an increasingly important role in diverse applications such as agriculture, forestry, hydrology, and ecological monitoring. Moreover, UAV-based remote sensing significantly contributes to the understanding of fundamental remote sensing science issues, such as scale variation and its impacts on multisource data collaboration. To cover extensive areas, UAVs often need to fly multiple strips to ensure complete coverage, leading to significant intervals between the start and end of missions. This can cause notable changes in brightness temperature (BT) due to the different observation times, introducing considerable uncertainty into the final BT mosaic, which, in turn, directly impacts subsequent applications, such as the calculation of land surface temperature (LST) and other temperature-related analyses. This study presents a temporal effect removal of LST (TERL) method to effectively correct for differences due to observation time and thereby enhance the temporal consistency of BT data. The core of TERL involves three key processes: 1) modeling the temporal information of TIR mosaic pixels; 2) deriving the temporal dynamics related to specific surface features by classifying image sequences; and 3) capturing the temporal variation of BT differences and temperature compensation. Validation results indicate that TERL significantly improves both the temporal comparability of pixels and the consistency between UAV temperature data and ground observations. Specifically, the root-mean-square error (RMSE) of the corrected data is 22.50%–77.14% smaller than that of the uncorrected data, with an impressive average reduction of 51.09%. Compared to the digital number probability density function fitting and radiative transfer simulation-based (DRAT) method, which primarily addresses temperature drift, TERL achieves an average RMSE reduction of 29.65%, showcasing its better performance. Moreover, the corrected data better reflect the temperature variation trends of surface features and show strong correlations with ground observation data, with most correlation coefficients exceeding 0.5. Thus, TERL facilitates more accurate comparisons and analyses of UAV TIR data, ultimately enhancing not only the reliability and effectiveness of quantitative remote sensing research with UAVs but also advancing the understanding of fundamental issues like scale variation in remote sensing science.

Land surface temperature (LST) retrieved from satellite data is a crucial parameter in the energy exchange between the Earth’s surface and the atmosphere and for environmental monitoring. One important application is permafrost modelling, where LST is a key input layer. Permafrost modelling requires continuous high spatiotemporal LST information to capture the heterogeneous nature of Arctic land cover accurately. Recent advances in spatiotemporal fusion and super-resolution models offer new solutions to downscale thermal infrared (TIR) data and, therefore, allow obtaining LST data at a high spatial and temporal resolution. This study compares the performances of different models to increase the spatial resolution of Advanced Very High Resolution Radiometer (AVHRR) LST data in the Arctic.

Land Surface Emissivity (LSE) is a critical variable in the quantification of the surface energy budget and for the estimation of surface parameters from earth observation data, in particular the Land Surface Temperature (LST). A new LSE product is proposed that combines two widely used methods: the Vegetation Cover Method (VCM) and the Temperature Emissivity Separation (TES) algorithm. The so-called V-TES approach maximizes the strengths of each method, considering their different performance over a wide range of surface conditions. As such, over vegetated areas, where thermal spectral contrasts are low and retrievals using TES are less accurate, we use the VCM method, while over bare areas, where the VCM relies entirely on ancillary information, the TES method is preferred. The proposed methodology was applied to observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG) satellites to derive emissivity channel and broad-band emissivities in the 3-14 μm range. Daily LSE maps are then derived using estimates of fraction of vegetation cover and snow cover. The product shows good agreement with in-situ data, with accuracies of 0.009 and 0.014 in the 8-14 μm and 3-8 μm regions, respectively. The methodology described in this article will be used to improve LST estimates and will be applied by the LSA-SAF for LST production from EUMETSAT’s Meteosat Second and Third Generation (MSG/MTG) and the Polar System-Second Generation (EPS-SG) missions.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station (ISS) on June 29, 2018, and currently provides the highest spatial resolution thermal infrared (TIR) data (38 m$\times \,\, 69$m) available from space. In this study, we validated the ECOSTRESS level-2 Land Surface Temperature (LST) and emissivity product at fourteen global sites to Stage-1 status. Two primary methods are recommended for the validation of LST data: Temperature-based (T-based) and Radiance-based (R-based) methods. The T-based method requires calibrated measurements of the ground leaving radiance concurrent with the satellite overpass. In contrast, the R-based method uses a radiative closure simulation with external atmospheric profiles and an$a$prioriknowledge of surface emissivity. Using these standard methods, we validated 1139 ECOSTRESS clear-sky observations between August 1, 2018, and March 31, 2020. For LST, the results show good agreement with ground-based measurements with an average root mean square error (RMSE) of 1.07 K, mean absolute error (MAE) of 0.40 K, and$r^{2}>0.988$at all sites. However, a cold bias of ~0.75 K was identified for temperatures below 295 K linked to calibration issues that will be addressed in future reprocessing of the data. Retrieved emissivity comparisons with laboratory spectra had an RMSE of 0.023 (2.3%) for all bands on average. With the decommissioning of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on Terra in 2023, the multispectral and high-spatial-resolution characteristics of ECOSTRESS data serve as a pathfinder to the National Aeronautics and Space Administration’s (NASA) Surface Biology and Geology (SBG) designated observable with an expected launch in 2026.

Land surface emissivity (LSE) is a key variable for land surface temperature (LST) retrieval from satellite data. In this study, five emissivity radiative transfer models (RTMs) for vegetation canopies are investigated together with a classification-based methodology to produce canopy LSE maps over the Iberian Peninsula with EOS Aqua – MODIS data. The five canopy RTMs are: FR97, Mod3, REN15, 4SAIL and CE-P. The analysis of the RTMs performance with satellite data gain interest over partially vegetated surfaces, for which these models can obtain accurate emissivities. The sensitivity analyses showed that FR97, REN15, 4SAIL and CE-P models have higher uncertainty for low LAIs, while Mod3 model increase the uncertainty with LAI. The produced LSEs were first intercompared with emissivities from the MODIS MYD21A1 product, which is obtained with the Temperature and Emissivity Separation (TES) method. The RTMs agreed with the TES emissivities within the given uncertainty. Additionally, the RTM emissivities were compared with MYD11A1 and MYD11B1 MODIS products and the IREMIS and CAMEL databases, and they were used to estimate the LST at three specific homogeneous sites: shrubland, vineyard and olive orchard. The LSTs estimated with each modelled emissivity and emissivity products were validated against reference data at these sites. All RTMs provided accurate LST data, equal or even better than the MODIS products, with median values of differences between -0.7 and 0.4 K depending on the site. Therefore, the canopy emissivity RTMs used in this study, together with the classification-based methodology, showed to be suitable for satellite LST retrieval.

Land surface temperature (LST) is a key variable at the land-atmosphere boundary. For many research projects and applications an all-weather LST product at moderate spatial resolution (e.g., 1 km) would be highly useful, especially in frequently cloudy areas. Merging thermal infrared (TIR) and microwave (MW) observations is able to overcome shortcomings of single-source remote sensing to derive such an LST. However, in current merging methods, models adopted for downscaling MW LST fail to quantify the effect of temporal variation of LST. Thus, accuracy of the merged LST can be deteriorated and therefore remain a major impediment for these methods to be generalized over large areas. In this context, we propose a new practical method to merge TIR and MW observations from a perspective of decomposition of LST in temporal dimension. The physical basis of the method is decomposing LST into three temporal components: annual temperature cycle component, diurnal temperature cycle component prescribed by solar geometry, and weather temperature component driven by weather change. The method was applied to MODIS and AMSR-E/AMSR2 data to generate an 11-year record of 1-km all-weather LST over Northeast China: the resulting merged LST has an accuracy of 1.29-1.71 K when validated against in situ LST; besides, no obvious differences in accuracy of the merged LST were found between clear-sky and unclear-sky conditions. Furthermore, the proposed method outperforms the previous method in both accuracy and image quality, indicating its good capability to generate daily 1-km all-weather LST, which will benefit continuous monitoring of earth's surface temperature.

Satellite passive microwave (MW) remote sensing has a better ability to observe land surface temperature (LST) in cloudy conditions than thermal infrared (TIR) remote sensing. Due to the much greater thermal sampling depth (TSD) of MW, currently available MW LST do not represent the thermodynamic temperature of the land surface and, therefore, yield systematic differences from TIR LST. The TSD effect is particularly prominent over barren land and sparsely vegetated surfaces. Here, we present a novel TSD correction (TSDC) method to estimate the MW LST over barren land. The core of this method is a new formulation of the passive MW radiation balance equation, which allows linking MW effective physical temperature to the soil temperature at a specific depth. The TSDC method is applied to the 6.9-GHz channel of AMSR-E in northwestern China-western Mongolia and western Namibia (WN). Evaluation shows that LST estimated by the TSDC method agrees well with the MODIS LST. Validation based on in situ LSTs measured at the Gobabeb site in WN demonstrates the high accuracy of the TSDC method: it yields a root mean squared error of about 2-3 K and slight systematic error. In contrast, other methods without TSDC yield lower accuracies and significantly underestimate LST. Therefore, the TSDC method has the potential to generate MW LST with the same physical meaning and similar accuracy as TIR LST. This study provides implications for developing practical and accurate methods to estimate MW LST over other land surface types and at the global scale.

In this paper, we analyze the feasibility of applying the temperature and emissivity separation (TES) algorithm to thermal-infrared data acquired with three bands of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation platform (SEVTES). The performance of the SEVTES algorithm was tested using data simulated over different atmospheric conditions and surface emissivities, with errors around 1.5% for emissivity and 1.5 K for temperature when atmospheric correction is accurate enough. In contrast, errors on land-leaving radiances higher than 2% or uncertainties on total atmospheric water vapor amount higher than 5% lead to errors on emissivity higher than 2% and errors on land surface temperature higher than 3 K, especially when the atmospheric absorption is overestimated. SEVIRI data acquired in August 2011 were also used to validate SEVTES emissivities against in situ measurements collected in five different homogeneous areas over Africa. Values were also intercompared to Moderate Resolution Imaging Spectroradiometer (MODIS)-derived and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-derived emissivities and to the LSA SAF emissivity product. Results show that SEVTES-derived emissivity values are consistent with MODIS-TES and ASTER-TES retrievals and that SEVTES also improves the retrievals included in LSA SAF and MOD11Cx v5 products. When compared to laboratory measurements, accuracies of around 1%-2% were obtained, although occasional inaccuracies (2%-3%) were also found in some cases at band 8.7 μm. The results presented in this paper show the potential SEVTES has for improving the LSA SAF product over arid and semiarid areas.

Validation of the land surface temperature (LST) algorithm and product is a challenging task for future Geostationary Operational Environmental Satellite R-Series (GOES-R) applications. Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) full-disk data have been used as the key proxy data for the GOES-R LST algorithm and product development. A split window algorithm developed to generate GOES-R LST was applied to MSG SEVIRI data with the algorithm coefficients adjusted to the specific SEVIRI bands. The retrieved LST values were evaluated with in situ LST obtained from four validation stations with different surface features over various time periods. The results presented here clearly highlight the importance of accurate and seasonally representative site characterizations for the LST validation process. Furthermore, the study gives valuable insights into the limitations of the current version of the LST retrieval algorithm and on how to further refine it for the next generation of satellite sensors.

Thermal infrared satellite observations of the Earth's surface are key components in estimating the surface skin temperature over global land areas. This work presents validation methodologies to estimate the quantitative uncertainty in Land Surface Temperature (LST) product derived from the Visible Infrared Imager Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (NPP) using ground-based measurements currently made operationally at many field and weather stations around the world. Over heterogeneous surfaces in terms of surface types or biophysical properties (e.g., vegetation density, emissivity), the validation protocol accounts for land surface spatial variability around the ground station. Over sparse vegetation canopies, the methodology accounts for viewing directional effects and sun configuration when validating VIIRS LST products.

Thermal infrared satellite observations of the Earth's surface are key components in estimating the surface skin temperature over global land areas. However, depending on sun illumination and viewing directional configurations, satellites measure different surface radiometric temperatures, particularly over sparsely vegetated regions where the radiometric contributions from soil and vegetation vary with the sun and viewing geometry. Over an oak tree woodland located near the town of Evora, Portugal, we compare different satellite-based land surface temperature (LST) products from the Moderate Resolution Imaging Spectroradiometer on board the Terra and Aqua polar-orbiting satellites and from the Spinning Enhanced Visible and Infrared Imager on board the geostationary Meteosat satellite with ground-based LST. The observed differences between LSTs derived from polar and geostationary satellites are up to 12 K due to directional effects. In this letter, we develop a methodology based on a radiative transfer model and dedicated field radiometric measurements to interpret and validate directional remote sensing measurements. The methodology is used to estimate the quantitative uncertainty in LST products derived from polar-orbiting satellites over a sparse vegetation canopy.

Structured canopies can show pronounced directional effects which influence land surface temperature (LST) estimates from thermal infrared satellite data. The effects depend on illumination and viewing geometries, because changes in these two geometries effectively cause the sensor to “see” different fractions of the canopy and the “background” surface (bare soil or low vegetation). Furthermore, parts of these two components will be in shadow, depending on the specific geometry of the canopy and its structure. This paper investigates these directional effects for a specific savanna site in West Africa and extends the findings to areas with denser tree crown cover. This is achieved by modeling the combined effects of the structured surface with a geometric optics model. The model assumes that the surface consists of four components: shaded and sunlit tree canopies and shaded and sunlit backgrounds. The brightness temperatures of these four surface components are provided by in situ measurements at the validation site, and emissivities are taken from the Land Surface Analysis Satellite Applications Facility (LSA-SAF) project. The LST modeling is performed for the geometry of the geostationary Meteosat Second Generation and for nadir geometry. Analyses of the temperature differences between the LST estimates for the two geometries show that, in many cases, the directional effects exceed 1°C within a day and that the timing and the sign of the effects change with season. Directional errors due to structured canopies are currently not considered in error estimates of operationally available LST products, e.g., the LSA-SAF LST product or the Moderate Resolution Imaging Spectroradiometer (MODIS) LST/emissivity products.

Land surface temperature (LST) is estimated from thermal infrared data provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), using a generalized split-window (GSW) algorithm. The uncertainty of the LST retrievals is highly dependent on the input accuracy and retrieval conditions, particularly the sensor view angle and the atmospheric water vapor content. This paper presents a quantification of the uncertainty of LST estimations, taking into account error statistics of the GSW under a globally representative collection of atmospheric profiles, and a careful characterization of the uncertainty of input data, particularly the surface emissivity and forecasts of the total water vapor content. Such analysis is the basis for LST uncertainty estimation, also distributed to users, in the form of error bars, along with the LST retrievals. Moreover, the spatial coverage of SEVIRI LST is essentially determined by the LST expected uncertainty, instead of being restricted to view zenith angles below a given threshold (e.g., 60°). Within the MSG disk, the atmosphere is often dry for clear-sky conditions where angles are large (e.g., Northern and Eastern Europe and Saudi Arabia). By considering several factors that contribute to LST inaccuracies, it is possible to increase the spatial coverage to regions such as those mentioned earlier. Retrieved values are also compared within situobservations collected in Namibia, covering a seasonal cycle. The two data sets are in good agreement with root-mean-square differences ranging between 1°C and 2°C, which is well below the average error estimated for the satellite retrievals.