A Temperature and Emissivity Separation Algorithm for Landsat-8 Thermal Infrared Sensor Data

This work presents Land Surface Temperature (LST) retrieval from Landsat-8 data using the Generalized Split-Window (GSW) algorithm. First, radiative transfer modeling experiments were conducted using the moderate spectral resolution atmospheric transmittance algorithm and computer model (MODTRAN) 4.0 fed with SeeBor V5.0 atmospheric profile database to simulate the brightness temperatures in Thermal InfraRed Sensor (TIRS) bands 10 (centered at 10.9 μm) and 11 (centered at 12.0 μm) related to Land Surface Emissivities (LSEs) and Total Precipitable Water (TPW). Then, the unknown coefficients of the GSW algorithm were obtained through multi-variable regressions, in which the simulated data were grouped into several subranges to improve fitting accuracy. Next, LSTs were derived from the clear-sky TIRS/Landsat-8 data in 2015, where LSEs were estimated from Operational Land Imager (OLI) measurements using the Normalized Difference Vegetation Index (NDVI) Based Emissivity Method (NBEM), and TPWs were extracted from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data. Finally, the derived LSTs were validated with MOD11_L2 V5 product. The results show that the GSW algorithm developed in this work can accurately retrieve LST from the Landsat-8 data, and the relative error is 0.18±0.68 K against the MOD11_L2 V5 product.

The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST from Landsat 8 and made an intercomparison among them. Apart from the direct use of the Radiative Transfer Equation (RTE), Single-Channel Algorithm and two Split-Window Algorithms were used taking an agricultural region in Bangladesh as the study area. The LSTs retrieved in the four methods were validated in two ways: first, an indirect validation against reference LST, which was obtained in the Atmospheric and Topographic CORection (ATCOR) software module; second, cross-validation with Terra MODerate Resolution Imaging Spectroradiometer (MODIS) daily LSTs that were obtained from the Application for Extracting and Exploring Analysis Ready Samples (AEEARS) online tool. Due to the absence of LST-monitoring radiosounding instruments surrounding the study area, in situ LSTs were not available; hence, validation of satellite retrieved LSTs against in situ LSTs was not performed. The atmospheric parameters necessary for the RTE-based method, as well as for other methods, were calculated from the National Centers for Environmental Prediction (NCEP) database using an online atmospheric correction calculator with MODerate resolution atmospheric TRANsmission (MODTRAN) codes. Root-mean-squared-error (RMSE) against reference LST, as well as mean bias error against both reference and MODIS daily LSTs, was used to interpret the relative accuracy of LST results. All four methods were found to result in acceptable LST products, leaving atmospheric water vapor content (w) as the important determinant for the precision result. Considering a set of several Landsat 8 images of different dates, Jiménez-Muñoz et al.’s (2014) Split-Window algorithm was found to result in the lowest mean RMSE of . Du et al.’s (2015) Split-Window algorithm resulted in mean RMSE of . The RTE-based direct method and the Single-Channel algorithm provided the mean RMSE of and , respectively. For Du et al.’s algorithm, the w range of to g cm−2 was considered, whereas for the other three methods, w values as retrieved from the NCEP database were considered for corresponding images. Land surface emissivity was retrieved through the Normalized Difference Vegetation Index (NDVI)-threshold method. This intercomparison study provides an LST retrieval methodology for Landsat 8 that involves four algorithms. It proves that (i) better LST results can be obtained using both thermal bands of Landsat 8; (ii) the NCEP database can be used to determine atmospheric parameters using the online calculator; (iii) MODIS daily LSTs from AEEARS can be used efficiently in cross-validation and intercomparison of Landsat 8 LST algorithms; and (iv) when in situ LST data are not available, the ATCOR-derived LSTs can be used for indirect verification and intercomparison of Landsat 8 LST algorithms.

The importance of land surface temperature (LST) retrieved from high to medium spatial resolution remote sensing data for many environmental studies, particularly the applications related to water resources management over agricultural sites, was a key factor for the final decision of including a thermal infrared (TIR) instrument on board the Landsat Data Continuity Mission or Landsat-8. This new TIR sensor (TIRS) includes two TIR bands in the atmospheric window between 10 and 12 μm, thus allowing the application of split-window (SW) algorithms in addition to single-channel (SC) algorithms or direct inversions of the radiative transfer equation used in previous sensors on board the Landsat platforms, with only one TIR band. In this letter, we propose SC and SW algorithms to be applied to Landsat-8 TIRS data for LST retrieval. Algorithms were tested with simulated data obtained from forward simulations using atmospheric profile databases and emissivity spectra extracted from spectral libraries. Results show mean errors typically below 1.5 K for both SC and SW algorithms, with slightly better results for the SW algorithm than for the SC algorithm with increasing atmospheric water vapor contents.

The thermal infrared (TIR) data from the Medium Resolution Spectral Imager II (MERSI-2) on the Chinese meteorological satellite FY-3D have high spatiotemporal resolution. Although the MERSI-2 land surface temperature (LST) products have good application prospects, there are some deviations in the TIR band radiance from MERSI-2. To accurately retrieve LSTs from MERSI-2, a method based on a cross-calibration model and split window (SW) algorithm is proposed. The method is divided into two parts: cross-calibration and LST retrieval. First, the MODTRAN program is used to simulate the radiation transfer process to obtain MERSI-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) simulation data, establish a cross-calibration model, and then calculate the actual brightness temperature (BT) of the MERSI-2 image. Second, according to the characteristics of the near-infrared (NIR) bands, the atmospheric water vapor content (WVC) is retrieved, and the atmospheric transmittance is calculated. The land surface emissivity is estimated by the NDVI-based threshold method, which ensures that both parameters (transmittance and emissivity) can be acquired simultaneously. The validation shows the following: 1) The average accuracy of our algorithm is 0.42 K when using simulation data; 2) the relative error of our algorithm is 1.37 K when compared with the MODIS LST product (MYD11A1); 3) when compared with ground-measured data, the accuracy of our algorithm is 1.23 K. Sensitivity analysis shows that the SW algorithm is not sensitive to the two main parameters (WVC and emissivity), which also proves that the estimation of LST from MERSI-2 data is feasible. In general, our algorithm exhibits good accuracy and applicability, but it still requires further improvement.

The retrieval of land surface temperature (LST) from remote sensing techniques has been studied and validated during the past 40 years, leading to important improvements. Accurate LST values are currently obtained through measurements using medium resolution thermal infrared (TIR) sensors. However, the most recent review reports demonstrated that the future TIR LST products need to obtain reliable temperature values at a high spatial resolution (100 m or higher) to study temperature variations between different elements in a heterogeneous kilometric area. The launch of high-resolution TIR sensors in the near future requires studies of the temporal evolution and spatial heterogeneities of the elements in a mixed region. The present study analyzes the LST in a sub-kilometric highly heterogeneous area, combining the use of LST products from high-resolution TIR orbiting sensors with the LST maps created from a TIR camera onboard an unmanned aerial vehicle (UAV). The aim is to estimate the LST variability in a heterogeneous area containing different surfaces (roads, buildings, and grass), observed from different TIR sensors at different spatial resolutions, covering from the meter to the kilometer scales. Several results showed that variations in the LST up to 18 °C were identified with the UAV-TIR camera, and significant differences were also present in the LST products obtained from simultaneous overpasses of high-resolution satellite TIR sensors. A second objective of the study, due to the availability of the high-resolution LST fields, was to explore the thermal advection between different elements and determine if it correlates with the surface energy budget in the same area, thus indicating that this process is of importance for heterogeneous terrains at these scales. This paper also highlights the relevance of the UAV-TIR camera flight for future studies since it is not commonly used in TIR remote sensing but has substantial potential advantages.

Accurate inversion of land surface geo/biophysical variables from remote sensing data for earth observation applications is an essential and challenging topic for the global change research. Land surface temperature (LST) is one of the key parameters in the physics of earth surface processes from local to global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from the space. Landsat 8 Thermal Infrared Sensor (TIRS) is the newest thermal infrared sensor for the Landsat project, providing two adjacent thermal bands, which has a great benefit for the LST inversion. In this paper, we compared three different approaches for LST inversion from TIRS, including the radiative transfer equation-based method, the split-window algorithm and the single channel method. Four selected energy balance monitoring sites from the Surface Radiation Budget Network (SURFRAD) were used for validation, combining with the MODIS 8 day emissivity product. For the investigated sites and scenes, results show that the LST inverted from the radiative transfer equation-based method using band 10 has the highest accuracy with RMSE lower than 1 K, while the SW algorithm has moderate accuracy and the SC method has the lowest accuracy.

To compare and evaluate the performance of iterative spectrally smooth temperature and emissivity separation method (ISSTES) and linear emissivity constraint temperature and emissivity separation method (LECTES) on land surface temperature (LST) and land surface emissivity (LSE) estimation, the simulation data for hyperspectral infrared spectroradiometer are used. The results reveal that the LST can be retrieved within the accuracy of 1 K at various conditions for both methods. However, the 0.01 accuracy of LSE depends on the method selected and the noise level. The ISSTES method should be taken full consideration when it used to retrieve LSE for the warm and wet atmosphere. It is advised that the ISSTES method is used for cold and dry atmosphere and the LECTES method for warm and wet atmosphere. The noises in the ground measurements may be have more effects on the accuracies of LST and LSE than those in the atmospheric downwelling measurements.

• LAI prediction accuracy improves by integrating remote sensing VNIR and TIR data. • The relationship between LAI and LST is found to be insignificant. • LSE has a positive correlation with LAI. • Accurate measurement of the percentage of vegetation cover is crucial for LSE retrieval accuracy. • LAI prediction accuracy is higher with LST retrieved from a lower flight altitude. The leaf area index (LAI) is a crucial biophysical variable for remote sensing vegetation studies. LAI estimation through remote sensing data has mostly been investigated using visible and near-infrared (0.4–1.3 μm, VNIR) and Shortwave Infrared (1.4–3 μm, SWIR) data. However, Thermal Infrared (3–14 μm, TIR) data for LAI retrieval has rarely been explored. This study aims to predict LAI by integrating VNIR and TIR data from Unmanned Aerial Systems (UAS) in a mixed temperate forest, the Haagse Bos, Enschede, the Netherlands. The VNIR and TIR images were acquired in September 2020, in conjunction with fieldwork to collect LAI in situ data for 30 plots. TIR images were acquired at two heights (i.e., 85 m and 120 m above ground) to investigate the effect of flight height on the LAI prediction accuracy by means of UAS data. Land Surface Temperature (LST) and Land Surface Emissivity (LSE) were computed and extracted from the collected images. LAI was estimated using seven vegetation indices and Partial Least Squares Regression (PLSR). LAI prediction accuracy using VNIR reflectance spectra was compared to the accuracy achieved by integrating VNIR data with LST or LSE applying vegetation indices as well as PLSR. Among the applied vegetation indices, the Reduced Simple Ratio (RSR) gained the highest prediction accuracy using VNIR data (R 2 = 0.5815, RMSE = 0.6972); the prediction accuracy was not improved when LST was integrated with VNIR data but increased when LSE was included (RSR: R 2 = 0.7458, RMSE = 0.5081). However, when LST from 85 m altitude and VNIR data was applied as an input using PLSR (R 2 = 0.5565, RMSE CV = 0.7998), the LAI prediction accuracy was slightly increased compared to when only VNIR data was used (R 2 = 0.4452, RMSE CV = 0.8668). Integrating VNIR data with LSE significantly improved the LAI retrieval accuracy (R 2 = 0.7907, RMSE CV = 0.8351). These findings corroborate prior research indicating that combining LSE with VNIR data can increase the prediction accuracy of LAI. However, LST was found to be ineffective for this purpose. The relationship between LAI and LSE should be the subject of more investigation through various approaches to bridge the existing scientific gap.

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.

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

Land surface temperature (LST) is an important parameter in global climate change and urban thermalenvironmental studies. The significance of land surface temperature is being acknowledged gradually and interest isincreasing in developing methodologies for the retrieval of LST from Satellite Remote Sensing (SRS) data. ThermalInfrared Sensor (TIRS) of Landsat-8 is the newest TIR sensor for the Landsat Data Continuity Mission (LDCM),offering two adjacent thermal infrared bands (10, 11), having significant beneficiary for the land surface temperatureinversion. The spectral radiance can be estimated through TIR bands 10 and 11 of Landsat-8 OLI_TIRS satellite image.In the present study, the radiative transfer equation-based method has been employed in estimating LST of Lahore andthe analysis demonstrated that estimated LST has the highest accuracy from the radiative transfer method through band10. Land Surface Emissivity (LSE) was derived with the aid of the NDVI’s threshold technique. The present studyresults show that as the built-up area increases and vegetation cover decreases in urban surface, they are linked toincrease in urban land surface temperature and conversely larger vegetation cover associated with lower urbantemperature. The output exposed that LST was high in built-up and barren land, whereas it was low in the area wherethere were more vegetation cover and water.

The main disadvantage of Land surface temperature (LST) retrieval methods from Landsat TM thermal channel images is that atmospheric profile parameters are needed, and MODIS has several near infrared bands that can be used to estimate atmospheric profile parameters. Two methods that could be used to retrieve the LST from Landsat TM and MODIS data were compared in this paper, the first of them is the mono-window algorithm developed by Qin et al. and the second is the single-channel algorithm developed by Jimenez-Munoz and Sobrino. Atmospheric profile parameters such as atmospheric moisture content, atmospheric transmittance and average atmospheric temperature have been estimated from MODIS data, and the land surface emissivity values have been estimated from a methodology based on spectral mixture analysis. Finally, a comparison between the LST measured in situ and retrieved by the algorithms over urban area of Changsha city in China is present. Result indicates that the two LST retrieval algorithms can get high-precision results in support of atmospheric parameters from MODIS images, the average deviation of mono-window algorithm is 0.76K, and the deviation of generalized single-channel algorithm is 1.23k.

A method for the retrieval of land surface temperature (LST) from the two thermal bands of Landsat 8 data is proposed in this paper. The emissivities of vegetation, bare land, buildings, and water are estimated using different features of the wavelength ranges and spectral response functions. Based on the Planck function of the Thermal Infrared Sensor (TIRS) band 10 and band 11, the radiative transfer equation is rebuilt and the LST is obtained using the modified emissivity parameters. A sensitivity analysis for the LST retrieval is also conducted. The LST was retrieved from Landsat 8 data for the city of Zoucheng, Shandong Province, China, using the proposed algorithm, and the LST reference data were obtained at the same time from a geosensor network (GSN). A comparative analysis was conducted between the retrieved LST and the reference data from the GSN. The results showed that water had a higher LST error than the other land-cover types, of less than 1.2°C, and the LST errors for buildings and vegetation were less than 0.75°C. The difference between the retrieved LST and reference data was about 1°C on a clear day. These results confirm that the proposed algorithm is effective for the retrieval of LST from the Landsat 8 thermal bands, and a GSN is an effective way to validate and improve the performance of LST retrieval.

Urban ecological parameters, such as land surface emissivity, temperature, and dryness, play important role in urban ecological system. The present study aimed to explore these ecological parameters using Landsat ETM+ data. The land surface emissivity (LSE) was calculated from the normalized vegetation index (NDVI) considering different cases; land surface temperature (LST) was retrieved from the thermal band based on the method of mono-window algorithm; and land surface dryness (LSD) was derived from the NDVI via LST space, in which the two edges for the minimum and maximum LST values (LSTmin and LSTmax) were simulated using NDVI values. The results showed that there was an obvious gradient as progressed from the core city out into the countryside for the three parameters. The values for LST and LSD over water surface and vegetated surface were obvious lower than that over impervious surface, demonstrating water and vegetation had the ability to improve environmental quality. These simulation results could provide quantitative evaluations and suggestions for the environmental planning and management by government.

Land surface temperature (LST) is a key parameter for a wide number of applications, including hydrology, meteorology and surface energy balance. In this study, we first proposed a new land surface emissivity (LSE) scheme, including a lookup table-based method to determine the vegetated surface emissivity and an empirical method to derive the bare soil emissivity from the Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product. Then, the Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis data and the Feng Yun-3C/Medium Resolution Spectral Imager (FY-3C/MERSI) precipitable water vapor product were used to correct the atmospheric effects. After resolving the land surface emissivity and atmospheric effects, the LST was derived in a straightforward manner from the FY-3C/MERSI data by the radiative transfer equation algorithm and the generalized single-channel algorithm. The mean difference between the derived LSE and field-measured LSE over seven stations is approximately 0.002. Validation of the LST retrieved with the LSE determined by the new scheme can achieve an acceptable accuracy. The absolute biases are less than 1 K and the STDs (RMSEs) are less than 1.95 K (2.2 K) for both the 1000 m and 250 m spatial resolutions. The LST accuracy is superior to that retrieved with the LSE determined by the commonly used Normalized Difference Vegetation Index (NDVI) threshold method. Thus, the new emissivity scheme can be used to improve the accuracy of the LSE and further the LST for sensors with broad spectral ranges such as FY-3C/MERSI.