Land surface temperature retrieval from Landsat-8 data

Currently,application-oriented researches on the data of Medium Resolution Spectral Imager(MERSI),which is on board China's new generation polar orbit meteorological satellite FY-3,are very insufficient,due to the reason that the data as a new source have been delivered only since 2008.With the normal operation of FY-3 satellite system,it is necessary to develop an operational module for FY-3/MERSI regional land surface temperature(LST) retrieval and its post-processing,since LST is required for a wide variety of scientific studies but FY-3/MERSI's operational LST products have not yet been provided by National Satellite Meteorological Center(NSMC).Based on an analysis of FY-3/MERSI L1 data's HDF5 format and its channel characteristics,the authors selected the generalized single-channel algorithm developed by Jimenez-Mu n～oz Sobrino to directly realize the LST retrieval at 250 m spatial resolution with MATLAB programming and the thematic mapping of LST derivative products.This paper describes the parametric processes of LST retrieval algorithm in detail,which include radiometric calibration,cloud detection,estimation of two intermediate parameters-surface emissivity and atmospheric water vapor,and calculation of thermal indexes from LST.On these bases,an automatic flowchart for FY-3/MERSI LST retrieval and thematic mapping was established.Experimental results of this flowchart applied in Shanghai thermal environmental monitoring show that it can process FY-3/MERSI L1 data in a fast,real-time and automatic way,thus suitable for operational products producing and sharing,with the saving of human resources.It is also proved that FY-3/MERSI data and various forms of LST products can reveal the spatial pattern of Shanghai thermal field and the urban heat island effect more finely and intuitively.

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.

On-board the Landsat-8 satellite, the Thermal Infrared Sensor (TIRS), which has two adjacent thermal channels centered roughly at 10.9 and 12.0 μm, has a great benefit for the land surface temperature (LST) retrieval. The single-channel algorithm (SC) and split-window algorithm (SW) have been applied to retrieve the LST from TIRS data, which need the land surface emissivity (LSE) as prior knowledge. Due to the big challenge of determining the LSE, this study develops a temperature and emissivity separation algorithm which can simultaneously retrieve the LST and LSE. Based on the laboratory emissivity spectrum data, the minimum-maximum emissivity difference module (MMD module) for TIRS data is developed. Then, an emissivity log difference method (ELD method) is developed to maintain the emissivity spectrum shape in the iterative process, which is based on the modified Wien’s approximation. Simulation results show that the root-mean-square-errors (RMSEs) are below 0.7 K for the LST and below 0.015 for the LSE. Based on the SURFRAD ground measurements, further evaluation demonstrates that the average absolute error of the LST is about 1.7 K, which indicated that the algorithm is capable of retrieving the LST and LSE simultaneously from TIRS data with fairly good results.

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.

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.

Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval

Land surface temperature (LST) is a key parameter for a wide number of applications. The Advanced Himawari Imager (AHI) onboard Himawari-8 has four thermal infrared (TIR) bands in the atmospheric window, which provides the possibility of separating LST and LSE from AHI TIR data. In this paper, the TES algorithm and water vapor scaling (WVS) method were combined to retrieve LST and LSE from the AHI TIR data. The retrieval results are evaluated by the ground measurements collected from one site in the Baseline Surface Radiation Network (BSRN) network. The average Bias and RMSE of the estimated LST are -0.096K and 0.705K, respectively. This study demonstrates the capability of the combinations of TES algorithm and WVS method in retrieving LST and LSE from AHI TIR data.

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) and Surface Net Radiation (SNR) are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions. Although high-resolution LST and SNR retrievals are available, they have large gaps due to cloud-cover that hinder their use as input in models. Here, we present a downscaled and continuous daily LST and SNR product across Europe for 2018–2019. The LST product is based on all-sky LST retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellite, and clear-sky LST retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the polar-orbiting Sentinel 3 satellites. The product combines the medium spatial (approx. 5–7 km) but high temporal (30 minute) resolution, gap-free data from MSG, with the low temporal (2–3 days) but high spatial (1 km) resolution of the Sentinel 3 LST retrievals. The resulting 1 km and daily LST dataset is based on an hourly merging of both datasets through bias-correction and Kalman Filter assimilation. Longwave outgoing radiation is computed from the merged LST product in combination with MSG-based emissivity data. Shortwave outgoing radiation is computed from the incoming shortwave radiation from MSG and downscaled albedos using 1 km PROBA-V data. MSG incoming shortwave and longwave radiation and the outgoing radiation components at 1 km spatial resolution are used together to compute the final daily SNR dataset in a consistent manner. Validation results indicate an improvement of the root mean squared error by ca. 8 % with a substantial increase in spatial detail compared to the original MSG product. The resulting pan-European LST and SNR dataset can be used for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes and will be regularly updated in the future.

<strong class="journal-contentHeaderColor">Abstract.</strong> Land Surface Temperature (LST) and Surface Net Radiation (SNR) are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions. Although high-resolution LST and SNR retrievals are available, they have large gaps due to cloud-cover that hinder their use as input in models. Here, we present a downscaled and continuous daily LST and SNR product across Europe for 2018&ndash;2019. The LST product is based on all-sky LST retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellite, and clear-sky LST retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the polar-orbiting Sentinel 3 satellites. The product combines the medium spatial (approx. 5&ndash;7 km) but high temporal (30 minute) resolution, gap-free data from MSG, with the low temporal (2&ndash;3 days) but high spatial (1 km) resolution of the Sentinel 3 LST retrievals. The resulting 1 km and daily LST dataset is based on an hourly merging of both datasets through bias-correction and Kalman Filter assimilation. Longwave outgoing radiation is computed from the merged LST product in combination with MSG-based emissivity data. Shortwave outgoing radiation is computed from the incoming shortwave radiation from MSG and downscaled albedos using 1 km PROBA-V data. MSG incoming shortwave and longwave radiation and the outgoing radiation components at 1 km spatial resolution are used together to compute the final daily SNR dataset in a consistent manner. Validation results indicate an improvement of the root mean squared error by ca. 8 % with a substantial increase in spatial detail compared to the original MSG product. The resulting pan-European LST and SNR dataset can be used for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes and will be regularly updated in the future.

Atmospheric water vapor is one of the key factors affecting the retrieval of land surface temperature(LST) from the single-band infrared image.It is conductive to the rational usage of single-band infrared data to find out the influence of spatial heterogeneity of AWV on the retrieval of LST.With the MODIS MOD05_L2 products as data sources,this paper discussed the spatial heterogeneity of AWV at the regional scale and its impact on the retrieval of LST from Landsat image.Firstly,an error model was deduced with regard to the influence of AWV on the retrieval of LST from remote sensing data.Then,in order to explore the retrieval error of LST possibly caused by the spatial heterogeneity of AWV at the regional scale,the authors conducted simulation tests followed by comparative analyses under spatially different distribution conditions of AWV in several regions,particularly for the thermal infrared band of Landsat 5.Results obtained in this paper indicate that the retrieval error of LST caused by the spatial heterogeneity of AWV has to do with the value and the heterogeneity degree of AWV.For example,when AWV had relatively high heterogeneity,an error was relatively great,ranging from 0.5℃ to 2℃ with relative error from 2% to 20% accordingly;by contrast,when AWV was relatively uniform,error was comparatively insignificant and ranged from 0.1℃ to 0.2℃ with relative error from 0.5% to 2% accordingly.In conclusion,with regard to regions with a relatively high degree of atmospheric heterogeneity,it is vital to get atmospheric parameters properly at the regional scale so as to retrieve LST precisely from remote sensing data.

Much achievements have been presented for retrieving land surface temperature(LST) from thermal infrared satellite sensor data.The accuracy of retrieval results can reach 1K.But the thermal infrared remote sensing is greatly influenced much by cloud,atmospheric water content and rainfall,which may cause many difficulties in LST retrieval studies.However,the passive microwave remote sensing can just overcome these disadvantages.Passive microwave emission can penetrate non-precipitating clouds,thereby providing a better representation of LST under nearly all sky conditions.Passive microwave remote sensing holds a unique advantage in retrieving LST.But passive microwave emission can also be influenced by certain surface factors.So more efforts are needed for the algorithms improvement of LST retrieval from passive microwave remote sensing data at present.This paper systematically reviews the empirical LST retrieval models,physical LST retrieval models and semi-empirical LST retrieval models from passive microwave data based on different passive microwave radiometers.The weakness and difficulties for LST inversion at the present study stage are analyzed,which may be very helpful to the future researches related to the kind of microware sensors.

Detection of geothermal anomalies in the Northern Lake Abaya geothermal field, Main Ethiopian Rift

Land Surface Temperature (LST) is not only a necessary physical parameter of global climate system but an important input parameter in some models based on remote sensing for estimating crop yield and surface flux (e.g., sensible heat, latent heat, CO 2 flux), monitoring soil water content and crop water deficit. This paper selected two AVHRR images derived at 11:00am and 1:00pm on April 15, 2003 to retrieve the instantaneous LST in Yangtze Delta by using Splitwindow Algorithm raised by Price (1984). To validate the retrieval effect, daily average LST was calculated on the retrieved instantaneous LST and then compared with the daily average LST measured by the ground weather observation stations. The results show that the total average error is 0.18°C, the maximum error is 2.9°C and the stations with retrieval error less than 1.1°C accounted for 77.78%. Therefore, the retrieval method of instantaneous LST in the paper can obtain more accurate results of the surface temperature in the Yangtze Delta. And the study could provide a scientific basis for the application of thermal infrared remote sensing data on the retrieval of large-scale regional or global surface temperature.

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.