Surface Temperature Retrieval Research Articles

Geometry and adjacency effects in urban land surface temperature retrieval from high-spatial-resolution thermal infrared images

Urban land surface temperature (ULST) is a key surface feature parameter in urban heat island studies. However, the geometry and adjacency effects are usually neglected in conventional land surface temperature (LST) retrieval methods. In this study, considering the geometry and adjacency effects, a new urban canopy multiple-scattering thermal radiative transfer (UCM-RT) model and an urban temperature-emissivity separation (UTES) algorithm were developed for ULST retrieval from Chinese GaoFen-5 (GF-5) satellite thermal infrared (TIR) images. The UCM-RT model and the UTES algorithm incorporate multiple scattering within the urban building canopy and consider the thermal radiance contribution from adjacent pixels and the atmosphere. Two schemes were designed to evaluate the geometry and adjacency effects quantitatively in this study: (1) evaluating their impact on ground temperature and top of atmosphere (TOA) brightness temperature for GF-5 four TIR bands from the simulation dataset, and (2) comparing the retrieval temperature difference between the UTES algorithm and conventional temperature-emissivity separation (TES) algorithm (without considering geometry and adjacency effects). For a specific simulation situation, the magnitude of the geometric effects on urban ground temperatures were found to be approximately 3.2 K, 4.2 K, 4.0 K, and 3.9 K for the four GF-5 TIR bands, while the effects on the TOA brightness temperature reached 2.4 K, 3.3 K, 3.3 K, and 3.0 K for those bands, respectively. The results show that the geometry effects have a significant influence on the thermal radiative transfer process. The temperature error of the UTES algorithm was generally up to 0.8 K, much lower than that of the conventional TES algorithm (~2.2 K) in urban surface. The results indicate that the UTES algorithm is suitable for ULST retrieval, and the retrieval temperature difference between the UTES algorithm and conventional TES algorithm ranges from 0.1 K to 2.1 K, especially for dense building pixels. Additionally, the thermal radiation from adjacent pixels in high-spatial-resolution TIR images has significant influence on ULST retrieval. The comparison between the ULST algorithm for urban land surface and the conventional TES algorithm for the flat surface indicated that the three-dimensional structure inside the pixel has a significant influence on the ULST retrieval. The findings of this study indicate that the geometry and adjacency effects must be considered in ULST retrieval for highly accurate results.

A new model for an improved AMSR2 satellite soil moisture retrieval over agricultural areas

This study evaluates the potential of AMSR2 (Advance Microwave Scanning Radiometer2) data for the estimation of Volumetric Soil Moisture (VSM) for bare and agricultural areas. At the first step, the sensitivity of the Microwave Polarization Difference Index (MPDI) to variations in soil and vegetation characteristics were examined at different frequencies. At lower frequencies, the signal attenuation due to vegetation is minimal and thus, denser vegetation usually depolarizes the soil emission. Interestingly, the results also reveal that at higher frequencies, the sensitivity of V and H polarizations over relatively dense vegetation covers is not the same at all. Therefore, MPDI at both low and high frequencies can be a good indicator of the soil moisture and Vegetation Water Content (VWC), respectively. After evaluation of AMSR2 datasets, a model called Multi-channel/MPDI-based Land Parameters Retrieval Model (MMLPRM) is proposed. The MMLPRM optimizes optical depth of vegetation and soil dielectric constant, with simultaneous retrieval of soil moisture and surface temperature by using the AMSR2 brightness temperature data. This algorithm also includes the surface roughness parameters to increase the soil moisture retrieval efficiency. In this way, calibration and validation have been done, using in situ observations of 50 monitoring stations obtained from the International Soil Moisture Network (ISMN) over the United States. Consequently, the analysis on the MMLPRM retrieval model demonstrates its potential and usefulness for soil moisture retrieval. The outcome of this study will help in estimating the accurate soil moisture to optimize the irrigation management strategies and help in water conservation.

Cross-calibration of Chinese Gaofen-5 thermal infrared images and its improvement on land surface temperature retrieval

Thermal infrared (TIR) remote sensing technology is capable of acquiring large-scale land surface temperature (LST), which is a key factor in the energy exchange between land surface and atmosphere. The visible and infrared multispectral sensor (VIMS) equipped in the Chinese Gaofen-5 (GF-5) satellite can obtain four channels of TIR images with a 40 m spatial resolution. However, due to the change of working environment, the TIR sensor suffers a low-accurate radiometric calibration that needs improvement. This paper puts forward a new cross-calibration for GF-5/VIMS TIR images by linking the top of the atmosphere (TOA) radiance of the vertical angle MODIS observation with the GF-5/VIMS image to estimate the radiometric calibration coefficients, Gain and Offset. To verify the recalibration performance, a new nonlinear two-channel split-window (SW) algorithm and a light gradient boosting machine (LightGBM) method which is used to refine LST from the SW algorithm by minimizing the residuals, were developed for the recalibrated GF-5/VIMS TIR 3 and TIR 4 channels. The radiometric cross-calibration algorithm and the optimized SW algorithm were applied to real GF-5/VIMS TIR images. The validation results showed that the brightness temperature and LST were improved significantly, and the LST retrieval error was reduced greatly to 1.79 K after recalibration, indicating a large improvement of the LST retrieval for GF-5/VIMS TIR image using the proposed cross-calibration and SW algorithms.

Spatial Temporal of Urban Green Space in Tropical City Of Kuching, Sarawak, Malaysia.

Urban green space is one of the essential variables to influence urban climate. Urban green spaces offer evaporative cooling. The evaporative process is vital to mitigate the urban heat island. This paper investigates the spatial-temporal pattern changes of urban green space in the tropical city of Kuching, Malaysia, using remote sensing and GIS. To achieve the objective, this study required three steps. The first was a performed pre-processing, namely geometric correction, atmosphere correction, and radiometric correction. The next step was a retrieval of land surface temperature at the thermal band for every selected data for the year 1988, 2000, 2011, and 2019. The third step performed supervised classification for every selected data to generate a land cover map every selected year. The final step was to identify a correlation between urban greens space and LST. The results discovered spatial patterns of urban greens space significantly effects of cooling potential which the more urban green space areas encounter the stronger cooling effect of the urban heat island. The expansion of the urban areas significantly decreases urban green spaces areas. Correct spatial planning is an essential tool for improving urban green spaces infrastructure. This output can improve the knowledge planners, and policymakers understand green spaces recognized in urban areas and plan the urban green space strategically to mitigate UHI effects.

Retrieval of sea surface temperature from the scanning microwave radiometer aboard HY-2B

ABSTRACT The HY-2B satellite, equipped with the scanning microwave radiometer (SMR), was launched on 25 October 2018 and began observation on 30 October 2018. With the SMR data, we retrieved the sea surface temperature (SST), along with the ocean wind speed (OWS), total columnar water vapour (TCWV), and cloud liquid water (TCLW), within an optimal estimation (OE) Framework. Through the comparison with Argo profiling floats data, mean difference of 0.01°C, and standard deviation of 0.47 are inferred for SST within the latitude range of 60°S–60°N. The contrast against the WindSat standard products also shows an encouraging result, in which the standard deviation for SST, OWS, TCWV, and TCLW are 0.49, 0.64 m s−1, 1.23 mm, and 0.029 mm, respectively. To investigate the effect of oceanic and atmospheric state on SST retrieval, the sensitivities of brightness temperature (TB) at considered frequency to SST, OWS, TCWV, and TCLW are carefully analysed, and seven combinations of SMR frequencies are tested to retrieve different parameters. The results emphasize the importance of low-frequency observations in retrieving accurate SST and suggest that the influence of wind speed and atmospheric vapour content is non-negligible. It is also indicated that the role of the higher frequency (23.8 and 37 GHz) is limited for SST retrieval. This study presents an assessment for SST retrieval from SMR measured data and provides a reference about frequency selection for measuring SST from passive microwave radiometer.

Comparing different space-borne sensors and methods for the retrieval of land surface temperature

The importance of land surface temperature (LST) is increasingly recognized, and various methodologies have been proposed for the retrieval of LST using space-borne thermal infrared data. However, the selection of LST retrieval from Thermal Infrared Sensor (TIRS) of Landsat-8 based on different methods and the readily available MODIS LST products is still a challenging topic for local and global environmental studies. In this study, the potential of three different methods for retrieving LST using Landsat-8 TIRS data, including Radiative Transfer Equation (RTE), Single Channel (SC), and Split Window (SW) method in comparison with MODIS MOD11A1 LST product was evaluated. For accuracy assessment, 0 cm ground surface temperature (LSTGST) data was used. Our results almost showed same accuracy for RTEB10 with RMSE = 0.35 °C, followed by MODIS with RMSE = 0.36 °C, and SCB10 with RMSE = 0.38 °C. Secondly, SCmean (Mean of B10 and B11), and RTEmean (Mean of B10 and B11) generate nearly the same accuracy with RMSE = 0.53 °C, and RMSE = 0.54 °C, respectively. The other methods viz., SCB11, RTEB11, and SW method slightly showed lower accuracy with RMSE = 0.87 °C, RMSE = 0.88 °C, and RMSE = 0.91 °C, respectively. We found all the methods highly accurate and can be used successfully by climatologists, environmentalists, hydrologists, and urban planners concerning planning, and monitoring of the ever-increasing LST at local and global scale studies.

Sea surface temperature retrieval based on simulated microwave polarimetric measurements of a one-dimensional synthetic aperture microwave radiometer

Sea surface temperature retrieval based on simulated microwave polarimetric measurements of a one-dimensional synthetic aperture microwave radiometer

A New Strategy for Comparison of Land Surface Temperature Retrieval Methods with Landsat Remote Sensing Images Considering Regional Consistency

Landsat remote sensing images are widely used in fields such as land surface temperature retrieval, urban expansion, and urban heat islands, due to their high spatial resolution and the availability of long time series data. Long-term land surface temperature (LST) change analysis usually requires comprehensive utilization of remote sensing data from different sensors, such as Landsat 7, Landsat 8. A LST retrieval algorithm with generalization for Landsat thermal infrared image can effectively improve the reliability of long-term analysis using multi-source images. In order to evaluate the performances of different LST retrieval methods for Landsat images, a new strategy based on regional consistency was proposed in this paper so that different LST retrieval algorithms can be compared with each other without utilizing reference data from ground observation. The general hypothesis is that there is a significant positive correlation between the obtained Landsat 7 and Landsat 8 LST products with adjacent imaging time, similar imaging environment for the identical area. Firstly, the Landsat 7 and Landsat 8 image pairs from Shenzhen were selected under aforementioned constraints. Secondly, four representative LST retrieval methods, radiative transfer equation method (RTEM), image-based method (IBM), mono-window algorithm (MWA) and single-channel algorithm (SCA) were used to generate the LST products from the Landsat 7 and Landsat 8 image pairs respectively. Lastly, the correlation between the Landsat 7 and Landsat 8 LST products from different methods can be calculated as different indexes, including the goodness of fit, Pearson correlation coefficient and Euclidean distance. It is convincing that the optimal LST retrieval method should exhibit a higher regional consistency between the Landsat 7 and Landsat 8 LST product pair given the certain area. The experimental results show that the radiative transfer equation method generates the highest correlation and it is considered as a suitable option for long-term LST research with multi-source Landsat datasets.

Influencing Factors and Quality Evaluation of Urban Thermal Environment Based on Artificial Neural Network

To alleviate the urban heat island effect that is getting increasingly serious these days, the research on the monitoring, simulation, and regulation of the thermal environment of cities has become a necessity. Aiming at figuring out the correlations between influencing factors and giving accurate quality evaluation of urban thermal environment, this study extracted 10 influencing factors of urban thermal environment and gave their influence, and then performed the Land Surface Temperature (LST) retrieval of a target city. After that, this paper constructed a Multiple Linear Regression (MLR) model and explored the law of the numerical changes of the influencing factors of urban thermal environment. At last, this paper also built a BP neural network to predict the quality evaluation of urban thermal environment and used experimental results to prove the effectiveness of the proposed algorithm and model.

Comparison of the accuracy of daytime land surface temperature retrieval methods using Landsat 8 images in arid regions

Land surface temperature (LST) is a significant parameter in surface and atmospheric systems which can be used to describe the physical processes of energy and water exchange. There are several ways to retrieve land surface temperature using satellite imagery. The purpose of the current study is to compare the accuracy of different LST retrieval algorithms, including 11 types of split-window algorithms, two mono-window algorithms, and three single-channel algorithms. The validation of LST retrieval algorithms was done using temperature-based and cross-validation methods. In this study, 20 images from Landsat 8 satellite related to 2017 and 2018 were used to retrieve LST in different ways. The temperatures obtained from different methods were compared in one day, and the average of LST of the different methods was about 5 ℃ different, which indicates the importance of the existing validation methods. Using Sentinel-2 image classification, 20 points in homogeneous areas with pure pixels were selected to measure the LST using a thermometer for land validation. Results showed that in a split window algorithm which was applied on the water vapor image, the RMSE error has been reduced by 2 ℃. In cross-validation, MODIS LST images were used, and RMSE temporal image was prepared, and classified into five groups including less than 2, 2–3, 3–4 and more than 5 ℃ .Comparing the area of each of these groups in different methods showed that MW2 and SW1 methods have less accuracy than the other methods. The SW9 algorithm can retrieve temperature with an error of less than 2 ℃ in 80% of the area. The results of cross-validation showed that the SC2 algorithm had the highest accuracy among the algorithms that use a thermal band for LST retrieval with a RMSE spatial of 5.3 °C and the SW10 and SW11 have shown the highest accuracy among the algorithms that use two thermal bands with RMSE less than 5 ℃. In general, the algorithms that use atmospheric transmittance coefficients and water vapor as inputs are more accurate than the others.

A machine learning approach to estimating the error in satellite sea surface temperature retrievals

Global, repeated, and accurate measurements of Sea Surface Temperature (SST) are critical for weather and climate projections. While thermometers on buoys measure SST relatively accurately, only sensors aboard satellites give global and repeated SST measurements necessary for many applications, including climate modeling. For satellite-based thermal infrared sensors, an atmospheric correction converts calibrated brightness temperatures measured at orbital height into an SST estimate, but imperfect assumptions in the correction algorithm coupled with variability in atmospheric conditions and viewing geometries can lead to a wide range of errors and uncertainties in the satellite-derived SST retrievals. Estimates of the resulting errors are imperative for satellite-derived SST assimilation into climate models. This paper evaluates the use of machine learning Decision Tree algorithms to predict the central tendency and dispersion of errors in satellite-derived SST retrievals. First, using records from the NASA R2014.1 MODIS Aqua SST Matchup Database, which includes matched-up satellite and in situ SST measurements, a set of seven variables was derived that addresses the assumptions and known issues in the satellite SST retrieval process. Then, both Random Forest and Cubist Decision Trees were used to predict the SST residual (satellite SST minus skin-corrected buoy SST) for each matchup. While both Decision Tree methods performed similarly well, the Cubist model is more easily interpreted and yields better predictions of errors for relatively infrequent conditions. Various characteristics of the groups of matchups identified by Cubist were explored, and uncertainty values for the error estimates were derived for each group. Overall, the Cubist model predicted the skin SST residual with a root-mean-squared-error of 0.380 °C across nighttime cloud-filtered domains, demonstrating that a Cubist model is viable for quantitatively and accurately predicting Single Sensor Error Statistics per pixel as required by the Group for High Resolution SST. In this paper, we present the training and testing of both Decision Tree models. Because of its interpretability, we explore in detail the characteristics of the Cubist-derived groups to gain new geophysical insight into the satellite-derived SST retrieval error across different measurement conditions.

Subpixel Temperature Estimation by Information Transfer With Adaptive Ensemble Extreme Learning Machine (IT-AEELM)

The retrieval of land surface temperature (LST) using thermal infrared (TIR) data is important in many applications. However, TIR data usually suffer from low spatial resolution. We introduce a novel subpixel LST estimation model using the information-transfer-based adaptive ensemble extreme learning machine (IT-AEELM). The proposed method constructs a reliable relationship between subpixel LST and the input high-resolution visible and near-infrared (VNIR) data, short-wave infrared (SWIR) data, and low-resolution TIR data. Based on a detailed analysis of different ground objects, we divide the input data into multiple subsets. Instead of using consistent land surface parameters (LSPs), we utilize different LSPs to characterize the land surface properties in each subset. The VNIR-SWIR-LSPs data and the low-resolution LST are used to train a novel IT-AEELM network, where a feedback ensemble learning scheme is introduced to effectively remove inaccurate estimates. The main difference of the model against existing methods is that it builds a robust architecture at different spatial scales, which provides benefits including lower demand for training data, more rapid and accurate acquisition of subpixel LST, and better adaption to heterogeneous land surface. Numerical experiments demonstrate that the proposed method significantly improves the accuracy of subpixel LST compared with the state-of-the-art algorithms.

Retrieval and validation of the land surface temperature from FY-3D MERSI-II

Retrieval and validation of the land surface temperature from FY-3D MERSI-II

Land Surface Temperature and Emissivity Retrieval from Nighttime Middle and Thermal Infrared Images of Chinese Fengyun-3D MERSI-II

The second-generation Chinese polar-orbit meteorological satellite Fengyun-3D carries the MEdium-Resolution Spectral Imager (MERSI-II). MERSI-II has 19 solar channels and 6 middle and thermal infrared channels and can provide daily global coverage observation at daytime and nighttime. Land surface temperature (LST) products have a wide range of application requirements. Unfortunately, there is no official LST product of MERSI-II yet. This article proposed a temperature-emissivity separation algorithm to estimate LST and emissivity from two mid-infrared (MIR) and two thermal infrared (TIR) nighttime MERSI-II images. The sensitive analysis and ground validation show the following. First, the algorithm can theoretically retrieve LST with an error of less than 0.7 K, emissivity with errors of about 0.025 for MIR channel and 0.01 for TIR channel, respectively. Second, the nighttime LST retrieval error was approximately 2.19 K, and the bias was approximately 0.60 K among 6 SURFRAD sites and 2 PKULSTNet sites. The retrieved emissivity was cross-validated using MODIS emissivity product over a desert area and its difference was obtained as 0.01 and 0.016 for two TIR channels. Finally, the TES algorithm was applied to obtain LST and emissivity images over North China Plain as an example, and the nighttime LST and land surface emissivity (LSE) were obtained well. As a result, this study shows that the proposed TES algorithm provides an effective way to get LST and LSE image from FY-3D/ MERSI-II nighttime observation, which will improve their applications in different fields.

Land surface temperature and emissivity retrieval from airborne hyperspectral thermal infrared hyperspectral data and application

Land surface temperature and emissivity retrieval from airborne hyperspectral thermal infrared hyperspectral data and application

Destriping and evaluating FY-3D MERSI-2 data with the moment matching method based on synchronous reference image

Medium Resolution Spectral Imager II (MERSI-2) is a payload of the China meteorological satellite FY-3D. The sensor bands 24 (10.3 to 11.3 μm) and 25 (11.5 to 12.5 μm) images, which are most suitable for land surface temperature (LST) retrieval, have higher spatial resolution than that of similar sensors. However, bands 24/25 images with spatial resolution of 250 m (hereafter bands 24/25 250-m images) have considerable horizontal stripe noise caused by the limitation of the sensor imaging system’s multi-detector parallel scanning and the multiple interferences from space. Thus, the application value of LST retrieval using bands 24/25 250-m images is severely affected. At present, existing methods used to remove stripe noise often cannot retain the spectral information of the original image. Our research considers that MERSI-2 bands 24/25 images with spatial resolution of 1000 m (hereafter bands 24/25 1000-m images) and no stripe noise can provide reasonable reference statistics for bands 24/25 250-m images. Thus, the moment matching method considering synchronous reference image is applied to remove the stripe noise of bands 24/25 250-m images in the land area. Results shows that the moment matching method considering the synchronous reference image can effectively remove the stripe noise in the land area. In addition, quantitative evaluation criteria, such as information entropy H, mean-absolute-error (MAE), and peak signal-to-noise ratio, are better than those of the traditional moment matching method. After destriping, the LST values using MERSI-2 data are retrieved with better effect and decrease by 0.339 and 0.504 K using MAE and root-mean-square error, respectively, in contrast to the Moderate Resolution Imaging Spectroradiometer LST product.

Surface Temperature Retrieval From Gaofen-5 Observation and its Validation

Surface temperature (ST) plays a great role in urban heat island effect, environment monitoring, earth resources monitoring, and water balance at local and global scales. The Chinese Gaofen-5 (GF5) satellite can capture Earth’s thermal infrared information for use in national high-resolution Earth observations. In this article, the nonlinear split-window algorithm and the refined generalized split-window method are used to retrieve sea surface temperature (SST) and land surface temperature (LST) from GF5 observations. For different atmospheric and surface conditions, the algorithm coefficients are calculated using a statistical regression method from the numerical values simulated with the atmospheric radiative transfer model MODTRAN 5.0. The simulation results show that the root mean square error (RMSE) for SST and LST retrieval ranges from 0.09 K to 0.46 K and 0.19 K to 0.69 K, respectively, with increasing water vapor content (WVC). To validate the retrieved STs, Moderate Resolution Imaging Spectroradiometer (MODIS) STs extracted from the MYD11A1 product are used. Note that the RMSEs of both the LST and SST are less than 3.3 K. The RMSE for SST retrieval varies from 1.2 K to 1.45 K, with a mean value of 1.33 K; the RMSE for LST retrieval ranges from 1.57 K to 3.3 K, with a mean value of 2.41 K.

Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data

Land surface temperature (LST) is an important parameter for the biosphere, cryosphere, and climate change studies. In this study, we estimate LST, NDMI, and NDWI over the Ravi basin, India, and parts of Pakistan, using Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data. The study develops an ERDAS IMAGINE image processing method by using the LANDSAT 8 band 3(Green), band 4(Red), band 5(NIR), band 6(SWIR 1), and band 10 (TIR) data for determining the various spectral indices. The LST results show that most of the areas experienced extreme anomalies ranging from −350Cand 36 ​°C. The normalized difference moisture index (NDMI) value ranges from 0.685 to - 0.154. The normalized difference water index (NDWI) value ranges from 0.146 to - 0.444. Further, the LST result validated with in situ temperature observations at six locations in the study area, providing excellent correlation.

Enhancing Information Content in the Satellite-Derived Daytime Infrared Sea Surface Temperature Dataset Using a Transformative Approach

The operational daytime Sea Surface Temperature (SST) retrieval from remote sensing Infrared (IR) measurements does not conventionally use Mid-Wave IR (MWIR) channels due to solar contamination. However, MWIR channels are desirable to obtain unambiguous surface information and a transformative approach, Physical Deterministic Sea Surface Temperature (PDSST) retrieval scheme, including MWIR channels, to enhance the information content in satellite-derived SST data. This paper mainly emphasizes on the quality and availability of swath-processed daytime SSTs from MODIS-AQUA radiances using PDSST suite, including MWIR channels, for coastal and near-coastal areas, which are needed the most. The focus areas of this study are the California coast, which is a highly dynamic oceanographic region, the Indian Ocean, which has a sparse population of in-situs, and the Chesapeake Bay, which is best known for seafood production. Apart from in-situ validation using iQuam (NOAA), indirect validation, by comparing different SST products, is also performed. The results of PDSST-suite are compared with the currently operational MODIS-AQUA SSTs, obtained from the NASA website, and microwave SSTs from AMSR2, obtained from RSS website. Both the operational products are based on the regression method. It is found that the PDSST suite including MWIR channels can extract 3-5 times as much information as the currently operational NASA-produced regression-based daytime SSTs from MODIS-AQUA radiances for coastal areas.

Development of a Land Surface Temperature Retrieval Algorithm from GK2A/AMI

Land surface temperature (LST) is an important geophysical element for understanding Earth systems and land–atmosphere interactions. In this study, we developed a nonlinear split-window LST retrieval algorithm for the observation area of GEO-KOMPSAT-2A (GK2A), the next-generation geostationary satellite in Korea. To develop the GK2A LST retrieval algorithm, radiative transfer model simulation data, considering various impacting factors, were constructed. The LST retrieval algorithm was developed with a total of six equations as per day/night and atmospheric conditions (dry/normal/wet), considering the effects of diurnal variation of LST and atmospheric conditions on LST retrieval. The emissivity of each channel required for LST retrieval was calculated in real-time with the vegetation cover method using the consecutive 8-day cycle vegetation index provided by GK2A. The indirect validation of the results of GK2A LST with Moderate Resolution Imaging Spectroradiometer (MODIS) LST Collection 6 showed a high correlation coefficient (0.969), slightly warm bias (+1.227 K), and root mean square error (RMSE) (2.281 K). Compared to the MODIS LST, the GK2A LST showed a warm bias greater than +1.8 K during the day, but a relatively small bias (<+0.7 K) at night. Based on the results of the validation with in situ measurements from the Tateno station of the Baseline Surface Radiation Network, the correlation coefficient was 0.95, bias was +0.523 K, and RMSE was 2.021 K.