This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based ...
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This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance-variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.
One of the most important parameters in the exchange of energy between the ground and the atmosphere is the land surface temperature (LST). In this work, a split-window algorithm for deriving LST from the first Meteos...
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One of the most important parameters in the exchange of energy between the ground and the atmosphere is the land surface temperature (LST). In this work, a split-window algorithm for deriving LST from the first Meteosat Second Generation satellite (MSG-1) data using the two thermal infrared channels IR10.8 and IR12.0 is proposed and validated with in-situ measured temperatures. This algorithm is obtained starting from the radiance transfer equation and requires the knowledge of total atmospheric water vapor content (TAWV) and the surface emissivity (epsilon(10.8) and epsilon(12)). First, we have simplified the Planck function to retrieve the radiance at MSG-1 from the temperature. Then, the Roberts model was used to create a relationship between atmospheric transmittance and TAWV. Next, sensitivity analysis of the algorithm indicates that the possible error of viewing angle has relatively insignificant impact on the probable LST estimation error, which is sensible to the possible error of ground emissivity and TAWV. Finally, two methods have been used to validate the proposed algorithm. On the one hand, the results show a root mean square error (RMSE) equals 0.79 K and an average accuracy equals 0.45 K for the comparison with another algorithm. On the other hand, the results show a RMSE equals 2.75 K and an average accuracy equals 1.96 K for the comparison with in-situ measurements. We conclude that the proposed algorithm is able to provide an accurate LST.
A practical split-window algorithm which involves two parameters (transmittance and emissivity) utilized to retrieve land-surface temperature over agricultural areas from the Advanced Spaceborne Thermal Emission and R...
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A practical split-window algorithm which involves two parameters (transmittance and emissivity) utilized to retrieve land-surface temperature over agricultural areas from the Advanced Spaceborne Thermal Emission and Reflection Radiometer data is presented. First, by calculating the relationship between thermal radiation intensity and temperature, the Planck function is simplified using exponential function which is applied to deduce the split-window algorithm. Second, how to obtain transmittance from water vapor content and the method for estimating emissivity using normalized difference vegetation index are discussed in detail. Sensitivity analysis demonstrates that the algorithm is not sensitive to these two parameters. Finally, a standard atmospheric simulation method has been used to validate the proposed algorithm, and comparison between the algorithm and the prior study has been carried out. The results indicate that the average accuracy is 0.32 K for the case without error in both transmittance and emissivity, which is better than the prior algorithm. The accuracy is also 0.32 K when the transmittance is computed from the water content by piecewise cubic polynomial fit. The accuracy is about 0.30 K similar to 0.33 K corresponding to different Pv (Pv is the proportion of vegetation) values, which indicates that this algorithm is suitable for different land surface types over agricultural areas. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Land surface temperature (LST) is a crucial parameter in analyzing and evaluating climate change at various scales, the surface energy balance, soil moisture, evapotranspiration and urban heat islands. Currently, meth...
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ISBN:
(纸本)9781479979295
Land surface temperature (LST) is a crucial parameter in analyzing and evaluating climate change at various scales, the surface energy balance, soil moisture, evapotranspiration and urban heat islands. Currently, methods for its estimation from space have continuously been developed, while most studies focus on the split-window (SW) algorithms. According to the published works, some approximations and assumptions were used to develop SW algorithms. This paper investigated and revised the error caused by these approximations and assumptions with the help of TIGR 2000 database and MODTRAN 4.0 software. Then a new SW method to estimate LST from FY-3A/VIRR was proposed in this paper. The primarily accuracy evaluation of the proposed method shows that the root mean square error (RMSE) of LST estimation using TIGR atmospheric profiles is 0.768 K, with the bias of -0.122 K.
The main objective of this paper is to compare eighteen data-driven split-window (SW) algorithms to derive land surface temperature (LST) and identify the most efficient techniques. We also aim to compare the results ...
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The main objective of this paper is to compare eighteen data-driven split-window (SW) algorithms to derive land surface temperature (LST) and identify the most efficient techniques. We also aim to compare the results of the SW algorithms against the MODIS LST products (MOD11_L2/MYD11_L2). To archive these goals, the radiometric correction was accomplished to the MODIS satellite image, and then, the LST was derived using the SW algorithms for the year 2019. This LST was validated using RMSE, MSE, MAPE, and MAD based on the observed data in local meteorological stations. Validation analysis reveals that the Sobrino 1993 algorithm with a RMSE value of 1.79 and Qin algorithm with a RMSE value of 5.28 have respectively high and low accuracy to calculate LST in the study area. Furthermore, we compared the MODIS LST products against the local meteorological station data and achieved respectively the RMSE values of 13.3, 13.96, 18.83, 10.84, 13.91, 13.51, and 5.2 in Ahar, Tabriz, Jolfa, Sarab, Maraghe, Ligvan, and Kalibar stations. These findings demonstrated that the SW algorithms have better performance in comparison with MODIS LST products to estimate LST. Results of this research are of great importance for applying and comparing different data-driven approaches and identifying the most efficient techniques. The obtained results support future research by means of exploring the capability of each method and deriving validate results through efficient techniques.
Cloud cover and satellite angle significantly impact sea surface temperature (SST) retrievals from remote sensing imagery, yet traditional methods often overlook these factors. This study takes Moderate Resolution Ima...
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Cloud cover and satellite angle significantly impact sea surface temperature (SST) retrievals from remote sensing imagery, yet traditional methods often overlook these factors. This study takes Moderate Resolution Imaging Spectroradiometer remote sensing imagery from the northern South China Sea in winter as a case study to investigate the effects of cloud cover and satellite angle on SST retrieval. By employing adaptive cloud detection, cloud masking, and angle correction techniques, a SST retrieval model based on the split-window algorithm was established and compared with microwave and in-situ data. The research results indicate that: (1) The established cloud removal and satellite angle correction model for SST retrieval demonstrates high precision and accuracy, with an average error of less than 0.5 degrees C. (2) Combination a (cloud removal + angle correction), Combination b (non-cloud removal + angle correction), and Combination c (non-cloud removal+ non-angle correction) have an average error of -0.916 degrees C, -0.311 degrees C, -1.047 degrees C respectively. It is evident that the inversion results of the proposed model (Combination a) exhibit a higher level of agreement with the measured data compared to t Combination b and Combination c. (3) The average error of the microwave inversion data (combination x) is 0.560 degrees C;Furthermore, combination x shows a different temperature curve trend compared to the measured data, whereas combination a aligns more closely with the temperature curve trend of the measured data. Accurate retrieval of SST is of great significance for understanding large-scale oceanic circulation systems, boundary currents, eddies, and ocean currents, as well as studying seasonal and interannual variations in marine ecosystems.
Land surface temperature (LST) is a crucial parameter for representing the earth's surface energy balance. Thermal infrared remote sensing is the primary method for rapidly retrieving LST over large areas. The Chi...
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Land surface temperature (LST) is a crucial parameter for representing the earth's surface energy balance. Thermal infrared remote sensing is the primary method for rapidly retrieving LST over large areas. The Chinese Atmospheric Environment Monitoring Satellite (DQ-1) is equipped with the wide swath imager (WSI), which includes three thermal infrared bands capable of providing global LST retrieval. This article introduces a nonlinear hybrid algorithm that combines the split-window (SW) algorithm and the temperature and emissivity separation (TES) algorithm, and the accuracies of the three algorithms, including hybrid, SW and TES algorithm are analyzed. The results demonstrated that the root mean square errors of LST for SW, TES, and hybrid algorithm are approximately 2.11, 1.78, and 1.64 K, with mean absolute errors (of 1.72, 1.40, and 1.21 K using in situ measurements from the SURFRAD sites. Cross-validation with moderate-resolution imaging spectroradiometer (MODIS) LST products showed that the hybrid algorithm outperforms the SW and TES algorithms in retrieving LST, achieving reductions in LST error of 0.43 and 0.16 K at the Qinghai Lake site, and 0.67 and 0.06 K at the Dunhuang site, respectively. In summary, this study demonstrates that the nonlinear hybrid algorithm can accurately estimate LST from DQ1/WSI data.
The split-window (SW) and temperature-and-emissivity separation (TES) algorithms have been widely used for land surface temperature (LST) estimation from thermal infrared (TIR) observations for various missions. Howev...
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The split-window (SW) and temperature-and-emissivity separation (TES) algorithms have been widely used for land surface temperature (LST) estimation from thermal infrared (TIR) observations for various missions. However, the SW algorithm requires prior estimates of land surface emissivity (LSE). The TES algorithm encompasses an atmospheric correction module, which increases the complexity and uncertainty of operational LST retrieval. To address this, we proposed a split-window-driven temperature-and-emissivity separation (SWDTES) algorithm in this study to estimate LST and LSE simultaneously without the need of atmospheric correction by combining the respective advantages of SW and TES. The inputs to the SWDTES algorithm are largely simplified, which only requires atmospheric water vapor content (AWVC) apart from the top-of-atmosphere TIR radiance. The developed SWDTES algorithm was applied to the high spatial resolution Thermal Infrared Spectrometer (TIS) data from the newly launched Sustainable Development Science Satellite-1 (SDGSAT-1) mission, and its performance was assessed using the MODIS data and ground measurements. The cross validation shows that the correlation coefficient (r), bias and root mean square error (RMSE) between MODIS-converted LSE and retrieved LSE using the SWDTES algorithm for the nighttime case is 0.904, -0.033 and 0.038 for band 1;0.677, -0.008 and 0.014 for band 2;and 0.576, -0.000 and 0.008 for band 3, indicating a good consistency between the two LSE estimates. In addition, the evaluation using ground measurements shows that the r, bias and RMSE between the in-situ LST and retrieved LST using the SWDTES algorithm are 0.99, -0.67 K and 2.10 K, respectively. Compared to the OSW and TES algorithms, the SWDTES algorithm reduces the RMSE by 0.34 K and 0.90 K, respectively, indicating an improvement in LST retrieval accuracy. We conclude that the proposed SWDTES algorithm can achieve high-accuracy and high-resolution LST retrieval from the S
Land surface temperature (LST) is an important climate variable used to assess the effects of climate change. This research project aims to compare the results of mono-window (MW) and split-window (SW) algorithms agai...
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Land surface temperature (LST) is an important climate variable used to assess the effects of climate change. This research project aims to compare the results of mono-window (MW) and split-window (SW) algorithms against the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 Collection 2 Level-2 surface temperature (L8-C2L2) products and identify the most suitable techniques. In-situ measurements of the earth's surface temperature and relative humidity have been recorded in 2020. LSTs have been validated using root mean square error (RMSE) based on the in-situ meteorological data. Validation analysis has indicated that the SW algorithm combined with in-situ micro-scale atmospheric water vapor content values was more accurate for LST. In the overall study area, the RMSE values of 1.09 degrees C, 3.97 degrees C, 4.36 degrees C, and 6.80 degrees C have been calculated for SW, MODIS, L8-C2L2, and MW LSTs, respectively. These results have demonstrated that the SW algorithm outperformed the other LST products. The maximum difference between the in-situ earth surface temperature and the SW algorithm was 0.79 degrees C. These findings are essential for comparing different data-driven approaches and identifying the most efficient techniques. The study's significance lies in identifying the most appropriate method for LST retrieval, which can aid in climate change studies and inform decision-making processes.(c) 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)
This paper addresses the retrieval of land surface temperature (LST) from the measurements acquired by the Medium Resolution Spectral Imager - Low Light (MERSI-LL) on Fengyun 3E (FY-3E) satellite. First, a generalized...
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ISBN:
(纸本)9798350360332;9798350360325
This paper addresses the retrieval of land surface temperature (LST) from the measurements acquired by the Medium Resolution Spectral Imager - Low Light (MERSI-LL) on Fengyun 3E (FY-3E) satellite. First, a generalized split-window algorithm is developed using radiative transfer modelling experiments. Then, LSTs are retrieved from the FY-3E MERSI-LL measurements in Sept. of 2022 over a study area with longitude from 105 degrees E to 135 degrees E and latitude from 25 degrees N to 50 degrees N. Finally, the LSTs retrieved in this work are validated against the MOD11C1 V61 product, in which the LST product of the Advanced Geostationary Radiation Imager on Fengyun 4A (FY-4A) satellite serves as a connecting bridge. The results show that the accuracy of the LSTs retrieved from the FY-3E MERSI- LL measurements in this work is 0.01 +/- 0.75 K against the MOD11C1 V61 product.
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