High-resolution land surface temperature (LST) product holds significant importance in quantifying surface heat, monitoring climate change, assessing environmental health, and water resource management. Therefore, acc...
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ISBN:
(纸本)9798350360332;9798350360325
High-resolution land surface temperature (LST) product holds significant importance in quantifying surface heat, monitoring climate change, assessing environmental health, and water resource management. Therefore, accurate LST retrieval improves our understanding of detailed thermal characteristics of the Earth's surface. In this research, we developed an operational split-window algorithm for generationg high-resolution LST products from Gaofen5-02 (GF5-02) Visible and Infrared Multispectral Imager (VIMI) data. The coefficients of the split-window algorithm are simulated utilizing the MODTRAN 5.2 atmospheric radiative transfer model, with the global atmospheric profile library of SeeBor V5.0. The land surface emissivities in VIMI bands 11 and 12 are estimated using the ASTER global emissivity dataset (GED) based on the vegetation cover method. The GF5-02 VIMI LSTs are validated using in-situ data collected from the Huailai experiment site in China. Preliminary results show the accuracy of the GF5-02 LST products is satisfactory, exhibiting a bias of -0.03 K and a Root Mean Square Error (RMSE) of 2.42 K.
Sea Surface Temperature (SST) is a pivotal parameter in studying the energy balance and material exchange between the ocean and the atmosphere. Obtaining high-precision SST is of significant importance for a deep unde...
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ISBN:
(纸本)9798350360332;9798350360325
Sea Surface Temperature (SST) is a pivotal parameter in studying the energy balance and material exchange between the ocean and the atmosphere. Obtaining high-precision SST is of significant importance for a deep understanding of the dynamic changes in the ocean and atmospheric systems. This study utilized a nonlinear split-window algorithm for deriving 40m SST from Chinese Gaofen5-02 (GF5-02) Visible and Infrared Multispectral Imager (VIMI). The coefficients of the algorithm were simulated utilizing the MODTRAN 5.2 atmospheric radiative transfer model and the global atmospheric profile library of SeeBor V5.0. The retrieved VIMI SST was validated using iQuam SST measurements (in-situ measurements) and the MODIS SST products. The results of the cross-validation demonstrate a reasonable accuracy of the produced VIMI SST products, exhibiting a bias of 1.34 K and an RMSE of 2.68 K.
The system of observation and capturing the earth resource features have been improving with the scientific revolution and technological development in remote sensing techniques. In comparison with the previous Landsa...
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High-resolution land surface temperature (LST) retrieval is a hot research topic in recent ten years, and the development of various high-resolution satellite sensors provides a data basis for this study. The Visual a...
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ISBN:
(数字)9781665427920
ISBN:
(纸本)9781665427920
High-resolution land surface temperature (LST) retrieval is a hot research topic in recent ten years, and the development of various high-resolution satellite sensors provides a data basis for this study. The Visual and Infrared Multispectral Imager (VIMI) on Gaofen5-02 (GF5-02) satellite provides 40m spatial resolution thermal infrared data ranging from 8 mu m to 12.5 mu m, including four thermal infrared channels. In this paper, we developed a split-window algorithm for retrieving LST from VIMI data. First, the two thermal infrared channels 11 and 12 of VIMI are cross-calibrated using MODIS bands 31 and 32, and then high-resolution LST was derived using the generalized splitwindowalgorithm. The GF5-02 LST was cross-validated with the MODIS MOD21 LST products, the preliminary results indicate that GF5-02 LST shows a reasonable accuracy, with a mean bias of 0.05 K and a mean RMSE of 3.29 K.
In this study, a split-window(SW) algorithm is tested to retrieve the land surface temperature (LST) from nighttime mid-infrared MODIS data. At first, the SW algorithm's coefficients were derived using MODTRAN sim...
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ISBN:
(纸本)9781665403696
In this study, a split-window(SW) algorithm is tested to retrieve the land surface temperature (LST) from nighttime mid-infrared MODIS data. At first, the SW algorithm's coefficients were derived using MODTRAN simulations with the TIGR atmospheric profile database. Then, the input emissivities of the SW algorithm were directly calculated using the MODIS MYD11B1 products. At last, the LST was retrieved using the Aqua MODIS bands 22 and 23 data. The retrieved LSTs were compared with the MODIS MYD11A1 products and validated using ground measurements collected from four ground sites in northwest China. The validation results indicate that the developed SW algorithm provides better accuracy than the MYD11A1 LST products, with a mean bias of -0.76K and a mean root-mean-square error (RMSE) of 1.3K. Experiments show that the split-window algorithm is also applicable in the mid-infrared channel at night, and can produce accurate land surface temperature results.
Land surface temperature (LST) is an essential parameter widely used in environmental studies. The Medium Resolution Spectral Imager II (MERSI-II) boarded on the second generation Chinese polar-orbiting meteorological...
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Land surface temperature (LST) is an essential parameter widely used in environmental studies. The Medium Resolution Spectral Imager II (MERSI-II) boarded on the second generation Chinese polar-orbiting meteorological satellite, Fengyun-3D (FY-3D), provides a new opportunity for LST retrieval at a spatial resolution of 250 m that is higher than that of the already widely used Moderate Resolution Imaging Spectrometer (MODIS) LST data of 1000 m. However, there is no operational LST product from FY-3D MERSI-II data available for free access. Therefore, in this study, we developed an improved two-factor split-window algorithm (TFSWA) of LST retrieval from this data source as it has two thermal-infrared (TIR) bands. The essential coefficients of the TFSWA algorithm have been carefully and precisely estimated for the FY-3D MERSI-II TIR thermal bands. A new approach for estimating land surface emissivity has been developed using the ASTER Global Emissivity Database (ASTER GED) and the International Geosphere-Biosphere Program (IGBP) data. A model to estimate the atmospheric water vapor content (AWVC) from the three atmospheric water vapor absorption bands (bands 16, 17, and 18) has been developed as AWVC has been recognized as the most important factor determining the variation of AT. Using MODTRAN 5.2, the equations for the AT estimate from the retrieved AWVC were established. In addition, the AT of the pixels at the far edge of FY-3D MERSI-II data may be strongly affected by the increase of the optical path. Viewing zenith angle (VZA) correction equations were proposed in the study to correct this effect on AT estimation. Field data from four stations were applied to validate the improved TFSWA in the study. Cross-validation with MODIS LST (MYD11) was also conducted to evaluate the improved TFSWA. The cross-validation result indicates that the FY-3D MERSI-II LST from the improved TFSWA are comparable with MODIS LST while the correlation coefficients between FY-3D MERSI-I
The split-window algorithm is the most commonly used method for land surface temperature (1ST) retrieval from satellite data. Simplification of the Planck's function, as an important step in developing the SWA, al...
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The split-window algorithm is the most commonly used method for land surface temperature (1ST) retrieval from satellite data. Simplification of the Planck's function, as an important step in developing the SWA, allows us to directly relate the radiance to the temperature toward solving the radiative transfer equation (RTE) set. In this study, Planck's radiance relationship between two adjacent thermal infrared channels was modeled to solve the RTE set instead of simplification of the Planck's function. A radiance-based split-window algorithm (RBSWA) was developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data. The performance of the RBSWA was assessed and compared with three most common brightness temperature-based split-window algorithms (BTBSWAs) by using the simulated data and satellite measurements. Simulation analysis showed that the 1ST retrieval using RBSWA had a Root Mean Square Error (RMSE) of 0.5 K and achieved an improvement of 0.3 K compared with three BTBSWAs, and the 1ST retrieval accuracy using RBSWA was better than 1.5 K considering uncertainties in input parameters based on the sensitivity analysis. For application of RBSWA to MODIS data, the results showed that: 1) comparison between 1ST from MODIS 1ST product and 1ST retrieved using RBSWA showed a mean RMSE of 1.33 K for 108 groups of MODIS image covering continental US, which indicates RBSWA is reliable and robust;2) when using the measurements from US surface radiation budget network as real values the RMSE of the RBSWA algorithm was 2.55 K and was slightly better than MODIS 1ST product;and 3) through the cross validation using Advanced Spaceborne Thermal Emission and Reflection Radiometer 1ST product, the RMSE of the RBSWA algorithm was 2.23 K and was 0.28 K less than that of MODIS 1ST product. We conclude that the RBSWA for 1ST retrieval from MODIS data can attain a better accuracy than the BTBSWA.
Land surface temperature (LST) is a crucial parameter in the interaction between the ground and the atmosphere. The Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) provides global daily coverage of day...
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Land surface temperature (LST) is a crucial parameter in the interaction between the ground and the atmosphere. The Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) provides global daily coverage of day and night observation in the wavelength range of 0.55 to 12.0 mu m. LST retrieved from SLSTR is expected to be widely used in different fields of earth surface monitoring. This study aimed to develop a split-window (SW) algorithm to estimate LST from two-channel thermal infrared (TIR) and one-channel middle infrared (MIR) images of SLSTR observation. On the basis of the conventional SW algorithm, using two TIR channels for the daytime observation, the MIR data, with a higher atmospheric transmittance and a lower sensitivity to land surface emissivity, were further used to develop a modified SW algorithm for the nighttime observation. To improve the retrieval accuracy, the algorithm coefficients were obtained in different subranges, according to the view zenith angle, column water vapor, and brightness temperature. The proposed algorithm can theoretically estimate LST with an error lower than 1 K on average. The algorithm was applied to northern China and southern UK, and the retrieved LST captured the surface features for both daytime and nighttime. Finally, ground validation was conducted over seven sites (four in the USA and three in China). Results showed that LST could be estimated with an error mostly within 1.5 to 2.5 K from the algorithm, and the error of the nighttime algorithm involved with MIR data was about 0.5 K lower than the daytime algorithm.
Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and La...
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Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) thermal infrared data. The National Centers for Environmental Prediction (NCEP) reanalysis atmospheric profiles combined with the radiation transport model MODerate resolution atmospheric TRANsmission version 5.2 (MODTRAN 5.2) were utilized to obtain atmospheric water vapor content (WVC). The ASTER Global Emissivity Database Version 3 (ASTER GED v3) product was utilized to estimate surface emissivity in order to improve the accuracy of LST estimation over barren surfaces. Using a simulation database, the coefficients of the algorithm were fitted and the performance of the algorithm was evaluated. The root-mean-square error (RMSE) values of the differences between the estimated LST and the actual LST of the MODTRAN radiative transfer simulation at each WVC subrange of 0-6.5 g/cm(2) were less than 1.0 K. To validate the retrieval accuracy, ground-based LST measurements were collected at two relatively homogeneous desert study sites in Dalad Banner and Wuhai, Inner Mongolia, China. The bias between the retrieved LST and the in situ LST was about 0.2 K and the RMSE was about 1.3 K at the Dalad Banner site, whereas they were approximately -0.4 and 1.0 K at the Wuhai site. As a reference, the retrieved LST was compared with the operational SLSTR LST product in this study. The bias between the SLSTR LST product and the in situ LST was approximately 1 K and the RMSE was approximately 2 K at the Dalad Banner site, whereas they were approximately 1.1 and 1.4 K at the Wuhai site. The results demonstrate that the split-window algorithm combined with improved emissivity estimation based on the ASTER GED product can distinctly obtain better accuracy of LST over barren surfaces.
作者：
Wang, LijuanGuo, NiWang, WeiZuo, HongchaoLanzhou Univ
Coll Atmospher Sci Lanzhou 730000 Gansu Peoples R China China Meteorol Adm
Key Lab Drought Climate Change & Disaster Reduct Key Lab Drought Climate Change & Disaster Reduct Inst Arid Meteorol Lanzhou 730020 Gansu Peoples R China
FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). I...
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FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.
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