Abstract
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-II LST and MODIS LST over the Mid-East China region are in the range of 0.84~0.98 for different seasons and land cover types. Validation with 318 field LST samples indicates that the average MAE and R2 of the scenes at the four stations are about 1.97 K and 0.98, respectively. Thus, it could be concluded that the improved TFSWA developed in the study can be a good algorithm for LST retrieval from FY-3D MERSI-II data with acceptable accuracy.
Highlights
Land surface temperature (LST) is an important variable determining the process of land-atmosphere interactions over the Earth’s surface [1,2]
In order to determine the atmospheric transmittance for LST retrieval from the data, we developed a model to estimate atmospheric water vapor content (WVC) from the three atmospheric water bands
LSTs with the two-factor split-window algorithm (TFSWA) are very close to the radiation transfer (RTE) LSTs in all cases for the six atmospheric models
Summary
Land surface temperature (LST) is an important variable determining the process of land-atmosphere interactions over the Earth’s surface [1,2]. Many algorithms have been developed for LST retrieval from TIR remote sensing data [13,14,15]. Accurate and reliable LST retrieval is not easy because it needs to consider many variables in the process of TIR remote sensing. Such variables as land surface emissivity (LSE) of the TIR bands, the downward atmospheric thermal radiance, the atmospheric upward thermal radiance, and the atmospheric transmittance are all critical for the retrieval of LST. To address the effects of both land surface and atmospheric variability on TIR remote sensing, researchers have developed various algorithms for LST retrieval. The single-channel algorithms [11,16,17,18] were generally developed for the thermal data with only one TIR band, while the split-window algorithms (SWA) [19,20,21,22] were proposed for the data with, at least, two neighboring TIR bands
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