Abstract
Medium resolution spectral imager II (MERSI-2) is a payload for the Chinese meteorological satellite Feng Yun 3 (FY-3). China's satellite remote sensing observation capabilities such as climate change research can be improved during MERSI-2's operation in orbit, and the sensor is the world's first imaging instrument that can obtain a global infrared split-window data with a spatial resolution of 250 m. We developed an operational spilt-window (SW) algorithm to retrieve land surface temperature (LST) accurately from the MERSI-2 data. The SW algorithm coefficients were derived from a simulation dataset that was established with the Moderate spectral resolution atmospheric Transmittance model version 5.2 and the thermodynamic initial guess retrieval dataset. In the practical retrieval, the precise algorithm coefficients were determined by view zenith angle and atmospheric water vapor content (WVC), the atmospheric WVC were obtained from the ERA5 dataset, and the land surface emissivity was dynamically estimated using the advanced spaceborne thermal emission and reflection radiometer global emissivity dataset, considering the fractional of vegetation cover and snow cover. The retrieved LST compared with in situ LST, which was highly consistent with the in situ LST and that the root-mean-square error of the two is within 3 K. The retrieved LST was compared with the MYD11_L2 and MYD21_L2 LST products, and the results indicated that MERSI-2 LST was more consistent with the MYD21 LST. The operational SW algorithm for FY-3D MERSI-2 developed in this study could retrieve LST accurately and has a wide range of popularization and application values.
Highlights
L AND surface temperature (LST) is one of the key parameters in land surface physical process on regional and global scales, and accurate land surface temperature (LST) is essential for the study, such as climate, hydrology, and ecology [1]–[4]
The root-mean-square error (RMSE) is less than 1.5 K at each water vapor content (WVC) subrange when viewing zenith angles (VZA) is less than 40° and is less than 1.5 K in all VZAs when WVC is less than 2.5 g/cm2
The results indicated that the influence of WVC. (a) LST RMSE caused by Land surface emissivity (LSE) uncertainty. (b) LST RMSE caused by the sensor noise (0.4 K) (W1:WVCࢠ[0, 1.5]; W2:WVCࢠ[1, 2.5]; W3:WVCࢠ[2, 3.5]; W4:WVCࢠ[3, 4.5]; W5:WVCࢠ[4, 5.5]; W6:WVCࢠ[5, 6.5])
Summary
L AND surface temperature (LST) is one of the key parameters in land surface physical process on regional and global scales, and accurate LST is essential for the study, such as climate, hydrology, and ecology [1]–[4]. A large number of LST retrieval algorithms have been proposed. Several typical algorithms, such as the split-window (SW) algorithm [6]–[9], single-channel algorithm [10], [11], and temperature and emissivity separation (TES) algorithm [12]–[14]. Soil emissivity varies greatly in the TIR bands [38] These methods cannot well characterize the emissivity variation over the bare soil surface in arid and semiarid lands [39], [40]. Several studies have indicated that the Collection 5 MODIS LST product using classification-based emissivity method overestimates bare soil emissivity leads to the serious underestimation of the LST [41], [42]. The National Aeronautics and Space Administration released a new emissivity dataset named the ASTER global emissivity dataset (ASTER GED) in 2014, which is a global mean emissivity database produced using all clear-sky ASTER images from 2000 to 2008
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