Abstract
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.
Highlights
LAND surface temperature (LST) characterizes the thermal radiation information and temporal and spatial changes of the surface under different conditions
This study shows that the proposed temperatureemissivity separation (TES) algorithm provides an effective way to get LST and land surface emissivity (LSE) image from FY-3D/ MERSI-II nighttime observation, which will improve their applications in different fields
The retrieval of LST from thermal infrared (TIR) data observed via remote sensing can be traced back to the 1970s, and it has attracted widespread attention from the scientific community[10]
Summary
LAND surface temperature (LST) characterizes the thermal radiation information and temporal and spatial changes of the surface under different conditions. In the normalized emissivity (NEM) module of the TES algorithm, after atmospheric correction, we usually set a maximum initial emissivity value εmax=0.99 in all channels to obtain surface temperatures (Tsl, Ts2, Ts3 and Ts4) from each channel. For the tropical profile (Fig. 3(b)), when the surface temperature is approximately 280 K, surface emission radiance (εiBi(Ts)) is close to atmospheric downward thermal radiance Ra↓i, especially in the second TIR channel. In this case, the land surface and atmospheric environment are like a blackbody, which disables the TES algorithm from separating the temperature and emissivity well. The RMSE of emissivity for the two TIR channels is stable, maintaining around 0.01 under varying surface temperature
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