Remote sensing inversion of land surface temperature for cloud coverage areas based on NDVI in the North China Plain

As the cloud covers the surface thermal radiation during the transmission process and the remote sensor in the air is difficult to detect, Thermal Infrared (TIR) remote sensing inversion of Land Surface Temperature (LST) must be performed under clear-sky and cloudless conditions. This study has established the functional relationship between LST and the vegetation index of cloudless vegetation pixels around the cloud coverage areas in the North China Plain based on the Normalized Difference Vegetation Index (NDVI) to address the issues of TIR remote sensing inversion for LST affected by cloud coverage. By acquiring NDVI and using short-term, relatively stable characteristics of vegetation, the LST of the cloud cover area is estimated. The findings show a linear negative correlation between LST and NDVI in vegetation pixels, with the vegetation type remaining essentially unchanged over time. When there are 20 pixels in each of the two thermal infrared channels of FY-3 D, the MAE value and RMSE value of the 24th thermal infrared channel are 0.77 and 0.88, respectively, and the MAE value and RMSE value of the 25th thermal infrared channel are 0.64 and 0.80, respectively. When the number of pixels is 200, the 24th thermal infrared channel's MAE value and RMSE values are 0.96 and 0.99, respectively, while the 25th thermal infrared channel's MAE value and RMSE values are 0.90 and 0.95, respectively. In other words, the estimation is more accurate and closer to the true value, and the land surface temperature retrieved by the 25th channel deviates from the true value to a lesser extent. The average absolute error and root mean square error are both less than 1, which may satisfy the accuracy demands of practical applications such as agricultural drought monitoring, ecological evaluation, and crop yield estimation.