A new approach for modeling near surface temperature lapse rate based on normalized land surface temperature data

摘要

The modeling of Near-Surface Temperature Lapse Rate (NSTLR) is of great importance in various environmental applications. This study proposed a new approach for modeling the NSTLR based on the Normalized Land Surface Temperature (NLST). A set of remote sensing imagery including Landsat images, MODIS products, and ASTER Digital Elevation Model (DEM), land cover maps, and climatic data recorded in meteorological stations and self-deployed devices located in the three study area were used for modeling and evaluation of NSTLR. First, the Split Window (SW) and Single Channel (SC) algorithms were used to estimate LST, and the spectral indices were used to model surface biophysical characteristics. The solar local incident angle was obtained based on topographic and time conditions for different dates. In the second step, the NSTLR value was calculated based on the LST-DEM feature space at the regional scale. The LST was normalized relative to the surface characteristics based on Random Forest (RF) regression and the NSTLR was calculated based on the NLST-DEM feature space. Finally, the coefficient of determination (R-2) and Root Mean Square Error (RMSE) between the modeled NSTLR and the observed NSTLR were calculated to evaluate the accuracy of the modeled NSTLR. The mean values of R-2 between DEM and NLST were improved 0.3, 0.42 and 0.35, rather than between DEM and LST for the study area A, B and C, respectively. The R-2 and RMSE between the observed NSTLR and the Landsat derived NSTLR based on NLST for the study area A (B, C) were improved 0.30 (0.26, 0.35) and 0.81 (0.80, 0.94) degrees C Km(-1), respectively, rather than the Landsat derived NSTLR based on LST. Also, for the study area A, the R-2 and RMSE between the observed NSTLR and the MODIS derived NSTLR based on NLST in spring, summer, autumn and winter were improved 0.17, 0.12, 0.10, and 0.22; and 0.51, 0.44, 0.27, and 0.51 degrees C Km(-1), respectively, rather than the MODIS derived NSTLR based on LST. Model assessment results (R-2 and RMSE) and comparing modeled NSTLR (all strategies) with observed NSTLR, for both Landsat and MODIS, showed that the use of NLST instead of LST, significantly improved the accuracy of the obtained NSTLR in the mountainous regions.