Land Surface Temperature Retrieval Using High-Resolution Vertical Profiles Simulated by WRF Model

Reconstruction of all-weather land surface temperature based on a combined physical and data-driven model

At present, the remote sensing (RS) thermal infrared (TIR) images that are commonly used to obtain land surface temperature (LST) are contaminated by clouds and thus cannot obtain spatiotemporal integrity of LST. To solve this problem, this study combined a physical model with strong interpretability with a data-driven model with high data adaptability. First, the physical model (Weather Research and Forecast (WRF) model) was used to generate LST source data. Then, combined with multisource RS data, a data-driven method (random forest (RF)) was used to improve the accuracy of the LST, and a model framework for a data-driven auxiliary physical model was formed. Finally, all-weather MODIS-like data with a spatial resolution of 1 km were generated. Beijing, China, was used as the study area. The results showed that in cases of more clouds and fewer clouds, the reconstructed all-weather LST had a high spatial continuity and could restore the spatial distribution details of the LST well. The mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (ρ) in the case of more (fewer) clouds were ranked as follows: MAE < 1 K (< 2 K), RMSE < 2 K (< 2 K), and ρ > 0.9. The errors obeyed an approximately normal distribution. The total MAE, RMSE, and ρ were 0.80 K, 1.09 K, and 0.94 K, respectively. Generally, the LST reconstructed in this paper had a high accuracy, and the model could provide all-weather MODIS-like LST to compensate for the disadvantages of satellite TIR images (i.e., contamination by clouds and inability to obtain complete LST values).

Seasonal Differences in Land Surface Temperature under Different Land Use/Land Cover Types from the Perspective of Different Climate Zones

The process of urbanization is accelerating, and land surface temperature (LST) is increasing, seriously threatening human health. Therefore, it is crucial to explore the differences in LST of different land use/land cover (LULC) types. Using MOD11A2 and MCD12Q1 data, this study explored the seasonal differences in LST of each LULC type from the perspective of different climate zones. The results showed that the maximum and minimum LSTs during the day were higher than those at night. During the day, the LSTs of urban and built-up and barren lands were higher than those of forests, grasslands, and water bodies; at night, the LSTs of urban and built-up lands decreased but remained high, while barren lands showed a significant decrease to LSTs even lower than those of water bodies. In addition, the difference in daytime LST of the LU16 type (barren lands) in different climatic zones was the most obvious and was much higher than that of other LULC types in the middle temperate and south temperate zones, but much lower than those in the middle subtropical and north subtropical zones. This comparison of the LST differences of each LULC type under different climate backgrounds provides an important reference for rational urban planning.