Construction of cloud-free MODIS-like land surface temperatures coupled with a regional weather research and forecasting (WRF) model

Abstract

Land surface temperature (LST) is a key parameter in the physics of land surface processes. Currently, the technique most commonly used to obtain LST is thermal infrared (TIR) remote sensing (RS). However, this technique is affected by clouds and cannot obtain complete spatiotemporal LST images. To solve this problem, RS and weather research and forecasting (WRF) coupled model (RS-WRF coupled model) was developed to produce cloud-free MODIS-like LST data. (i) The WRF model is used to simulate the cloud-free LST with a 1 km resolution. (ii) The optimal machine learning model is utilized to fit the simulated LSTs and produce cloud-free MODIS-like LSTs. (iii) Combined with a median filtering algorithm, the salt and pepper noise in the fitted image is optimized. Taking Beijing as a test site. Under relatively little cloud cover and greater cloud contamination, the root mean square error of the LST constructed by the RS-WRF coupled model is approximately 1.2 and 1.8 K, respectively. The correlation coefficients under both conditions exceed 0.9. Overall, the RS-WRF coupled model can provide cloud-free time series MODIS-like LST images in areas with frequent cloud cover, thereby compensating for the disadvantage that satellite TIR images contaminated by clouds cannot obtain complete LST estimates.