Estimating All-Weather Land Surface Temperature: A Method Considering Cloud Fraction and Energy Balance

Spatiotemporally continuous land surface temperature (LST) is crucial for monitoring extreme weather and providing disaster warnings. It captures abnormal temperature fluctuations, offering timely early warning and response for sudden climate events and natural disasters. However, cloud cover and satellite observation gaps often limit the spatial completeness of LST, while previous reconstruction methods seldom consider the effects of solar radiation and cloud cover on LST. To address these challenges, this study proposed the all-weather real estimation (AWRE) method, which integrated thermal infrared (TIR) and passive microwave (PMW) data with environmental factors to estimate the LST under all-weather conditions. By incorporating deep learning and land surface energy balance (SEB) models, and analyzing the impact of clouds on temperature fluctuations, the proposed method retrieves all-weather LST. Applied to the 2022 data of China, the AWRE method demonstrated high accuracy in estimating LST. The overall average root mean square error (RMSE) and Bias were 2.90 and 0.56 K, respectively, with daytime and nighttime RMSEs of 2.97 and 2.83 K, respectively. Specifically, for daytime (nighttime) conditions, the RMSEs under clear sky were 2.94 K (2.58 K), partially cloudy 3.08 K (2.76 K), and fully cloudy 2.9 K (3.14 K). The estimated all-weather LST effectively captured diurnal and seasonal variations, with accuracy comparable to in situ LST measurements, maintaining temporal continuity. This approach improves the detection of extreme heat events and addresses spatiotemporal coverage gaps, providing more accurate data for climate models, weather monitoring, and public health decisions.