Estimating Near-Surface Air Temperature From Satellite-Derived Land Surface Temperature Using Temporal Deep Learning: A Comparative Analysis

This study develops and compares three deep learning methods—LSTM, TCN, and N-BEATS—for estimating near-surface air temperature (T2M) from satellite-derived land surface temperature (LST) and land cover metrics such as NDVI and NDBI. By incorporating temporal context through varying look-back windows, these models substantially outperform non-temporal baselines, reducing root-mean-square error (RMSE) from around 2.6–2.8°C to below 1.8°C, and underscoring the value of historical LST observations for capturing the evolving surface-air temperature relationship. Longer lags generally improve accuracy, although N-BEATS performance plateaus beyond a certain window, reflecting both diminishing returns and practical limitations linked to missing cloud-free satellite data. Seasonal and diurnal evaluations show higher errors in spring and midday hours, likely due to rapid vegetation changes and stronger physical and dynamical processes that make T2M less predictable. Spatially, stations with denser vegetation exhibit elevated errors, suggesting that transpiration and canopy effects complicate the LST-T2M linkage. For extreme-event detection, LSTM provides the fewest false alarms (highest precision), N-BEATS captures the most extremes (highest recall), and TCN offers the best overall balance in precision and recall (highest F1). While cloud-free satellite coverage remains a limitation, future work could explore adaptive lag strategies, additional data sources, and more advanced data-fusion techniques. These results highlight that satellite-based temperature monitoring, when combined with suitable deep learning architectures, can reliably estimate T2M based on LST, further addressing gaps in near-surface observations and facilitating the detection of critical T2M extremes. This framework has direct applications in heat-warning systems, resource management, precision agriculture, and urban climate adaptation, and stands to benefit further from ongoing advancements in satellite sensing technology.