Comprehensive Evaluation of Eight Methods for Generating 1‐km Monthly Composite Hourly Land Surface Temperature in 2011 Under Clear Sky

Abstract The monthly composite hourly land surface temperature (LST) under clear sky at 1‐km resolution (denoted as T mh ) plays a critical role in various fields such as urban and agricultural managements. Existing methods for estimating T mh fall into four categories: single‐source methods employing spatial downscaling or diurnal temperature cycle (DTC) models and multisource methods employing spatiotemporal fusion or DTC models. Despite this methodological diversity, a comprehensive evaluation of their respective strengths and weaknesses remains lacking, posing a challenge for advancing T mh estimation. To address this critical gap, we performed a wide‐ranging comparison of eight representative approaches for estimating T mh , comprising two from each category. Their accuracies were assessed over various timescales and conditions, utilizing in situ LST observations from 77 ground‐based stations worldwide. Our evaluations show that DTC‐based multisource approaches exhibit the highest overall accuracy, outperforming both spatial downscaling‐based single‐source approaches and spatiotemporal fusion‐based multisource approaches. Conversely, the performance of DTC‐based single‐source methods exhibits substantial disparity. This observed pattern of overall accuracy remains valid across months, seasons, land cover types, and climatic zones. Furthermore, our assessments indicate that accuracies are time‐of‐day dependent. Spatial downscaling and spatiotemporal fusion approaches are most effective around 2 hr after sunrise, while DTC‐based approaches show better performance around midday. Our findings suggest that this work holds potential significance for generating fine‐resolution hourly LST data with enhanced accuracy.