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.