Comparison of gap-filling methods for generating landsat-like land surface temperatures under all-weather conditions

Abstract

Thermal infrared remote sensors provide cost-effective and widespread land surface temperatures (LSTs) but often with spatiotemporal gaps due to discrete sampling and synoptic disturbance, greatly limiting their reliability and application. Current gap-filling methods have been primarily developed and validated for medium- to low-resolution LSTs; however, with rising demand for spatiotemporally continuous, high-resolution (tens of meters like Landsat) LSTs across disciplines, there is an urgent need to assess these methods’ applicability and uncertainty at higher spatial resolutions under a unified framework. In this study, we apply eight typical and hybrid methods, including temporal interpolation, spatiotemporal interpolation, weight-based fusion, learning-based fusion, and four standard annual temperature cycle (ATC)-based hybrid reconstructions, to fill gaps in irregularly spaced Landsat series over Weishan, Huairou, and Yulin, China. These sites represent cropland in a sub-humid plain, forest in a sub-humid mountain region, and grassland in the semi-arid Loess Plateau. We evaluate their performance in terms of spatiotemporal pattern, statistical accuracy, sensitivity to input data quality and distribution, and adaptability to different synoptic and surface conditions based on cloudy Landsat data and in-situ measurements. Results reveal that the enhanced ATC (EATC) method is optimal among these methods, capturing all-weather spatiotemporal dynamics at the Landsat scale with superior accuracy and robustness under various input, cloud, and ground conditions. In addition, the ATC-based hybrid methods do not necessarily improve the statistical accuracy over their respective typical ones. This comprehensive evaluation provides valuable insights into the selection of appropriate gap-filling methods for generating Landsat-like LSTs under all-weather conditions and highlights the need for further advancements especially in addressing abrupt changes in land cover types and temporal sparsity in high-resolution LST observations to improve accuracy, stability, and generality.