Long-Term Trend and Seasonal Cycles of Gap-Free Downscaled Diurnal/Nocturnal LST and the Interaction to Functional Plant Trait Under Tropical Monsoon Climate

Abstract

Land surface temperature (LST) monitoring via Earth observation constellation will become optimized and consistent with spatiotemporal-explicit characteristics. Besides, scientific evidence for the interaction between LST and vegetation biophysical variables remains limited through spatial large-scale assessment and seamless long-term tracking. This study addresses this gap by utilizing gap-filled fine spatial resolution LST products in understanding the dynamic over the period 2000–2023 and the spatiotemporal relationship with leaf area index (LAI). Firstly, Moderate Resolution Imaging Spectroradiometer (MODIS) LST 1,000 m of both daytime and nighttime were downscaled to a finer resolution of 250 m using the Random Forest algorithm. The Whittaker algorithm was then applied to obtain gap-free LST products due to the typical cloud cover under tropical monsoon climate. Time series decomposition of gap-filled fine resolution LST revealed slight warming trends in daytime (0.005°C year⁻¹), nighttime (0.036°C year⁻¹), and mean of all-day time (0.02°C year⁻¹) over recent 24 years, while seasonal amplitude in daytime (−3.7°C–4.8°C) is more fluctuated than in nighttime (−2.5°C–1.9°C). Spatial correlations of monthly LSTs and LAI indicated a consistent negative correlation (R ranging from −0.717 to −0.45). These findings shed light on the quantitative relationship between vegetation LAI and LST, contributing to a more unified theoretical framework for understanding functional vegetation responses under diverse climatic conditions.