Time-series analysis of Leaf Area Index and Land Surface Temperature Association using Sentinel-2 and Landsat OLI data

Background: Understanding the complex relationship between vegetation dynamics and Land Surface Temperature (LST) is crucial for comprehending ecosystem functioning, climate change impacts, and sustainable land management. Hence, this study conducts a time-series analysis of Leaf Area Index (LAI) and LST derived from Sentinel-2 and Landsat Operational Land Imager (OLI) data. LAI data was generated using Sentinel-2 imagery processed with the SNAP toolbox, while Landsat OLI data was utilized for precise LST calculations. Mann-Kendall test was used to detect trends in the time series data. Results: The trends of LAI were statistically significant at P-values of 0.05 and 0.1 for annual and seasonal trends, respectively. The mean LST trends were statistically insignificant throughout the study period except for the summer season at a P-value of 0.07. The correlation between LAI and LST was weak (R ² = 0.36) during crop-growing seasons, but moderate in winter (R ² = 0.46) and autumn (R ² = 0.41). Conclusion: The findings of this research clarify the complex relationships between variations in surface temperature and vegetation growth patterns, providing insight into the environmental mechanisms driving the dynamics of localized ecosystems. The study underscores the implications of these findings for informed decision-making in sustainable land management, biodiversity conservation, and climate change mitigation strategies.