Temporal dynamics of leaf area index and land surface temperature correlation using Sentinel-2 and Landsat OLI data

BackgroundUnderstanding 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 temporal analysis of leaf area index (LAI) and LST data derived from Sentinel-2 and Landsat Operational Land Imagery (OLI) in the Mille River Basin, a tropical region in Ethiopia. LAI data were generated using Sentinel-2 imagery processed with the Sentinel Application Platform (SNAP) toolbox, an open-access earth observation analysis tool, while Landsat OLI collection 2 level 2 data were utilized for precise LST retrieval. The Mann-Kendall test was used to detect trends in the time series data.ResultsThe trends in the mean LAI were statistically significant at P values of 0.05 and 0.10 for the annual and seasonal trends, respectively. The mean LST trends were insignificant throughout the study period except for the summer season, for which the P value was 0.07. The correlation between the LAI and LST was weak (R2 = 0.36) during the crop-growing seasons (summer and spring) but moderate in winter (R2 = 0.46) and autumn (R2 = 0.41).ConclusionThe 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.