Understanding vegetation–temperature interactions is essential for climate-resilient agricultural planning in Uzbekistan’s diverse agro-climatic zones. This study analyzes the relationship between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) during the 2024 vegetation season in two contrasting regions: Jarqo‘rg‘on district (Surxondaryo Region) in the arid south and Bo‘ka district (Tashkent Region) in the temperate north. Monthly MODIS NDVI and LST datasets were processed and temporally aligned in Google Earth Engine (GEE). Pearson correlation and linear regression models were applied to quantify vegetation responses to temperature variability. Results show notable regional differences. In Bo‘ka district, NDVI and LST exhibited a strong positive correlation (r = 0.71), with R² = 0.51, indicating that more than half of NDVI variability is explained by LST. The regression slope (b = 0.0085) suggests that vegetation greenness increases consistently with rising temperature during the active growth phase. In contrast, Jarqo‘rg‘on district demonstrated a moderate correlation (r = 0.61), with R² = 0.37, and a slightly lower regression slope (b = 0.0076), reflecting the district’s stronger heat exposure and potentially higher temperature-induced stress. These findings highlight the importance of integrating remote sensing–based vegetation and thermal indicators into regional climate adaptation strategies. The comparison demonstrates that vegetation in cooler agro-climatic zones responds more favorably to temperature increases, whereas vegetation in hotter regions exhibits dampened sensitivity due to thermal stress thresholds.

NDVI; LST; climate–vegetation interaction; thermal stress; MODIS; temporal alignment; Pearson correlation; linear regression; climate-smart agriculture; Uzbekistan.

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