The interaction between Land Surface Temperature (LST) and albedo plays a crucial role in regulating surface energy dynamics and environmental variability. This study presents the first comprehensive, nation-scale diagnostic analysis of the LST-albedo relationship in Iran, using daily MODIS MCD43A4 and MOD11A1 datasets spanning 8035 days from 1 January 2001, to 31 December 2022. Data preprocessing involved standardizing the spatial resolution of 500-m albedo data to match the 1000-m LST data using the reshape function in MATLAB. Seasonal and annual mean values were computed for 1,884,077 pixels, followed by statistical correlation analysis through the calculation of the correlation coefficient (r) and coefficient of determination (r2), and corresponding p-values. We introduce a spatiotemporal correlation-based diagnostic framework. The results reveal that distinct correlation patterns (positive vs. negative) serve as robust diagnostic signatures for specific land cover changes: positive correlations in desiccated lakes and wetlands (e.g., Lake Urmia) signal surface aridification, while strong negative correlations in highlands are indicative of snow-cover decline via the snow-albedo feedback, where declining albedo due to snow cover reduction was accompanied by increasing LST, particularly in winter. These findings establish that LST-albedo correlation patterns are not merely descriptive statistics but a scalable diagnostic tool. This framework provides a powerful transferable approach for monitoring critical environmental changes, such as drought intensification and snowpack loss, in Iran and similar arid to semi-arid regions worldwide.