Spatio-Temporal Monitoring and Statistical Analysis of Snow-Cover Area and Land Surface Temperature in the USRB Using Geospatial Techniques and Google Earth Engine

: Land surface temperature is a crucial meteorological parameter that affects the hydrological cycle and the complex nature of snow cover. This study aims to evaluate the effects of global warming by evaluating 36 years (1990–2025) of time series Land Surface Temperature (LST) and Normalised Difference Snow Index (NDSI) data from the Upper Sutlej River Basin (USRB) to determine the trend and slope magnitude of these series. Temperature records from the Indian Meteorological Department are used to validate the NDSI and LST data obtained from Landsat (4–5 TM, Landsat 7 ETM+, and Landsat 8/9 OLI/TIRS). A trend analysis was performed using the non-parametric Mann–Kendall test to assess statistical significance and Sen’s slope estimator is used to determine the magnitude of the trend. The Mann-Kendall test, along with Sen’s slope estimator, was applied to establish a functional relationship between variables, specifically a linear trend of LST and NDSI data for the studied area. The USRB was found to be significantly warming, with Landsat-derived LST rising at a rate of roughly 0.01-0.02°C annually and IMD temperature data showing a steady annual increase of roughly 0.03°C annually. On the other hand, there was a noticeable downward trend in the NDSI based snow covered area. A significant negative correlation (r = -0.80, p < 0.05) corroborated the strong inverse correlation between LST and snow cover, suggesting that rising temperatures are strongly linked to the loss of snow cover. Additionally, there was a significant positive correlation (r = 0.69, p < 0.05) between IMD temperature and Landsat-derived temperature, confirming the consistency of the observed warming pattern. These results highlight the imperative for persistent monitoring and sustainable management of water resources in the region, due to the heightened vulnerability of Himalayan cryospheric systems to climate change. The data indicates a distinct and ongoing warming trend in recent decades, along with its substantial effects on the area climate. A robust concordance between in-situ observations and satellite-derived datasets is indicated by the significant correlation between IMD temperature records and Landsat-derived LST, underscoring the dependability of satellite-based measurements for long-term climate monitoring in mountainous areas.