Fine-scale satellite-based monitoring of temperature and vegetation cover in microclimates, distribution ranges, and landscape connectivity for Neurergus kaiseri (Kaiser’s mountain newt) during the breeding season

• Fine-scale time-series monitoring remains a key gap in conservation planning. • Landsat data analyzed with Mann-Kendall test can monitor suitable habitat. • Standard score below −1.654 is a key indicator for monitoring water flow in linkages. • Highest population core temperature heterogeneity was 12.19 percent in sub-basin 4. • Sub-basin 2 experienced the highest temperature increase: 35.92 square kilometers. Amphibians’ sensitivity to climate change makes them effective environmental indicators. Fine-scale species distribution models (SDMs) aid in analyzing short-distance species movement and landscape connectivity. However, a gap remains in monitoring time-specific environmental indicators using fine-scale satellite remote sensing (SRS). This study integrates spatiotemporal indicators with fine-scale SDMs to provide insights into Neurergus kaiseri ’s response to vegetation cover and temperature changes during the breeding season, informing conservation and management strategies. Data on localities, habitat suitability, and population cores connectivity from Karami et al. (2024) were divided into five sub-basins in Iran. Using Landsat 8 in Google Earth Engine (GEE), we calculated annual average land surface temperature (LST) and normalized difference vegetation index (NDVI) from 2013 to 2024. LST was downscaled using NDVI and elevation through geographically weighted regression (GWR). We performed point-based analyses of LST and NDVI trends and microclimate heterogeneity using Mann-Kendall tests (MK) and principal component analysis (PCA). We calculated LST and NDVI trends with MK for sub-basins in suitable habitats, examining significant changes by land use/land cover (LULC). At the linkage level, linear regression between LST and NDVI time series was performed, and linkage fluctuations were analyzed using the Z-score (ZMK). Results showed NDVI performed better than elevation due to local collinearity. NDVI trends varied with significant changes ( P < 0.05), but no significant LST changes were observed ( P > 0.05). Microclimatic heterogeneity decreased with latitude, with sub-basins 2 and 3 having the most favorable conditions relative to the surrounding environment. In sub-basin 2, an area of 35.92 km 2 within the suitable habitat had the highest LST increase, whereas sub-basin 3 recorded the highest LST decrease over an area of 3.23 km 2 . Sub-basin 1 had the highest NDVI increase in 307.35 km 2 of suitable habitat, and sub-basin 2 experienced the highest NDVI decrease over 46.45 km 2 . Crops in sub-basins 1 and 2 experienced the largest LST decreases, while rangeland areas in sub-basins 2 and 3 exhibited the largest increases. Decreases in ZMKNDVI along linkages indicated water presence, whereas increases in ZMKLST were due to water loss and exposed riverbeds. By integrating fine-scale habitat suitability and connectivity findings with detailed, time-specific SRS indicators, we create a comprehensive environmental monitoring strategy. This approach bridges critical knowledge gaps, enabling effective real-time conservation planning in arid and semi-arid regions.