Soil moisture is a significant unit of hydrological cycle that influences groundwater recharge and surface runoff. The expensive and laborious nature of ground-based measurement of soil moisture has called for the application of remote sensing and geographic information system techniques. An investigation into soil moisture status is paramount to land degradation and desertification assessment. This study is therefore aimed at assessing the satellite-derived LST-NDVI relationship for evaluation of geospatial distribution of soil moisture and evaluating its distributional pattern in mountainous Cameron Highlands watershed. Satellite sensors of Landsat-8 were used for estimation of soil moisture in ArcMap. The results of this study indicated that the mean land surface temperature (LST) for the area was estimated to be 23.30°C. Linear slope equations representing the maximum LST (dry edge) and minimum LST (wet edge) for LST-NDVI space scatter plot were evaluated for soil moisture index (SMI) estimation. The SMI values between-0.1484 and 0.3577 were observed with mean and standard deviation of 0.0965 and 0.0238 respectively. The results indicated that the highest mean SMI value occurred in the "very high" elevation class. This was because most high elevated areas are abundantly vegetated (high NDVI values) with the relatively low level of human activities that expose the land surface. This study explores multispectral satellite-based soil moisture assessment for sustainable watershed management. © 2018 Construction Research Institute of Malaysia. All rights reserved.