Soil moisture availability affects agricultural productivity and in turn food security. Estimating the moisture content of soil is imperative for proper water resource management and agricultural productivity. However, field based method is expensive and covers limited spatial variation. The advancement of remote sensing technology eases the soil moisture estimation over large geographic area. Hence, this study intended to apply the optical and thermal remote sensing data for estimating SM in the Lake Tana sub basin. Temperature vegetation dryness index (TVDI) model which is used in this study to estimate soil moisture is derived from the wet and dry edge of the LST-NDVI triangular scatterplot. The finding revealed that NDVI and LST have inverse relationship where LST decrease with increasing NDVI. Spatially, northern and north western part has experienced high LST. The estimated soil moisture result ranging from 0 to 1 where the soil moisture is higher in areas with TVDI value is near 1. Thus, soil moisture is higher in the east, and northeast part of the sub basin whereas the central, western and northwest part experienced low soil moisture. Therefore, applying remote sensing enables estimation of soil moisture across large geographical area with scarcity of field data (in-situ observations).