Conventional methods for estimating evapotranspiration (ET) through experiments are accurate but limited to specific point locations, restricting their applicability for regional-scale estimations. However, the utilization of remote sensing (RS) data from satellites enables the assessment of ET over large areas. An important advantage of RS is the ability to compute ET without the need to quantify additional complex hydrological processes. The objective of this study is to estimate ETcrop (crop evapotranspiration) and Kc (crop coefficient) by utilizing RS and Geographic Information System (GIS) techniques in the Lower Manair Dam (LMD) command area, encompassing the Karimnagar, Warangal, and Khammam districts in India. The estimation of ETcrop is performed using the Surface Energy Balance Algorithm for Land (SEBAL) model, with Landsat 8 imagery selected for processing due to its high spatial resolution, ensuring enhanced accuracy. SEBAL is preferred over other methods due to its ability to minimize reliance on ground-based measurements and its automatic internal calibration capabilities. To calculate ET, SEBAL employs a series of computations, including the determination of net surface radiation (Rn), soil heat flux (G), and sensible heat flux (H) to the air. A residual energy flux is derived by subtracting the soil heat flux and sensible heat flux from the net radiation at the surface, which is then utilized for estimating ET. The crop coefficient (Kc) is obtained by relating ETcrop to the FAO-56 Penman's equation. In summary, this study demonstrates the integration of RS, GIS, and the SEBAL model to estimate ETcrop and Kc within the LMD command area. Landsat 8 imagery with high spatial resolution is utilized for reliable analysis, and SEBAL is recommended due to its ability to minimize the need for ground-based measurements and its automatic internal calibration capabilities.