Evaluation of spatially distributed crop coefficient (K_c) for estimating evapotranspiration (ET_c) based on remotely sensed imagery has become an essential topic in managing the demand for agricultural water. Currently, satellite (MODIS, Landsat, etc.) imageries are not insufficient to detect variability within the small agricultural field due to its lack of desired spatial and temporal resolutions. Unmanned Aerial Vehicle (UAV) equipped with various sensors like Multispectral (MS), Thermal, and Hyperspectral cameras is becoming an emerging technology to overcome these limitations over small agricultural fields. A field experiment is carried out in the Agricultural and Food Engineering (AGFE) Department, IIT Kharagpur, to estimate K_c over the small Agri. Field using UAV-based MS cameras during Kharif (monsoon) 2019-2020 season. Lysimeters are used for estimating daily ET_c for conventionally irrigated paddy crops. Reference evapotranspiration (ET₀) is also calculated using the weather data of the study area. High-resolution multispectral imageries are acquired using a quad-copter UAV. The imageries are pre-processed using Pix4Dmapper software, and various vegetation indices (such as NDVI, TNDVI, NDRE, RVI, GNDVI, and LCI) are evaluated. The vegetation indices (VI_s) are correlated with ground truth K_c values and spatially distributed K_c maps for the whole study area are generated based upon the excellent correlation between the VI_s and ground K_c. The spatial K_c maps clearly show the variation in K_c within the plots and will be helpful for the calculation of K_c for any field without a lysimeter experiment. Generated K_c maps describe the crop water demand by visual color variations within the field. This approach may be helpful in understanding the variability in crop water requirements within the field Keywords: UAV, Crop Coefficient (K_c), Crop Evapotranspiration (ET_c), Vegetation Indices, Remote Sensing.