Soil moisture deficit is an essential element in the estimation of irrigation demands, both spatially and temporarily. The determination of temporal and spatial variations of soil moisture in a river basin is challenging in many aspects; however, distributed hydrological modelling with remote sensing inputs is an effective way to determine soil moisture. In this research, a water demand module was developed for a satellite-based National Hydrological Model—India (NHM-I) to estimate distributed irrigation demands based on soil moisture deficits. The NHM-I is a conceptual distributed model that was explicitly developed to utilize the products from remote sensing satellites. MOD13Q1.5 data were used in this study to classify paddy and irrigated dry crops. Along with the above data, the DEM, Leaf Area Index, FAO soil map, and crop characteristics data were also used as inputs. The NHM-I with water demand module was evaluated in the Damodar river basin, India, from 2009 to 2018. The integrated NHM-I model simulated the irrigation demands effectively with remote sensing data. The temporal analysis reveals that soil moisture deficits in the Kharif season varied annually from 2009 to 2018; however, soil moisture deficits in the Rabi season were almost constant. The 50% Allowable Moisture Depletion (AMD-50) scenario can reduce the irrigation demand of 1966 MCM compared to the Zero Allowable Moisture Depletion (AMD-0) scenario. The highest annual irrigation demand (8923 MCM) under the AMD-50 scenario occurred in the 2015–2016 season, while the lowest (6344 MCM) happened in 2013–2014 season. With a new water demand module and remote sensing inputs, the NHM-I will provide a platform to assess spatial and temporal irrigation demands and soil moisture.