Crop coefficient (K_c) represents the actual crop growth of the crop. It plays an important role in estimating water requirements at the different growth stages of the crop. However, FAO 56 Penman–Monteith K_c method does not account for spatial heterogeneity and uncertainty for regional climatic conditions significantly. Therefore, this study aims to develop the relation between K_c and normalized difference vegetation index (NDVI) using a linear regression and back calculations. These relationships were adjusted to local conditions using information from survey data obtained during Rabi season (2014–2015). The NDVI–K_c model (r² = 0.86) has developed using NDVI–K_c from a fine resolution Landsat 8 remote sensing data. NDVI–K_c regression equation was utilized for generating crop coefficient for different month of season. The Vegetation Index-based AET estimated was evaluated with lysimeter data for different crop growth stage across the season. The results have shown that NDVI–K_c estimated AET has been better correlated with NDVI–K_c remote sensing model. Thus, the output of this research can help to calculate actual water demand in a command area and be helpful in allocating water from less demand area toward more demand area.

作物系数（K _c）代表作物的实际作物生长。它在估算作物不同生长阶段的需水量方面起着重要作用。但是，FAO 56 Penman–Monteith K _c方法并未显着说明区域气候条件的空间异质性和不确定性。因此，本研究旨在利用线性回归和反向计算来发展K _c与归一化植被指数（NDVI）之间的关系。利用拉比季节（2014-2015年）获得的调查数据中的信息，将这些关系调整为适合当地条件。NDVI– K _c模型（r ² = 0.86）是使用NDVI– K _c从高分辨率Landsat 8遥感数据中获得的。NDVI – K _c回归方程用于生成季节不同月份的作物系数。在整个季节不同作物生长阶段，用蒸渗仪数据对基于植被指数的AET进行了评估。结果表明，NDVI– K _c估计的AET与NDVI– K _c遥感模型具有更好的相关性。因此，这项研究的结果可以帮助计算命令区域内的实际需水量，并有助于将水从需求量较小的区域分配到需求量较大的区域。