The knowledge about spatial variation of annual rainfall is important for many applications ranging from agriculture planning to flood risk management in a tropical low-lying country like Bangladesh. The remotely sensed data has emerged as a suitable addition to the data source which is often suggested for use at ungauged conditions. This study investigates whether the remotely sensed outputs on its own or its incorporation as a covariate can outperform the mapping estimate of annual average rainfall. The work primarily considers a multivariate kriging approach, kriging with external drift (KED), which can take covariates to good effect for the spatial interpolation. Other than remotely sensed annual average rainfall (RAAR), the study includes easily accessible: geographical coordinates (LON, LAT) and elevation as potential covariates. The suitability of the KED model is assessed against the widely used classical univariate, ordinary kriging (OK), and the inverse distance weighting (IDW) methods. The annual average rainfall calculated at 34 stations based on observed daily rainfall data from 1970 to 2016 was used for the assessment. Based on cross-validation techniques, the KED with LON is identified as the best interpolation method. The IDW performed poorly and came last among all the interpolation methods. The performance of remotely sensed outputs on its own is not as good as the interpolation estimate; in fact, it is outperformed by the IDW quite convincingly. The integration of RAAR as a covariate with the KED performed superior to IDW but could not outperform the chosen KED (LON) model. Overall, remotely sensed data could be served better with the integration of an appropriate kriging approach rather than to be used as model outputs.

有关年降雨量空间变化的知识对于从农业规划到孟加拉国等热带低洼国家的洪水风险管理的许多应用都很重要。遥感数据已成为数据源的合适补充，通常建议在非测量条件下使用。本研究调查了遥感输出本身或其作为协变量的结合是否可以胜过年平均降雨量的测绘估计。该工作主要考虑了一种多元克里金方法，即带有外部漂移的克里金法 (KED)，它可以利用协变量对空间插值产生良好的效果。除了遥感年平均降雨量 (RAAR) 外，该研究还包括易于获取的：地理坐标（LON、LAT）和海拔作为潜在协变量。KED 模型的适用性根据广泛使用的经典单变量、普通克里金法 (OK) 和反距离加权 (IDW) 方法进行评估。评估采用 34 个站点根据 1970 年至 2016 年观测到的日降雨量数据计算的年平均降雨量。基于交叉验证技术，带有 LON 的 KED 被确定为最佳插值方法。IDW 表现不佳，在所有插值方法中排在最后。遥感输出本身的性能不如插值估计；事实上，IDW 的表现令人信服。RAAR 作为协变量与 KED 的整合优于 IDW，但无法超越所选的 KED (LON) 模型。全面的，