Identification of the spatial and temporal variation of groundwater level in Beijing plain via the fusion of remotely sensed data and ground observation data

Predicting groundwater level changing accurately is important to ensure the sustainable and efficient utilization of water resources. The study of groundwater model generally includes multi-source of data as input. The spatial and temporal patterns of groundwater is associated with surface water and rainfall, resulting in the difficulty of groundwater level predicting. Satellite data has been gradually valued and utilized more popularly. The effective fusion of remotely sensed data and ground observation data will greatly improve the spatial and temporal resolution of groundwater level mapping. At present, most of the models used solely the ground data or remote sensing data for prediction. The development and application of data analysis technology will effectively improve the level of prediction. How to merge multi-source data to enhance the accuracy is the goal of the study. In this study, the Beijing plain area will be selected as a typical research. Ground observed data and satellite remotely sensed data will be unified used for data fusion to predict groundwater dynamics. The groundwater level distribution after multi-source data fusion is simulated to analyze the evolution trend and spatial patterns of groundwater level in Beijing plain over the years. Analyzing the difference between single source data and multi-source data fusion is another goal in this study.