The article presents a novel method for integrating diverse environmental sensor data to improve spatial predictions. The authors propose an adaptive distance attention framework combining geostatistical techniques like kriging with deep learning models to enhance data fusion. Applied to case studies involving topography and air pollution, the method demonstrates improved predictive accuracy over traditional approaches, offering a scalable solution for environmental monitoring in complex and data-sparse regions.