In this paper, we introduce a novel framework for multimodal image translation utilizing the Diffusion Schrödinger Bridge (DSB). By integrating diffusion models with the Schrödinger Bridge problem, our approach addresses stability and interpretability challenges in image translation tasks. Explainable Diffusion Model via Schrödinger Bridge Multimodal Image Translation (xDSBMIT) leverages the unique distribution characteristics of different modalities, achieving high-quality translations with limited datasets. The DSB framework enhances interpretability by elucidating the diffusion process between paired images, providing insights into how the translation is achieved. Specifically, we apply our framework to the translation of Synthetic Aperture Radar (SAR) images to Infrared (IR) and Electro-Optical (EO) images, demonstrating its effectiveness in remote sensing applications. Experimental results show that xDSBMIT framework outperforms established methods such as pix2pix, significantly enhancing image translation performance and model interpretability while requiring fewer training data.