Water resource asset assessment and financial decision support based on multi-source remote sensing data

Introduction Assessing water resource assets in dynamic environmental conditions presents significant scientific and operational challenges. Remote sensing data are often multi-source, high-dimensional, and temporally inconsistent, making it difficult to construct models that are both accurate and generalizable. Moreover, existing financial decision support systems struggle with integrating environmental variability, spatiotemporal noise, and the real-time interpretability required for practical deployment. Addressing these issues requires a fundamentally new approach that unifies data fusion, spatiotemporal modeling, and financial risk assessment into a cohesive system. Methods This study introduces the Contextual Multi-source Decision Network (CMDN), a hybrid deep learning framework that incorporates adaptive volatility modeling, multi-scale temporal analysis, and cross-modal attention mechanisms. By doing so, we aim to bridge the gap between remote sensing technologies and financial planning, enabling more accurate, transparent, and timely decision-making in water resource management. Results Extensive experiments on GRACE, MODIS, ERA5-Land, and SEN12MS datasets demonstrate that CMDN reduces RMSE by up to 12.3% and improves R 2 scores by 2%–4% compared to state-of-the-art baselines. Discussion The study identifies two key limitations. The complexity and computational intensity of integrating multi-source data and machine learning models may restrict accessibility, especially in regions with limited technological resources. These results confirm its value as a scalable and actionable tool for sustainable resource management under uncertain and evolving environmental conditions.