In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume,
variety and velocity of water-related data are increasing due to large-scale sensor networks …

This paper proposes an approach that combines reduced-order models with machine
learning in order to create physics-informed digital twins to predict high-dimensional output …

Using geophysical inversion for three-dimensional (3D) geological modeling is an effective
way to model underground geological structures. In this study, we propose and investigate a …

The consistent and long-term spaceborne synthetic aperture radar (SAR) missions such as
Sentinel-1 (S-1) provide high-quality dual-polarized C-band images particularly suited to …

Oceans cover approximately 71% of the Earth's surface and provide numerous services to
the environment and humans. Precise, real-time, and large-scale monitoring of the …

Despite the ever-growing number of ocean data, the interior of the ocean remains
undersampled in regions of high variability such as the Gulf Stream. In this context, neural …

Through the Synthetic Aperture Radar (SAR) embarked on the satellites Sentinel-1A and
Sentinel-1B of the Copernicus program, a large quantity of observations is routinely …

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the
traditional deep learning models are difficult to effectively extract key features of the targets …

A three‐state global estimator of marine surface layer atmospheric stratification is
demonstrated using more than 600,000 Sentinel‐1 synthetic aperture radar wave mode …