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# Explainable SAR Measurements for Wind Assessment with Artificial Intelligence (ESAWAAI)
The **Explainable SAR Measurements for Wind Assessment with Artificial Intelligence (ESAWAAI)** project, funded by the **European Space Agency (ESA) Phi-Lab**, is dedicated to advancing the retrieval of sea surface wind fields using a diverse set of observables derived from multi-polarization **Single-Look Complex (SLC) Synthetic Aperture Radar (SAR)** data, with a primary focus on **Sentinel-1** sensors. By integrating advanced SAR processing techniques with **Artificial Intelligence (AI)** and **Explainable AI (XAI)** methodologies, the project is developing a comprehensive legacy dataset to support both data-driven and physics-informed **Deep Learning (DL)** models for wind estimation.
A central objective of ESAWAAI is to enhance the interpretability of the **Geophysical Model Function (GMF)** used in SAR-based wind retrieval. This includes improving transparency in the relationships between radar observables and geophysical wind parameters, proposing wind retrieval schemes that avoid the use of *a priori* ancillary wind information, and achieving improved performance compared to state-of-the-art wind estimation algorithms.
## Dataset Overview
Our legacy dataset incorporates features derived from the **NORA3** and **CERRA** numerical weather prediction and regional reanalysis models, corresponding respectively to the North Sea and Mediterranean Sea domains. These model-derived variables, along with official ESA Level-2 Ocean (L2 OCN) products—such as sea surface wind fields and rain flags predicted by the AI algorithm developed by Collins et al. (2025)—are embedded directly within the Level-1B (L1B) product as complementary features. The dataset aggregates more than three years of Sentinel-1 SLC SAR data, processed using the SAR WAVE framework.
Only **Sentinel-1 Interferometric Wide (IW) mode** data in **VV polarization** are included in this release.
## Data Structure and Processing
The dataset preserves the structure of the original **Level-1 SLC** products:
- Each subswath (**IW1, IW2, IW3**) is processed independently
- Within each subswath, data are segmented by **burst**
- Each burst is further divided into smaller **tiles**, enabling wind and wave-related analyses
### Spatial Grids
Two spatial grids are defined to organize the data:
- **Intra-burst grid**: tiles extracted within individual bursts
- **Inter-burst grid**: tiles corresponding to overlap regions between successive bursts
This structured tiling strategy ensures flexibility and consistency for downstream processing tasks, including **machine learning model training and evaluation**.
## Consortium and Applications
The ESAWAAI project is developed by a consortium of leading institutions:
- **ESA Phi-Lab**
- **CLS** – Collecte Localisation Satellites Group, France
- **IFREMER**– Institut Français de Recherche pour l'Exploitation de la Mer, France
- **DTU** – Technical University of Denmark
- **UPB** – University Politehnica of Bucharest, Romania
Beyond dataset development, ESAWAAI aims to deepen scientific understanding of **SAR observables** across a wide range of **metocean** and **geometric conditions**. The resulting products support applications in:
- Meteorology
- Climate science
- Wind energy resource assessment
- Joint wind and wave retrieval