## 2 Materials and Methods
### Data-Collecting Sensors Used in the Project: GNSS, Meteorrones, In Situ Sensors, Sentinel
#### 2.2.4 Copernicus Sentinel Earth Observation Data  
The Copernicus Sentinel satellite constellation, developed by the European Space Agency (ESA), provides high-resolution Earth observation data that are critical to environmental monitoring, land surface characterization, and hydrological modeling. The MAGDA project integrates data from Sentinel-1, Sentinel-2, and Sentinel-3, leveraging their complementary capabilities to retrieve key parameters such as soil moisture, land cover, vegetation indices, and land surface temperature (LST).  
Sentinel-1, a synthetic aperture radar (SAR) mission, is particularly valuable for all-weather soil moisture estimation. Unlike optical sensors, SAR data acquisition is unaffected by cloud cover and sunlight availability, making it suitable for continuous soil moisture monitoring. Within the MAGDA framework, Sentinel-1 backscatter coefficients are processed to derive surface soil moisture variability, complementing GNSS-Reflectometry (GNSS-R) and in situ soil moisture sensors. The integration of Sentinel-1 data enables frequent and high-resolution monitoring of soil moisture conditions, which is critical for hydrological applications.  
Sentinel-2, a multi-spectral imaging mission, provides high-resolution optical data for vegetation monitoring, agricultural land use classification, and water stress assessment. The project utilizes the Normalized Difference Vegetation Index (NDVI) and the Moisture Stress Index (MSI) to evaluate crop health and irrigation needs. NDVI is calculated using the near-infrared (NIR) and red spectral bands to assess vegetation vigor, with values ranging from \(-1\) (water bodies) to \(1\) (dense vegetation). The MSI, derived from the shortwave infrared (SWIR) and NIR bands, provides a quantitative estimate of vegetation moisture stress, where higher values indicate greater water stress. These vegetation indices are processed at \(10\,\mathrm{m}\) spatial resolution and analyzed for the period from 2015 to the present, ensuring a long-term perspective on crop conditions.  
Sentinel-3, specifically its Sea and Land Surface Temperature Radiometer (SLSTR), provides Land Surface Temperature (LST) products at a \(1\,\mathrm{km}\) resolution with a daily frequency of four observations (two from Sentinel-3A and two from Sentinel-3B). LST is a critical variable for evapotranspiration (ET) estimation, an essential component of the agricultural water balance and drought assessment. The MAGDA project processes actual evapotranspiration using the Sen-ET SNAP plugin, developed by EOatDHI (Hersholm, Denmark) in the Sen-ET project ([[https://www.esa-sendet.org/](https://www.esa-sendet.org/)]([https://www.esa-sendet.org/](https://www.esa-sendet.org/))) and financed by ESA. It is a model based on the Two-Source Energy Balance (TSEB) approach [41]. This methodology integrates solar radiation flux, latent heat flux, and atmospheric parameters to compute actual evapotranspiration (ET\({}_{\text{daily}}\)), which is crucial for precision irrigation and water resource management.  
The integration of Sentinel-derived products with in situ observations and numerical weather prediction (NWP) models ensures that remote sensing datasets are properly validated and assimilated. The Sentinel datasets were assessed for temporal and spatial alignment with key weather events, ensuring that their outputs could be effectively incorporated into MAGDA's forecasting and hydrological modeling framework. However, the feasibility of operational integration is influenced by satellite revisit times, particularly for real-time monitoring of fast-developing weather phenomena. This limitation is addressed through continuous in situ monitoring of soil moisture, which offers high temporal resolution. By integrating Sentinel-derived products with in situ data, it is possible to bridge the spatial and temporal gaps inherent to each monitoring approach, thereby enhancing high-resolution environmental analysis within MAGDA.