## 2 Methods and Tools  
Understanding the Dead Sea environmental dynamics requires a comprehensive variety of tools capable of monitoring both surface and subsurface processes. Remote sensing and geophysical methods are pivotal in capturing the intricate interactions between tectonic, hydrological, and geomorphological forces. Table 1 provides a critical assessment of the principal methodologies employed: satellite imagery, drone-based photogrammetry, InSAR, LiDAR, and ground-based geophysics. This table evaluates these tools in terms of their technological advancements, operational capabilities, and limitations, providing a quantitative and qualitative foundation for understanding their applicability in detecting and monitoring, e.g., subsidence, sinkholes, and shoreline retreat. The insights offered in Table 1 aim to facilitate informed decision-making for future research and hazard mitigation strategies. \begin{table}
\begin{tabular}{l l} \hline \hline
**Method/Tool** & **Critical Assessment** \\ \hline \multirow{7}{*}{Satellite Time Series Analysis} & - Strengths: Offers long-term monitoring (e.g., Corona: 1960s, Sentinel-2: 2015–present) with temporal resolution of 5–16 days; spatial resolution: Sentinel-2 (10–60 m), Landsat (15–60 m). \\  & - Challenges: Insufficient spatial resolution (<10 m) for small-scale deformation or \\  & microfeatures like sinkholes; atmospheric effects reduce data quality for optical sensors. \\  & - Quantitative Limitations: Cloud cover impacts \(\sim\)30\% of scenes in arid regions; \\  & moderate revisit frequency delays rapid event detection. \\  & - Opportunities: Fusing Sentinel-2 with VHR imagery (<1 m) or SAR datasets could \\  & achieve resolutions \(\sim\)1–5 m while preserving temporal continuity. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Critically assesses the strengths, challenges, quantitative limitations, and opportunities associated with key remote sensing and geophysical methods applied in the Dead Sea area [1; 2; 11; 14]. The evaluation encompasses parameters such as spatial and temporal resolution, accuracy, operational constraints, and potential for technological integration, offering a comprehensive perspective on the capabilities and trade-offs of each methodology. \begin{table}
\begin{tabular}{l l} \hline
**Method/Tool** & **Critical Assessment** \\ \hline \multirow{7}{*}{Very High-Resolution Imagery} & - Strengths: Exceptional spatial resolution (WorldView-3: 30 cm; Pleiades Neo: 30 cm) \\  & enables precise microfeature analysis (e.g., sinkholes \textgreater{} 50 cm). \\  & - Challenges: Temporal resolution is limited to revisit times of \textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasci{}\textasciasci{}\textasci{}\textasciasci{}\textasci{}\textasciasci{}\textasciasci{}}\textasci{}\textasci{}\textasciasci{}\textasciasci{}\textasciasci{}\textasci{}\textasciasci{}\textasciasci{}\textasciasci{}\textasciasciasciasciasci{}}{\textasci}{\textasci}{\textasci  
All these methodologies include strategies to manage data noise, decorrelation, and instabilities. In InSAR, techniques like adaptive spatial-temporal filtering and phase unwrapping mitigate atmospheric delays and scattering. InSAR phase decorrelation uses coherence enhancement algorithms and integrates high-resolution datasets to mitigate effects in rapidly changing or vegetated areas. In electro-optical satellite imagery, radiometric calibration, and atmospheric correction, such as the Dark Object Subtraction method, are used to enhance data quality, while cloud masking algorithms address visibility issues.