Multitemporal remote sensing for ecology and conservation: a fusion of Sentinel-1&2 time series applied to small seasonal ponds in semiarid environments (Conference Presentation)

Remote sensing offers a cost-effective solution for monitoring water resources across vast areas and timeframes. This study introduces an innovative framework within Google Earth Engine (GEE) to process high-quality Sentinel-1&2 data for detecting and monitoring water surfaces. The approach focuses on neglected small water bodies in semi-arid regions of SW Iberia. By employing Sentinel-1&2-based local surface water (SLSW) models, this research surpasses existing methods in accuracy. The comparison with Landsat-based global water (LGSW) models indicates a SLSW superior performance in capturing seasonal patterns and aligning with validation data. The findings underscore the importance of understanding dynamic surface water characteristics, especially in small-sized water bodies crucial for ecological systems. This research contributes to sustainable water management strategies, aiding in identifying anomalies and supporting rural development and biodiversity conservation efforts. The proposed approach holds significant potential for addressing water scarcity challenges in regions susceptible to climate change and agricultural intensification.