Multi-Temporal Remote Sensing of Inland Surface Waters: A Fusion of Sentinel-1 Data Applied to Small Seasonal Ponds in Semiarid Environments

Abstract

Inland freshwater resources in semiarid environments play a key role in maintaining ecological systems and supporting human development. Space-based remote sensing spatiotemporal data have emerged as a new paradigm for understanding ecohydrological processes and trends, particularly in water-stressed areas. However, comprehensive cataloging is still lacking, especially in semi-arid regions and for small-sized water bodies (i.e., ponds), which are often overlooked despite their ecological relevance. In this study, high-resolution optical and radar Sentinel data (Sentinel-1 and Sentinel-2) were used to construct Sentinel-1&2-based local surface water (SLSW) models, to infer surface water occurrence and extent. To assess the reliability of this model, the results were compared with verification data, and separately with Landsat-based global water (LGSW) models. Three distinct semiarid regions were selected in SW Iberia, within a Mediterranean climate, each encompassing special protection areas for conservation and subjected to marked seasonality and bioclimatic changes. Surface water attributes were modeled using Random Forests for SLSW time series forecasting, which included the period from January 1, 2020, to December 31, 2021. During this period, the completeness of the archived information was compared between SLSW and LGSW, considering both intra-annual and inter-annual variations. The predictive performance of these models was then compared for specific periods (dry and wet), and each was independently validated with verification data. The results showed that SLWM achieved satisfactory predictive performances in detecting surface water occurrence ({approx}72%), with far greater completeness and reconstructed seasonality patterns compared to LGSW. The relatedness between SLSW and LGSW was stronger during wet periods (R2=0.38) than dry periods (R2=0.05), and SLSW related much better with the verification data (R2=0.66) than when compared to LGSW (R2=0.24). The proposed SLSW approach may therefore provide advantages in the delineation of dynamic surface water characteristics (occurrence and extent) in very small-sized water bodies (i.e., <0.5 ha), allowing for uninterrupted surface water time series forecasting at high spatiotemporal detail, and over extensive areas. Given the water constraints in semiarid regions and water resources vulnerability to climate change, our results show high potential for supporting a variety of activities underlying rural development and biodiversity conservation. Additionally, the socio-ecological applications of this research may help identify surface water anomalies (e.g., drought events) and enhance sustainable water supply governance, a particular priority in climate change hotspots. HighlightsO_LISurface water occurrence and extent was modeled across three semiarid regions C_LIO_LISentinel-1&2 data was compared with Landsat for characterizing very small water bodies C_LIO_LIModels based on Sentinel-1&2 resulted in a satisfactory classification precision C_LIO_LIVery high series completeness across seasons was found using Sentinel-1&2 data C_LIO_LISentinel-1&2 data was more reliable than Landsat when compared to verification data C_LI