Inland freshwaters are essential in maintaining ecological balance and supporting human development. However, comprehensive water data cataloguing remains insufficient, especially for small water bodies (i.e., ponds), which are overlooked despite their ecological importance. To address this gap, remote sensing has emerged as a possible solution for understanding ecohydrological characteristics of water bodies, particularly in water-stressed areas. Here, we propose a novel framework based on a Sentinel-1&2 local surface water (SLSW) model targeting very small (<0.5 ha, Mdn ≈ 0.031 ha) and seasonal water bodies. We tested this framework in three semiarid regions in SW Iberia, subjected to distinct seasonality and bioclimatic changes. Surface water attributes, including surface water occurrence and extent, were modelled using a Random Forests classifier, and SLSW time series forecasts were generated from 2020 to 2021. Modelreliability was first verified through comparative data completeness analyses with the established Landsat-based global surface water (LGSW) model, considering both intra-annual and inter-annual variations. Further, the performance of the SLSW and LGSW models was compared by examining their correlations for specific periods (dry and wet seasons) and against a validation dataset. The SLSW model demonstrated satisfactory results in detecting surface water occurrence (μ ≈ 72 %), and provided far greater completeness and reconstructed seasonality patterns than the LGSW model. Additionally, SLSW model exhibited a stronger correlation with LGSW during wet seasons (R2 = 0.38) than dry seasons (R2 = 0.05), and aligned more closely with the validation dataset (R2 = 0.66) compared to the LGSW model (R2 = 0.24). These findings underscore the SLSW model’s potential to effectively capture surface characteristics of very small and seasonal water bodies, which are challenging to map over broad regions and often beyond the capabilities of conventional global products. Also, given the vulnerability of water resources in semiarid regions to climate fluctuations, the present framework offers advantages for the local reconstruction of continuous, high-resolution time series, useful for identifying surface water trends and anomalies. This information has the potential to better guide regional water management and policy in support of Sustainable Development Goals, focusing on ecosystem resilience and water sustainability.

中文翻译：

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内陆表层水多时相遥感：应用于半干旱环境中小型季节性池塘的 sentinel-1&2 数据的融合


内陆淡水对于维持生态平衡和支持人类发展至关重要。然而，全面的水数据编目仍然不足，特别是对于小型水体（即池塘），尽管它们具有重要的生态意义，但被忽视了。为了解决这一差距，遥感已成为了解水体生态水文特征的可能解决方案，尤其是在缺水地区。在这里，我们提出了一个基于 Sentinel-1&2 局部地表水 （SLSW） 模型的新框架，该模型针对非常小（<0.5 ha，Mdn ≈ 0.031 ha）和季节性水体。我们在伊比利亚西南的三个半干旱地区测试了这个框架，这些地区受到明显的季节性和生物气候变化的影响。地表水属性（包括地表水的出现和范围）使用随机森林分类器进行建模，并生成了 2020 年至 2021 年的 SLSW 时间序列预测。首先通过与已建立的基于 Landsat 的全球地表水 （LGSW） 模型进行比较数据完整性分析来验证模型可靠性，同时考虑了年内和年际变化。此外，通过检查它们在特定时期（旱季和雨季）的相关性以及与验证数据集的相关性，比较了 SLSW 和 LGSW 模型的性能。SLSW 模型在检测地表水出现方面表现出令人满意的结果 （μ ≈ 72 %），并且提供了比 LGSW 模型更高的完整性和重建的季节性模式。此外，SLSW 模型在雨季 （R2 = 0.38） 比旱季 （R2 = 0.05） 与 LGSW 的相关性更强，并且与 LGSW 模型 （R2 = 0.24） 相比，与验证数据集 （R2 = 0.66） 的一致性更紧密。 这些发现强调了 SLSW 模型有效捕获非常小和季节性水体的表面特征的潜力，这些水体在广阔的区域绘制具有挑战性，并且通常超出了常规全球产品的能力。此外，鉴于半干旱地区的水资源对气候波动的脆弱性，本框架为连续、高分辨率时间序列的局部重建提供了优势，有助于识别地表水趋势和异常。这些信息有可能更好地指导区域水管理和政策，以支持可持续发展目标，重点关注生态系统复原力和水可持续性。