Each component of the terrestrial water storage is a key hydrological variable to understand floods and drought events Their monitoring at river basin scale and over long periods of time is facilitated by large scale sensors. The combination of Earth observations with other datasets can be an asset for the prediction of hydrological events and for monitoring. Each component of the terrestrial water storage is a key hydrological variable to understand floods and drought events Their monitoring at river basin scale and over long periods of time is facilitated by large scale sensors. The combination of Earth observations with other datasets can be an asset for the prediction of hydrological events and for monitoring. Hydrological extremes, in particular floods and droughts, impact all regions across planet Earth. They are mainly controlled by the temporal evolution of key hydrological variables like precipitation, evaporation, soil moisture, groundwater storage, surface water storage and discharge. Precise knowledge of the spatial and temporal evolution of these variables at the scale of river basins is essential to better understand and forecast floods and droughts. In this article, we present recent advances on the capability of Earth observation (EO) satellites to provide global monitoring of floods and droughts. The local scale monitoring of these events which is traditionally done using high-resolution optical or SAR (synthetic aperture radar) EO and in situ data will not be addressed. We discuss the applications of moderate- to low-spatial-resolution space-based observations, e.g., satellite gravimetry (GRACE and GRACE-FO), passive microwaves (i.e. SMOS) and satellite altimetry (i.e. the JASON series and the Copernicus Sentinel missions), with supporting examples. We examine the benefits and drawbacks of integrating these EO datasets to better monitor and understand the processes at work and eventually to help in early warning and management of flood and drought events. Their main advantage is their large monitoring scale that provides a “big picture” or synoptic view of the event that cannot be achieved with often sparse in situ measurements. Finally, we present upcoming and future EO missions related to this topic including the SWOT mission.

中文翻译：

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使用卫星遥感进行大尺度洪水和干旱探测

陆地蓄水的每个组成部分都是了解洪水和干旱事件的关键水文变量。大型传感器促进了它们在流域规模和长时间内的监测。地球观测与其他数据集的结合可以成为预测水文事件和监测的资产。陆地蓄水的每个组成部分都是了解洪水和干旱事件的关键水文变量。大型传感器促进了它们在流域规模和长时间内的监测。地球观测与其他数据集的结合可以成为预测水文事件和监测的资产。水文极端事件，尤其是洪水和干旱，会影响地球上的所有地区。它们主要受降水、蒸发、土壤水分、地下水储存、地表水储存和排放等关键水文变量的时间演变控制。准确了解这些变量在流域尺度上的空间和时间演变对于更好地理解和预测洪水和干旱至关重要。在本文中，我们介绍了地球观测 (EO) 卫星提供全球洪水和干旱监测能力的最新进展。传统上使用高分辨率光学或 SAR（合成孔径雷达）EO 和原位数据对这些事件进行局部尺度监测将不会得到解决。我们讨论了中低空间分辨率空基观测的应用，例如卫星重力测量（GRACE 和 GRACE-FO）、无源微波（即 SMOS）和卫星测高（即 JASON 系列和哥白尼哨兵任务），以及支持的例子。我们研究了整合这些 EO 数据集的利弊，以更好地监控和了解工作过程，并最终帮助洪水和干旱事件的早期预警和管理。它们的主要优点是它们的监测规模大，可以提供事件的“大图”或概要视图，而这通常是稀疏的现场测量无法实现的。最后，我们介绍了与该主题相关的即将到来和未来的 EO 任务，包括 SWOT 任务。我们研究了整合这些 EO 数据集的利弊，以更好地监控和了解工作过程，并最终帮助洪水和干旱事件的早期预警和管理。它们的主要优点是它们的监测规模大，可以提供事件的“大图”或概要视图，而这通常是稀疏的现场测量无法实现的。最后，我们介绍了与该主题相关的即将到来和未来的 EO 任务，包括 SWOT 任务。我们研究了整合这些 EO 数据集的利弊，以更好地监控和了解工作过程，并最终帮助洪水和干旱事件的早期预警和管理。它们的主要优点是它们的监测规模大，可以提供事件的“大图”或概要视图，而这通常是稀疏的现场测量无法实现的。最后，我们介绍了与该主题相关的即将到来和未来的 EO 任务，包括 SWOT 任务。