Abstract Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

中文翻译：

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雪盖研究中的遥感、水文建模和原位观测：综述

摘要 雪是水文循环的重要组成部分。作为冰冻圈的重要组成部分，积雪也是一种宝贵的陆地水资源。在气候变化的背景下，积雪的动态在重新平衡全球能源和水资源预算方面发挥着至关重要的作用。遥感、水文建模和原位观测是积雪调查中常用的三种技术。然而，系统误差、尺度差距和复杂的雪物理等因素造成的不确定性限制了这三种方法在雪研究中的可用性。在本文中，概述了三种方法的优点、局限性和最新进展，并评估了更有效的积雪特性估计方法。讨论了使用地面观测改进遥感雪信息的可能性。作为快速增长的自愿地理信息 (VGI) 来源，基于网络的地理标记照片具有为遥感产品和水文模型提供地面实况数据的巨大潜力，从而有助于云去除、校正、验证、强迫和同化程序。最后，这篇综述为雪盖研究的未来提出了一个协同框架。该框架强调了原位和遥感雪测量的跨尺度整合，以及将改进的遥感数据同化到水文模型中。基于网络的地理标记照片具有为遥感产品和水文模型提供地面实况数据的巨大潜力，从而有助于云去除、校正、验证、强迫和同化程序。最后，这篇综述为雪盖研究的未来提出了一个协同框架。该框架强调了原位和遥感雪测量的跨尺度整合，以及将改进的遥感数据同化到水文模型中。基于网络的地理标记照片具有为遥感产品和水文模型提供地面实况数据的巨大潜力，从而有助于云去除、校正、验证、强迫和同化程序。最后，这篇综述为雪盖研究的未来提出了一个协同框架。该框架强调了原位和遥感雪测量的跨尺度整合，以及将改进的遥感数据同化到水文模型中。