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首页> 外文期刊>Water resources research >Recurrent Snowmelt Pattern Synthesis Using Principal Component Analysis of Multiyear Remotely Sensed Snow Cover
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Recurrent Snowmelt Pattern Synthesis Using Principal Component Analysis of Multiyear Remotely Sensed Snow Cover

机译：基于多年遥感积雪主成分分析的循环融雪模式合成

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摘要

Snow acts as a vital source of water especially in areas where streamflow relies on snowmelt. The spatiotemporal pattern of snow cover has tremendous value for snowmelt modeling. Instantaneous snow extent can be observed by remote sensing. Cloud cover often interferes. Many complex methods exist to resolve this but often have requirements which delay the availability of the data and prohibit its use for real-time modeling. In this research, we propose a new method for spatially modeling snow cover throughout the melting season. The method ingests multiple years of MODerate Resolution Imaging Spectroradiometer snow cover data and combines it using principal component analysis to produce a spatial melt pattern model. Development and application of this model relies on the interannual recurrence of the seasonal melting pattern. This recurrence has long been accepted as fact but to our knowledge has not been utilized in remote sensing of snow. We develop and test the model in a large watershed in Wyoming using 17 years of remotely sensed snow cover images. When applied to images from 2 years that were not used in its development, the model represents snow-covered area with accuracy of 84.9-97.5% at varied snow-covered areas. The model also effectively removes cloud cover if any portion of the interface between land and snow is visible in a cloudy image. This new principal component analysis method for modeling the interannually recurring spatial melt pattern exclusively from remotely sensed images possesses its own intrinsic merit, in addition to those associated with its applications.Plain Language Summary Mountain snow provides an important source of water. The ability to model snowmelt and the resulting streamflow helps predict the amount and timing of when water will be available for irrigation, drinking water, and other uses. Satellite remote sensing can produce maps of snow-covered area that can improve our ability to model snowmelt, but during the melt season, clouds often block the view of watersheds from space. However, snowmelt follows a repeatable spatial pattern as it melts, year after year. We used this observation to develop a model of the spatial pattern of melt using multiple years of satellite snow cover images. This model can remove the cloud interference from daily satellite snow cover images. Our model achieved 85-98% accuracy in representing the spatial pattern of snow observed in satellite images even when starting with 95% cloud cover. Other models achieve similar accuracy but require much more data to accomplish this, which prevents them from being used for cloud removal in real time. We expect the model to improve our ability to model streamflow from snowmelt runoff.
机译：雪是水的重要来源，尤其是在水流依赖融雪的地区。积雪的时空格局对融雪建模具有重要价值。瞬时降雪量可通过遥感观测。云层经常会干扰。存在许多复杂的方法来解决此问题，但通常存在一些要求，这些要求会延迟数据的可用性并禁止将其用于实时建模。在这项研究中，我们提出了一种在整个融化季节对雪盖进行空间建模的新方法。该方法吸收多年的MODerate分辨率成像光谱辐射仪的积雪数据，并使用主成分分析将其合并以生成空间融化模式模型。该模型的开发和应用依赖于季节性融化模式的年际复发。这种复发早已被接受为事实，但据我们所知尚未在雪的遥感中利用。我们使用17年的遥感积雪图像在怀俄明州的一个大流域开发和测试了该模型。当将其应用于未开发的2年图像时，该模型表示的是大雪覆盖的区域，在不同的大雪覆盖区域中的准确度为84.9-97.5％。如果在阴天图像中可见土地和雪之间的接口的任何部分，该模型还可以有效地去除云层。这种新的仅通过遥感图像模拟年际重复空间融化模式的主成分分析方法，除了具有与应用相关的优点外，还具有其固有的优点。平原语言摘要山雪提供了重要的水源。对融雪和由此产生的水流进行建模的能力有助于预测何时有水可用于灌溉，饮用水和其他用途。卫星遥感可以生成积雪区域的地图，从而可以提高我们对融雪进行建模的能力，但是在融雪季节，云层通常会挡住太空中的分水岭。但是，融雪年复一年地遵循着可重复的空间格局。我们使用多年的卫星积雪图像，利用这一观察结果建立了融化空间模式的模型。该模型可以消除日常卫星积雪图像中的云干扰。即使从95％的云量开始，我们的模型在表示卫星图像中观测到的雪的空间模式方面也达到了85-98％的精度。其他模型可以达到类似的精度，但是需要更多的数据才能完成此任务，从而阻止了它们实时用于云去除。我们希望该模型能够提高我们对融雪径流的流量进行建模的能力。

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