Snow cover plays an essential role in climate change and the hydrological cycle of the Tibetan Plateau. The widely used Moderate Resolution Imaging Spectroradiometer (MODIS) snow products have two major issues: massive data gaps due to frequent clouds and relatively low estimate accuracy of snow cover due to complex terrain in this region. Here we generate long-term daily gap-free snow cover products over the Tibetan Plateau at 500 m resolution by applying a hidden Markov random field (HMRF) technique to the original MODIS snow products over the past two decades. The data gaps of the original MODIS snow products were fully filled by optimally integrating spectral, spatiotemporal, and environmental information within HMRF framework. The snow cover estimate accuracy was greatly increased by incorporating the spatiotemporal variations of solar radiation due to surface topography and sun elevation angle as the environmental contextual information in HMRF-based snow cover estimation. We evaluated our snow products, and the accuracy is 98.29 % in comparison with in situ observations, and 91.36 % in comparison with high-resolution snow maps derived from Landsat images. Our evaluation also suggests that the incorporation of spatiotemporal solar radiation as the environmental contextual information in HMRF modeling, instead of the simple use of surface elevation as the environmental contextual information, results in the accuracy of the snow products increases by 2.71 % and the omission error decreases by 3.59 %. The accuracy of our snow products is especially improved during snow transitional period, and over complex terrains with high elevation and sunny slopes. The new products can provide long-term and spatiotemporally continuous information of snow cover distribution, which is critical for understanding the processes of snow accumulation and melting, analyzing its impact on climate change, and facilitating water resource management in Tibetan Plateau. This dataset can be freely accessed from the National Tibetan Plateau Data Center at https://doi.org/10.11888/Cryos.tpdc.272204 (Huang and Xu, 2022).

中文翻译：

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HMRFS-TP：基于隐马尔可夫随机场模型的青藏高原2002-2021年长期日无间隙积雪产品

积雪在气候变化和青藏高原水文循环中起着至关重要的作用。广泛使用的中分辨率成像光谱仪（MODIS）雪产品存在两个主要问题：由于频繁的云层导致的大量数据空白和由于该地区复杂的地形导致的积雪估计精度相对较低。在这里，我们通过对过去二十年的原始 MODIS 雪产品应用隐马尔可夫随机场 (HMRF) 技术，以 500 m 的分辨率在青藏高原上生成长期每日无间隙雪覆盖产品。通过在 HMRF 框架内优化整合光谱、时空和环境信息，完全填补了原始 MODIS 雪产品的数据空白。在基于 HMRF 的积雪估计中，通过将由于地表地形和太阳仰角导致的太阳辐射的时空变化作为环境上下文信息，大大提高了积雪估计的准确性。我们评估了我们的雪产品，与现场观测相比，准确率为 98.29 %，与从 Landsat 图像中提取的高分辨率雪图相比，准确率为 91.36 %。我们的评估还表明，在 HMRF 建模中将时空太阳辐射作为环境上下文信息，而不是简单地使用地表高程作为环境上下文信息，使得雪产品的准确性提高了 2.71%，遗漏误差减少 3.59%。我们的雪产品的准确性在雪过渡期间以及在高海拔和阳光充足的斜坡的复杂地形中得到了特别提高。新产品可以提供长期、时空连续的积雪分布信息，对于了解积雪和融化过程、分析其对气候变化的影响以及促进青藏高原水资源管理至关重要。该数据集可从国家青藏高原数据中心免费访问，网址为 促进青藏高原水资源管理。该数据集可从国家青藏高原数据中心免费访问，网址为 促进青藏高原水资源管理。该数据集可从国家青藏高原数据中心免费访问，网址为https://doi.org/10.11888/Cryos.tpdc.272204 （黄和徐，2022）。