Abstract We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k-nearest neighbors (k-NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k-NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k-NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k-NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k-NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of −10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k-NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20–25 cm to 10–15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions.

中文翻译：

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利用无线传感器网络和遥感产品估算山区空间雪水当量

摘要 我们开发了一种通过使用 k 最近邻 (k-NN) 算法和历史空间 SWE 数据对空间代表性点测量值进行插值来估计雪水当量 (SWE) 的方法。它使用不同的数据源进行训练和评估，准确再现了测量的 SWE。在中美洲河流域，我们使用 k-NN 算法通过将 10 个传感器集群中的连续雪深测量数据与 14 年的每日 500 米分辨率 SWE 重建地图融合来插入数据。对融化季节的准确 SWE 估计显示了提供每日、近乎实时的分布式融雪估计的潜力。再往南，在 Merced-Tuolumne 盆地，我们评估了 k-NN 方法改进实时 SWE 估计的潜力。缺乏密集的地面测量网络，我们使用双周激光雷达衍生的雪水当量产品的选定像素模拟了传感器数据的 k-NN 插值。k-NN 外推法低估了激光雷达衍生的 SWE，在海拔低于 3000 m 和高于 3000 m 时最大偏差为 -10 cm 和 +15 cm。通过使用高斯过程回归模型在空间上分布残差，可以减少这种偏差。使用 2014 年激光雷达衍生的 SWE 的 10 个场景作为 k-NN 中的训练数据来估计 2016 年空间 SWE，与使用 SWE 重建相比，RMSE 和 MAE 都从大约 20-25 厘米减少到 10-15 厘米作为训练数据。我们发现历史数据的空间准确性对于学习 SWE 的空间分布比可用的历史场景数量更重要。将连续的具有空间代表性的地面传感器与同一盆地的 SWE 重建历史库相结合，可以提供实时的空间 SWE 地图，准确地表示激光雷达测量的雪深；并且可以通过使用历史激光雷达扫描而不是 SWE 重建来改进估计。