Snowpack in mountainous areas often provides water storage for summer and fall, especially in the Western United States. In situ observations of snow properties in mountainous terrain are limited by cost and effort, impacting both temporal and spatial sampling, while remote sensing estimates provide more complete spacetime coverage. Spatial estimates of fractional snow covered area (fSCA) at 30m are available every 16 days from the series of multispectral scanning instruments on Landsat platforms. Daily estimates at 463m spatial resolution are also available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite. Fusing Landsat and MODIS fSCA images creates high resolution daily spatial estimates of fSCA that are needed for various uses: to support scientists and managers interested in energy and water budgets for water resources and to understand the movement of animals in a changing climate. Here, we propose a new machine learning approach conditioned on MODIS fSCA, as well as a set of physiographic features, and fit to Landsat fSCA over a portion of the Sierra Nevada USA. The predictions are daily 30m fSCA. The approach relies on two stages of spatially-varying models. The first classifies fSCA into three categories and the second yields estimates within (0, 100) percent fSCA. Separate models are applied and fitted within sub-regions of the study domain. Compared with a recently-published machine learning model (Rittger, Krock, et al., 2021), this approach uses spatially local (rather than global) random forests, and improves the classification error of fSCA by 16%, and fractionally-covered pixel estimates by 18%.

中文翻译：

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一种用于积雪覆盖率分数估计的空间分布式机器学习方法


山区的积雪通常为夏季和秋季提供蓄水，尤其是在美国西部。山区地形中雪特性的原位观测受到成本和工作量的限制，影响了时间和空间采样，而遥感估计提供了更完整的时空覆盖。每 16 天可从 Landsat 平台上的一系列多光谱扫描仪器获得 30m 处积雪分数 （fSCA） 的空间估计值。Terra 卫星上的中分辨率成像光谱仪 （MODIS） 仪器也提供了 463m 空间分辨率的每日估计值。融合 Landsat 和 MODIS fSCA 影像可创建各种用途所需的 fSCA 的高分辨率每日空间估计：以支持对水资源的能源和水预算感兴趣的科学家和管理人员，并了解动物在不断变化的气候中的移动。在这里，我们提出了一种以 MODIS fSCA 以及一组地形特征为条件的新机器学习方法，该方法适用于美国内华达山脉部分地区的 Landsat fSCA。预测为每日 30m fSCA。该方法依赖于空间变化模型的两个阶段。第一个将 fSCA 分为三类，第二个得出的估计值在 （0， 100） % 的 fSCA 以内。在研究域的子区域内应用和拟合单独的模型。与最近发布的机器学习模型 （Rittger， Krock， et al.， 2021） 相比，这种方法使用空间局部（而不是全局）随机森林，并将 fSCA 的分类误差提高了 16%，将部分覆盖像素估计提高了 18%。