In addition to providing water for nearly 2 billion people, snow drives resource selection by wildlife and influences the behavior and demography of many species. Because snow cover is highly spatially and temporally variable, mapping its extent using currently available satellite data remains a challenge. At present, there are no sensors acquiring daily data of Earth's entire surface at fine spatial resolutions (< 30 m) in wavelengths required for snow cover retrieval, namely: visible, near-infrared, and shortwave infrared. Fine scale observations at 30 m from Landsat are available at 16-day intervals since 1982 and at 8-day intervals since 1999. However, over this duration, snow can accumulate, ablate, or both, making the Landsat data ineffective for many applications. Conversely, the Moderate Resolution Imaging Spectroradiometer (MODIS) atmospherically corrected daily reflectance data, have a coarse spatial resolution of 463 m and thus, are not ideal for snow cover mapping either. This spatial and temporal resolution tradeoff limits the use of these data for a wide range of snow cover applications and indicates a pressing need for data fusion. To address this need, we use a physically-based, spectral-mixture-analysis approach for mapping fractional snow cover (fSCA) and a two-stage random forest algorithm to produce daily 30 m fSCA. We test our algorithm in the US Sierra Nevada and find MODIS fSCA is the most important predictor. We cross validate using 170 Landsat scenes and while snow cover varies immensely in time we find little variation in errors between seasons, a small bias of 0.01, and an overall accuracy of 0.97 with slightly higher precision than recall. This technique for accurate, daily, high-resolution snow cover retrievals could be applied more broadly for analyses of regional energy budget, validating snow cover in global and regional models, and for quantifying changes in the availability of biotic resources in ecosystems.

除了为近 20 亿人提供水源外，雪还推动野生动物选择资源，并影响许多物种的行为和人口结构。由于积雪在空间和时间上高度可变，使用当前可用的卫星数据绘制其范围仍然是一个挑战。目前，还没有传感器以精细空间分辨率（< 30 m）获取覆盖雪覆盖所需波长的日常数据，即：可见光、近红外和短波红外。自 1982 年以来每 16 天间隔一次，自 1999 年以来每 8 天间隔一次，可以从 Landsat 获得 30 m 处的精细尺度观测。然而，在此期间，雪会积累、消融或两者兼有，使得 Landsat 数据对许多应用无效。反过来，中分辨率成像光谱仪 (MODIS) 经大气校正的日反射率数据具有 463 m 的粗空间分辨率，因此也不适用于积雪测绘。这种空间和时间分辨率的权衡限制了这些数据在广泛的积雪应用中的使用，并表明对数据融合的迫切需求。为了满足这一需求，我们使用基于物理的光谱混合分析方法来绘制部分积雪 (fSCA) 和两阶段随机森林算法，以产生每天 30 m 的 fSCA。我们在美国内华达山脉测试我们的算法，发现 MODIS fSCA 是最重要的预测因子。我们使用 170 个 Landsat 场景进行交叉验证，虽然积雪随时间变化很大，但我们发现季节之间的误差变化很小，偏差为 0.01，总体准确度为 0。97 精度略高于召回率。这种用于准确、每日、高分辨率的积雪反演的技术可以更广泛地应用于区域能量收支分析、验证全球和区域模型中的积雪以及量化生态系统中生物资源可用性的变化。