We compare five daily MODIS-derived snow albedo products to terrain-corrected, in situ data from sites in California and Colorado, USA, and to snow albedo derived from airborne hyperspectral imagery over several basins in California and Colorado. The MODIS-derived products we consider are NASA standard products MOD10A1, MCD43A3, and MCD19A3D, along with STC-MODSCAG/MODDRFS and MODIS SPIReS. These products vary in their retrieval algorithms, including whether, for mixed pixels, they represent the albedo of snow within the pixel or the albedo of the whole pixel. When compared to in situ data, STC-MODSCAG/MODDRFS and SPIReS products have the highest accuracy (RMSE ≤0.093) and most spatially and temporally complete data records (∼99 %) because the algorithms each have independently developed gap filling and interpolation methods. The MOD10A1 and MCD43A3 products underestimate snow albedo (RMSE ≤0.248) because they incorporate non-snow land surfaces into their calculations and have less complete data records (∼76 %) due to less accurate snow detection and lack of interpolation. The MCD19A3D product has accuracy similar to STC-MODSCAG/MODDRFS and SPIReS (RMSE = 0.090) but the lowest data completeness of all datasets (56 %). We found similar performance trends when comparing the MODIS products to airborne hyperspectral data. Our analysis shows algorithms that account for fractional snow cover and incorporate all available spectral information result in the best snow albedo products across time and space. Similar algorithms applied to hyperspectral data can better resolve spectral features to retrieve optical properties of snow; hence we can expect improvements in snow albedo retrievals from future hyperspectral satellite missions.

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评估 MODIS 绘制雪反照率的方法

我们将 5 个每日 MODIS 衍生的雪反照率产品与来自美国加利福尼亚州和科罗拉多州站点的地形校正原位数据，以及来自加利福尼亚州和科罗拉多州几个盆地的航空高光谱图像的雪反照率进行了比较。我们考虑的 MODIS 衍生产品是 NASA 标准产品 MOD10A1、MCD43A3 和 MCD19A3D，以及 STC-MODSCAG/MODDRFS 和 MODIS SPIReS。这些产品的检索算法各不相同，包括对于混合像素，它们是否表示像素内雪的反照率或整个像素的反照率。与原位数据相比，STC-MODSCAG/MODDRFS 和 SPIReS 产品具有最高的精度 （RMSE ≤0.093） 和最完整的空间和时间数据记录 （∼99 %），因为每种算法都具有独立开发的间隙填充和插值方法。MOD10A1 和 MCD43A3 产品低估了雪反照率 （RMSE ≤0.248），因为它们在计算中考虑了非雪地表，并且由于雪检测精度较低和缺乏插值，数据记录不完整 （∼76%）。MCD19A3D 产品的准确性与 STC-MODSCAG/MODDRFS 和 SPIReS 相似 （RMSE = 0.090），但数据完整性在所有数据集中最低 （56 %）。在将 MODIS 产品与机载高光谱数据进行比较时，我们发现了类似的性能趋势。我们的分析显示了考虑积雪分数并结合所有可用光谱信息的算法，从而在时间和空间上产生最佳的雪反照率产品。应用于高光谱数据的类似算法可以更好地解析光谱特征以检索雪的光学特性;因此，我们可以预期未来高光谱卫星任务对雪反照率检索的改进。