| 摘 要： |
Advances in snow-cover mapping techniques have resulted in more accurate estimation of fractional snow cover (FSC) in areas with no vegetation; however, vegetation interference limits the accuracy of available snow cover information from satellite observations. The aim of this study was to develop a robust and enhanced FSC-retrieval algorithm using Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data for vegetated areas. The experiments were conducted in North America, where vegetation cover is complex and heterogeneous, using 28 Landsat-8 - MODIS image pairs acquired for the entire snow cover season (September 2015-May 2016). The FSC retrieval models were established from 20 sub-models based on the Extremely Randomized Trees method incorporating input information from multiple sources, such as commonly used variables, vegetationand snow-related variables, location and geometry related variables, and other auxiliary variables. The FSC retrieval models were divided into forest- and non-forest types. We further investigated a canopy correction method to mitigate vegetation interference effects caused by the viewing geometry of satellite observations. The results show that the integration of 20 sub-models largely decreased model dependence on the training sample quality and improved the robustness of the model predictions. In the validation of the independent dataset, there was a noticeable improvement in FSC estimation for different land-cover and vegetation-cover types, with rootmean-square errors (RMSEs) reduced by an average of 11% compared to the Trimmed-Model. The application of canopy correction under the Recommend conditions (i.e., viewing zenith angle in [45 degrees , 70 degrees] and fraction of forest cover in [0, 0.3]) improved the FSC prediction accuracy. Moreover, based on a comparison with the MOD10A1-based FSC map, our FSC estimation showed improved consistency across various vegetation coverages based on the Landsat reference FSC values, with 40% lower RMSEs and 8% increase in overall accuracy. |
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