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Estimating fractional snow cover in vegetated environments using MODIS surface reflectance data
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-26 , DOI: 10.1016/j.jag.2022.103030
Xiongxin Xiao, Tao He, Shunlin Liang, Xinyan Liu, Yichuan Ma, Shuang Liang, Xiaona Chen

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, vegetation- and 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 root-mean-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°,70°] 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.



中文翻译:

使用 MODIS 表面反射率数据估计植被环境中的部分积雪

积雪测绘技术的进步导致在没有植被的地区更准确地估计部分积雪(FSC);然而,植被干扰限制了来自卫星观测的可用积雪信息的准确性。本研究的目的是利用中分辨率成像光谱仪 (MODIS) 的植被区域表面反射率数据开发一种强大且增强的 FSC 检索算法。这些实验是在北美进行的,那里的植被覆盖复杂且异质,使用了整个雪覆盖季节(2015 年 9 月至 2016 年 5 月)获取的 28 幅 Landsat-8 - MODIS 图像对。FSC 检索模型是由 20 个子模型基于极端随机树方法建立的,该方法结合了来自多个来源的输入信息,例如常用变量、植被和雪相关变量、位置和几何相关变量以及其他辅助变量。FSC 检索模型分为森林类型和非森林类型。我们进一步研究了一种冠层校正方法,以减轻由卫星观测的观测几何引起的植被干扰效应。结果表明,20个子模型的集成大大降低了模型对训练样本质量的依赖,提高了模型预测的鲁棒性。在独立数据集的验证中,不同土地覆盖和植被覆盖类型的 FSC 估计有显着改善,与 Trimmed 模型相比,均方根误差 (RMSE) 平均降低了 11% . 在“推荐”条件下应用冠层校正(即,[45°,70°]和森林覆盖率的一部分[0,0.3]) 提高了 FSC 预测精度。此外,基于与基于 MOD10A1 的 FSC 地图的比较,我们的 FSC 估计显示,基于 Landsat 参考 FSC 值,各种植被覆盖的一致性得到改善,RMSE 降低 40%,整体准确度提高 8%。

更新日期:2022-09-26
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