MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area
MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area
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DOI：
10.3390/rs12060962
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Changy Liu;Xiaodong Huang;Xubing Li;Tiangang Liang
中科院分区：
文献类型：
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作者：
Changy Liu;Xiaodong Huang;Xubing Li;Tiangang Liang
针对青藏高原地形复杂（RMSE = 0.30）地区MODIS（Moderate Resolution Imaging Spectroradiometer，中分辨率成像光谱仪）逐日积雪覆盖率产品精度不高的问题，采用无人机和机器学习技术，对青藏高原地形进行了基于MODIS的积雪覆盖率制图。三种机器学习模型，包括随机森林，支持向量机和反向传播人工神经网络模型，在这项研究中进行训练和比较。结果表明，与MODIS逐日积雪覆盖率产品相比，将无人机获取的高精度积雪图作为参考引入到机器学习模型中，可以显著提高MODIS积雪覆盖率制图精度。随机森林模型在三种机器学习模型中显示出最好的准确性，RMSE（均方根误差）为0.23，特别是在林地和灌木丛中，RMSE分别为0.13和0.18。虽然支持向量机和反向传播人工神经网络模型的精度较差的林地和灌丛，他们的平均误差仍然优于MOD 10A1。不同的分数积雪梯度也会影响机器学习算法的准确性。然而，随机森林模型在不同的分数雪盖梯度保持稳定，因此，是最好的机器学习算法的MODIS分数雪盖制图在青藏高原地区复杂的地形和严重破碎的积雪。
To improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based on MODIS over this terrain. Three machine learning models, including random forest, support vector machine, and back-propagation artificial neural network models, are trained and compared in this study. The results indicate that compared with the MODIS daily fractional snow cover product, the introduction of a highly accurate snow map acquired by unmanned aerial vehicles as a reference into machine learning models can significantly improve the MODIS fractional snow cover mapping accuracy. The random forest model shows the best accuracy among the three machine learning models, with an RMSE (root-mean-square error) of 0.23, especially over forestland and shrubland, with RMSEs of 0.13 and 0.18, respectively. Although the accuracy of the support vector machine and back-propagation artificial neural network models are worse over forestland and shrubland, their average errors are still better than that of MOD10A1. Different fractional snow cover gradients also affect the accuracy of the machine learning algorithms. Nevertheless, the random forest model remains stable in different fractional snow cover gradients and is, therefore, the best machine learning algorithm for MODIS fractional snow cover mapping in Tibetan Plateau areas with complex terrain and severely fragmented snow cover.