Retrieval of fractional snow covered area from MODIS data by multivariate adaptive regression splines
Retrieval of fractional snow covered area from MODIS data by multivariate adaptive regression splines
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DOI：
10.1016/j.rse.2017.11.021
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发表时间：
2018-02-01
影响因子：
13.5
通讯作者：
Weber, Gerhard-Wilhelm
中科院分区：
文献类型：
--
作者：
Kuter, Semih;Akyurek, Zuhal;Weber, Gerhard-Wilhelm
在本文中，一种新的方法来估计分数积雪覆盖（FSC）从MODIS数据在一个复杂的和异质的阿尔卑斯山地形表示使用一个国家的最先进的非参数样条回归方法，即多元自适应回归样条（MARS）。为此，使用了2013年4月至2016年12月在欧洲阿尔卑斯山上空采集的20幅MODIS - Landsat 8图像对。在模型训练过程中使用了15个图像对，并保留了5个图像作为独立的测试数据集。以MODIS 1-7波段的大气顶反射率、归一化差异积雪指数、归一化差异植被指数和土地覆盖类别为预报变量，对MARS模型进行训练。参考FSC地图是从空间分辨率较高的Landsat 8二进制积雪图生成的。多层前馈人工神经网络（ANN）模型也使用相同的输入数据进行训练。在训练和测试过程中，研究了训练数据大小和样本类型对ANN和MARS模型预测性能的影响。一个额外的搜索也进行，以揭示是否在ANN的输出层中使用的传递函数的选择有显着的贡献，网络的FSC映射性能。最终的ANN和MARS FSC产品的空间分辨率为500米。独立测试场景的结果表明，开发的神经网络模型与线性和双曲正切传递函数在输出层和MARS模型是在良好的协议与参考FSC数据具有相同的平均值R = 0.93。相比之下，标准的MODIS雪分数产品，即MOD 10 FSC，表现出略差的性能，平均R = 0.88。所提出的MARS方法在统计上被证明具有与ANN相同的性能，但它在模型构建中的计算效率更高。
In this paper, a novel approach to estimate fractional snow cover (FSC) from MODIS data in a complex and heterogeneous Alpine terrain is represented by using a state-of-the-art nonparametric spline regression method, namely, multivariate adaptive regression splines (MARS). For this purpose, twenty MODIS - Landsat 8 image pairs acquired between April 2013 and December 2016 over European Alps are used. Fifteen of the image pairs are employed during model training and five images are reserved as an independent test dataset. MARS models are trained by using MODIS top-of-atmosphere reflectance values of bands 1-7, normalized difference snow index, normalized difference vegetation index and land cover class as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat 8 binary snow cover maps. Multilayer feedforward artificial neural network (ANN) models are also trained by using the same input data. During the training and the testing, the effects of the training data size and the sampling type on the predictive performance of ANN and MARS models are investigated. An additional search is also conducted to reveal whether the choice of the transfer function used in the output layer of ANN has a significant contribution to the network's FSC mapping performance. The final ANN and MARS FSC products are at 500 m spatial resolution. The results on the independent test scenes indicate that the developed ANN models with linear and hyperbolic tangent transfer functions in the output layer and the MARS models are in good agreement with reference FSC data with the same average values of R = 0.93. In contrast, the standard MODIS snow fraction product, namely, MOD10 FSC, exhibits slightly poorer performance with average R = 0.88. The proposed MARS approach is statistically proven to have the same performance with ANN, yet it is computationally more efficient in model building.