Improving SWE Estimation by Fusion of Snow Models with Topographic and Remotely Sensed Data
Improving SWE Estimation by Fusion of Snow Models with Topographic and Remotely Sensed Data
复制标题
通过融合雪模型与地形和遥感数据改进 SWE 估计
DOI：
10.3390/rs11172033
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
C. Notarnicola
中科院分区：
文献类型：
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作者：
L. D. Gregorio;Daniel Günther;M. Callegari;U. Strasser;M. Zebisch;L. Bruzzone;C. Notarnicola
本文提出了一种新的概念，推导雪水当量（SWE）的基础上，联合使用积雪模型（AMUNDSEN）模拟，地面数据，以及辅助产品从遥感。其主要目的是表征时空分布的模型推导的SWE偏差相对于真实的SWE值来自地面测量。这种偏差是由于任何理论模型的内在不确定性，与分析公式中的近似值有关。该方法基于k-NN算法，计算某些标记样本的偏差，即，地面测量可用的样本，以便通过假设样本的偏差根据特征空间内的位置而变化来表征和建模与未标记样本（没有可用的地面测量）相关联的偏差。所获得的结果表明，相对于AMUNDSEN模型，通过降低RMSE和MAE与地面数据，平均从154到75毫米和从99到45毫米，分别提高了性能。此外，估计的SWE和地面参考样本之间的回归线的斜率从AMUNDSEN模拟的0.6达到0.9，通过减少数据传播和异常值的数量。
This paper presents a new concept to derive the snow water equivalent (SWE) based on the joint use of snow model (AMUNDSEN) simulation, ground data, and auxiliary products derived from remote sensing. The main objective is to characterize the spatial-temporal distribution of the model-derived SWE deviation with respect to the real SWE values derived from ground measurements. This deviation is due to the intrinsic uncertainty of any theoretical model, related to the approximations in the analytical formulation. The method, based on the k-NN algorithm, computes the deviation for some labeled samples, i.e., samples for which ground measurements are available, in order to characterize and model the deviations associated to unlabeled samples (no ground measurements available), by assuming that the deviations of samples vary depending on the location within the feature space. Obtained results indicate an improved performance with respect to AMUNDSEN model, by decreasing the RMSE and the MAE with ground data, on average, from 154 to 75 mm and from 99 to 45 mm, respectively. Furthermore, the slope of regression line between estimated SWE and ground reference samples reaches 0.9 from 0.6 of AMUNDSEN simulations, by reducing the data spread and the number of outliers.
DOI：
10.3390/rs11050479
发表时间：
2019-02
期刊：
Remote. Sens.
影响因子：
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作者：
M. Martin;D. Ghent;A. Pires;F. Göttsche;J. Cermak;J. Remedios
通讯作者：
M. Martin;D. Ghent;A. Pires;F. Göttsche;J. Cermak;J. Remedios