Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere
Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere
复制标题
基于机器学习方法的北半球雪深融合
DOI：
10.3390/rs13071250
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发表时间：
2021
期刊：
影响因子：
5
通讯作者：
Xiao Lin
中科院分区：
文献类型：
--
作者：
Hu Yanxing;Che Tao;Dai Liyun;Xiao Lin
在这项研究中，引入了一种机器学习算法来融合网格积雪深度数据集。机器学习方法的输入变量包括地理位置(经纬度)、地形数据(海拔)、网格积雪深度数据集和现场观测。总共使用了29,565个现场观测数据来训练和优化机器学习算法。共五个网格化雪深数据集--先进对地观测系统微波扫描辐射计(AMSR-E)雪深、全球雪气候监测(GlobSnow)雪深、北半球日雪深长时间序列、ERA--临时雪深和现代研究和应用追溯分析--第二版(MERRA-2)雪深被用作输入变量。前三个雪深数据集由被动微波亮温或同化与现场观测资料反演出，后两个雪深数据集由陆面模式和数据同化系统的气象再分析资料获得。然后，使用人工神经网络(ANN)、支持向量回归(SVR)和随机森林回归(RFR)三种机器学习方法生成了2002-2004年的融合积雪深度数据集。RFR模型的表现最好，因此被用来从2002年至2011年北半球的五个雪深数据集和辅助数据的融合中产生新的雪深产品。融合积雪深度产品在五个著名的积雪观测点进行了验证。索丹基勒山脉、老阿斯彭山脉和雷诺兹山脉东部的R2分别为0.88、0.69和0.63。在平均积雪深度超过200厘米的沼泽天使研究区和Weissfluhjoch观测点，熔融积雪深度表现不佳。对平均积雪深度的空间分布进行了季节性分析，秋季、冬季和春季的平均积雪深度分别为5.7、25.8和21.5 cm。未来，随机森林回归将被用来产生北半球或其他特定地区的融合积雪深度数据集的长时间序列。
In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.
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影响因子：
5.4
作者：
C. Landry;Kimberly A. Buck;M. Raleigh;M. Clark
通讯作者：
C. Landry;Kimberly A. Buck;M. Raleigh;M. Clark
影响因子：
3.4
作者：
Yungang Cao;Xiuchun Yang;Xiaohua Zhu
通讯作者：
Yungang Cao;Xiuchun Yang;Xiaohua Zhu
DOI：
10.3390/rs9050465
发表时间：
2017-05
期刊：
Remote. Sens.
影响因子：
--
作者：
E. Cho;S. Tuttle;J. Jacobs
通讯作者：
E. Cho;S. Tuttle;J. Jacobs
DOI：
10.5194/tc-2017-56
发表时间：
2017
期刊：
--
影响因子：
--
作者：
A. Snauffer;W. Hsieh;Alex J. Cannon;M. Schnorbus
通讯作者：
A. Snauffer;W. Hsieh;Alex J. Cannon;M. Schnorbus
影响因子：
13.5
作者：
M. Takala;K. Luojus;J. Pulliainen;C. Derksen;J. Lemmetyinen;Juha-Petri Kärnä;J. Koskinen;B. Bojkov-
通讯作者：
M. Takala;K. Luojus;J. Pulliainen;C. Derksen;J. Lemmetyinen;Juha-Petri Kärnä;J. Koskinen;B. Bojkov-