DOI: 10.5194/tc-14-1763-2020
论文题名: Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach
作者: Yang J. ; Jiang L. ; Luojus K. ; Pan J. ; Lemmetyinen J. ; Takala M. ; Wu S.
刊名: Cryosphere
ISSN: 19940416
出版年: 2020
卷: 14, 期: 6 起始页码: 1763
结束页码: 1778
语种: 英语
英文关键词: algorithm
; correlation
; data set
; estimation method
; land cover
; machine learning
; model validation
; snow cover
; spatial variation
; spatiotemporal analysis
; trend analysis
; China
; Qinghai-Xizang Plateau
英文摘要: We investigated the potential capability of the random forest (RF) machine learning (ML) model to estimate snow depth in this work. Four combinations composed of critical predictor variables were used to train the RF model. Then, we utilized three validation datasets from out-of-bag (OOB) samples, a temporal subset, and a spatiotemporal subset to verify the fitted RF algorithms. The results indicated the following: (1) the accuracy of the RF model is greatly influenced by geographic location, elevation, and land cover fractions; (2) however, the redundant predictor variables (if highly correlated) slightly affect the RF model; and (3) the fitted RF algorithms perform better on temporal than spatial scales, with unbiased root-mean-square errors (RMSEs) of 4:4 and 7:3 cm, respectively. Finally, we used the fitted RF2 algorithm to retrieve a consistent 32-year daily snow depth dataset from 1987 to 2018. This product was evaluated against the independent station observations during the period 1987 2018. The mean unbiased RMSE and bias were 7.1 and 0:05 cm, respectively, indicating better performance than that of the former snow depth dataset (8.4 and 1:20 cm) from the Environmental and Ecological Science Data Center for West China (WESTDC). Although the RF product was superior to the WESTDC dataset, it still underestimated deep snow cover (> 20 cm), with biases of 10:4, 8:9, and 34:1 cm for northeast China (NEC), northern Xinjiang (XJ), and the Qinghai Tibetan Plateau (QTP), respectively. Additionally, the long-term snow depth datasets (station observations, RF estimates, and WESTDC product) were analyzed in terms of temporal and spatial variations over China. On a temporal scale, the ground truth snow depth presented a significant increasing trend from 1987 to 2018, especially in NEC. However, the RF and WESTDC products displayed no significant changing trends except on the QTP. The WESTDC product presented a significant decreasing trend on the QTP, with a correlation coefficient of 0:55, whereas there were no significant trends for ground truth observations and the RF product. For the spatial characteristics, similar trend patterns were observed for RF and WESTDC products over China. These characteristics presented significant decreasing trends in most areas and a significant increasing trend in central NEC. © 2020 Copernicus GmbH. All rights reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/164631
Appears in Collections: 气候变化与战略
There are no files associated with this item.
作者单位: State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Norm. Univ. and Aerosp. Info. Res. Inst. of Chinese Academy of Sciences, Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Finnish Meteorological Institute, P.O. Box 503, Helsinki, 00101, Finland; State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China; National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081, China
Recommended Citation:
Yang J.,Jiang L.,Luojus K.,et al. Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach[J]. Cryosphere,2020-01-01,14(6)