气候变化领域集成服务门户(CCPortal): Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach

CCPortal

DOI	10.5194/tc-14-1763-2020
	Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach
	Yang J.; Jiang L.; Luojus K.; Pan J.; Lemmetyinen J.; Takala M.; Wu S.
发表日期	2020
ISSN	19940416
起始页码	1763
结束页码	1778
卷号	14 期号:6
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.
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
语种	英语
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/202253
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
推荐引用方式 GB/T 7714	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],2020,14(6).
APA	Yang J..,Jiang L..,Luojus K..,Pan J..,Lemmetyinen J..,...&Wu S..(2020).Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach.Cryosphere,14(6).
MLA	Yang J.,et al."Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach".Cryosphere 14.6(2020).