气候变化领域集成服务门户(CCPortal): Diversity of Remote Sensing-Based Variable Inputs Improves the Estimation of Seasonal Maximum Freezing Depth

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DOI	10.3390/rs13234829
	Diversity of Remote Sensing-Based Variable Inputs Improves the Estimation of Seasonal Maximum Freezing Depth
	Wang, Bingquan; Ran, Youhua
通讯作者	Ran, YH (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Heihe Remote Sensing Expt Res Stn, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China. ; Ran, YH (通讯作者)，Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China.
发表日期	2021
EISSN	2072-4292
卷号	13 期号:23
英文摘要	The maximum soil freezing depth (MSFD) is an important indicator of the thermal state of seasonally frozen ground. Its variation has important implications for the water cycle, ecological processes, climate and engineering stability. This study tested three aspects of data-driven predictions of MSFD in the Qinghai-Tibet Plateau (QTP), including comparison of three popular statistical/machine learning techniques, differences between remote sensing variables and reanalysis data as input conditions, and transportability of the model built by reanalysis data. The results show that support vector regression (SVR) performs better than random forest (RF), k-nearest neighbor (KNN) and the ensemble mean of the three models. Compared with the climate predictors, the remote sensing predictors are helpful for improving the simulation accuracy of the MSFD at both decadal and annual scales (at the annual and decadal scales, the root mean square error (RMSE) is reduced by 2.84 and 1.99 cm, respectively). The SVR model with climate predictor calibration using the in situ MSFD at the baseline period (2001-2010) can be used to simulate the MSFD over historical periods (1981-1990 and 1991-2000). This result indicates the good transferability of the well-trained machine learning model and its availability to simulate the MSFD of the past and the future when remote sensing predictors are not available.
关键词	LAND-SURFACE TEMPERATURE SNOW-COVER AIR-TEMPERATURE TIME-SERIES GROUND SURFACE CLIMATE-CHANGE HEIHE RIVER PERMAFROST MODIS VALIDATION
英文关键词	seasonally frozen ground; soil freezing depth; statistical learning; machine learning; Qinghai-Tibet Plateau
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000734631300001
来源期刊	REMOTE SENSING
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254516
作者单位	[Wang, Bingquan; Ran, Youhua] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Heihe Remote Sensing Expt Res Stn, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China; [Wang, Bingquan; Ran, Youhua] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Wang, Bingquan,Ran, Youhua. Diversity of Remote Sensing-Based Variable Inputs Improves the Estimation of Seasonal Maximum Freezing Depth[J]. 中国科学院西北生态环境资源研究院,2021,13(23).
APA	Wang, Bingquan,&Ran, Youhua.(2021).Diversity of Remote Sensing-Based Variable Inputs Improves the Estimation of Seasonal Maximum Freezing Depth.REMOTE SENSING,13(23).
MLA	Wang, Bingquan,et al."Diversity of Remote Sensing-Based Variable Inputs Improves the Estimation of Seasonal Maximum Freezing Depth".REMOTE SENSING 13.23(2021).