气候变化领域集成服务门户(CCPortal): Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

CCPortal

DOI	10.3390/rs13020228
	Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products
	Kang, Jian; Jin, Rui; Li, Xin; Zhang, Yang
通讯作者	Jin, R (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. ; Jin, R (通讯作者)，CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China.
发表日期	2021
EISSN	2072-4292
卷号	13 期号:2
英文摘要	In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm(3) cm(-3) and 0.077 cm(3) cm(-3,) respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm(3) cm(-3) and the CCI with an RMSE of 0.039 cm(3) cm(-3) will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm(3) cm(-3).
关键词	SATELLITE SMOS NETWORK SIMULATIONS RESPECT ERRORS
英文关键词	error; remote sensing product; soil moisture; upscaling; validation
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:000611969300001
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/254254
作者单位	[Kang, Jian; Jin, Rui; Zhang, Yang] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Heihe Remote Sensing Expt Res Stn, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China; [Jin, Rui; Li, Xin] CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China; [Li, Xin] Chinese Acad Sci, Inst Tibetan Plateau Res, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Kang, Jian,Jin, Rui,Li, Xin,et al. Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products[J]. 中国科学院西北生态环境资源研究院,2021,13(2).
APA	Kang, Jian,Jin, Rui,Li, Xin,&Zhang, Yang.(2021).Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products.REMOTE SENSING,13(2).
MLA	Kang, Jian,et al."Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products".REMOTE SENSING 13.2(2021).