气候变化领域集成服务门户(CCPortal): Quantifying Uncertainties in Passive Microwave Remote Sensing of Soil Moisture via a Bayesian Probabilistic Inversion Method

CCPortal

DOI	10.1109/TGRS.2021.3123464
	Quantifying Uncertainties in Passive Microwave Remote Sensing of Soil Moisture via a Bayesian Probabilistic Inversion Method
	Ma, Chunfeng; Li, Xin; Wang, Shuguo; Wang, Zengyan; Che, Tao; Jin, Rui; Wang, Weizhen
通讯作者	Li, X (通讯作者)，Chinese Acad Sci, Inst Tibetan Plateau Res, Natl Tibetan Plateau Data Ctr, Beijing 100101, Peoples R China. ; Li, X (通讯作者)，Chinese Acad Sci, Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China.
发表日期	2022
ISSN	0196-2892
EISSN	1558-0644
卷号	60
英文摘要	Improving the accuracy of remotely sensed soil moisture (SM) is a challenging and popular topic. Quantifying the uncertainty in the SM inversion process and enhancing the confidence of SM retrieval are promising ways to address this challenge but have received little attention. We present a Bayesian probabilistic inversion algorithm that can simultaneously retrieve SM, surface roughness, and vegetation optical depth and quantify the uncertainty in the inversion. The proposed algorithm is evaluated using airborne polarimetric L-band multibeam radiometer (PLMR) observations. We use three combinations, 3-angular observations at V-polarization (3CV), 3-angular observations at H-polarization (3CH), and 6-channel observations (6CA), to identify the optimal configuration for SM retrieval by taking advantage of PLMR's dual-polarization and multiple angles. Uncertainties are quantified by introducing multiple uncertainty quantification metrics into Bayesian posterior distributions of SM retrievals. The estimates are validated against multiscale ground-based measurements, including manual measurements and wireless sensor network (WSN) measurements, and the spatial representativeness of the ground-based reference regarding the validation of pixel-scale SM retrievals is discussed. The 6CA attempt yields the best SM estimates (correlation coefficient (R) >= 0.864 , root mean square error (RMSE) <= 0.04 m(3)/m(3), and unbiased RMSE (ubRMSE) <= 0.035 m(3)/m(3)), while the 3CH attempt yields the lowest uncertainty. In addition, dense manual measurements are more representative than sparsely distributed WSN measurements. Overall, combining dual-polarized observations yields the best SM estimates but introduces additional uncertainty. This study highlights uncertainties quantification in SM inversion and thus provides confidence in SM inversion, facilitating improved SM retrieval algorithms.
关键词	SURFACE EMISSION MODEL L-BAND SENSED DATA RETRIEVAL VALIDATION SMOS VEGETATION ROUGHNESS ALGORITHM FIELD
英文关键词	Microwave radiometry; Uncertainty; Microwave theory and techniques; Microwave measurement; L-band; Wireless sensor networks; Sea measurements; Bayesian probabilistic inversion; passive microwave remote sensing; soil moisture (SM); uncertainty quantification
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000754264200016
来源期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND 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/255075
作者单位	[Ma, Chunfeng; Che, Tao; Jin, Rui; Wang, Weizhen] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China; [Ma, Chunfeng; Che, Tao; Jin, Rui; Wang, Weizhen] Chinese Acad Sci, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China; [Li, Xin] Chinese Acad Sci, Inst Tibetan Plateau Res, Natl Tibetan Plateau Data Ctr, Beijing 100101, Peoples R China; [Li, Xin] Chinese Acad Sci, Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China; [Wang, Shuguo] Jiangsu Normal Univ, Sch Geog Geomat & Planning, Xuzhou 221116, Jiangsu, Peoples R China; [Wang, Zengyan] Henan Univ, Minist Educ, Key Lab Geospatial Technol Middle & Lower Yellow, Kaifeng 475004, Peoples R China; [Wang, Zengyan] Henan Univ, Coll Geog & Environm Sci, Kaifeng 475004, Peoples R China
推荐引用方式 GB/T 7714	Ma, Chunfeng,Li, Xin,Wang, Shuguo,et al. Quantifying Uncertainties in Passive Microwave Remote Sensing of Soil Moisture via a Bayesian Probabilistic Inversion Method[J]. 中国科学院西北生态环境资源研究院,2022,60.
APA	Ma, Chunfeng.,Li, Xin.,Wang, Shuguo.,Wang, Zengyan.,Che, Tao.,...&Wang, Weizhen.(2022).Quantifying Uncertainties in Passive Microwave Remote Sensing of Soil Moisture via a Bayesian Probabilistic Inversion Method.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60.
MLA	Ma, Chunfeng,et al."Quantifying Uncertainties in Passive Microwave Remote Sensing of Soil Moisture via a Bayesian Probabilistic Inversion Method".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022).