气候变化领域集成服务门户(CCPortal): Microwave retrievals of soil moisture and vegetation optical depth with improved resolution using a combined constrained inversion algorithm: Application for SMAP satellite

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DOI	10.1016/j.rse.2020.111662
	Microwave retrievals of soil moisture and vegetation optical depth with improved resolution using a combined constrained inversion algorithm: Application for SMAP satellite
	Gao L.; Sadeghi M.; Ebtehaj A.
发表日期	2020
ISSN	00344257
卷号	239
英文摘要	A new algorithm called combined constrained multi-channel algorithm (C-CMCA) is presented for simultaneous retrieval of soil moisture (SM) and vegetation optical depth (VOD) in L-band with improved resolution. Unlike widely used algorithms, the new approach optimally fuses multiple sources of surface temperatures into the inversion process and confines the retrievals to their feasible climatological range rather than the mean and paves the way to account for the slow changes of VOD through a Sobolev-norm regularization. Through controlled numerical experiments that assume a random error in the surface temperatures, it is shown that the algorithm can decrease the root mean squared error (RMSE) by 78% and 81% when compared with the unconstrained version and 54% and 7% when a single source of surface temperature is used in retrievals of SM and VOD, respectively. The use of the Sobolev-norm regularization decreases the RMSE by more than 25% at the expense of a negligible bias. Implementation of the algorithm, using data from the NASA's Soil Moisture Active Passive (SMAP) satellite in 2016, demonstrates that the monthly RMSE of SM retrievals improves by more than 6% when compared with the SMAP enhanced products considering the ground measurements from the International SM Network (ISMN) as a reference while the monthly RMSE of VOD decreases by more than 62% when compared with the VOD climatology used in the SMAP SCA products. Analysis of the results demonstrates that, without increasing the native resolution of radiometric observations, the information content of the a priori constraints cannot only improve resolutions of the retrievals but also make them robust to the background water contamination in the vicinity of coastal zones and over lowland floodplains. For example, over Florida peninsula, the annual SM RMSE and bias are reduced by more than 65%. © 2020 Elsevier Inc.
英文关键词	L-band radiometry; Soil moisture; Vegetation optical depth
语种	英语
scopus关键词	Atmospheric temperature; Coastal zones; Mean square error; NASA; Optical properties; Radiometry; Soil moisture; Surface properties; Vegetation; Video on demand; Water pollution; Constrained inversions; Information contents; L-band radiometry; Numerical experiments; Root mean squared errors; Soil moisture active passive (SMAP); Surface temperatures; Vegetation optical depth; Soil surveys; algorithm; experimental study; optical depth; radiometric method; satellite altimetry; soil moisture; spectral resolution; vegetation dynamics; Florida [United States]
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179442
作者单位	Saint Anthony Falls Laboratory, Department of Civil Environmental and Geo- Engineering, University of Minnesota, Minneapolis, MN 55414, United States
推荐引用方式 GB/T 7714	Gao L.,Sadeghi M.,Ebtehaj A.. Microwave retrievals of soil moisture and vegetation optical depth with improved resolution using a combined constrained inversion algorithm: Application for SMAP satellite[J],2020,239.
APA	Gao L.,Sadeghi M.,&Ebtehaj A..(2020).Microwave retrievals of soil moisture and vegetation optical depth with improved resolution using a combined constrained inversion algorithm: Application for SMAP satellite.Remote Sensing of Environment,239.
MLA	Gao L.,et al."Microwave retrievals of soil moisture and vegetation optical depth with improved resolution using a combined constrained inversion algorithm: Application for SMAP satellite".Remote Sensing of Environment 239(2020).