气候变化领域集成服务门户(CCPortal): A D-vine copula quantile regression approach for soil moisture retrieval from dual polarimetric SAR Sentinel-1 over vegetated terrains

CCPortal

DOI	10.1016/j.rse.2021.112283
	A D-vine copula quantile regression approach for soil moisture retrieval from dual polarimetric SAR Sentinel-1 over vegetated terrains
	Nguyen H.H.; Cho S.; Jeong J.; Choi M.
发表日期	2021
ISSN	00344257
卷号	255
英文摘要	Soil moisture retrieval from Synthetic Aperture Radar (SAR) over vegetated terrains requires an isolation of soil and canopy signals from observed backscatter (σ°). This study develops a probabilistic soil moisture retrieval method from dual polarimetric C-band SAR Sentinel-1 (S-1) with uncertainty quantification at distinct vegetation covers (VCs). Both σVV° and σVH° were used to represent ground and volume scattering due to the high respective sensitivity to soil moisture and vegetation dynamics. A novel D-vine copula quantile regression (DVQR) was adopted to provide the soil moisture estimates based on modelling trivariate dependence of σVV°-σVH°-soil moisture anomalies (VV-VH-Mv), with a support from the innovative cosmic-ray soil moisture as ground-truth data. The feasibility of DVQR was underlined for: (1) multivariate nonlinear dependence structure modelling and (2) soil moisture retrieval with associated uncertainty. An inter-dependence analysis, which assesses the correlations among three major variables, indicated that the dependence between each pair of variables decreased as canopy density increases from herbs to forests, mainly due to the σ° attenuated by vegetation effect. The dependence structures simulated from the D-vine copula revealed highly nonlinear and asymmetric shapes with tail dependences occurred in most VCs, which can be well captured by different associated Archimedean copulas. Soil moisture anomaly (Mv) estimated using the DVQR and Multiple linear quantile regression (MLQR) were compared against ground-truth data for both in-sample and out-of-sample predictions. Superior performances of the DVQR in most VCs, with 10% and 16% improved in RMSE at grasslands and broadleaf forests, respectively, demonstrated the robustness of this method for S-1 soil moisture retrieval due to the highly nonlinear dependence structures captured by the D-vine models. Over VCs, better performances were obtained at low-canopy herbaceous regions (grasslands and croplands); whereas extremely dry conditions and complex structures in shrublands and dense forests resulted in inferior performances. A sensitivity analysis was then conducted to evaluate the change in Mv estimation accuracy given distinct VV and VH quantile levels. Result underlines that VV is the primary factor controlling the retrieval accuracy, but the increase in VH level also contributes to higher errors in soil moisture estimation, especially under wet conditions. © 2021 Elsevier Inc.
英文关键词	Quantile regression; SAR Sentinel-1; Soil moisture retrieval; Vegetation covers; Vine copula
语种	英语
scopus关键词	Binary alloys; Cosmology; Forestry; Nonlinear analysis; Polarimeters; Sensitivity analysis; Soil surveys; Synthetic aperture radar; Uncertainty analysis; Vegetation; Archimedean copula; Dependence structures; Nonlinear dependence; Quantile regression; Soil moisture estimation; Soil moisture retrievals; Uncertainty quantifications; Vegetation dynamics; Soil moisture; backscatter; broad-leaved forest; detection method; grassland; regression analysis; Sentinel; soil moisture; soil-vegetation interaction; synthetic aperture radar
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/178950
作者单位	Center for Built Environment, Sungkyunkwan University, Suwon, 440-746, South Korea; Environment and Remote Sensing Laboratory, Department of Water Resources, Graduate School of Water Resources, Sungkyunkwan University, Suwon, 440-746, South Korea
推荐引用方式 GB/T 7714	Nguyen H.H.,Cho S.,Jeong J.,et al. A D-vine copula quantile regression approach for soil moisture retrieval from dual polarimetric SAR Sentinel-1 over vegetated terrains[J],2021,255.
APA	Nguyen H.H.,Cho S.,Jeong J.,&Choi M..(2021).A D-vine copula quantile regression approach for soil moisture retrieval from dual polarimetric SAR Sentinel-1 over vegetated terrains.Remote Sensing of Environment,255.
MLA	Nguyen H.H.,et al."A D-vine copula quantile regression approach for soil moisture retrieval from dual polarimetric SAR Sentinel-1 over vegetated terrains".Remote Sensing of Environment 255(2021).