A three-variables cokriging method to estimate bare-surface soil moisture using multi-temporal, VV-polarization syntheti
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PAPER
A three-variables cokriging method to estimate bare-surface soil moisture using multi-temporal, VV-polarization synthetic-aperture radar data Ling Zeng 1 & Qingyun Shi 2 & Ke Guo 1 & Shuyun Xie 3 & Jason Scott Herrin 4 Received: 20 August 2019 / Accepted: 28 April 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract A cokriging model using three variables is developed to estimate bare-surface soil moisture content based on multi-temporal synthetic-aperture radar (SAR) data. This model utilizes cross-semivariogram function to take into account spatially varied correlation among multiple variables. Here, five sentinel-1 SAR scenes were acquired on different dates using the interferometric wide-swath (IW) mode and a mean incidence angle of 39.02° to build the backscatter temporal-ratio in VV polarization. This algorithm is generally based on the assumption of contributions of soil moisture and surface roughness to the backscattering coefficient under the given radar configurations. In this study, soil moisture is the target variable, and the surface roughness and backscatter temporal-ratio in VV polarization are the auxiliary variables. A cross-semivariogram relationship is formulated among those three spatial variables; then ordinary cokriging is used, based on that cross-semivariogram formula, to estimate the spatial distribution of bare soil moisture content. The root mean square error (RMSE) of soil-moisture retrieval ranges from 2.62 to 2.66 vol%. The new empirical model described in this paper will provide new insights into the study of soil environments. Keywords Geostatistics . Remote sensing . Soil moisture . Sentinel-1 . Backscatter coefficient
Introduction Knowledge of soil-moisture content is of vital importance to the understanding of hydrological processes and for land management, especially agriculture, as well as for effective drought monitoring (Brocca et al. 2010; Kerr et al. 2010; Western et al. 2002; Zhao et al. 2017). Remote sensing is one of the most effective tools used in surface soil-moisture monitoring due to its ability of record a wide range of * Ling Zeng [email protected] 1
Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China
2
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
3
State Key Laboratory of Geological Processes and Mineral Resources (GPMR), Faculty of Earth Sciences, China University of Geosciences, Wuhan 430074, China
4
Facility for Analysis Characterization Testing Simulation, Nanyang Technological University, Singapore 639798, Singapore
observations and the high frequency of repeated measurements without expensive in-situ monitoring networks (Kornelsen and Coulibaly 2017; Zhu et al. 2016). Compared to optical sensing, satellite microwave observations from active and passive sensors are the most suitable for the retrieval of soil-moisture content (Mohanty et al. 2017; Peng et al. 2017; Schmugge et
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