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Interpolation of GPS and Geological Data Using InSAR Deformation Maps: Method and Application to Land Subsidence in the Alto Guadalentín Aquifer (SE Spain)

M. Béjar-Pizarro,C. Guardiola‐Albert,Ramón P. García-Cárdenas,G. Herrera,A. Barra,Antonio López Molina,S. Tessitore,A. Staller,José A. Ortega-Becerril,Ramón P. García-García

Published 2016 in Remote Sensing

ABSTRACT

Land subsidence resulting from groundwater extractions is a global phenomenon adversely affecting many regions worldwide. Understanding the governing processes and mitigating associated hazards require knowing the spatial distribution of the implicated factors (piezometric levels, lithology, ground deformation), usually only known at discrete locations. Here, we propose a methodology based on the Kriging with External Drift (KED) approach to interpolate sparse point measurements of variables influencing land subsidence using high density InSAR measurements. In our study, located in the Alto Guadalentin basin, SE Spain, these variables are GPS vertical velocities and the thickness of compressible soils. First, we estimate InSAR and GPS rates of subsidence covering the periods 2003–2010 and 2004–2013, respectively. Then, we apply the KED method to the discrete variables. The resulting continuous GPS velocity map shows maximum subsidence rates of 13 cm/year in the center of the basin, in agreement with previous studies. The compressible deposits thickness map is significantly improved. We also test the coherence of Sentinel-1 data in the study region and evaluate the applicability of this methodology with the new satellite, which will improve the monitoring of aquifer-related subsidence and the mapping of variables governing this phenomenon.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    Remote Sensing

  • Publication date

    2016-11-23

  • Fields of study

    Geology, Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs8110965
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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