Spatio-temporal estimation of soil moisture in a tropical region using a remote sensing algorithm

Liliana Marrufo, Fernando González, Alejandro Monsiváis-Huertero, Judith Ramos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

To achieve a soil moisture estimation (ms) in an accurate way is crucial to understand the water cycle response and avoid traditional runoff estimations where the ms is assumed as a constant value. The aim of this study is the implementation of optical-radar images into a model to estimate ms in the Zapotes Lagoon System in Tabasco, Mexico. The satellite images used were Landsat TM and ETM+ sensors, Envisat and also in situ measurements and MDT were available. The field measurements at the soil profile showed a clear pattern of the water movement into the basin corresponding to the lower parts of the system (lagoons). The land use analysis obtained with the optical images indicated a strong change in the floodplain due to the construction of protect barriers around the Villahermosa city losing its hydrological capacity. This allows the identification of three main covers (soil, vegetation and air) that were monitored and feed to the model (MIMICS) in order to estimate ms using the Envisat image. Results provided an empirical equation that relates the ms with the backscattering coefficient.

Original languageEnglish
Title of host publication2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Proceedings
Pages3089-3092
Number of pages4
DOIs
StatePublished - 2011
Event2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 - Vancouver, BC, Canada
Duration: 24 Jul 201129 Jul 2011

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011
Country/TerritoryCanada
CityVancouver, BC
Period24/07/1129/07/11

Keywords

  • ENVISAT
  • MIMICS
  • microwave remote sensing
  • soil moisture
  • supervised classification

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