Process modeling for soil moisture using sensor network data

Souparno Ghosh, David M. Bell, James S. Clark, Alan E. Gelfand, Paul G. Flikkema

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

The quantity of water contained in soil is referred to as the soil moisture. Soil moisture plays an important role in agriculture, percolation, and soil chemistry. Precipitation, temperature, atmospheric demand and topography are the primary processes that control soil moisture. Estimates of landscape variation in soil moisture are limited due to the complexity required to link high spatial variation in measurements with the aforesaid processes that vary in space and time. In this paper we develop an inferential framework that takes the form of data fusion using high temporal resolution environmental data from wireless networks along with sparse reflectometer data as inputs and yields inference on moisture variation as precipitation and temperature vary over time and drainage and canopy coverage vary in space. We specifically address soil moisture modeling in the context of wireless sensor networks.

Original languageEnglish (US)
Pages (from-to)99-112
Number of pages14
JournalStatistical Methodology
Volume17
Issue numberC
DOIs
StatePublished - Jan 1 2014

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Keywords

  • Data fusion
  • Euler discretization
  • Hierarchical nonlinear model
  • Partial differential equation
  • State space model

ASJC Scopus subject areas

  • Statistics and Probability

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