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 language | English (US) |
|---|---|
| Pages (from-to) | 99-112 |
| Number of pages | 14 |
| Journal | Statistical Methodology |
| Volume | 17 |
| Issue number | C |
| DOIs | |
| State | Published - 2014 |
Keywords
- Data fusion
- Euler discretization
- Hierarchical nonlinear model
- Partial differential equation
- State space model
ASJC Scopus subject areas
- Statistics and Probability