# Additive Time Series Decomposition in R: Soil Moisture and Temperature Data

Oct 27, 2008 metroadmin**Premise**

Simple demonstration of working with time-series data collected from Decagon Devices soil moisture and temperature sensors. These sensors were installed in a potted plant, that was semi-regularly watered, and data were collected for about 80 days on an hourly basis. Several basic operations in **R**are demonstrated:

- reading raw data in CSV format
- converting date-time values to R's date-time format
- applying a calibration curve to raw sensor values
- initialization of R time series objects
- seasonal decomposition of additive time series (trend extraction)
- plotting of decomposed time series, ACF, and cross-ACF

**Process the raw sensor values with standard calibrations**

**Decompose each time series into additive components**

**Auto-Correlation Function (ACF)**

**Interesting Results**

Variation in temperature with time dominated by diurnal fluctuations superposed over underlying fluctuations caused by building heating/cooling system. The magnitude of the diurnal cycle appears to be related to the moisture content- as expected due to high heat capacity of water. Diurnal variation in moisture values appears to account for less than < 2% absolute change in volumetric water content.

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Access Data Stored in a Postgresql Database

R: advanced statistical package

## Software

- General Purpose Programming with Scripting Languages
- LaTeX Tips and Tricks
- PostGIS: Spatially enabled Relational Database Sytem
- PROJ: forward and reverse geographic projections
- GDAL and OGR: geodata conversion and re-projection tools
- R: advanced statistical package
- Access Data Stored in a Postgresql Database
- Additive Time Series Decomposition in R: Soil Moisture and Temperature Data
- Aggregating SSURGO Data in R
- Cluster Analysis 1: finding groups in a randomly generated 2-dimensional dataset
- Color Functions
- Comparison of Slope and Intercept Terms for Multi-Level Model
- Comparison of Slope and Intercept Terms for Multi-Level Model II: Using Contrasts
- Creating a Custom Panel Function (R - Lattice Graphics)
- Customized Scatterplot Ideas
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- Exploration of Multivariate Data
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- Numerical Integration/Differentiation in R: FTIR Spectra
- Plotting XRD (X-Ray Diffraction) Data
- Using lm() and predict() to apply a standard curve to Analytical Data
- Working with Spatial Data
- Comparison of PSA Results: Pipette vs. Laser Granulometer

- GRASS GIS: raster, vector, and imagery analysis
- Generic Mapping Tools: high quality map production