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Related Experiment Video

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In Situ Soil Moisture Sensors in Undisturbed Soils

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Published on: November 18, 2022

Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework.

Anthony Giorgio¹, Nicoletta Del Buono², Marco Berardi³

¹Ernst & Young, 70126 Bari, Italy.

Sensors (Basel, Switzerland)

|October 27, 2022

Summary

This study forecasts soil water content and salinity for 48 hours using time series models. Accurate predictions aid water resource management and irrigation in semi-arid regions using reclaimed water.

Keywords:

infiltration irrigation with reused water soil salinity soil water content time series

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Area of Science:

Agricultural Science
Environmental Science
Data Science

Background:

Understanding soil water content and salinity is crucial for water resource management and irrigation.
Semi-arid environments face challenges with water scarcity and soil health, especially when using reclaimed water.

Purpose of the Study:

To develop a 48-hour forecast model for soil water content and salinity.
To assess the impact of reclaimed water irrigation on soil parameters in semi-arid conditions.

Main Methods:

Utilized time series data of soil water content and salinity (15-min resolution) at 50 cm depth.
Incorporated hourly atmospheric data and daily irrigation amounts into statistical models.
Performed exploratory data analysis and pre-processing before model construction.

Main Results:

Developed statistical models demonstrating good forecasting accuracy for soil water content and salinity.
The models provided interpretable results, enhancing understanding of soil dynamics.
Successfully forecasted soil parameters for a 48-hour period.

Conclusions:

Time series modeling effectively predicts soil water content and salinity under reclaimed water irrigation.
Accurate forecasting supports optimized irrigation and fertilization strategies in water-scarce regions.
The study highlights the importance of integrated data for environmental and agricultural management.