Impact of calibrating a low-cost capacitance-based soil moisture sensor on AquaCrop model performance
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View abstract on PubMed
Summary
This summary is machine-generated.Calibrating low-cost soil moisture sensors is essential for accurate agro-hydrological modeling. Field calibration improved crop water productivity by enhancing soil moisture simulations in the AquaCrop model.
Area Of Science
- Agricultural Science
- Hydrology
- Sensor Technology
Background
- Effective irrigation management relies on integrating sensor data with agro-hydrological models.
- Data-scarce regions require parsimonious crop water models and low-cost, maintainable soil moisture sensors for practical application.
- Site-specific calibration of soil moisture sensors is crucial for reliable irrigation management.
Purpose Of The Study
- To calibrate low-cost capacitance-based soil moisture sensors using various methods.
- To assess the impact of sensor calibration on the FAO AquaCrop Open Source (AquaCrop-OS) model's performance.
- To recommend best practices for sensor calibration in crop modeling.
Main Methods
- Calibration of Spectrum Inc. SM100 soil moisture sensors using multiple least squares and machine learning models with laboratory and field data.
- Field-based piece-wise linear regression was identified as the optimal calibration technique.
- The calibrated sensor data was used to adjust soil hydraulic parameters in the AquaCrop-OS model.
Main Results
- Field-based piece-wise linear regression yielded the best calibration results (r²=0.76).
- Calibrated low-cost sensor data significantly improved AquaCrop-OS soil moisture simulations and water productivity.
- Machine learning models showed poor field validation due to overfitting; using literature values for calibration was a viable alternative if field calibration was not feasible.
Conclusions
- Calibrating low-cost soil moisture sensors is essential for improving crop water productivity through enhanced agro-hydrological modeling.
- Field calibration against a reference sensor is recommended for optimal results.
- In the absence of calibration, using literature values for soil hydraulic parameters can be a cost-effective alternative without compromising model performance.
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