Data-Driven Calibration of Soil Moisture Sensor Considering Impacts of Temperature: A Case Study on FDR Sensors

Liping Chen; Lili Zhangzhong; Wengang Zheng; Jingxin Yu; Zehan Wang; Long Wang; Chao Huang

doi:10.3390/s19204381

Data-Driven Calibration of Soil Moisture Sensor Considering Impacts of Temperature: A Case Study on FDR Sensors

Liping Chen, Lili Zhangzhong, Wengang Zheng, Jingxin Yu, Zehan Wang, Long Wang^*, Chao Huang^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

31 Citations (Scopus)

48 Downloads (CityUHK Scholars)

Abstract

Commercial soil moisture sensors have been widely applied into the measurement of soil moisture content. However, the accuracy of such sensors varies due to the employed techniques and working conditions. In this study, the temperature impact on the soil moisture sensor reading was firstly analyzed. Next, a pioneer study on the data-driven calibration of soil moisture sensor was investigated considering the impacts of temperature. Different data-driven models including the multivariate adaptive regression splines and the Gaussian process regression were applied into the development of the calibration method. To verify the efficacy of the proposed methods, tests on four commercial soil moisture sensors were conducted; these sensors belong to the frequency domain reflection (FDR) type. The numerical results demonstrate that the proposed methods can greatly improve the measurement accuracy for the investigated sensors.

Original language	English
Article number	4381
Journal	Sensors (Switzerland)
Volume	19
Issue number	20
Online published	10 Oct 2019
DOIs	https://doi.org/10.3390/s19204381
Publication status	Published - Oct 2019
Externally published	Yes

Funding

Funding: This research was funded by the Innovation Ability Construction Project of Beijing Academy of Agriculture and Forestry Sciences under grant 2017YFD0201503, the National Key Research and Development Program of China under grant 2016YFC0403102, the Foundation of Key Laboratory of Wind Energy and Solar Energy Technology, Ministry of Education under grant 2018ZD02, the Fundamental Research Funds for the Central Universities under Grants 06500103 and 06500078, the University of Science and Technology Beijing– National Taipei University of Technology Joint Research Program under Grant TW2018008, the National Natural Science Foundation of China under Grant 71473155, the National Key Research and Development Program of China under Grant 2018YFC0808306, and the National Key R&D Program of China under grant 2018YFC0810600.

Research Keywords

Calibration
Data-driven
Impacts of temperature
Soil moisture sensor

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This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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title = "Data-Driven Calibration of Soil Moisture Sensor Considering Impacts of Temperature: A Case Study on FDR Sensors ",

abstract = "Commercial soil moisture sensors have been widely applied into the measurement of soil moisture content. However, the accuracy of such sensors varies due to the employed techniques and working conditions. In this study, the temperature impact on the soil moisture sensor reading was firstly analyzed. Next, a pioneer study on the data-driven calibration of soil moisture sensor was investigated considering the impacts of temperature. Different data-driven models including the multivariate adaptive regression splines and the Gaussian process regression were applied into the development of the calibration method. To verify the efficacy of the proposed methods, tests on four commercial soil moisture sensors were conducted; these sensors belong to the frequency domain reflection (FDR) type. The numerical results demonstrate that the proposed methods can greatly improve the measurement accuracy for the investigated sensors.",

keywords = "Calibration, Data-driven, Impacts of temperature, Soil moisture sensor",

author = "Liping Chen and Lili Zhangzhong and Wengang Zheng and Jingxin Yu and Zehan Wang and Long Wang and Chao Huang",

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T1 - Data-Driven Calibration of Soil Moisture Sensor Considering Impacts of Temperature

T2 - A Case Study on FDR Sensors

AU - Chen, Liping

AU - Zhangzhong, Lili

AU - Zheng, Wengang

AU - Yu, Jingxin

AU - Wang, Zehan

AU - Wang, Long

AU - Huang, Chao

PY - 2019/10

Y1 - 2019/10

N2 - Commercial soil moisture sensors have been widely applied into the measurement of soil moisture content. However, the accuracy of such sensors varies due to the employed techniques and working conditions. In this study, the temperature impact on the soil moisture sensor reading was firstly analyzed. Next, a pioneer study on the data-driven calibration of soil moisture sensor was investigated considering the impacts of temperature. Different data-driven models including the multivariate adaptive regression splines and the Gaussian process regression were applied into the development of the calibration method. To verify the efficacy of the proposed methods, tests on four commercial soil moisture sensors were conducted; these sensors belong to the frequency domain reflection (FDR) type. The numerical results demonstrate that the proposed methods can greatly improve the measurement accuracy for the investigated sensors.

AB - Commercial soil moisture sensors have been widely applied into the measurement of soil moisture content. However, the accuracy of such sensors varies due to the employed techniques and working conditions. In this study, the temperature impact on the soil moisture sensor reading was firstly analyzed. Next, a pioneer study on the data-driven calibration of soil moisture sensor was investigated considering the impacts of temperature. Different data-driven models including the multivariate adaptive regression splines and the Gaussian process regression were applied into the development of the calibration method. To verify the efficacy of the proposed methods, tests on four commercial soil moisture sensors were conducted; these sensors belong to the frequency domain reflection (FDR) type. The numerical results demonstrate that the proposed methods can greatly improve the measurement accuracy for the investigated sensors.

KW - Calibration

KW - Data-driven

KW - Impacts of temperature

KW - Soil moisture sensor

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Data-Driven Calibration of Soil Moisture Sensor Considering Impacts of Temperature: A Case Study on FDR Sensors

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Research Keywords

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