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Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry

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Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry. / Li, P.; Zha, Y.; Tso, Michael et al.
In: Geoderma, Vol. 374, 114432, 01.09.2020.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Li, P, Zha, Y, Tso, M, Shi, L, Yu, D, Zhang, Y & Zeng, W 2020, 'Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry', Geoderma, vol. 374, 114432. https://doi.org/10.1016/j.geoderma.2020.114432

APA

Li, P., Zha, Y., Tso, M., Shi, L., Yu, D., Zhang, Y., & Zeng, W. (2020). Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry. Geoderma, 374, Article 114432. https://doi.org/10.1016/j.geoderma.2020.114432

Vancouver

Li P, Zha Y, Tso M, Shi L, Yu D, Zhang Y et al. Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry. Geoderma. 2020 Sept 1;374:114432. Epub 2020 May 22. doi: 10.1016/j.geoderma.2020.114432

Author

Li, P. ; Zha, Y. ; Tso, Michael et al. / Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry. In: Geoderma. 2020 ; Vol. 374.

Bibtex

@article{36ddc6c74acc4cf79185141a7196a355,
title = "Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry",
abstract = "Accurate measurements of soil moisture are essential for hydrological, agricultural and environmental sciences. Among many indirect measurement approaches, Frequency-Domain Reflectometry (FDR) soil moisture sensors are popular but are prone to be affected by many factors (e.g., temperature, bulk density, texture, mineralogy) at different installation sites. To avoid the enormous effort required for site-specific FDR calibration, we propose a calibration-free framework, in which a linear calibration model (that links FDR observation and true soil moisture) is incorporated. Based on the classical bias-blind methods using the ensemble Kalman filter (EnKF) and the iterative ensemble smoother (IES), two such bias-aware data assimilation methods are developed to simultaneously identify the unknown hydraulic and the linear calibration parameters based on uncalibrated FDR observations as well as meteorological data. We thoroughly discuss the effects of various factors (i.e., observation noise and number of observations, ensemble size, number of unknown parameters and two potential model errors) on their performances in the synthetic cases and make an application in a real-world case, besides comparing them with their previous versions simultaneously. In particular, the linear calibration model coupled with IES is more favored. From a pragmatic point of view, we demonstrate the usefulness of the proposed approaches in calibrating the FDR data in an online manner with easily-accessible meteorological data.",
keywords = "Bias, Data assimilation, Frequency-domain reflectometry, Observation, Soil moisture, Agricultural robots, Frequency domain analysis, Iterative methods, Meteorology, Minerals, Moisture control, Moisture meters, Reflection, Reflectometers, Textures, Data assimilation methods, Ensemble Kalman Filter, Environmental science, Frequency domain reflectometry, Indirect measurements, Iterative ensemble smoothers, Soil moisture measurement, Soil moisture sensors, Soil surveys, accuracy assessment, calibration, data assimilation, error analysis, frequency analysis, installation, Kalman filter, measurement method, performance assessment, real time, reflectometry, soil moisture",
author = "P. Li and Y. Zha and Michael Tso and L. Shi and D. Yu and Y. Zhang and W. Zeng",
year = "2020",
month = sep,
day = "1",
doi = "10.1016/j.geoderma.2020.114432",
language = "English",
volume = "374",
journal = "Geoderma",
issn = "0016-7061",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry

AU - Li, P.

AU - Zha, Y.

AU - Tso, Michael

AU - Shi, L.

AU - Yu, D.

AU - Zhang, Y.

AU - Zeng, W.

PY - 2020/9/1

Y1 - 2020/9/1

N2 - Accurate measurements of soil moisture are essential for hydrological, agricultural and environmental sciences. Among many indirect measurement approaches, Frequency-Domain Reflectometry (FDR) soil moisture sensors are popular but are prone to be affected by many factors (e.g., temperature, bulk density, texture, mineralogy) at different installation sites. To avoid the enormous effort required for site-specific FDR calibration, we propose a calibration-free framework, in which a linear calibration model (that links FDR observation and true soil moisture) is incorporated. Based on the classical bias-blind methods using the ensemble Kalman filter (EnKF) and the iterative ensemble smoother (IES), two such bias-aware data assimilation methods are developed to simultaneously identify the unknown hydraulic and the linear calibration parameters based on uncalibrated FDR observations as well as meteorological data. We thoroughly discuss the effects of various factors (i.e., observation noise and number of observations, ensemble size, number of unknown parameters and two potential model errors) on their performances in the synthetic cases and make an application in a real-world case, besides comparing them with their previous versions simultaneously. In particular, the linear calibration model coupled with IES is more favored. From a pragmatic point of view, we demonstrate the usefulness of the proposed approaches in calibrating the FDR data in an online manner with easily-accessible meteorological data.

AB - Accurate measurements of soil moisture are essential for hydrological, agricultural and environmental sciences. Among many indirect measurement approaches, Frequency-Domain Reflectometry (FDR) soil moisture sensors are popular but are prone to be affected by many factors (e.g., temperature, bulk density, texture, mineralogy) at different installation sites. To avoid the enormous effort required for site-specific FDR calibration, we propose a calibration-free framework, in which a linear calibration model (that links FDR observation and true soil moisture) is incorporated. Based on the classical bias-blind methods using the ensemble Kalman filter (EnKF) and the iterative ensemble smoother (IES), two such bias-aware data assimilation methods are developed to simultaneously identify the unknown hydraulic and the linear calibration parameters based on uncalibrated FDR observations as well as meteorological data. We thoroughly discuss the effects of various factors (i.e., observation noise and number of observations, ensemble size, number of unknown parameters and two potential model errors) on their performances in the synthetic cases and make an application in a real-world case, besides comparing them with their previous versions simultaneously. In particular, the linear calibration model coupled with IES is more favored. From a pragmatic point of view, we demonstrate the usefulness of the proposed approaches in calibrating the FDR data in an online manner with easily-accessible meteorological data.

KW - Bias

KW - Data assimilation

KW - Frequency-domain reflectometry

KW - Observation

KW - Soil moisture

KW - Agricultural robots

KW - Frequency domain analysis

KW - Iterative methods

KW - Meteorology

KW - Minerals

KW - Moisture control

KW - Moisture meters

KW - Reflection

KW - Reflectometers

KW - Textures

KW - Data assimilation methods

KW - Ensemble Kalman Filter

KW - Environmental science

KW - Frequency domain reflectometry

KW - Indirect measurements

KW - Iterative ensemble smoothers

KW - Soil moisture measurement

KW - Soil moisture sensors

KW - Soil surveys

KW - accuracy assessment

KW - calibration

KW - data assimilation

KW - error analysis

KW - frequency analysis

KW - installation

KW - Kalman filter

KW - measurement method

KW - performance assessment

KW - real time

KW - reflectometry

KW - soil moisture

U2 - 10.1016/j.geoderma.2020.114432

DO - 10.1016/j.geoderma.2020.114432

M3 - Journal article

VL - 374

JO - Geoderma

JF - Geoderma

SN - 0016-7061

M1 - 114432

ER -