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Comparison of In Situ Soil Moisture Measurements: An Examination of the Neutron and Dielectric Measurements within the Illinois Climate Network

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  • 1 Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland, and Department of Civil and Environmental Engineering, University of New Hampshire, Durham, New Hampshire
  • | 2 Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland
  • | 3 Department of Civil and Environmental Engineering, University of New Hampshire, Durham, New Hampshire
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Abstract

The continuity of soil moisture time series data is crucial for climatic research. Yet, a common problem for continuous data series is the changing of sensors, not only as replacements are necessary, but as technologies evolve. The Illinois Climate Network has one of the longest data records of soil moisture; yet, it has a discontinuity when the primary sensor (neutron probes) was replaced with a dielectric sensor. Applying a simple model coupled with machine learning, the two time series can be merged into one continuous record by training the model on the latter dielectric model and minimizing errors against the former neutron probe dataset. The model is able to be calibrated to an accuracy of 0.050 m3 m−3 and applying this to the earlier series and applying a gain and offset, an RMSE of 0.055 m3 m−3 is possible. As a result of this work, there is now a singular network data record extending back to the 1980s for the state of Illinois.

Corresponding author address: Evan Coopersmith, Hydrology and Remote Sensing Laboratory, U.S. Department of Agriculture, 10300 Baltimore Avenue, Beltsville, MD 20705. E-mail: ecooper2@gmail.com

Abstract

The continuity of soil moisture time series data is crucial for climatic research. Yet, a common problem for continuous data series is the changing of sensors, not only as replacements are necessary, but as technologies evolve. The Illinois Climate Network has one of the longest data records of soil moisture; yet, it has a discontinuity when the primary sensor (neutron probes) was replaced with a dielectric sensor. Applying a simple model coupled with machine learning, the two time series can be merged into one continuous record by training the model on the latter dielectric model and minimizing errors against the former neutron probe dataset. The model is able to be calibrated to an accuracy of 0.050 m3 m−3 and applying this to the earlier series and applying a gain and offset, an RMSE of 0.055 m3 m−3 is possible. As a result of this work, there is now a singular network data record extending back to the 1980s for the state of Illinois.

Corresponding author address: Evan Coopersmith, Hydrology and Remote Sensing Laboratory, U.S. Department of Agriculture, 10300 Baltimore Avenue, Beltsville, MD 20705. E-mail: ecooper2@gmail.com
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