Journal Article FZJ-2019-03813

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On the Accuracy of Factory-Calibrated Low-Cost Soil Water Content Sensors

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2019
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Sensors 19(14), 3101 - () [10.3390/s19143101]

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Abstract: Soil water content (SWC) monitoring is often used to optimize agricultural irrigation. Commonly, capacitance sensors are used for this task. However, the factory calibrations have been often criticized for their limited accuracy. The aim of this paper is to test the degree of improvement of various sensor- and soil-specific calibration options compared to factory calibrations by taking the 10HS sensor as an example. To this end, a two-step sensor calibration was carried out. In the first step, the sensor response was related to dielectric permittivity using calibration in media with well-defined permittivity. The second step involved the establishment of a site-specific relationship between permittivity and soil water content using undisturbed soil samples and time domain reflectometry (TDR) measurements. Our results showed that a model, which considered the mean porosity and a fitted dielectric permittivity of the solid phase for each soil and depth, provided the best fit between bulk permittivity and SWC. Most importantly, it was found that the two-step calibration approach (RMSE: 1.03 vol.%) provided more accurate SWC estimates compared to the factory calibration (RMSE: 5.33 vol.%). Finally, we used these calibrations on data from drip-irrigated almond and apple orchards and compared the factory calibration with our two-step calibration approach.

Classification:

Contributing Institute(s):
  1. Agrosphäre (IBG-3)
Research Program(s):
  1. 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) (POF3-255)

Appears in the scientific report 2019
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Medline ; Creative Commons Attribution CC BY 4.0 ; DOAJ ; OpenAccess ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Ebsco Academic Search ; IF < 5 ; JCR ; NCBI Molecular Biology Database ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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 Record created 2019-07-15, last modified 2021-01-30