Effective Calibration of Low-Cost Soil Water Content Sensors

This page lists all metadata that was entered for this dataset. You can download the dataset.

Citation Options
Title:Main Title: Effective Calibration of Low-Cost Soil Water Content Sensors
Description:Abstract: Soil water content is a key variable for understanding and modelling ecohydrological processes. Low-cost electromagnetic sensors are increasingly being used to characterize the spatio-temporal dynamics of soil water content, despite the reduced accuracy of such sensors as compared to reference electromagnetic soil water content sensing methods such as time domain reflectometry. Here, we present an effective calibration method to improve the measurement accuracy of low-cost soil water content sensors taking the recently developed SMT100 sensor (Truebner GmbH, Neustadt, Germany) as an example. We calibrated the sensor output of more than 700 SMT100 sensors to permittivity using a standard procedure based on five reference media with a known apparent dielectric permittivity (1 < Ka < 34.8). Our results showed that a sensor-specific calibration improved the accuracy of the calibration compared to single “universal” calibration. The associated additional effort in calibrating each sensor individually is relaxed by a dedicated calibration setup that enables the calibration of large numbers of sensors in limited time while minimizing errors in the calibration process.
Identifier:10.3390/s17010208 (DOI)
Responsible Party
Creators:Heye Bogena (Author), Johan A. Huisman (Author), Bernd Schilling (Author), Ansgar Weuthen (Author), Harry Vereecken (Author)
Publication Year:2017
TR32 Topic:Soil
Related Subproject:C1
Subject:Keyword: Hydrological Modelling
File Details
Data Type:Text - Article
File Size:2.3 MB
Date:Available: 21.01.2017
Mime Type:application/pdf
Data Format:PDF
Download Permission:Free
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Licence:[TR32DB] Data policy agreement
Specific Information - Publication
Publication Status:Accepted
Review Status:Peer reviewed
Publication Type:Article
Article Type:Journal
Source Website:http://www.mdpi.com/1424-8220/17/1/208
Number of Pages:12 (1 - 12)
Metadata Details
Metadata Creator:Michael Stockinger
Metadata Created:31.05.2017
Metadata Last Updated:31.05.2017
Funding Phase:3
Metadata Language:English
Metadata Version:V50
Metadata Export
Metadata Schema:
Dataset Statistics
Page Visits:701
Metadata Downloads:0
Dataset Downloads:4
Dataset Activity