Sep 20, 2024
USDA LTAR Common Experiment measurement: Soil water content
- Michael Cosh1,
- Claire Baffaut2
- 1USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD;
- 2USDA Agricultural Research Service, Cropping Systems and Water Quality Research Unit, Columbia, MO
External link: https://ltar.ars.usda.gov
Protocol Citation: Michael Cosh, Claire Baffaut 2024. USDA LTAR Common Experiment measurement: Soil water content. protocols.io https://dx.doi.org/10.17504/protocols.io.261ge542yg47/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: April 15, 2024
Last Modified: September 20, 2024
Protocol Integer ID: 98806
Keywords: Long-Term Agroecosystem Research, LTAR, USDA LTAR, Common Experiment, crops, soil water, volumetric water content, gravimetric water content, water content, soil water content soil water content, gravimetric soil water content, water content of the soil, soil water content, volumetric water content, volumetric water content in the unit, soil water, volume of soil, soil moisture, weight of soil, volume of water, saturated water content, soil, weight of water, surface soil layer, vadose zone of the surface soil layer, usda ltar common experiment measurement, volume, water, measurement
Funders Acknowledgements:
United States Department of Agriculture
Grant ID: -
Abstract
Soil water content, also known as soil moisture, is commonly defined as the volume of water per volume of soil (volumetric soil water content). Gravimetric soil water content is the weight of water per weight of soil. For most purposes, we speak of volumetric water content in the units of m3/m3. Soil water content usually refers to the water contained within the vadose zone of the surface soil layer. Soil water content below the saturated water content of the soil usually ranges from 0-0.5 m3/m3.
Guidelines
Readers are encouraged to reference the protocol paper by Caldwell et al (2022) published in JOVE for illustrations and more detailed explanations. The text contained here in protocols.io is an overview for implementation in the USDA LTAR network.
Caldwell, T.G., Cosh, M.H., Evett, S.R., Edwards, N., Hofman, H., Illston, B.G., Meyers, T., Skumanich, M., Sutcliffe, K. In situ Soil Moisture Sensors in Undisturbed Soils. J. Vis. Exp. (189), e64498, https://doi.org/10.3791/64498 (2022)
Troubleshooting
Data collection
Equipment
Various technologies are available for monitoring soil water content, many using the dielectric nature of water to provide an estimate. Others are based on capacitance, cosmic ray neutron counting, time domain reflectometry, transmission line oscillators, time domain transmission, lysimeters, and GPS.
Measurement
Most common soil water content data are collected at 1 h intervals, but some soils and hydrologic regions may benefit from more frequent intervals. A paper by Caldwell et al. (2022) has more details on the installation and maintenance of an installation; see references.
Common measurement depths depend on the sensor technology, although every location and study differ.
- USDA SCAN/SNOTEL: 5, 10, 20, 50, 100 cm
- NOAA CRN: 5, 10, 20, 50, 100 cm
- NEON: 6, 16, 26, and greater cm
Site Maintenance
The operations and maintenance cost is a primary station installation cost. Keeping short grass to minimize root intrusion for long-term installations is necessary. Depending on the purpose of the study, locating sensors inside a cultivated field may be desirable.
Data processing and quality control
Data are often collected from in situ sensors and recorded on a data logger. Quality control can be implemented. Common indicators of suspicious data are, for example, increases in SWC without precipitation, no increase in SWC with precipitation, and SWC greater than saturation.
Quality control for soil moisture data relies on multiple complementary techniques to assess the magnitude and variability of these data, including tests for absolute magnitude, measurement-to-measurement variability, observation persistence, and spatial coherence.
Quality assessment and quality control (QA/QC) approaches for soil moisture data in the North American Soil Moisture Database (Quiring et al., 2016) and International Soil Moisture Network (Derigo et al. 2013 & 2021) rely on automated algorithms.
The International Soil Moisture Network’s QC procedures for in situ soil moisture data are available on GitHub (Aberer et al., 2021).
Specific to the LTAR network, please refer to the "Quality control" section in the USDA LTAR Common Experiment measurement: Best practices for collection, handling, and analyses of water quantity measurements protocol (Baffaut et al., 2024).
Data file formats and metadata
Files are usually time series with raw voltages or readings stored. Intermediary measurements are usually made of properties such as soil temperature and the real dielectric constant.
Metadata should include GPS coordinates of the sampling site, sampling frequency, sensor depths (cm), probe manufacturer, model, and type, land cover, and installation orientation (horizontal/vertical).
Recommendations for data collection
Table 1. Summary of recommendations for measuring soil water content.
| A | B | C | D | |
| Attribute | Preferred | Minimum | Comments | |
| Spatial scale | Field scale ~100 m | Point scale | For management scale, field scale is desirable but difficult to capture. Most sensors are point scale, ~ 5 cm. | |
| Frequency | 15 min | Hourly | ||
| Covariate metrics | Precipitation, soil temperature, solar radiation, ET | Precipitation, soil temperature | ||
| Other | Depth to 1 m | 50 cm depth | Measurement depths can be at set depths or set as a function of the soil profile |
Protocol references
Aberer, D., Xaver, A., and Preimesberger, W.: TUW-GEO/flagit, GitHub [code], https://github.com/TUW-GEO/flagit
Baffaut, C., Schomberg, H., Cosh, M. H., O'Reilly, A. M., Saha, A., Saliendra, N. Z., Schreiner-McGraw, A., & Snyder, K. A. (2024). USDA LTAR Common Experiment measurement: Best practices for collection, handling, and analyses of water quantity measurements. protocols.io
Caldwell, T.G., Cosh, M.H., Evett, S.R., Edwards, N., Hofman, H., Illston, B.G., Meyers, T., Skumanich, M., Sutcliffe, K. In situ Soil Moisture Sensors in Undisturbed Soils. J. Vis. Exp. (189), e64498, https://doi.org/10.3791/64498 (2022)
Cooper, J. David, Soil Water Measurement: A Practical Handbook, Wiley/Blackwell, West Sussex, UK, 2016.
Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., …, and Sabia, R. (2021). The International Soil Moisture Network: Serving Earth systemscience for over a decade. Hydrol. Earth Syst. Sci., 25, 5749–5804, 2021 https://doi.org/10.5194/hess-25-5749-2021
Dorigo, W. A., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiová, A., Sanchis-Dufau, A. D., Zamojski, D., Cordes, C., Wagner, W., & Drusch, M. (2013). Global Automated Quality Control of In Situ Soil Moisture Data from the International Soil Moisture Network. Vadose Zone Journal, 12(3), vzj2012.0097. https://doi.org/10.2136/vzj2012.0097
Field Estimation of Soil Water Content https://www-pub.iaea.org/MTCD/publications/PDF/TCS-30_web.pdf
Montzka, C., M. Cosh, B. Bayat, A. Al Bitar, A. Berg, R. Bindlish, et al. (2020): Soil Moisture Product Validation Good Practices Protocol Version 1.0. In: C. Montzka, M. Cosh, J. Nickeson, F. Camacho (Eds.): Good Practices for Satellite-Derived Land Product Validation (p. 123), Land Product Validation Subgroup (WGCV/CEOS), https://doi.org/10.5067/doc/ceoswgcv/lpv/sm.001
Quiring, S. M., Ford, T. W., Wang, J. K., Khong, A., Harris, E., Lindgren, T., Goldberg, D. W., & Li, Z. (2016). The North American Soil Moisture Database: Development and Applications. Bulletin of the American Meteorological Society, 97(8), 1441–1459. https://doi.org/10.1175/BAMS-D-13-00263.1
Acknowledgements
This research is a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. The use of trade, firm, or corporation names in this publication is for the information and convenience of the reader. Such use does not constitute an official endorsement or approval by the United States Department of Agriculture or the Agricultural Research Service of any product or service to the exclusion of others that may be suitable. USDA is an equal opportunity provider and employer.