Predicting the campbell soil water retention function: Comparing visible–near-infrared spectroscopy with classical pedotransfer function

Zampela Pittaki-Chrysodonta*, Per Moldrup, Maria Knadel, Bo V. Iversen, Cecilie Hermansen, Mogens H. Greve, Lis Wollesen de Jonge

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

21 Citations (Scopus)
178 Downloads (Pure)

Abstract

The soil water retention curve (SWRC) is essential for the modeling of water flow and chemical transport in the vadose zone. The Campbell function and its b (poresize distribution index) parameter fitted to measured data is a simple method to quantify retention under relatively moist conditions. Measuring soil water retention is time consuming, and a method to accurately predict the Campbell relation from either typically available soil parameters such as bulk density, clay-size fraction, and organic matter content (soil fines) or from visible–near-infrared (vis–NIR) spectroscopy may provide a fast and inexpensive alternative. However, the traditional Campbell model has a reference point at saturated water content, and this soil-structure-dependent water content will typically be poorly related to basic texture properties and thus be poorly predicted from vis–NIR spectra. In this study, we anchor the Campbell model at the water content at −1000 cm H2O matric potential [log(1000)= pF 3]. Agricultural soil samples with a wide textural range from across Denmark were used. Soil water retention was measured at a number of matric potentials between pF 1 and 3. The soil water content at pF 3 and Campbell b were both well predicted using either a soil-fines-based pedotransfer function or vis–NIR spectroscopy. The resulting Campbell function anchored at pF 3 compared closely to measured water retention data for a majority of soils. The ability of the two methods to also predict field average SWRC was evaluated for three fields. Field average, predicted SWRC compared well with field average measured data, with vis–NIR overall performing better.

Original languageEnglish
Article number170169
JournalVadose Zone Journal
Volume17
Issue number1
ISSN1539-1663
DOIs
Publication statusPublished - 2018

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