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An improved method of soil moisture meter calibration for satellite data validation at watershed scale

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Abstract

This work presents an improved gravimetric algorithm to derive reference soil moisture, with removal of some of the hypothesis on which its original expression was based, and addition of a new corrective term that takes into account the interdependence between temperature and non-unitary water density. The temperature correction term improves reference measurements by up to 0.55% of their values in the temperature range 10–35℃. The temperature-corrected reference measurements were applied to the calibration of a hand-held soil moisture meter (Lutron PMS-714) for three soil texture types: medium, fine, and very fine. Linear regression models were used to calibrate the meter for each soil type, and the resulting calibration equations were validated with field data sampled from Sondu-Miriu watershed in Western Kenya. The validation produced errors (RMSE = 0.022, 0.010, 0.010 m3/m3) that are significantly better than the meter’s reported factory calibration errors of ± 0.05 m3/m3. While calibrations did not improve correlation statistics (R2 and RMSE), they did significantly reduce biases (+ 0.009, + 0.004, -0.001 m3/m3) compared to uncalibrated ones (-0.216, -0.181, -0.184 m3/m3). Additionally, the calibrated meter values compared well with Soil Moisture Active Passive (SMAP) surface moisture data, with errors (RMSE = 0.010, 0.007, 0.008 m3/m3) well within SMAP recommended value of ± 0.04 m3/m3. A spatial scalability test showed that the calibrations are adequately robust (with R2 = 0.81, RMSE = 0.016 m3/m3, and Bias =  + 0.005 m3/m3), permitting calibration equations derived from one site to be scaled out to other sites of similar soil texture regime.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Physics, University of Nairobi, P.O. Box 30197, Nairobi, 00100, Kenya

    Elijah Cheruiyot & Collins Mito

  2. Department of Geoscience and Remote Sensing, Delft University of Technology, P.O. Box 5048, Delft, GA, 2600, The Netherlands

    Massimo Menenti

Authors
  1. Elijah Cheruiyot
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  2. Collins Mito
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Contributions

All authors contributed to the conceptual design, methodology, investigation, and validation. C.M. and M.M. provided supervision. E.C. drafted the manuscript, and all authors reviewed the manuscript.

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Correspondence to Elijah Cheruiyot.

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Communicated by: H. Babaie

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Cheruiyot, E., Mito, C. & Menenti, M. An improved method of soil moisture meter calibration for satellite data validation at watershed scale. Earth Sci Inform 17, 117–129 (2024). https://doi.org/10.1007/s12145-023-01170-w

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