Articles | Volume 11, issue 1
https://doi.org/10.5194/soil-11-435-2025
https://doi.org/10.5194/soil-11-435-2025
Original research article
 | 
13 Jun 2025
Original research article |  | 13 Jun 2025

Pooled error variance and covariance estimation of sparse in situ soil moisture sensor measurements in agricultural fields in Flanders

Marit G. A. Hendrickx, Jan Vanderborght, Pieter Janssens, Sander Bombeke, Evi Matthyssen, Anne Waverijn, and Jan Diels

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Cited articles

Albertson, J. D. and Montaldo, N.: Temporal dynamics of soil moisture variability: 1. Theoretical basis, Water Resour. Res., 39, 1274, https://doi.org/10.1029/2002WR001616, 2003. 
Allen, R. G., Pereira, L. S., Raes, D., Smith, M.: Crop Evapotranspiration. Guidelines for Computing Crop Water Requirements, in: Irrigation and Drainage Paper No. 56, FAO, Food & Agriculture Organization of the United Nations (FAO), ISBN 9251042195, 1998. 
Ammann, L., Fenicia, F., and Reichert, P.: A likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelation, Hydrol. Earth Syst. Sci., 23, 2147–2172, https://doi.org/10.5194/hess-23-2147-2019, 2019. 
Brocca, L., Melone, F., Moramarco, T., and Morbidelli, R.: Spatial-temporal variability of soil moisture and its estimation across scales, Water Resour. Res., 46, W02516, https://doi.org/10.1029/2009WR008016, 2010. 
Chaney, N. W., Roundy, J. K., Herrera-Estrada, J. E., and Wood, E. F.: High-resolution modeling of the spatial heterogeneity of soil moisture: Applications in network design, Water Resour. Res., 51, 619–638, https://doi.org/10.1002/2013WR014964, 2015. 
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Short summary
We developed a method to estimate errors in soil moisture measurements using limited sensors and infrequent sampling. By analyzing data from 93 cropping cycles in agricultural fields in Belgium, we identified both systematic and random errors for our sensor setup. This approach reduces the need for extensive sensor networks and is applicable to agricultural and environmental monitoring and ensures more reliable soil moisture data, enhancing water management and improving model predictions.
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