Articles | Volume 11, issue 2
https://doi.org/10.5194/soil-11-811-2025
https://doi.org/10.5194/soil-11-811-2025
Original research article
 | 
15 Oct 2025
Original research article |  | 15 Oct 2025

High-resolution frequency-domain electromagnetic mapping for the hydrological modeling of an orange orchard

Luca Peruzzo, Ulrike Werban, Marco Pohle, Mirko Pavoni, Benjamin Mary, Giorgio Cassiani, Simona Consoli, and Daniela Vanella

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Cited articles

Abedi-Koupai, J., Dorafshan, M.-M., Javadi, A., and Ostad-Ali-Askari, K.: Estimating Potential Reference Evapotranspiration Using Time Series Models (Case Study: Synoptic Station of Tabriz in Northwestern Iran), Appl. Water Sci., 12, 212, https://doi.org/10.1007/s13201-022-01736-x, 2022. a
Ahmad, A. Y., Al-Ghouti, M. A., AlSadig, I., and Abu-Dieyeh, M.: Vertical Distribution and Radiological Risk Assessment of 137Cs and Natural Radionuclides in Soil Samples, Sci. Rep., 9, 12196, https://doi.org/10.1038/s41598-019-48500-x, 2019. a
Autovino, D., Minacapilli, M., and Provenzano, G.: Modelling Bulk Surface Resistance by MODIS Data and Assessment of MOD16A2 Evapotranspiration Product in an Irrigation District of Southern Italy, Agr. Water Manage., 167, 86–94, https://doi.org/10.1016/j.agwat.2016.01.006, 2016. a
Autovino, D., Rallo, G., and Provenzano, G.: Predicting Soil and Plant Water Status Dynamic in Olive Orchards under Different Irrigation Systems with Hydrus-2D: Model Performance and Scenario Analysis, Agr. Water Manage., 203, 225–235, https://doi.org/10.1016/j.agwat.2018.03.015, 2018. a
Bagagiolo, G., Biddoccu, M., Rabino, D., and Cavallo, E.: Effects of Rows Arrangement, Soil Management, and Rainfall Characteristics on Water and Soil Losses in Italian Sloping Vineyards, Environ. Res., 166, 690–704, https://doi.org/10.1016/j.envres.2018.06.048, 2018. a
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Short summary
Both spatial and temporal information is important in agriculture. Information regarding the aboveground variables is ever increasing in terms of density and precision. On the contrary, belowground information lags behind and has been typically limited to time series. This study uses methods that map the subsurface spatial variability. Numerical simulations of aboveground and belowground water fluxes are then based on such spatial information and additional time-oriented datasets that are common in agriculture.
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