Modelling soil water conent in a tomato field: proximal gamma ray spectroscopy and soil-crop system models

TL;DR

This study demonstrates that continuous gamma ray spectroscopy can reliably monitor soil water content in a tomato field, with results validated against gravimetric data and compared to soil-crop models over seven months.

Contribution

It introduces a feasible method for field-scale soil water monitoring using proximal gamma ray sensors and evaluates its effectiveness against established models.

Findings

Gamma ray spectroscopy accurately estimates soil water content with ~2% error.

CRITeRIA model best fits data in bare soil conditions.

IRRINET model performs best during tomato crop growth.

Abstract

Proximal soil sensors are taking hold in the understanding of soil hydrogeological processes involved in precision agriculture. In this context, permanently installed gamma ray spectroscopy stations represent one of the best space-time trade off methods at field scale. This study proved the feasibility and reliability of soil water content monitoring through a seven-month continuous acquisition of terrestrial gamma radiation in a tomato test field. By employing a 1 L sodium iodide detector placed at a height of 2.25 m, we investigated the gamma signal coming from an area having a ~25 m radius and from a depth of approximately 30 cm. Experimental values, inferred after a calibration measurement and corrected for the presence of biomass, were corroborated with gravimetric data acquired under different soil moisture conditions, giving an average absolute discrepancy of about 2%. A quantitative comparison was carried out with data simulated by AquaCrop, CRITeRIA, and IRRINET soil-crop system models. The different goodness of fit obtained in bare soil condition and during the vegetated period highlighted that CRITeRIA showed the best agreement with the experimental data over the entire data-taking period while, in presence of the tomato crop, IRRINET provided the best results.