Can Drip Irrigation be Scheduled with Cosmic‐Ray Neutron Sensing?

Abstract

Cosmic‐ray neutron sensing (CRNS) was used in a drip‐irrigated field. Soil water content was estimated from CRNS. Neutron transport was simulated for the drip‐irrigated field. CRNS has limitations for irrigation scheduling of drip‐irrigated fields. Irrigation is essential for maintaining food production in water‐scarce regions. The irrigation need depends on the water content of the soil, which we measured with the novel technique of cosmic‐ray neutron sensing (CRNS). The potential of the CRNS technique for drip irrigation scheduling was explored in this study for the Picassent site near Valencia, Spain. To support the experimental evidence, the neutron transport simulation URANOS was used to simulate the effect of drip irrigation on the neutron counts. The overall soil water content (SWC) in the CRNS footprint was characterized with a root mean square error <0.03 cm3/cm3, but the experimental dataset indicated methodological limitations to detect drip water input. Both experimental data and simulation results suggest that the large‐area neutron response to drip irrigation is insignificant in our specific case using a standard CRNS probe. Because of the small area of irrigated patches and short irrigation time, the limited SWC changes due to drip irrigation were not visible from the measured neutron intensity changes. Our study shows that CRNS modeling can be used to assess the suitability of the CRNS technique for certain applications. While the standard CRNS probe was not able to detect small‐scale drip irrigation patterns, the method might be applicable for larger irrigated areas, in drier regions, and for longer and more intense irrigation periods. Since statistical noise is the main limitation of the CRNS measurement, the capability of the instrument could be improved in future studies by larger and more efficient neutron detectors.

Comparison of Soil Water Estimates From Cosmic-Ray Neutron and Capacity Sensors in a Semi-arid Pine Forest: Which Is Able to Better Assess the Role of Environmental Conditions and Thinning?

“…In general, calibration (RMSE: 0.013 and 0.007 m 3 m −3 for T and C plots, respectively) and validation (KGE: 0.84 and 0.75 for T and C plots, respectively) procedures showed values that indicate a good performance of CRNP, which in the case of the non-thinned forest (C-plot), improved with the application of the vegetation correction factor. These values are comparable to those obtained in other studies such as that of Bogena et al (2013); they obtained a RMSE value of 0.025 m 3 m −3 for a forested area, and Li et al (2019), who found a RMSE value of 0.025 m 3 m −3 for a semi-arid environment, or Lv et al (2014), who found RMSE values varying between 0.011 and 0.023 m 3 m −3 for a humid forest (gross precipitation of 950 mm y −1 ). However, when comparing O CRNP to O g , there is a general overestimation by CRNP that can be attributed to the influence of biomass and the different measurement depths of both methodologies and changes under the different subperiods ( Figure 5).…”

Large-Scale Boron-Lined Neutron Detection Systems as a 3He Alternative for Cosmic Ray Neutron Sensing

“…It uses the validated near-ground cosmic-ray neutron spectrum by Sato (2016). The code was employed for CRNS footprint revision by Köhli et al (2015) and Schrön et al (2017), in roving (Schrön et al, 2018) and irrigation studies (Li et al, 2019) as well as understanding the signal for snow height measurements (Schattan et al, 2019). It also features special input options for conducting detector-related neutron transport studies (Köhli et al, 2018).…”

“…Mobile campaigns have also extended the spatial scale up to several km 2 and therefore could contribute to closing the measurement scale gap, especially relevant for small catchments (Schrön et al, 2018). Furthermore, CRNS has shown to be a prominent candidate for agricultural applications (Franz et al, 2016;Li et al, 2019), for validation of satellite based measurements (Montzka et al, 2017) and to improve hydrological modeling (Shuttleworth et al, 2013). The success of this technique (Andreasen et al, 2017) lead to a worldwide deployment of meanwhile more than 100 sensors.…”

Requirements for Sustainable Irrigated Agriculture

“…Common approaches for developing and improving irriga-tion management include calculating crop water requirements (e.g., [62]), monitoring soil water status (e.g., [63,64]), combining soil water balancing and soil sensing (e.g., [65,66]), and monitoring canopy temperature (e.g., [67,68]). Also, an experimental approach involving cosmic-ray neutron sensing for drip irrigation scheduling can be found in recent literature [69]. The overall objective of these studies is to maximise water productivity, which can be achieved by increasing irrigation efficiency (reducing unproductive losses) and at the same time stabilising yield.…”