Calibration and crop-induced footprint dynamics of cosmic-ray neutron sensing in maize cropland

Cosmic-ray neutron sensing (CRNS) provides continuous, non-invasive estimates of field-scale soil water content, but retrieval performance depends on site calibration, crop growth, and temporal variation in the sensing footprint. This study combined five field calibration campaigns, continuous CRNS observations, maize growth measurements, and URANOS simulations to evaluate CRNS-based soil water monitoring in humid maize cropland in the black soil region of Northeast China. Under the representative simulation conditions, maize exerted the largest effect among the evaluated farmland features, reducing the detected neutron count by 13.6% and shortening the 86% cumulative contribution radius ( R 86 ) by 23.8 m. Within the five-date dataset, two-date calibrations generally produced lower RMSE values than single-date calibrations. The best-performing two-date combination yielded an RMSE of 0.025 cm 3 cm −3 . Using the selected fixed N 0 , CRNS captured rainfall-induced wetting and subsequent soil water dynamics. Accounting for maize growth reduced mean R 86 from 139 to 133 m, with a minimum of 108 m during rapid canopy development. Small biomass corrections improved some single-date calibrations but did not achieve the accuracy of the selected two-date calibration, whereas non-zero corrections generally increased RMSE for multi-date calibrations. Mean effective sensing depth was below 18 cm, with more than 45% of the horizontal contribution originating within 6 m of the sensor and more than 40% of the vertical contribution from the 0–5 cm layer. Simplifying the sampling design from five radial distances and eight soil layers to three radial distances and four soil layers reduced the number of samples by 70%, while RMSE increased by only 0.002 cm 3 cm −3 . These findings highlight the need to jointly consider calibration timing, crop-induced footprint dynamics, and spatial sampling design for efficient field-scale soil water monitoring in humid cropland.