Reconciling Top-Down and Bottom-Up Ammonia Emission Estimates over Queensland Sugarcane

Quantifying ammonia (NH₃) volatilization in intensive agriculture remains challenging due to the high spatiotemporal variability of emissions. A key difficulty is to reconcile field-scale processes with the coarser resolution of satellite retrievals. To address this issue, this study proposes a robust framework to bridge the gap between top-down (TD) satellite constraints and bottom-up (BU) process estimates of NH₃ volatilization, and demonstrates its application to Queensland sugarcane belt over seven cropping seasons (2017–2023).

BU estimates were simulated using the NH₃ module of DNDC process-based model, driven by Sentinel-2 leaf area index (LAI) to infer fertiliser application timing, ERA5 meteorology data, and local agronomic nitrogen rate guidelines (the ‘Six Easy Steps’ program). TD emissions were derived from IASI v4 NH₃ concentrations using an upwind-ring background subtraction method and a steady-state mass-balance operator, with the lifetime diagnosed from regional GEOS-Chem simulations. Initial comparisons revealed significant discrepancies between these two estimates, with the original TD estimates exceeding BU estimates by 3.7 times (mean bias = 36 kg N ha^-1).

To reconcile these differences, we developed a bridging model that links TD and BU estimates as a function of meteorological conditions (temperature, ventilation) and fractional cane cover. These predictors act as a multiplicative correction and can effectively capture sub-grid source mixing and meteorological biases inherent in the satellite operators. Robust regression of the TD/BU ratio on these variables provides a statistically valid correction. Applying this adjustment reduced the normalized mean bias from 380% to 27%. The harmonized estimates are consistent with an independent estimate of regional NH₃ emissions of approximately 3.3 kt NH₃ yr^-1, confirming the dominance of diffuse agricultural sources in the region.

This framework yields more coherent NH₃ emission estimates for Queensland sugarcane and could in principle be adapted to other cropping systems where ground measurement data are sparse and satellite constraints are essential.