The use of earth observation data for crop mapping and monitoring in West Africa has concentrated on rainfed systems due to its pre-dominance in the sub-region. However, irrigated systems, though of limited extent, provide critical livelihood support to many. Accurate statistics on irrigated crops are, thus, needed for effective management and decision making. This study explored the use of Sentinel 1 (S-1) and Sentinel 2 (S-2) data to map the extent and yield of irrigated crops in an informal irrigation scheme in Burkina Faso. Random Forest classification and regression were used together with an extensive field data comprising 842 polygons. Four irrigated crops (tomoto, onion, green bean and other) were classified while the yield of tomatoes was modelled through regression analysis. Apart from spectral bands, derivatives (e.g. biophysical parameters and vegetation indices) from S-2 were used. Different data configuration of S-1, S-2 and their derivatives were tested to ascertain optimal temporal windows for accurate irrigated crop mapping and yield estimation. Results of the crop classification revealed a greater overall accuracy (76.3%) for S-2 compared to S-1 (69.4%), with S-2 biophysical parameters (especially the fraction of absorbed photosynthetic active radiation i.e fAPAR) being prominent. For yield prediction, however, S-1 VV polarization came up as the most prominent predictor in the regression analysis ( $R_{\mathrm{a}\mathrm{d}\mathrm{j}}^2$= 0.63), while the addition of S-2 fAPAR marginally improved the fit ( $R_{\mathrm{a}\mathrm{d}\mathrm{j}}^2$= 0.64). Tomato yield in the study area was found to range from 1 to 16 kg m⁻², although about 83% of the area have yields of less than 10 kg m⁻². Our study revealed that early season images (acquired in December) perform better in classifying irrigated crop compared to mid or late season. On the other hand, the use of early to mid-season (December to February) images for yield modelling produced reasonable prediction accuracy. This indicates the possibility of using S-1 and S-2 data to predict crop yield prior to harvest season for efficient planning and food security attainment.

Denitrification represents a major nitrogen loss pathway in agriculture, reducing plant N uptake, lowering crop N use efficiency, while emitting the potent greenhouse gas N2O. However, due to methodological challenges, field measurements of N2 emissions remain rare, leaving denitrification losses poorly characterised across most cropping systems and comprehensive N budgets lacking. This constrains parameterization and validation of biogeochemical models, impeding efforts to forecast denitrification losses under changing conditions and design effective mitigation strategies. This study (i) reviewed the current literature on field-based N2 measurements, (ii) tested the ability of five different models to accurately simulate denitrification losses, and (iii) simulated denitrification losses from agricultural soils in Germany using gridded and point-based modelling frameworks. Results show that field studies suitable for model calibration remain scarce and mostly limited to temperate systems. Simulated cumulative N2 emissions varied widely between models, ranging from 0.89 to 6.07 kg N ha⁻¹, far below the observed N2 emissions of 21±3 kg N ha⁻¹. The ratio of N2O to (N2O+N2) emitted (RN2O), a key determinant of the climate impact of denitrification, was not only strongly overestimated by all models but also exhibited considerable variations, ranging from 0.056 to 0.707, compared to the measured ratio of 0.006. Models also failed to capture expected responses of RN2O to environmental drivers. For Germany, simulated average N2 emissions from agricultural soils ranged from 1.7 to 14.5 kg N ha⁻¹yr-1, with corresponding RN2O ranging from 0.18 to 0.59. Spatial patterns of emissions also differed significantly across modelling frameworks, reflecting large structural uncertainties. These findings highlight that both N2 emissions and RN2O remain poorly represented in current models, severely limiting our ability to establish reliable N budgets and develop targeted N management strategies. Coordinated advancements in field-based measurements and modelling are urgently needed to improve the representation of denitrification in biogeochemical models.