Optimizing Top Dressing Nitrogen Fertilization Using VENμS and Sentinel-2 L1 Data

Environmental and economic constraints are forcing farmers to be more precise in the rates timing of nitrogen (N) fertilizer application wheat. In practice, N is frequently applied without knowledge amount needed or likelihood significant protein enhancement. The objective this study was help optimize top dress by adopting use within-field reference strips. We developed an assisting app on Google Earth Engine (GEE) platform map spatial variability four different vegetation indices (VIs) each field calculating mean VI, masking extreme values (three standard deviations, 3σ) field, presenting anomaly as a deviation ±σ ±2σ percentage. VIs based red-edge bands (REIP, NDRE, ICCI) were very useful for detection wheat above ground uptake in-field anomalies. VENµS high temporal resolutions provide advantages over Sentinel-2 monitoring agricultural fields during growing season, representing variations decision making, but coverage accessibility data much better. already available GEE found optimizing topdressing wheat, applying it only where will increase grain yield and/or quality. Therefore, can used top-N dressing decisions. Nevertheless, there some issues that must tested, research required. To conclude, satellite images detection, rendering results within few seconds. ability L1 atmospheric correction through opens opportunity these several applications others.