Improving regional simulations of processing tomato using remote sensing

Accurate crop growth monitoring is essential for effective regional agricultural management. This study investigates how the use of growing-period data and crop-specific field masks derived from remote sensing improves the accuracy of processing-tomato growth simulations. This study extracted transplanting and harvesting dates from Sentinel-2 enhanced vegetation index (EVI) time series data, demonstrating high consistency with field observations (R² = 0.90; RMSE = 30.36 and 6.39 days, respectively). However, we observed systematic bias in transplanting dates derived from remote sensing data. For this reason, we applied a combined smoothing and interpolation approach prior to threshold detection to improve phenology retrieval; this substantially reduced transplanting date root mean square error (RMSE) from 30.36 to 27.89 days. We then calibrated and validated MONICA, a process-based crop model, using field experiments (four growing seasons) and on-farm data (49 fields – two growing seasons). Subsequently, we employed the model for eight provinces in the Emilia-Romagna region of Northern Italy to simulate crop yield under various combinations of basic cropland mask, and processing-tomato mask, with and without consideration of remotely sensed growing dynamics. Results showed that employing specific tomato field maps combined with dynamic growing periods significantly improved yield simulation accuracy compared to basic cropland mask (reducing RMSE by 24%) and specific maps without consideration of remotely sensed growing season dynamics (reducing RMSE by 10%). Incorporating sensing data and tomato maps into MONICA also improved the model’s ability to capture yield anomalies as an indicator of its sensitivity to climatic signals, with a 24% reduction in RMSE. Integrating remote sensing-derived growing periods into crop models resulted in a wider range of simulation values, enhancing the model’s capacity to simulate nitrate leaching under real-world conditions. This study demonstrates that using remote sensing data to inform crop models significantly enhances scholarly understanding of dynamic growth patterns, supporting regional yield estimation and nitrate leaching simulations, while providing crucial insights for agricultural resource management. • Processing tomato transplanting and harvesting dates were extracted from Sentinel-2 EVI time series in field scale. • Crop-specific field masks reduced uncertainty in regional simulations. • Tomato masks and dynamic growing periods significantly improved MONICA’s regional yield simulation accuracy. • Tomato masks and dynamic growing periods improved MONICA’s capacity to simulate N leaching under real world conditions.