CropINSs: A multi-objective irrigation and nitrogen scheduling optimization framework for silage maize based on coupling remote sensing and crop models

Developing efficient irrigation and nitrogen schedules (INSs) at regional scales remains challenging due to spatial heterogeneity in climate, soil conditions, and crop growth dynamics, which limits the transferability of field-calibrated crop model parameters and the reliability of regional management optimization. To address this challenge, this study developed an integrated remote sensing–crop model–optimization framework for regional-scale INSs optimization of silage maize. The framework integrates remote sensing observations, regional crop model parameterization, and multi-objective optimization to achieve spatially adaptive crop growth simulations and management decisions. Specifically, Sentinel-1-derived leaf area index (LAI) and aboveground biomass (AGB) was incorporated into the DSSAT model using the SCE-UA algorithm to identify regional cultivar parameters, which were subsequently coupled with the NSGA-III algorithm to optimize INSs under different typical-rainfall scenarios. Results demonstrated that Sentinel-1 backscattering information enabled accurate estimation of regional LAI and AGB. Compared with the direct application of field-scale calibrated cultivar parameters, the regional cultivar parameters identified through joint assimilation of LAI and AGB improved crop model performance, reducing the RMSE from 1.02 to 0.44 for LAI and from 3076 to 1624 kg ha −1 for AGB, while increasing the R 2 from 0.53 to 0.883 and from 0.672 to 0.872, respectively. Based on the calibrated regional DSSAT model, the optimized INSs reduced irrigation amounts by 9.4%–21.4% (37–101 mm) and nitrogen inputs by 8.4%–24.7% (32–90 kg ha −1 ) compared with current farmer practices, while increasing silage maize average yield, water use efficiency ( WUE ), and partial factor productivity of nitrogen ( NFP ) by 701 kg ha −1 , 0.15 kg m −3 , and 12.4 kg kg −1 , respectively. Overall, this study addressed the challenge of transferring field-calibrated crop model parameters to heterogeneous agricultural regions by developing a regional-scale INSs optimization framework, providing a reliable approach for optimizing INSs under spatially variable production conditions.