Precise fertilization of rice depends on the timely and effective acquisition of fertilizer application recommended by prescription maps in large-scale cropland, which can provide fertilization spatial information reference. In this paper, the prescription map was discussed based on the improved nitrogen fertilizer optimization algorithm (NFOA), using satellite and unmanned aerial vehicle (UAV) imagery, and supplemented by meteorological data. Based on the principles of NFOA, firstly, remote sensing data and meteorological data were collected from 2019 to 2021 to construct a prediction model for the potential yield of rice based on the in-season estimated yield index (INSEY). Secondly, based on remote sensing vegetation indices (VIs) and spectral features of bands, the grain nitrogen content (GNC) prediction model constructed using the Random Forest (RF) algorithm was used to improve the values of GNC taken in the NFOA. The nitrogen demand for rice was calculated according to the improved NFOA. Finally, the nitrogen fertilizer application recommended prescription map of rice in large-scale cropland was generated based on UAV multispectral images, and the economic cost-effectiveness of the prescription map was analyzed. The analysis results showed that the potential yield prediction model of rice based on the improved INSEY had a high fitting accuracy (R² = 0.62). The accuracy of GNC estimated with the RF algorithm reached 96.3% (RMSE = 0.07). The study shows that, compared with the non-directional and non-quantitative conventional tracking of N fertilizer, the recommended prescription map based on the improved NFOA algorithm in large-scale cropland can provide accurate information for crop N fertilizer variable tracking and provide effective positive references for the economic benefits of rice and ecological benefits of the field environment.

Properly managing the relationship between food security, ecological protection, and urbanization, and coordinating the trade-offs among these three factors for land demand are extremely important for environmental management and sustainable development. In this study, we attempt to analyze the state of land use trade-offs from a dynamic perspective in terms of both potential and efficiency. We have innovatively proposed a new land use trade-off analysis framework integrating the Estimation System for Land Productivity (ESLP) model, machine learning algorithms, ecosystem service value assessment, and spatial analysis method. By applying the framework, the potential and efficiency of the three land use functions of urban development, ecological protection, and agricultural production on the Huang-Huai-Hai (HHH) Plain were comprehensively estimated, and the trade-off relationship between the three land use functions was identified. The results showed a prominent conflict between urban development and agricultural production (around 8%) on the HHH Plain, especially in the Beijing–Tianjin–Hebei urban agglomeration and the southern Jiangsu urban agglomeration. In the mountainous areas, such as northern Hebei and central Shandong, there was an obvious trade-off between ecological land and agriculture land. Most cities had a trade-off between ecological land and urban land (approximately 6% of the study area), but it was relatively more relaxed in comparison. Finally, we found that on the HHH Plain, where land resources are fiercely competitive, spatial planning and land resource control depend not only on the suitability or potential of the land unit, but also on whether the efficiency of land use has reached an appropriate range. The smart way to use land resources is to scientifically trade-off different land use functions and improve the efficiency of land use to achieve maximum benefit.