基于多源数据融合的苏打盐碱地变量施肥智能分级研究OA

To achieve precise management of soda saline-alkali soil in northeast China and variable rate application of organic fertilizers,a multi-source data fusion deep learning method hybrid model driven by fertilizer recommendation index(FRI)has been proposed.A FRI-CNN-MLP hybrid model was constructed by integrating 2023 growing season Sentinel-2 time-series remote sensing data,based normalized difference vegetation index(NDVI)and soil-adjusted vegetation index(SAVI)with soil cation exchange capacity(CEC)and pH data from SoilGrids database.This model has achieved four-level classification of fertilization application intensity within plastic-mulched areas of saline-alkali soil.FRI labels for model training were generated based on expert rules derived from current technical standards.Prediction results demonstrated that light fertilizer application index(accounting for 70.53%)was prodominant in study area,while moderate and heavy application were concentrated in low-lying peripheral areas,exhibiting a heavier application at periphery and lighter application at center.Compared with baseline method,model achieved an overall accuracy rate of 89.7%,and macro average F1 score of 0.85,outperforming traditional threshold method and random forest model.FRI-CNN-MLP hybrid model effectively integrates spatiotemporal-soil features,overcoming strong subjectivity and poor generalization of traditional threshold methods.It can directly de-termine organic fertilizer application rates,providing technical support for intelligent saline-alkali soil improvement.