基于CNN-LSTM耦合模型的禹城市土壤有机质预测制图OA

To address the insufficient systematic consideration of phenological information in environmental variable selection for digital soil mapping(DSM)and the limited spatiotemporal multidimensional feature fusion capabilities of existing models,this study aims to explore a novel method by integrating moderate resolution imaging spectroradiometer(MODIS)long-term time-series phenological data to improve the prediction accuracy of soil organic matter(SOM)in cropland areas.Focusing on Yucheng City,Shandong Province,environmental covariates and MODIS phenological data from 2011 to 2020 were integrated,with spatial features extracted using a convolutional neural network(CNN)and temporal dynamic patterns captured with a long short-term memory(LSTM)network.An innovatively designed CNN-LSTM spatiotemporal feature coupling model was developed to achieve high-accuracy SOM prediction.Results indicated that the integration of phenological features significantly improved the prediction accuracy of the CNN-LSTM model(R2=0.523),with 6.3％and 12.0％enhancements compared with CNN(R2=0.492)and random forest model(R2=0.467)without phenological data,respectively.The spatial distribution of SOM exhibited a pattern of higher values in central and south regions and lower values in northern and southeastern areas,strongly aligned with local topography and farming practices.Environmental factors such as wind speed,vapor pressure,and topographic position index showed significant correlations with SOM,while vegetation indices during mid-growth seasons contributed most to SOM prediction among phenological variables.By combining MODIS long-term phenological data with the CNN-LSTM spatiotemporal fusion model,this study not only demonstrates the enhanced role of phenological dynamics in SOM prediction but also overcomes the technical limitations of traditional methods in spatiotemporal feature fusion for soil attributes.The generated 30-meter-resolution SOM digital soil mapping product provides robust scientific data support for regional cropland quality improvement and soil carbon sequestration potential assessment.