Remote Sensing, Volume 17, Issue 5
2025 March-1 - 212 articles
Cover Story: The following study introduces a novel framework for predicting soil organic carbon by integrating spectral data from a low-cost near-infrared device with open geospatial datasets derived from remote sensing. The proposed hybrid modeling approach processes multimodal data using convolutional neural networks to extract spectral features, which are then combined with climate, landform, and vegetation covariates in an extreme gradient boosting model. Applied at a national scale across the United States, this architecture significantly enhances predictive performance compared to traditional machine learning methods while enabling interpretability. By identifying key environmental drivers such as rainfall and valley bottom flatness, the model provides deeper insights into SOC variability, demonstrating the power of multimodal data fusion for soil assessment. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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