Attention-guided CNN-SM: Sentinel-1/2 fusion in bare soil season improves soil organic matter prediction in cultivated black soils of Northeast China

Attention-guided CNN-SM: Sentinel-1/2 fusion in bare soil season improves soil organic matter prediction in cultivated black soils of Northeast China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Attention-guided CNN-SM: Sentinel-1/2 fusion in bare soil season improves soil organic matter prediction in cultivated black soils of Northeast China qixun Ding, Lixia Ma, Dongsheng Yu, Jie Song, Yuchen Gao, Xin Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6207977/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aim A novel two-dimensional convolutional neural network model integrating attention mechanisms, designated as CNN-SM, was developed to advance spatial prediction of soil organic matter (SOM) in cultivated soils under complex environmental conditions. Methods This study incorporated 154 georeferenced soil samples from Lishu County, Jilin Province, China, synergistically fused with: (1) spectral indices and derivative features from Sentinel-2 multispectral data, (2) Sentinel-1 SAR textural metrics acquired during the bare soil season, and (3) topographic derivatives, vegetation proxies, and climatic covariates. Feature selection was optimized through Out-of-Bag (OOB) estimation, with model performance rigorously evaluated across 11 feature combination scenarios. Results Results demonstrated that bare soil-season Sentinel-2 near-infrared (NIR) and shortwave infrared (SWIR) band reflectance, combined with Sentinel-1 polarization-enhanced texture features significantly outperformed conventional vegetation indices in contributing to the SOM prediction model. The CNN-SM framework achieved optimal performance (R² = 0.695, MAE = 2.800 g kg -1 , RMSE = 3.064 g kg -1 ) through attention-driven prioritization of microwave-optical feature synergies. Conclusions This multi-sensor digital soil mapping approach provides a paradigm shift in precision SOM estimation, offering actionable insights for spatially explicit soil health management in intensively cultivated black soil regions. Soil organic matter Spectral feature indices Bare soil season Deep learning Neural network model Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6207977","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430076952,"identity":"cb9f4d9c-fc3c-480e-8e8a-8c37425ce0c9","order_by":0,"name":"qixun 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","lastPublishedDoi":"10.21203/rs.3.rs-6207977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6207977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/em\u003e A novel two-dimensional convolutional neural network model integrating attention mechanisms, designated as CNN-SM, was developed to advance spatial prediction of soil organic matter (SOM) in cultivated soils under complex environmental conditions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e This study incorporated 154 georeferenced soil samples from Lishu County, Jilin Province, China, synergistically fused with: (1) spectral indices and derivative features from Sentinel-2 multispectral data, (2) Sentinel-1 SAR textural metrics acquired during the bare soil season, and (3) topographic derivatives, vegetation proxies, and 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