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In this paper, we studied the possibility of using deep learning methods to establish a multi-feature model to predict SOM content. Moreover, using Nong\u2019an County of Changchun City as the study area, Sentinel-2A remote sensing images were taken as the data source to construct the dataset by using field sampling and image processing. The LeNet-5 convolutional neural network model was chosen as the deep learning model, which was improved based on the basic model. The evaluation metrics were selected as the root mean square error (RMSE) and the coefficient of determination R2. Through comparison, the R2 of the improved model was found to be higher than that of the linear regression method, Support Vector Machines (SVM) (RMSE = 2.471, R2 = 0.4035), and Random Forest (RF) (RMSE = 2.577, R2 = 0.4913). The result shows that: (1) It is feasible to use the multispectral data extracted from remote sensing images for soil organic matter content inversion based on the deep learning model with a minimum RMSE of 2.979 and with the R2 reaching 0.89. (2) The choice of features has an impact on the prediction of the model to a certain extent. After ranking the importance of features, selecting the appropriate number of features for inversion provides better results than full feature inversion, and the computational speed is improved.<\/jats:p>","DOI":"10.3390\/s22207777","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T01:44:13Z","timestamp":1665711853000},"page":"7777","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Inversion of Soil Organic Matter Content Based on Improved Convolutional Neural Network"],"prefix":"10.3390","volume":"22","author":[{"given":"Li","family":"Ma","sequence":"first","affiliation":[{"name":"College of Information and Technology, Jilin Agricultural University, Changchun 130118, China"},{"name":"Institute for the Smart Agriculture, Jilin Agricultural University, Changchun 130117, China"}]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Information and Technology, Jilin Agricultural University, Changchun 130118, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7413-8385","authenticated-orcid":false,"given":"Liying","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Information and Technology, Jilin Agricultural University, Changchun 130118, China"},{"name":"Institute for the Smart Agriculture, Jilin Agricultural University, Changchun 130117, China"}]},{"given":"Dongming","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Technology, Jilin Agricultural University, Changchun 130118, China"},{"name":"Institute for the Smart Agriculture, Jilin Agricultural University, Changchun 130117, China"}]},{"given":"Guifen","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Technology, Changchun Humanities and Sciences College, Changchun 130118, China"}]},{"given":"Ye","family":"Han","sequence":"additional","affiliation":[{"name":"College of Information and Technology, Jilin Agricultural University, Changchun 130118, China"},{"name":"Institute for the Smart Agriculture, Jilin Agricultural University, Changchun 130117, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1071\/SR14246","article-title":"Impact of Soil Organic Matter on Soil Properties\u2014A Review with Emphasis on Australian Soils","volume":"53","author":"Murphy","year":"2015","journal-title":"Soil Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1111\/j.1365-2389.2008.01114.x","article-title":"Challenges and Opportunities in Soil Organic Matter Research","volume":"60","author":"Lal","year":"2009","journal-title":"Eur. J. Soil Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1071\/SR9941043","article-title":"Soil Structure and Carbon Cycling","volume":"32","author":"Golchin","year":"1994","journal-title":"Soil Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/S0167-1987(97)00038-X","article-title":"The Role of Soil Organic Matter in Maintaining Soil Quality in Continuous Cropping Systems","volume":"43","author":"Reeves","year":"1997","journal-title":"Soil Tillage Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Guo, L., Chen, Y., Shi, T., Luo, M., Ju, Q., Zhang, H., and Wang, S. (2019). Prediction of Soil Organic Carbon Based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China. Remote Sens., 11.","DOI":"10.3390\/rs11141683"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Voltr, V., Men\u0161\u00edk, L., Hlisnikovsk\u00fd, L., Hru\u0161ka, M., Pokorn\u00fd, E., and Posp\u00ed\u0161ilov\u00e1, L. (2021). The Soil Organic Matter in Connection with Soil Properties and Soil Inputs. Agronomy, 11.","DOI":"10.3390\/agronomy11040779"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1038\/371783a0","article-title":"The Role of Soil Organic Matter in Sustaining Soil Fertility","volume":"371","author":"Tiessen","year":"1994","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.geoderma.2005.01.019","article-title":"Effect of Cover Crop Management on Soil Organic Matter","volume":"130","author":"Ding","year":"2006","journal-title":"Geoderma"},{"key":"ref_9","first-page":"311","article-title":"Comparison on determining the organic matter contents in the soils by different heating methods in the potassium dichromate-volumetric method","volume":"17","author":"Ji","year":"2005","journal-title":"Acta Agric. Zhejiangensis"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1366\/13-07288","article-title":"Visible, Near-Infrared, and Mid-Infrared Spectroscopy Applications for Soil Assessment with Emphasis on Soil Organic Matter Content and Quality: State-of-the-Art and Key Issues","volume":"67","author":"Gholizadeh","year":"2013","journal-title":"Appl. Spectrosc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/0034-4257(90)90037-M","article-title":"On the Information Content of Soil Reflectance Spectra","volume":"33","author":"Price","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106836","DOI":"10.1016\/j.microc.2021.106836","article-title":"Spectral Characteristics of Organic Soil Matter: A Comprehensive Review","volume":"171","author":"Sharma","year":"2021","journal-title":"Microchem. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105053","DOI":"10.1016\/j.compag.2019.105053","article-title":"Hyperspectral Inversion of Soil Organic Matter Content in Cultivated Land Based on Wavelet Transform","volume":"167","author":"Gu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102645","DOI":"10.1016\/j.jvcir.2019.102645","article-title":"Inversion of Organic Matter Content in Wetland Soil Based on Landsat 8 Remote Sensing Image","volume":"64","author":"Zhai","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_15","first-page":"134","article-title":"Invertion of Cultivated Soil Organic Matter Content Combining Multi-spectral Remote Sensing and Random Forest Algorithm","volume":"36","author":"Liu","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wei, L., Yuan, Z., Wang, Z., Zhao, L., Zhang, Y., Lu, X., and Cao, L. (2020). Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model. Sensors, 20.","DOI":"10.3390\/s20102777"},{"key":"ref_17","first-page":"103","article-title":"Hyperspectral Estimation of Soil Organic Matter Content Based on Partial Least Squares Regression","volume":"31","author":"Yu","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2017.12.003","article-title":"Hyperspectral Sensing of Heavy Metals in Soil and Vegetation: Feasibility and Challenges","volume":"136","author":"Wang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3939","DOI":"10.1080\/01431160110115960","article-title":"Spectral Unmixing of Vegetation, Soil and Dry Carbon Cover in Arid Regions: Comparing Multispectral and Hyperspectral Observations","volume":"23","author":"Asner","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","first-page":"127","article-title":"Soil Organic Matter Content Inversion Model with Remote Sensing Image in Field Scale of Blacksoil Area","volume":"34","author":"Liu","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mandanici, E., and Bitelli, G. (2016). Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sens., 8.","DOI":"10.3390\/rs8121014"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Flood, N. (2017). Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia. Remote Sens., 9.","DOI":"10.3390\/rs9070659"},{"key":"ref_23","first-page":"456","article-title":"Homogeneity of Retrieval Models for Soil Organic Matter of Different Soil Types in Northeast Plain Using Hyperspectral Data","volume":"17","author":"Lu","year":"2011","journal-title":"J. Plant Nutr. Fertil."},{"key":"ref_24","first-page":"535","article-title":"Study on Differential-Based Multispectral Modeling of Soil Organic Matter in Ebinur Lake Wetland","volume":"39","author":"Li","year":"2019","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_25","first-page":"2248","article-title":"Remote Sensed Estimation of Soil Organic Matter Using Cluster-based Deep Neural Network in the Nearshore Plains of Lai-zhou Bay, Eastern China","volume":"22","author":"Feng","year":"2022","journal-title":"J. Saf. Environ."},{"key":"ref_26","first-page":"196","article-title":"Comparative analysis of soil organic matter content based on different hyperspectral inversion models","volume":"33","author":"Luan","year":"2013","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_27","first-page":"1428","article-title":"Inversion of Soil Organic Matter Content Using Hyperspectral Data Based on Continuous Wavelet Transformation","volume":"36","author":"Yu","year":"2016","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tang, S., Du, C., and Nie, T. (2022). Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis. Land, 11.","DOI":"10.3390\/land11050608"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"115695","DOI":"10.1016\/j.geoderma.2022.115695","article-title":"Deep Learning-Based National Scale Soil Organic Carbon Mapping with Sentinel-3 Data","volume":"411","author":"Odebiri","year":"2022","journal-title":"Geoderma"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1109\/JETCAS.2021.3101740","article-title":"Automated Pest Detection with DNN on the Edge for Precision Agriculture","volume":"11","author":"Albanese","year":"2021","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105933","DOI":"10.1016\/j.asoc.2019.105933","article-title":"Attention Embedded Residual CNN for Disease Detection in Tomato Leaves","volume":"86","author":"Karthik","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in Vegetation Remote Sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"115167","DOI":"10.1016\/j.eswa.2021.115167","article-title":"Solar Radiation Forecasting Based on Convolutional Neural Network and Ensemble Learning","volume":"181","author":"Cannizzaro","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_35","first-page":"457","article-title":"Classification of chemical explosion and earthquake infrasound based on 1-D convolutional neural network","volume":"40","author":"Tan","year":"2021","journal-title":"J. Appl. Acoust."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/S1002-0160(09)60285-X","article-title":"Spatial Variability of Soil Organic Carbon Under Maize Monoculture in the Song-Nen Plain, Northeast China","volume":"20","author":"Wang","year":"2010","journal-title":"Pedosphere"},{"key":"ref_37","unstructured":"Xin, S. (2014). The Evaluation and Monitoring of Cultivated Land Quality for NongAn Country. [Master\u2019s Thesis, Jilin Agricultural University]."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gascon, F., Bouzinac, C., Th\u00e9paut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S., and Gaudel-Vacaresse, A. (2017). Copernicus Sentinel-2A Calibration and Products Validation Status. Remote Sens., 9.","DOI":"10.3390\/rs9060584"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Canty, M. (2014). Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI\/IDL and Python, CRC Press. [3rd ed.].","DOI":"10.1201\/b17074"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Thanh Noi, P., and Kappas, M. (2018). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_41","first-page":"1","article-title":"Comparison of Sentinel 2A MSI and Landsat 8 OLI for Soil Organic Matter Inversion in Southwestern Shandong Province, China","volume":"36","author":"Wei","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_42","first-page":"94:1","article-title":"Feature Selection: A Data Perspective","volume":"50","author":"Li","year":"2017","journal-title":"ACM Comput. Surv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1089\/cmb.2015.0189","article-title":"Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters","volume":"23","author":"Li","year":"2016","journal-title":"J. Comput. Biol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-Based Learning Applied to Document Recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/7777\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:53:26Z","timestamp":1760144006000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/7777"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,13]]},"references-count":44,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22207777"],"URL":"https:\/\/doi.org\/10.3390\/s22207777","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,13]]}}}