{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T02:48:15Z","timestamp":1772938095824,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:00:00Z","timestamp":1722902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","award":["001"],"award-info":[{"award-number":["001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Modeling spectral reflectance data using machine learning algorithms presents a promising approach for estimating soil attributes. Nevertheless, a comprehensive investigation of the most effective models, parameters, wavelengths, and data acquisition techniques is essential to ensure optimal predictive accuracy. This work aimed to (a) explore the potential of the soil spectral signature obtained in different spectral bands (VIS-NIR, SWIR, and VIS-NIR-SWIR) and, by using hyperspectral imaging and non-imaging sensors, in the predictive modeling of soil attributes; and (b) analyze the accuracy of different ML models in predicting particle size and soil organic carbon (SOC) applied to the spectral signature of different spectral bands. Six soil monoliths, located in the central north region of Parana, Brazil, were collected and scanned via hyperspectral cameras (VIS-NIR camera and SWIR camera) and spectroradiometer (VIS-NIR-SWIR) in the laboratory. The spectral signature of the soils was analyzed and subsequently applied to ML models to predict particle size and SOC. Each set of data obtained by the different sensors was evaluated separately. The algorithms used were k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), linear regression (LR), artificial neural network (NN), and partial least square regression (PLSR). The most promising predictive performance was observed for the complete VIS-NIR-SWIR spectrum, followed by SWIR and VIS-NIR. Meanwhile, KNN, RF, and NN models were the most promising algorithms in estimating soil attributes for the dataset obtained from both sensors. The general mean R2 (determination coefficient) values obtained using these models, considering the different spectral bands evaluated, were around 0.99, 0.98, and 0.97 for sand prediction, and around 0.99, 0.98, and 0.96 for clay prediction. The lower performances, obtained for the datasets from both sensors, were observed for silt and SOC, with R2 results between 0.40 and 0.59 for these models. KNN demonstrated the best predictive performance. Integrating effective ML models with robust sample databases, obtained by advanced hyperspectral imaging and spectroradiometers, can enhance the accuracy and efficiency of soil attribute prediction.<\/jats:p>","DOI":"10.3390\/rs16162869","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T08:07:59Z","timestamp":1722931679000},"page":"2869","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Predicting Particle Size and Soil Organic Carbon of Soil Profiles Using VIS-NIR-SWIR Hyperspectral Imaging and Machine Learning Models"],"prefix":"10.3390","volume":"16","author":[{"given":"Karym Mayara de","family":"Oliveira","sequence":"first","affiliation":[{"name":"Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7377-7070","authenticated-orcid":false,"given":"Jo\u00e3o Vitor Ferreira","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, Brazil"}]},{"given":"Renato Herrig","family":"Furlanetto","sequence":"additional","affiliation":[{"name":"Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA"}]},{"given":"Caio Almeida de","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, Brazil"}]},{"given":"Weslei Augusto","family":"Mendon\u00e7a","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, Brazil"}]},{"given":"Daiane de Fatima da Silva","family":"Haubert","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, Brazil"}]},{"given":"Lu\u00eds Guilherme Teixeira","family":"Crusiol","sequence":"additional","affiliation":[{"name":"Embrapa Soja (Empresa Brasileira de Pesquisa Agropecu\u00e1ria), Londrina 86044-764, Parana, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2343-5045","authenticated-orcid":false,"given":"Renan","family":"Falcioni","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7338-2666","authenticated-orcid":false,"given":"Roney Berti de","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, Brazil"}]},{"given":"Amanda Silveira","family":"Reis","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, Brazil"}]},{"given":"Arney Eduardo do Amaral","family":"Ecker","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Centro Universit\u00e1rio Ing\u00e1 (UNING\u00c1), Rod. PR 317, 6114, Maringa 87035-510, Parana, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-2661","authenticated-orcid":false,"given":"Marcos Rafael","family":"Nanni","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104485","DOI":"10.1016\/j.catena.2020.104485","article-title":"Prediction of Soil Texture Classes through Different Wavelength Regions of Reflectance Spectroscopy at Various Soil Depths","volume":"189","author":"Coblinski","year":"2020","journal-title":"Catena"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"114061","DOI":"10.1016\/j.geoderma.2019.114061","article-title":"High-Resolution and Three-Dimensional Mapping of Soil Texture of China","volume":"361","author":"Liu","year":"2020","journal-title":"Geoderma"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"S38","DOI":"10.1016\/j.rse.2008.09.019","article-title":"Using Imaging Spectroscopy to Study Soil Properties","volume":"113","author":"Chabrillat","year":"2009","journal-title":"Remote Sens. 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