{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T06:05:38Z","timestamp":1771913138622,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific and Technological Development","award":["28\/2018"],"award-info":[{"award-number":["28\/2018"]}]},{"name":"Scientific and Technological Development","award":["312121\/2021-0"],"award-info":[{"award-number":["312121\/2021-0"]}]},{"name":"Scientific and Technological Development","award":["11\/2018"],"award-info":[{"award-number":["11\/2018"]}]},{"name":"Research Support Foundation of the State of Rio de Janeiro","award":["28\/2018"],"award-info":[{"award-number":["28\/2018"]}]},{"name":"Research Support Foundation of the State of Rio de Janeiro","award":["312121\/2021-0"],"award-info":[{"award-number":["312121\/2021-0"]}]},{"name":"Research Support Foundation of the State of Rio de Janeiro","award":["11\/2018"],"award-info":[{"award-number":["11\/2018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Airborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (\u03bcR\/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh\/K, eU\/K, and eU\/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km\u00b2, with a database of 208 soil samples to a predefined depth (0\u201330 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model\u2019s performance was evaluated through the R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient of variation (CV%). The RF algorithm showed better performance with AGD (R2 values ranging from 0.15 to 0.23), as well as the SVM model (R2 values ranging from 0.08 to 0.23) when compared to RF (R2 values ranging from 0.10 to 0.20) and SVM (R2 values ranging from 0.04 to 0.10) models without AGD. Overall, the results suggest that AGD can be helpful for soil mapping. Nevertheless, it is crucial to acknowledge that the accuracy of AGD in predicting soil properties could vary depending on various common factors in DSM, such as the quality and resolution of the covariates and available soil data. Further research is needed to determine the optimal approach for using AGD in soil mapping.<\/jats:p>","DOI":"10.3390\/rs15153719","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T01:09:01Z","timestamp":1690333741000},"page":"3719","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Could Airborne Geophysical Data Be Used to Improve Predictive Modeling of Agronomic Soil Properties in Tropical Hillslope Area?"],"prefix":"10.3390","volume":"15","author":[{"given":"Blenda P.","family":"Bastos","sequence":"first","affiliation":[{"name":"Postgraduate Program in Modeling and Geological Evolution, Geoscience Institute, Federal Rural University of Rio de Janeiro (UFRRJ), Serop\u00e9dica 23890-000, RJ, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5742-7556","authenticated-orcid":false,"given":"Helena S. K.","family":"Pinheiro","sequence":"additional","affiliation":[{"name":"Soils Department, Agronomy Institute, Federal Rural University of Rio de Janeiro (UFRRJ), Serop\u00e9dica 23890-000, RJ, Brazil"}]},{"given":"Francisco J. F.","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Laboratory for Research in Applied Geophysics, Department of Geology, Federal University of Paran\u00e1 (UFPR), Curitiba 81530-000, PR, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8619-0989","authenticated-orcid":false,"given":"Waldir de","family":"Carvalho Junior","sequence":"additional","affiliation":[{"name":"Empresa Brasileira de Pesquisa Agropecu\u00e1ria (Embrapa Solos), 1.024 Jardim Bot\u00e2nico Street, Rio de Janeiro 22460-000, RJ, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0063-3521","authenticated-orcid":false,"given":"L\u00facia Helena C.","family":"dos Anjos","sequence":"additional","affiliation":[{"name":"Soils Department, Agronomy Institute, Federal Rural University of Rio de Janeiro (UFRRJ), Serop\u00e9dica 23890-000, RJ, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0166-2481(06)31001-X","article-title":"Chapter 1 Spatial Soil Information Systems and Spatial Soil Inference Systems: Perspectives for Digital Soil Mapping","volume":"Volume 31","author":"Lagacherie","year":"2006","journal-title":"Developments in Soil Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hartemink, A.E., McBratney, A., and Mendon\u00e7a-Santos, M.D.L. 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