Different approaches to estimating soil properties for digital soil map integrated with machine learning and remote sensing techniques in a sub-humid ecosystem
Yazarlar (4)
Fikret Saygın Sivas Bilim ve Teknoloji Üniversitesi, Türkiye
Dr. Öğr. Üyesi Hasan AKSOY Sinop Üniversitesi, Türkiye
Doç. Dr. Pelin Alaboz Isparta Uygulamalı Bilimler Üniversitesi, Türkiye
Prof. Dr. Orhan Dengiz Ondokuz Mayıs Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Environmental Monitoring and Assessment (Q3) | ||
| Dergi ISSN | 0167-6369 Dergi Bilgileri (2023) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 08-2023 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 17-08-2023 |
| Cilt / Sayı / Sayfa | 195 / 9 / 1061–23 | DOI | 10.1007/s10661-023-11681-0 |
| Makale Linki | http://dx.doi.org/10.1007/s10661-023-11681-0 | ||
| UAK Araştırma Alanları |
Orman Hasılatı ve Amenajmanı
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| Özet |
| Today, data mining has become a relevant topic in digital soil mapping. In this current study, prediction of some soil properties and their spatial distribution were examined by machine learning algorithms (Support Vector Machine, Artificial Neural Network) using reflectance values of Triplesat satellite image bands in Vezirköprü district of Samsun province. The band data obtained from different wavelengths revealed positive correlations between the electrical conductivity and calcium carbonate equivalent contents of the soils. The support vector machine algorithm was the most successful to estimate the textural fractions, organic matter, electrical conductivity, and calcium carbonate equivalent contents of the soils using the bands obtained from satellite images. The mean absolute error for estimating sand, silt, and clay contents by support vector machine was 4.05%, 3.05%, and 3.66%, respectively. Texture classes … |
| Anahtar Kelimeler |
| Artificial neural network | Interpolation methods | Satellite image | Soil structure |
| Atıf Sayıları | |
| Web of Science | 18 |
| Scopus | 21 |
| Google Scholar | 28 |