Digital mapping of soil drainage using remote sensing, DEM and soil color in a semiarid region of Central Iran

Autor(es)

Asgari, Najmeh ^[1] ; Ayoubi, Shamsollah ^[1] ; Dematte, Jose A. M. ^[2] ; Jafari, Azam ^[3] ; Safanelli, Jose Lucas ^[2] ; Desiderio Da Silveira, Ariane Francine ^[2]

Afiliação do(s) autor(es)

^[1] Isfahan Univ Technol, Coll Agr, Dept Soil Sci, Esfahan 84115683111 - Iran

^[2] Univ Sao Paulo Luiz de Queiroz, Coll Agr, Dept Soil Sci, Av Padua Dias 11, BR-13418900 Piracicaba, SP - Brazil

^[3] Shahid Bahonar Univ Kerman, Coll Agr, Dept Soil Sci, Kerman - Iran

• Tipo de documento: Artigo Científico
• Fonte: GEODERMA REGIONAL; v. 22, SEP 2020.
• Citações Web of Science: 0

Resumo

In this study, random forest (RF) and support vector machine (SVM) models were developed to evaluate different input variables for predicting and mapping soil drainage classes in the a part of Charmahal \& Bakhtiari Province, central Iran. Input variables included digital elevation model (DEM) derived topographic attributes, remote sensing-derived vegetation indices and diffuse reflectance spectroscopy-derived soil color qualifiers (chroma and value). Three soil drainage classes, comprising poorly drained (PD), moderately well drained (MWD) and well drained (WD) were identified. Totally, 102 profiles were described and soil samples were collected from various genetic horizons. Results showed that the best classification results were acquired for two extreme drainage classes (WD and PD) with 100% user accuracy and the greatest misclassification for MWD. Chroma following NDVI and SAVI were the most efficient predictors of soil drainage. The best performance of models was acquired when the topographic attributes, NDVI, SAVI, chroma and value were included as input variables for predicting soil drainage classes. The prediction overall accuracy and the Kappa coefficient of drainage classification were 0.83 and 0.73 for RF and 0.86 and 0.74 for SVM, respectively. In overall, results indicated that color qualifiers combined with topographic attributes and vegetation indices can be employed to successfully predict soil drainage classes cost-effectively with acceptable overall accuracy. (c) 2020 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 14/22262-0 - Geotecnologias no mapeamento digital pedológico detalhado e biblioteca espectral de solos do Brasil: desenvolvimento e aplicações
• Beneficiário: José Alexandre Melo Demattê
• Modalidade de apoio: Auxílio à Pesquisa - Temático