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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Digital mapping of soil parent material in a heterogeneous tropical area

Full text
Author(s):
Bonfatti, Benito R. [1] ; Dematte, Jose A. M. [1] ; Marques, Karina P. P. [1] ; Poppiel, Raul R. [1] ; Rizzo, Rodnei [1] ; Mendes, Wanderson de S. [1] ; Silvero, Nelida E. Q. [1] ; Safanelli, Jose L. [1]
Total Authors: 8
Affiliation:
[1] Univ Sao Paulo, Luiz de Queiroz Coll Agr, Dept Soil Sci, Ave Padua Dias 11, Postal Box 09, BR-13416900 Piracicaba, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: Geomorphology; v. 367, OCT 15 2020.
Web of Science Citations: 0
Abstract

Parent material is one of the five factors in soil formation. Studies on parent material allow interpreting soil genesis processes and improve our knowledge of specific soil attributes. However, soil parent material maps at detailed cartographic scale (finer than 1:100,000) are rare in tropical areas and it is usually inferred from poorly detailed geological data, which generally group different lithologies into single units. Thus, we propose a methodology to map soil parent material based on remote sensing and machine learning in a geologically very complex area. The study site covers 1378 km(2) in Sao Paulo State, Brazil. Prediction models used data from 280 geological observation points, a digital elevation model (spatial resolution of 5 m, upscale to 30 m) and multitemporal Landsat images in a range of 30 years. We evaluated six classification algorithms, namely random forest, decision tree, support vector machine, multinomial logistic regression, K-means (unsupervised classification), and object-based image analysis with maximum likelihood classification. Environmental covariates were grouped to create different scenarios combining terrain derivatives, hydrologic covariates, topsoil spectral reflectance, and spatial coordinates. A bare soil image, elaborated using 30 years of Landsat data, was evaluated as a covariate to predict soil parent material. Predictions were validated using three different strategies: crossvalidation, separate validation dataset (20%), and comparison with legacy geological maps (information from two areas with geological maps at fine scale). We also assessed the correspondence between the map of predicted soil parent material and data of soil particle size from 571 soil sampling points. Random forest algorithm presented the best validation performance, whereas the group of terrain derivatives and hydrologic covariates explained most of model variation. The produced parent material map was coherent with the spatial distribution of soil particle size across the study area. (C) 2020 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 18/12678-5 - Terrestrial, orbital and relief spectroscopy mediated by the Random forest kriging system in soil mapping by pedo-transfer of São Paulo State
Grantee:Benito Roberto Bonfatti
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 16/26124-6 - Precision pedology: soil characterisation and mapping in real time using geotechnologies
Grantee:Wanderson de Sousa Mendes
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
FAPESP's process: 15/16172-0 - Geomorphometric segmentation associated with soils types via geotechnologies
Grantee:Karina Patrícia Prazeres Marques
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 14/22262-0 - Geotechnologies on a detailed digital soil mapping and the Brazilian soil spectral library: development and applications
Grantee:José Alexandre Melo Demattê
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 16/01597-9 - Pedotransfer functions by geotecnologies associated with photopedology for pedological mapping in agricultural areas of São Paulo State
Grantee:José Lucas Safanelli
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
FAPESP's process: 18/23760-4 - Spatial technology applied on the remote sensing of soil and vegetation: Applications in the mapping of agricultural and preserved lands.
Grantee:Rodnei Rizzo
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 20/04306-0 - Pedometric soil mapping of São Paulo State using proximal and remote sensing data coupled with pedo-transfer functions and machine learning
Grantee:Raul Roberto Poppiel
Support Opportunities: Scholarships in Brazil - Post-Doctoral