Next Article in Journal
Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data
Next Article in Special Issue
Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification
Previous Article in Journal
Rapid Assessments of Amazon Forest Structure and Biomass Using Small Unmanned Aerial Systems
Previous Article in Special Issue
Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(8), 614; doi:10.3390/rs8080614

Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols)

1
Institute of Agricultural Sciences, Federal University of Jequitinhonha and Mucuri Valleys, Campus Unaí, Av. Vereador João Narciso, 1380, Cachoeira, Unaí 38610-000, Brazil
2
Department of Soil Science, Federal University of Lavras, P.O. Box 3037, Lavras 37200-000, Brazil
*
Author to whom correspondence should be addressed.
Academic Editors: José A.M. Demattê, Lenio Soares Galvao and Prasad S. Thenkabail
Received: 25 May 2016 / Revised: 18 July 2016 / Accepted: 21 July 2016 / Published: 25 July 2016
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
View Full-Text   |   Download PDF [3146 KB, uploaded 25 July 2016]   |  

Abstract

Digital terrain models (DTM) have been used in soil mapping worldwide. When using such models, improved predictions are often attained with the input of extra variables provided by the use of proximal sensors, such as magnetometers and portable X-ray fluorescence scanners (pXRF). This work aimed to evaluate the efficiency of such tools for mapping soil classes and properties in tropical conditions. Soils were classified and sampled at 39 locations in a regular-grid design with a 200-m distance between samples. A pXRF and a magnetometer were used in all samples, and DTM values were obtained for every sampling site. Through visual analysis, boxplots were used to identify the best variables for distinguishing soil classes, which were further mapped using fuzzy logic. The map was then validated in the field. An ordinary least square regression model was used to predict sand and clay contents using DTM, pXRF and the magnetometer as predicting variables. Variables obtained with pXRF showed a greater ability for predicting soil classes (overall accuracy of 78% and 0.67 kappa index), as well as for estimating sand and clay contents than those acquired with DTM and the magnetometer. This study showed that pXRF offers additional variables that are key for mapping soils and predicting soil properties at a detailed scale. This would not be possible using only DTM or magnetic susceptibility. View Full-Text
Keywords: magnetic susceptibility; portable X-ray fluorescence scanner; data mining; fuzzy logics; ordinary least square multiple linear regression magnetic susceptibility; portable X-ray fluorescence scanner; data mining; fuzzy logics; ordinary least square multiple linear regression
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Silva, S.H.G.; Poggere, G.C.; Menezes, M.D.; Carvalho, G.S.; Guilherme, L.R.G.; Curi, N. Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols). Remote Sens. 2016, 8, 614.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top