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Remote Sens. 2017, 9(4), 293; doi:10.3390/rs9040293

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

1
Departamento de Solos, Instituto de Agronomia, Universidade Federal Rural do Rio de Janeiro, BR 465, km 7, Seropédica 23890-000, RJ, Brazil
2
Pedometrics, Landscape Analysis, and GIS Laboratory, Soil and Water Science Department, University of Florida, 2181 McCarty Hall A, P.O. Box 110290, Gainesville, FL 32611-0290, USA
3
Embrapa Solos, Rua Jardim Botânico, 1024, Rio de Janeiro 22460-000, RJ, Brazil
*
Author to whom correspondence should be addressed.
Academic Editors: José A.M. Demattê, Lenio Soares Galvao, Clement Atzberger and Prasad S. Thenkabail
Received: 28 October 2016 / Revised: 10 March 2017 / Accepted: 12 March 2017 / Published: 23 March 2017
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
View Full-Text   |   Download PDF [2641 KB, uploaded 23 March 2017]   |  

Abstract

Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization. The first step in this process is to develop a comprehensive VIS-NIR soil library of multiple key soil properties to be used in future soil surveys. This paper presents the first VIS-NIR soil library for a remote region in the Central Amazon. We evaluated the performance of VIS-NIR for the prediction of soil properties in the Central Amazon, Brazil. Soil properties measured and predicted were: pH, Ca, Mg, Al, H, H+Al, P, organic C (SOC), sum of bases, cation exchange capacity (CEC), percentage of base saturation (V), Al saturation (m), clay, sand, silt, silt/clay (S/C), and degree of flocculation. Soil samples were scanned in the laboratory in the VIS-NIR range (350–2500 nm), and forty-one pre-processing methods were tested to improve predictions. Clay content was predicted with the highest accuracy, followed by SOC. Sand, S/C, H, Al, H+Al, CEC, m and V predictions were reasonably good. The other soil properties were poorly predicted. Among the soil properties predicted well, SOC is one of the critical soil indicators in the global carbon cycle. Besides the soil property of interest, the landscape position, soil order and depth influenced in the model performance. For silt content, pH and S/C, the model performed better in well-drained soils, whereas for SOC best predictions were obtained in poorly drained soils. The association of VIS-NIR spectral data to landforms, vegetation classes, and soil types demonstrate potential for soil characterization. View Full-Text
Keywords: chemometrics; spectral pre-processing; partial least squares regression; soil organic carbon; tropical soils chemometrics; spectral pre-processing; partial least squares regression; soil organic carbon; tropical soils
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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).

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MDPI and ACS Style

Pinheiro, É.F.M.; Ceddia, M.B.; Clingensmith, C.M.; Grunwald, S.; Vasques, G.M. Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon. Remote Sens. 2017, 9, 293.

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