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Remote Sens. 2016, 8(9), 701; doi:10.3390/rs8090701

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

1
Faculty of Agronomy and Veterinary Medicine, University of Brasilia; Campus Universitário Darcy Ribeiro, ICC Sul, Asa Norte, Postal Box 4508, Brasília 70910-960, Brazil
2
Department of Soil Science, College of Agriculture Luiz de Queiroz, University of São Paulo; Pádua Dias Av., 11, Piracicaba, Postal Box 09, São Paulo 13416-900, Brazil
*
Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao, Xiaofeng Li and Prasad S. Thenkabail
Received: 22 March 2016 / Revised: 16 August 2016 / Accepted: 17 August 2016 / Published: 26 August 2016
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
View Full-Text   |   Download PDF [3422 KB, uploaded 26 August 2016]   |  

Abstract

The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil texture using a regional spectral library and its usefulness on classification. We collected 3750 soil samples covering 3 million ha within strong soil class variations in São Paulo State. The spectral analyses of soil samples from topsoil and subsoil were measured in laboratory (400–2500 nm). The potential of a regional soil spectral library was evaluated on the discrimination of soil texture. We considered two types of soil texture systems, one related with soil classification and another with soil managements. The soil line technique was used to assess differentiation between soil textural groups. Soil spectra were summarized by principal component analysis (PCA) to select relevant information on the spectra. Partial least squares regression (PLSR) was used to predict texture. Spectral curves indicated different shapes according to soil texture and discriminated particle size classes from clayey to sandy soils. In the visible region, differences were small because of the organic matter, while the short wave infrared (SWIR) region showed more differences; thus, soil texture variation could be differentiated by quartz. Angulation differences are on a spectral curve from NIR to SWIR. The statistical models predicted clay and sand levels with R2 = 0.93 and 0.96, respectively. Indeed, we achieved a difference of 1.2% between laboratory and spectroscopy measurement for clay. The spectral information was useful to classify Ferralsols with different texture classification. In addition, the spectra differentiated Lixisols from Ferralsols and Arenosols. This work can help the development of computer programs that allow soil texture classification and subsequent digital soil mapping at detailed scales. In addition, it complies with requirements for sustainable land use and soil management. View Full-Text
Keywords: remote sensing; proximal sensing; soils attributes; sustainable land use; soils survey and mapping; digital soil mapping; soil management; chemometrics; spectroscopy remote sensing; proximal sensing; soils attributes; sustainable land use; soils survey and mapping; digital soil mapping; soil management; chemometrics; spectroscopy
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MDPI and ACS Style

Lacerda, M.P.C.; Demattê, J.A.M.; Sato, M.V.; Fongaro, C.T.; Gallo, B.C.; Souza, A.B. Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification. Remote Sens. 2016, 8, 701.

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