Data/hora: 01/07/2024 11:57:49

Biblioteca(s): 	Epagri-Sede.	
Data corrente: 	10/01/2019	
Data da última atualização: 	10/01/2019	
Autoria: 	DEMATTÊ, J. A. M.; SILVEIRA, A. F. D.; DOTTO, A. C.; SATO, M. V.; DALMOLIN, R. S. D.; ARAÚJO, M. S. B.; SILVA, E. B.; NANNI, M. R.; NORONHA, N. C.; LACERDA, M. P. C.; ARAÚJO FILHO, J. C.; RIZZO, R.	
Título: 	The geographic and environmental characterization of the Brazilian Soil Spectral Library (BSSL).	
Ano de publicação: 	2018	
Fonte/Imprenta: 	In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Abstracts... Viçosa: Sociedade Brasileira de Ciência do Solo, 2018.	
Idioma: 	Inglês	
Conteúdo: 	The Brazilian Soil Spectral Library (BSSL) started with a collection of soil samples in 1995 at the
Department of Soil Science (ESALQ-USP). Currently, BSSL has gathered data from 26 States of
Brazil, totaling more than 38,000 soil samples. Thus, the objective of this work was to present the
first results of this databank in relation to Brazilian soils and its soil attributes. The BSSL allows to
extract and associate the inherent spectral information with the geographic and environmental
variables. With the development of BSSL, it was possible to demonstrate the potential of this tool
for tropical soils management, and relate the soil spectral reflectance to the regions and states,
biomes, geology, soil classes, and vegetation. Principal component analysis was performed to
explore and visualize correlated data. The average spectrum was determined for each group. The
group of spectra of Amazonas and Pernambuco States, the North and Northeast regions showed
higher reflectance corroborating the presence of sandy soils. While spectra from the States of Rio
Grande do Sul, Rio de Janeiro, and Mato Grosso do Sul, also the South, Southeast and Center-West
regions revealed low spectral reflectance. This low reflectance is correlated with clayey soils, which
are iron oxides and organic matter abundant. In relation to the types of geology, spectral curves
from igneous rocks, rich in calcium and iron, had low reflectance, while metamorphic rocks
exhibited with high reflectance and well-defined features mainly linked to the minerals of feldspar,
quartz, and plagioclase. Among the soil classes, the behavior of the Oxisol spectral curve showed
the lowest reflectance due to the high levels of clay and iron oxide contents. The spectral curve of
the Histosol presented low reflectance in the visible region due to the high content of organic
matter in these soils. On the other hand, the Spodosol had the highest reflectance followed by
Aridisol class, since these soils present very high sand content. With SSLB, it was possible to
characterize and group the intrinsic spectral soil information geographically and environmentally.
The use of sensors and geotechnologies, due to their agility and low cost in the analysis, allows a
higher sample density practice, improve the problem of detailed soil mapping, and generate data
for the soils survey and mapping that can assist in the management of agriculture in a rational
way.	
Palavras-Chave: 	big data; library; remote sensing; soil mapping; Soil sensing.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
URL: 	http://intranetdoc.epagri.sc.gov.br/producao_tecnico_cientifica/DOC_41882.pdf	
Registro original: 	Epagri-Sede (Epagri-Sede)	
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