Data/hora: 26/06/2024 15:43:59

Biblioteca(s): | Epagri-Sede.
Data corrente: | 21/11/2018
Data da última atualização: | 21/11/2018
Autoria: | SILVA, E. B.; GIASSON, É.; DOTTO, A. C.; CATEN, A. T.; DEMATTÊ, J. A. M.; VEIGA, M.
Título: | VIS-NIR SPECTROSCOPY FOR PREDICTING SAND, SILT AND CLAY CONTENTS USING LEGACY SOIL SAMPLES OF SANTA CATARINA STATE.
Ano de publicação: | 2017
Fonte/Imprenta: | In: CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 36., 2017, Belém. Resumos... Viçosa: Sociedade Brasileira de Ciência do Solo, 2017.
Idioma: | Inglês

Conteúdo:

The quantitative distribution of particle size is one of the most important soil 
properties. Diffuse reflectance spectroscopy in the  visible and near infrared  (Vis-NIR)  region 
have been employed to characterize and quantify soil properties in a fast, non-destructive and 
low cost manner. The objective of this  study  was to  develop a calibration model with 
laboratory-based soil  Vis-NIR spectra for  sand, silt and clay content using multivariate 
analysis techniques.  Materials and Methods  -  A set of 1534 legacy soil samples 
representative of the crops from 260 municipalities (80% of total) of Santa Catarina state were 
used.  Sand, silt and clay content (in percentage) were determined  in the laboratory  by the 
pipette method. Soil spectra was obtained using the FieldSpec 3 (ASD) sensor with a range of 
370 to 2500 nm (Vis-NIR). Four multivariate techniques (Partial least-squares - PLS, Support 
vector machine  -  SVM,  Random  forest  -  RF,  Gaussian process regression  -  GPR) and  eight
pre-processing transformations (including raw spectra,  smoothing,  derivatives, normalization 
and non-linear transformations) of spectral data were compared.  The coefficient of 
determination (R 2 ) and the root mean square error (RMSE) were used to evaluate  the models.
All analyses and modeling were performed using the AlradSpectra package in R. Results and 
Discussion - On average, the predictive performance of the multivariate techniques decreased 
in the following order:  PLS>RF>GPR>SVM  for sand; RF>PLS>GPR>SVM  for silt;  and 
PLS>RF>GPR>SVM  for clay.  The combinations of pre-processing transformation and 
multivariate technique that  achieved  the best  predictive result  was  Savitzky-Golay first 
derivative/RF (SGD1-RF) for sand (R 2 v=0.67, RMSEv=11.56%); SGD1-RF for silt (R
2 v=0.43, RMSEv=8.51%);  and  Normalization/SVM  for clay  (R 2 v=0.76, RMSEv=8.68%).  Conclusions
- This study offers the possibility to perform different modeling strategies using legacy data in 
order to predict sand, silt and clay content, by  Vis-NIR  spectroscopy technique.  On the other 
hand,  silt  was not predictable with the same effectiveness and more studies should be 
conducted.

Palavras-Chave: | chemometrics modeling; Diffuse reflectance spectroscopy; spectral.
Categoria do assunto: | X Pesquisa, Tecnologia e Engenharia
URL: | http://intranetdoc.epagri.sc.gov.br/producao_tecnico_cientifica/DOC_41589.pdf
Registro original: | Epagri-Sede (Epagri-Sede)
Exemplares: | Epagri-Sede - (102954-1) - UPC - PL - DD -  -  -  - DIS