Stratification of a local VIS-NIR-SWIR spectral library by homogeneity criteria yields more accurate soil organic carbon predictions

Geoderma ◽  
2019 ◽  
Vol 337 ◽  
pp. 565-581 ◽  
Author(s):  
Jean Michel Moura-Bueno ◽  
Ricardo Simão Diniz Dalmolin ◽  
Alexandre ten Caten ◽  
André Carnieletto Dotto ◽  
José A.M. Demattê
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spectral Library

Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique

Geoderma ◽  
2012 ◽  
Vol 183-184 ◽  
pp. 41-48 ◽  
Author(s):  
A.H. Cambule ◽  
D.G. Rossiter ◽  
J.J. Stoorvogel ◽  
E.M.A. Smaling
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
National Park ◽  
Near Infrared ◽  
Spectral Library ◽  
Infrared Spectral

scholarly journals Application of Spectrally Derived Soil Type as Ancillary Data to Improve the Estimation of Soil Organic Carbon by Using the Chinese Soil Vis-NIR Spectral Library

2018 ◽  
Vol 10 (11) ◽  
pp. 1747 ◽  
Author(s):  
Yi Liu ◽  
Zhou Shi ◽  
Ganlin Zhang ◽  
Yiyun Chen ◽  
Shuo Li ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Type ◽  
Near Infrared ◽  
Expert Knowledge ◽  
Nir Spectroscopy ◽  
Soil Classification ◽  
Soil Types ◽  
Spectral Library ◽  
Ancillary Data

Ancillary data, such as soil type, may improve the visible and near-infrared (vis-NIR) estimation of soil organic carbon (SOC); however, they require data collection or expert knowledge. The application of a national soil spectral library to local SOC estimations usually requires soil type information, because the relationships between vis-NIR spectra and SOC from different populations may vary. Using 515 samples of five soil types (genetic soil classification of China, GSCC) from the Chinese soil spectral library (CSSL), we compared three strategies in the vis-NIR estimation of SOC. Different regression models were calibrated using the entire dataset (Strategy I, without using soil type as ancillary data) and the subsets stratified by soil type from CSSL as ancillary data (strategies II and III). In Strategy II, the subsets were stratified by soil type from the CSSL for validation. In Strategy III, the subsets were stratified by spectrally derived soil type for validation. The results showed that 86.72% of the samples were successfully discriminated for the soil types by using the vis-NIR spectra. The coefficients of determination in the prediction ( R p 2 ) of SOC estimation by strategies I, II, and III were 0.74, 0.83, and 0.82, respectively. The stratified calibration strategies (strategies II and III) improved the vis-NIR estimation of SOC. The misclassification of the soil type in the application of Strategy III slightly affected the SOC estimations. Nevertheless, this strategy is inexpensive and beneficial when expert knowledge on soil classification is lacking. We concluded that vis-NIR spectroscopy could be applied to distinguish some soil types in terms of GSCC, which further provided essential and easily accessible ancillary data for the application of stratified calibration strategies in the vis-NIR estimation of SOC.

When does stratification of a subtropical soil spectral library improve predictions of soil organic carbon content?

2020 ◽  
Vol 737 ◽  
pp. 139895
Author(s):  
Jean Michel Moura-Bueno ◽  
Ricardo Simão Diniz Dalmolin ◽  
Taciara Zborowski Horst-Heinen ◽  
Alexandre ten Caten ◽  
Gustavo M. Vasques ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Organic Carbon Content ◽  
Spectral Library ◽  
Soil Organic Carbon Content ◽  
Subtropical Soil

Predicting Soil Organic Carbon at Field Scale Using a National Soil Spectral Library

2013 ◽  
Vol 21 (3) ◽  
pp. 213-222 ◽  
Author(s):  
Yi Peng ◽  
Maria Knadel ◽  
René Gislum ◽  
Fan Deng ◽  
Trine Norgaard ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spectral Library ◽  
Field Scale
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