Prediction of Soil Fertility Properties from a Globally Distributed Soil Mid-Infrared Spectral Library

2010 ◽  
Vol 74 (5) ◽  
pp. 1792-1799 ◽  
Author(s):  
Thomas Terhoeven-Urselmans ◽  
Tor-Gunnar Vagen ◽  
Otto Spaargaren ◽  
Keith D. Shepherd
Keyword(s):  
Soil Fertility ◽  
Spectral Library ◽  
Mid Infrared ◽  
Infrared Spectral ◽  
Globally Distributed

scholarly journals Accurate and Precise Prediction of Soil Properties from a Large Mid-Infrared Spectral Library

Soil Systems ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 11 ◽  
Author(s):  
Shree Dangal ◽  
Jonathan Sanderman ◽  
Skye Wills ◽  
Leonardo Ramirez-Lopez
Keyword(s):  
Soil Properties ◽  
Diffuse Reflectance Spectroscopy ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Soil Survey ◽  
Spectral Library ◽  
Mid Infrared ◽  
Large N ◽  
Infrared Spectral ◽  
Precise Prediction

Diffuse reflectance spectroscopy (DRS) is emerging as a rapid and cost-effective alternative to routine laboratory analysis for many soil properties. However, it has primarily been applied in project-specific contexts. Here, we provide an assessment of DRS spectroscopy at the scale of the continental United States by utilizing the large (n > 50,000) USDA National Soil Survey Center mid-infrared spectral library and associated soil characterization database. We tested and optimized several advanced statistical approaches for providing routine predictions of numerous soil properties relevant to studying carbon cycling. On independent validation sets, the machine learning algorithms Cubist and memory-based learner (MBL) both outperformed random forest (RF) and partial least squares regressions (PLSR) and produced excellent overall models with a mean R2 of 0.92 (mean ratio of performance to deviation = 6.5) across all 10 soil properties. We found that the use of root-mean-square error (RMSE) was misleading for understanding the actual uncertainty about any particular prediction; therefore, we developed routines to assess the prediction uncertainty for all models except Cubist. The MBL models produced much more precise predictions compared with global PLSR and RF. Finally, we present several techniques that can be used to flag predictions of new samples that may not be reliable because their spectra fall outside of the calibration set.

Strategies to improve the prediction of bulk soil and fraction organic carbon in Brazilian samples by using an Australian national mid-infrared spectral library

Geoderma ◽  
2020 ◽  
Vol 373 ◽  
pp. 114401
Author(s):  
Clever Briedis ◽  
Jeff Baldock ◽  
João Carlos de Moraes Sá ◽  
Josiane Burkner dos Santos ◽  
Débora Marcondes Bastos Pereira Milori
Keyword(s):  
Organic Carbon ◽  
Bulk Soil ◽  
Spectral Library ◽  
Mid Infrared ◽  
Infrared Spectral

Novel Search Algorithms for a Mid-Infrared Spectral Library of Cotton Contaminants

2008 ◽  
Vol 62 (6) ◽  
pp. 661-670 ◽  
Author(s):  
J. Brian Loudermilk ◽  
David S. Himmelsbach ◽  
Franklin E. Barton ◽  
James A. de Haseth
Keyword(s):  
Search Algorithms ◽  
Spectral Library ◽  
Mid Infrared ◽  
Infrared Spectral

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 Group delay dispersion measurements in the mid-infrared spectral range of 2-20 µm

2016 ◽  
Vol 24 (15) ◽  
pp. 16705 ◽  
Author(s):  
Florian Habel ◽  
Michael Trubetskov ◽  
Vladimir Pervak
Keyword(s):  
Spectral Range ◽  
Group Delay ◽  
Group Delay Dispersion ◽  
Mid Infrared ◽  
Infrared Spectral Range ◽  
Infrared Spectral

scholarly journals Mid‐Infrared Spectral Diagnostics of Nuclear and Extranuclear Regions in Nearby Galaxies

2006 ◽  
Vol 646 (1) ◽  
pp. 161-173 ◽  
Author(s):  
D. A. Dale ◽  
J. D. T. Smith ◽  
L. Armus ◽  
B. A. Buckalew ◽  
G. Helou ◽  
...  
Keyword(s):  
Mid Infrared ◽  
Spectral Diagnostics ◽  
Infrared Spectral ◽  
Nearby Galaxies
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