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I n‐situ prediction of soil organic carbon by vis– NIR spectroscopy: an efficient use of limited field data
Author(s) -
Kühnel A.,
Bogner C.
Publication year - 2017
Publication title -
european journal of soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.244
H-Index - 111
eISSN - 1365-2389
pISSN - 1351-0754
DOI - 10.1111/ejss.12448
Subject(s) - in situ , oversampling , spectral line , diffuse reflectance infrared fourier transform , soil carbon , calibration , spectroscopy , field (mathematics) , soil science , remote sensing , partial least squares regression , environmental science , biological system , chemistry , computer science , soil water , machine learning , geology , mathematics , statistics , physics , computer network , bandwidth (computing) , biochemistry , photocatalysis , quantum mechanics , catalysis , organic chemistry , astronomy , pure mathematics , biology
Summary Visible–near‐infrared diffuse reflectance spectroscopy (vis– NIR DRS ) has been widely used to predict soil organic carbon ( SOC ) in the laboratory. Predictions made directly from soil spectra measured in situ under field conditions, however, remain challenging. This study addresses the issue of incorporating in‐situ reflectance spectra efficiently into calibration data when a few field measurements only are available. We applied the synthetic minority oversampling technique ( SMOTE ) to generate new data with in‐situ reflectance spectra from soil profiles. Subsequently, we combined existing spectral libraries with these new synthetic data to predict SOC by partial least squares regression ( PLSR ). We found that models with added synthetic spectra always outperformed models based on the spectral libraries alone and in most cases also those with added in‐situ spectra only. We used the models to predict the distribution of SOC in soil profiles under five different land uses at M ount K ilimanjaro ( T anzania). Based on our results, we propose a framework for predicting SOC with a limited number of in‐situ soil spectra. This framework could effectively reduce the costs of developing in‐situ models for SOC at the local scale. Highlights We compare predictions of soil organic carbon from spectra of dried and sieved samples, field samples and calculated spectra. We use the synthetic minority oversampling technique (SMOTE) to calculate new soil spectra. Models with SMOTE outperform models with dried and field spectra in most cases. SMOTE can be used to reduce prediction errors when a few field data only are available for calibration.