Abstract
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.
Highlights
The content of soil organic carbon (SOC; 1550 Gt) is higher than that of the combined carbon from global vegetation (420–620 Gt) and the atmosphere (760 Gt) [1,2]
Our study proposed a strategy of using the spectrally derived soil type as ancillary data to improve SOC estimation by utilizing visible and near-infrared (vis-NIR) spectroscopy and the Chinese soil library
The results allowed us to draw the following conclusions: (i) vis-NIR spectroscopy coupled with a soil library could be used for soil classification; (ii) stratifying samples by actual soil type (Strategy II) or spectrally derived soil type (Strategy III) significantly improved the quality of the SOC models for all of the soil types, and soil type was an adequate criterion for calibration set formation
Summary
The content of soil organic carbon (SOC; 1550 Gt) is higher than that of the combined carbon from global vegetation (420–620 Gt) and the atmosphere (760 Gt) [1,2]. Even though a small proportion of SOC is transformed into atmospheric carbon as greenhouse gases, its potential influence on the global climate is substantial [3]. It is well recognized that SOC is important for sustaining soil quality and food production, and inappropriate land-use management practices might cause the loss of SOC [4,5]. Due to the critical role of SOC in food production and climate regulation, the demand for monitoring the spatial and temporal variation of SOC is increasing [6]. Techniques for the rapid and inexpensive measurement of SOC should be developed
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
