Journal article
Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images
Geoderma, Vol.337, pp.32-41
03/01/2019
DOI: 10.1016/j.geoderma.2018.09.003
Abstract
Soil organic carbon (SOC) plays an important role in controlling the function and quality of soil and offsetting the emissions of greenhouse gases. However, the dynamic monitoring and estimation of SOC are very difficult due to the complex traditional methods and the changing environmental variables. For instance, the calculation of SOC stock requires measurement of a few relevant soil attributes, such as soil organic matter (SOM), soil bulk density (SBD), soil moisture, and soil weight, in the laboratory. Many studies have suggested that visible and near-infrared (vis–NIR) spectra are a practical and affordable alternative to accurately and rapidly estimate the soil attributes relevant to SOC stock, and airborne hyperspectral images can be used as a valuable data source to perform digital soil mapping with high spatial resolution. The objective of this research was to check the predicted capability of SOC stock through laboratory and airborne vis–NIR spectral data. A total of 50 topsoil samples (0–15 cm) from the farmland of Iowa City were used as the study object. The partial least squares regression model was used to predict SOC stock through the direct and indirect methods. In the direct method, the SOC stock was predicted using the spectral data. In the indirect method, the relevant soil properties (SOM and SBD) of the SOC stock were predicted using the spectral data, and then the SOC stock was calculated. The mechanism of the prediction methods and the potential influencing factors of the model performance were discussed from the aspect of electromagnetic theory and empirical statistics. Results showed the following: (i) SOC stock can be successfully predicted using the laboratory spectra and the airborne hyperspectral image through the direct and indirect methods; (ii) the SOC stock and its relevant soil properties (SOM and SBD) showed evident spectral absorption characteristics in the vis–NIR spectral band; (iii) the atmospheric environment and soil surface conditions were the main influencing factors of the prediction accuracy between the airborne and laboratory spectra. This research might be useful for the dynamic monitoring and modeling of SOC in agricultural and environmental fields. •Verify the potential of lab and airborne hyperspectral data in predicting SOC stock•Explore the prediction capability of vis–NIR spectra for SOC stocks•Discuss the potential influencing factors in predicting SOC stock
Details
- Title: Subtitle
- Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images
- Creators
- Long Guo - College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, ChinaHaitao Zhang - College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, ChinaTiezhu Shi - Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of National Administration of Surveying, Mapping and GeoInformation & Shenzhen, Key Laboratory of Spatial Smart Sensing and Services & College of Life Sciences and Oceanography, Shenzhen University, 518060 Shenzhen, ChinaYiyun Chen - Wuhan UniversityQinghu Jiang - Collaborative Innovation Platform for Geospatial Information Technology, Wuhan University, Wuhan 430079, ChinaM Linderman - Geographical and Sustainability Sciences, The University of Iowa, Iowa City 52246, USA
- Resource Type
- Journal article
- Publication Details
- Geoderma, Vol.337, pp.32-41
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.geoderma.2018.09.003
- ISSN
- 0016-7061
- eISSN
- 1872-6259
- Grant note
- DOI: 10.13039/501100003819, name: Natural Science Foundation of Hubei Province, award: 2018CFB372; DOI: 10.13039/501100012226, name: Fundamental Research Funds for the Central Universities, award: 2662016QD032; DOI: 10.13039/501100002367, name: Chinese Academy of Sciences, award: Y852721s04; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 41371227; name: National Undergraduate Innovation and Entrepreneurship Training Program, award: 201810504023, 201810504030
- Language
- English
- Date published
- 03/01/2019
- Academic Unit
- Geographical and Sustainability Sciences
- Record Identifier
- 9983983646402771
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