Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data

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dc.contributor.author

Meng, Xiangtian

Bao, Yilin

Liu, Jiangui

Liu, Huanjun

Zhang, Xinle

Zhang, Yu

Wang, Peng

Tang, Haitao

Kong, Fanchang

dc.date.accepted

2020-03-03

dc.date.accessioned

2023-12-18T16:56:51Z

dc.date.available

2023-12-18T16:56:51Z

dc.date.issued

2020-03-11

dc.date.submitted

2019-12-17

dc.description.abstract - en

Most studies have the achieved rapid and accurate determination of soil organic carbon (SOC) using laboratory spectroscopy; however, it remains difficult to map the spatial distribution of SOC. To predict and map SOC at a regional scale, we obtained fourteen hyperspectral images from the Gaofen-5 (GF-5) satellite and decomposed and reconstructed the original reflectance (OR) and the first derivative reflectance (FDR) using discrete wavelet transform (DWT) at different scales. At these different scales, as inputs, we selected the 3 optimal bands with the highest weight coefficient using principal component analysis and chose the normalized difference index (NDI), ratio index (RI) and difference index (DI) with the strongest correlation with the SOC content using a contour map method. These inputs were then used to build regional-scale SOC prediction models using random forest (RF), support vector machine (SVM) and back-propagation neural network (BPNN) algorithms. The results indicated that: 1) at a low decomposition scale, DWT can effectively eliminate the noise in satellite hyperspectral data, and the FDR combined with DWT can improve the SOC prediction accuracy significantly; 2) the method of selecting inputs using principal component analysis and a contour map can eliminate the redundancy of hyperspectral data while retaining the physical meaning of the inputs. For the model with the highest prediction accuracy, the inputs were all derived from the wavelength range of SOC variations; 3) the differences in prediction accuracy among the different prediction models are small; and 4) the SOC prediction accuracy using hyperspectral satellite data is greatly improved compared with that of previous SOC prediction studies using multispectral satellite data. This study provides a highly robust and accurate method for predicting and mapping regional SOC contents.

dc.identifier.citation

Meng, X., Bao, Y., Liu, J., Liu, H., Zhang, X., Zhang, Y., Wang, P., Tang, H., & Kong, F. (2020). Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data. International Jounal of Applied Earth Observation and Geoinformation, 89, 102111. https://doi.org/10.1016/j.jag.2020.102111

dc.identifier.doi

https://doi.org/10.1016/j.jag.2020.102111

dc.identifier.issn

1872-826X

dc.identifier.uri

https://open-science.canada.ca/handle/123456789/1357

dc.language.iso

en

dc.publisher

Elsevier B.V.

dc.rights - en

Creative Commons Attribution 4.0 International (CC BY 4.0)

dc.rights - fr

Creative Commons Attribution 4.0 International (CC BY 4.0)

dc.rights.openaccesslevel - en

Gold

dc.rights.openaccesslevel - fr

Or

dc.rights.uri - en

https://creativecommons.org/licenses/by/4.0/

dc.rights.uri - fr

https://creativecommons.org/licenses/by/4.0/deed.fr

dc.subject - en

Nature and environment

dc.subject - fr

Nature et environnement

dc.subject.en - en

Nature and environment

dc.subject.fr - fr

Nature et environnement

dc.title - en

Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data

dc.type - en

Article

dc.type - fr

Article

local.acceptedmanuscript.articlenum

102111

local.article.journaltitle

International Journal of Applied Earth Observation and Geoinformation

local.article.journalvolume

89

local.pagination

1-15

local.peerreview - en

Yes

local.peerreview - fr

Oui

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