Research on inversion model of cultivated soil moisture content based on hyperspectral imaging analysis

Wu, T; Yu, J; Lu, J; Zou, X; Zhang, W

Research on inversion model of cultivated soil moisture content based on hyperspectral imaging analysis

Wu, T Yu, J Lu, J Zou, X Zhang, W

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Agriculture (Switzerland), 2020, 10, (7), pp. 1-14
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Wu, T
dc.contributor.author	Yu, J
dc.contributor.author	Lu, J
dc.contributor.author	Zou, X
dc.contributor.author	Zhang, W https://orcid.org/0000-0002-2064-0326
dc.date.accessioned	2021-06-17T03:32:45Z
dc.date.available	2021-06-17T03:32:45Z
dc.date.issued	2020-07-01
dc.identifier.citation	Agriculture (Switzerland), 2020, 10, (7), pp. 1-14
dc.identifier.issn	2077-0472
dc.identifier.issn	2077-0472
dc.identifier.uri	http://hdl.handle.net/10453/149583
dc.description.abstract	Based on hyperspectral imaging technology, rapid and efficient prediction of soil moisture content (SMC) can provide an essential basis for the formulation of precise agricultural programs (e.g., forestry irrigation and environmental management). To build an efficient inversion model of SMC, this paper collected 117 cultivated soil samples from the Chair Hill area and tested them using the GaiaSorter hyperspectral sorter. The collected soil reflectance dataset was preprocessed by wavelet transform, before the combination of competitive adaptive reweighted sampling algorithm and successive projections algorithm (CARS‐SPA) was used to select the bands optimally. Seven wavelengths of 695, 711, 736, 747, 767, 778, and 796 nm were selected and used as the factors of the SMC inversion model. The popular linear regression algorithm was employed to construct this model. The result indicated that the inversion model established by the multiple linear regression algorithm (the predicted R was 0.83 and the RMSE was 0.0078) was feasible and highly accurate, indicating it could play an important role in predicting SMC of cultivated soils over a large area for agricultural irrigation and remote monitoring of crop yields. 2
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Agriculture (Switzerland)
dc.relation.isbasedon	10.3390/agriculture10070292
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0701 Agriculture, Land and Farm Management
dc.title	Research on inversion model of cultivated soil moisture content based on hyperspectral imaging analysis
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0701 Agriculture, Land and Farm Management
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2021-06-17T03:32:43Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	7

Abstract:

Based on hyperspectral imaging technology, rapid and efficient prediction of soil moisture content (SMC) can provide an essential basis for the formulation of precise agricultural programs (e.g., forestry irrigation and environmental management). To build an efficient inversion model of SMC, this paper collected 117 cultivated soil samples from the Chair Hill area and tested them using the GaiaSorter hyperspectral sorter. The collected soil reflectance dataset was preprocessed by wavelet transform, before the combination of competitive adaptive reweighted sampling algorithm and successive projections algorithm (CARS‐SPA) was used to select the bands optimally. Seven wavelengths of 695, 711, 736, 747, 767, 778, and 796 nm were selected and used as the factors of the SMC inversion model. The popular linear regression algorithm was employed to construct this model. The result indicated that the inversion model established by the multiple linear regression algorithm (the predicted R was 0.83 and the RMSE was 0.0078) was feasible and highly accurate, indicating it could play an important role in predicting SMC of cultivated soils over a large area for agricultural irrigation and remote monitoring of crop yields. 2

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http://hdl.handle.net/10453/149583