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Prediction of soil macro- and micro-elements in sieved and ground air-dried soils using laboratory-based hyperspectral imaging technique

journal contribution
posted on 05.09.2019, 00:00 by M Malmir, I Tahmasbian, Z Xu, MB Farrar, Shahla Hosseini BaiShahla Hosseini Bai
Hyperspectral image analysis in laboratory-based settings has the potential to estimate soil elements. This study aimed to explore the effects of soil particle size on element estimation using visible-near infrared (400–1000 nm) hyperspectral imaging. Images were captured from 116 sieved and ground soil samples. Data acquired from hyperspectral images (HSI) were used to develop partial least square regression (PLSR) models to predict soil available aluminum (Al), boron (B), calcium (Ca), copper (Cu), iron (Fe), potassium (K), magnesium (Mg), manganese (Mn), sodium (Na), phosphorus (P) and zinc (Zn). The soil available Al, Fe, K, Mn, Na and P were not predicted with high precision. However, the developed PLSR models predicted B (R2 CV = 0.62 and RMSECV = 0.15), Ca (R2 CV = 0.81 and RMSECV = 260.97), Cu (R2 CV = 0.74 and RMSECV = 0.27), Mg (R2 CV = 0.80 and RMSECV = 43.71) and Zn (R2 CV = 0.76 and RMSECV = 0.97) in sieved soils. The PLSR models using reflectance of ground soil were also developed for B (R2 CV = 0.53 and RMSECV = 0.16), Ca (R2 CV = 0.81 and RMSECV = 260.79), Cu (R2 CV = 0.73 and RMSECV = 0.29), Mg (R2 CV = 0.79 and RMSECV = 45.45) and Zn (R2 CV = 0.76 and RMSECV = 0.97). RMSE of different PLSR models, developed from sieved and ground soils for the corresponding elements did not significantly differ based on the Levene's test. Therefore, this study indicated that it was not necessary to grind soil samples to predict elements using HSI. © 2018 Elsevier B.V.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

340

Start Page

70

End Page

80

Number of Pages

11

eISSN

1872-6259

ISSN

0016-7061

Publisher

Elsevier BV

Language

en

Peer Reviewed

Yes

Open Access

No

Acceptance Date

27/12/2018

External Author Affiliations

University of the Sunshine Coast; Griffith University; Wagga Wagga Agricultural Institute; Tarbiat Modares University, Iran

Era Eligible

Yes

Journal

Geoderma