Journal article
Prediction of soil macro- and micro-elements in sieved and ground air-dried soils using laboratory-based hyperspectral imaging technique
Geoderma, Vol.340, pp.70-80
2019
Abstract
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 (R2CV = 0.62 and RMSECV = 0.15), Ca (R2CV = 0.81 and RMSECV = 260.97), Cu (R2CV = 0.74 and RMSECV = 0.27), Mg (R2CV = 0.80 and RMSECV = 43.71) and Zn (R2CV = 0.76 and RMSECV = 0.97) in sieved soils. The PLSR models using reflectance of ground soil were also developed for B (R2CV = 0.53 and RMSECV = 0.16), Ca (R2CV = 0.81 and RMSECV = 260.79), Cu (R2CV = 0.73 and RMSECV = 0.29), Mg (R2CV = 0.79 and RMSECV = 45.45) and Zn (R2CV = 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.
Details
- Title
- Prediction of soil macro- and micro-elements in sieved and ground air-dried soils using laboratory-based hyperspectral imaging technique
- Authors
- Mohammad Malmir (Author) - Griffith UniversityIman Tahmasbian (Author) - Griffith UniversityZhihong Xu (Author) - Griffith UniversityMichael B Farrar (Author) - University of the Sunshine Coast - School of Science & EngineeringShahla Hosseini Bai (Author) - University of the Sunshine Coast
- Publication details
- Geoderma, Vol.340, pp.70-80
- Publisher
- Elsevier BV
- DOI
- 10.1016/j.geoderma.2018.12.049
- ISSN
- 0016-7061
- Organisation Unit
- School of Science, Technology and Engineering; School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Health and Sport Sciences - Legacy
- Language
- English
- Record Identifier
- 99450854202621
- Output Type
- Journal article
Metrics
3 File views/ downloads
523 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Soil Science
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites