Water-absorption-trough dewatering machine for estimation of organic carbon in moist soil. (1st September 2021)
- Record Type:
- Journal Article
- Title:
- Water-absorption-trough dewatering machine for estimation of organic carbon in moist soil. (1st September 2021)
- Main Title:
- Water-absorption-trough dewatering machine for estimation of organic carbon in moist soil
- Authors:
- Lin, Lixin
Wang, Yunjia
Liu, Xixi
Zhang, Xinyu - Abstract:
- Abstract: Quantitative estimation of soil organic carbon (SOC) is essential for the study of the C cycle and global C storage. Soil spectroscopic technology provides a cost-effective and time-efficient method for SOC quantification and has been successfully used to determine SOC storage. However, the SOC estimation accuracy remains limited by other soil properties, particularly soil water. In this study, we proposed a new deep learning algorithm named the Water Absorption Trough Dewatering Machine (WATDM) to improve estimations of SOC from soil reflectance spectra and reduce the effect of soil water. Soil water and reflectance spectral data of soil samples were measured using spectrometry. Based on the soil water contents derived from the water absorption troughs around 1900 nm, the optimal WATDM model was obtained and treated as the final model of the WATDM method, which performed better than a multiple linear regression model based on moist soil samples. The findings of this study indicate that the WATDM method can improve the estimation accuracy of SOC content by reducing the effect of soil water and can be used as a valuable new methodology within the spectroscopic estimation of soil properties. Graphical abstract: Image 1 Highlights: The water-absorption-trough dewatering machine (WATDM) method was proposed. This method was used to improve the accuracy for SOC estimated using spectroscopy. The water absorption troughs around 1400 and 1900 nm were used to obtain waterAbstract: Quantitative estimation of soil organic carbon (SOC) is essential for the study of the C cycle and global C storage. Soil spectroscopic technology provides a cost-effective and time-efficient method for SOC quantification and has been successfully used to determine SOC storage. However, the SOC estimation accuracy remains limited by other soil properties, particularly soil water. In this study, we proposed a new deep learning algorithm named the Water Absorption Trough Dewatering Machine (WATDM) to improve estimations of SOC from soil reflectance spectra and reduce the effect of soil water. Soil water and reflectance spectral data of soil samples were measured using spectrometry. Based on the soil water contents derived from the water absorption troughs around 1900 nm, the optimal WATDM model was obtained and treated as the final model of the WATDM method, which performed better than a multiple linear regression model based on moist soil samples. The findings of this study indicate that the WATDM method can improve the estimation accuracy of SOC content by reducing the effect of soil water and can be used as a valuable new methodology within the spectroscopic estimation of soil properties. Graphical abstract: Image 1 Highlights: The water-absorption-trough dewatering machine (WATDM) method was proposed. This method was used to improve the accuracy for SOC estimated using spectroscopy. The water absorption troughs around 1400 and 1900 nm were used to obtain water contents. The WATDM method can quickly reduce the soil-water's effect during SOC modeling. … (more)
- Is Part Of:
- Environmental pollution. Volume 284(2021)
- Journal:
- Environmental pollution
- Issue:
- Volume 284(2021)
- Issue Display:
- Volume 284, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 284
- Issue:
- 2021
- Issue Sort Value:
- 2021-0284-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-09-01
- Subjects:
- Deep learning -- Water absorption trough dewatering machine -- Soil organic carbon
Pollution -- Periodicals
Pollution -- Environmental aspects -- Periodicals
Environmental Pollution -- Periodicals
Pollution -- Périodiques
Pollution -- Aspect de l'environnement -- Périodiques
Pollution -- Effets physiologiques -- Périodiques
Pollution
Pollution -- Environmental aspects
Periodicals
Electronic journals
363.73 - Journal URLs:
- http://www.sciencedirect.com/science/journal/02697491 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.envpol.2021.117445 ↗
- Languages:
- English
- ISSNs:
- 0269-7491
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3791.539000
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