Water-absorption-trough dewatering machine for estimation of organic carbon in moist soil
Creators
- 1. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044 (China)
- 2. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116 (China)
- 3. College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001 (China)
Description
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. 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.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.envpol.2021.117445Additional details
Identifiers
- DOI
- 10.1016/j.envpol.2021.117445;
- PII
- S0269749121010277;
Publishing Information
- Journal Title
- Environmental Pollution (1987)
- Journal Volume
- 284
- Journal Page Range
- vp.
- ISSN
- 0269-7491
- CODEN
- ENPOEK
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 54021397
- Subject category
- S54: ENVIRONMENTAL SCIENCES;
- Descriptors DEI
- ACCURACY; HUMIDITY; MACHINE LEARNING; SIMULATION; SOILS; SPECTRAL REFLECTANCE; SPECTROSCOPY; WATER REMOVAL
- Descriptors DEC
- ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING; MATHEMATICAL LOGIC; MOISTURE; OPTICAL PROPERTIES; PHYSICAL PROPERTIES; REMOVAL
Optional Information
- Copyright
- Copyright (c) 2021 Elsevier Ltd. All rights reserved.