Published September 2021 | Version v1
Journal article

Water-absorption-trough dewatering machine for estimation of organic carbon in moist soil

  • 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.2021.117445

Additional 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.