Inversion analysis of soil nitrogen content using hyperspectral images with different preprocessing methods

Jiachen, He; Jing, He; Gang, Liu; Weile, Li; Zhe, Li; Zhi, Li

doi:10.1016/j.ecoinf.2023.102381

Ecological Informatics

2023

DOI: 10.1016/j.ecoinf.2023.102381

|View full text |Cite

Inversion analysis of soil nitrogen content using hyperspectral images with different preprocessing methods

He Jiachen,

He Jing,

Liu Gang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 6 publications

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Exploring the optimal model for assessing SOC and TN in Zanthoxylum bungeanum forest on the Loess Plateau using VNIR spectroscopy

Hou,

Ai,

et al. 2024

Ecological Informatics

View full text Add to dashboard Cite

Exploring the optimal model for assessing SOC and TN in Zanthoxylum bungeanum forest on the Loess Plateau using VNIR spectroscopy

Hou,

Ai,

et al. 2024

Ecological Informatics

View full text Add to dashboard Cite

Accurate estimation of Jujube leaf chlorophyll content using optimized spectral indices and machine learning methods integrating geospatial information

Tuerxun,

Naibi,

Zheng

et al. 2024

Ecological Informatics

View full text Add to dashboard Cite

Hyperspectral Imaging Predicts Differences in Carbon and Nitrogen Status Among Representative Biocrust Functional Groups of the Colorado Plateau

Yan,

Reed,

Rutherford

et al. 2024

JGR Biogeosciences

View full text Add to dashboard Cite

Biological soil crusts (biocrusts) are widespread soil photosynthetic communities covering about 12% of Earth's land surface, and play crucial roles in terrestrial carbon (C) and nitrogen (N) cycles, yet scalable quantifications of biocrusts and their biogeochemical contributions are notably lacking. While remote sensing has enormous potential to assess, scale, and contextualize biocrusts and their functions, the applicability of hyperspectral data in predicting C‐ and N‐related biocrust traits remains largely unexplored. We address this issue by evaluating the potential of in situ hyperspectral data to predict C and N across a range of biocrust species and different environmental conditions. We found that in situ hyperspectral reflectance measurements can be used to predict biocrust tissue C/N ratios and N concentrations with relatively high accuracy but to a lesser extent for potential biocrust N2 fixation rates. Critical wavelength domains included the visible region of the spectrum from roughly 490–600 nm, which most effectively captured variations in biocrust tissue C, and the shortwave infrared region from 1,150 to 1,350 nm and 1,550–1,650 nm, which most effectively captured biocrust tissue N and N2 fixation potential. Finally, we provide evidence that multi‐ and hyperspectral missions with targeted band placement, such as the proposed 26‐band Landsat Next, could be effective in predicting biocrust traits. This work provides a critical step in understanding how to apply data from new and upcoming satellite missions to the monitoring of biocrusts.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Inversion analysis of soil nitrogen content using hyperspectral images with different preprocessing methods

Cited by 6 publications

References 51 publications

Exploring the optimal model for assessing SOC and TN in Zanthoxylum bungeanum forest on the Loess Plateau using VNIR spectroscopy

Exploring the optimal model for assessing SOC and TN in Zanthoxylum bungeanum forest on the Loess Plateau using VNIR spectroscopy

Accurate estimation of Jujube leaf chlorophyll content using optimized spectral indices and machine learning methods integrating geospatial information

Hyperspectral Imaging Predicts Differences in Carbon and Nitrogen Status Among Representative Biocrust Functional Groups of the Colorado Plateau

Contact Info

Product

Resources

About