A Novel Exposed Coal Index Combining Flat Spectral Shape and Low Reflectance

Pan, Xiaoquan; Zhang, Peng; Guo, Shanchuan; Zhang, Wei; Xia, Zilong; Fang, Hong; Du, Peijun

doi:10.1109/tgrs.2023.3333568

IEEE Trans. Geosci. Remote Sensing

2023

DOI: 10.1109/tgrs.2023.3333568

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A Novel Exposed Coal Index Combining Flat Spectral Shape and Low Reflectance

Xiaoquan Pan,

Peng Zhang,

Shanchuan Guo

et al.

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2024

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Cited by 5 publications

References 43 publications

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Mapping annual dynamics of surface mining disturbances in the northeastern Tibetan Plateau using Landsat imagery and LandTrendr algorithm

Xu,

Wang,

Zhou

et al. 2024

Environ Monit Assess

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Mapping annual dynamics of surface mining disturbances in the northeastern Tibetan Plateau using Landsat imagery and LandTrendr algorithm

Xu,

Wang,

Zhou

et al. 2024

Environ Monit Assess

View full text Add to dashboard Cite

A novel index for exposed coal mapping using Landsat imagery

Yang,

He,

Zhang

et al. 2024

Ecological Indicators

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Spectral index for estimating leaf water content across diverse plant species using multiple viewing angles

Yasir,

Zhang,

Ren

et al. 2024

J. Appl. Rem. Sens.

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Understanding the impact of climate change on Earth presents a significant scientific challenge. Monitoring changes in terrestrial ecosystems, including leaf water content, is essential for assessing plant transpiration, water use efficiency, and physiological processes. Optical remote sensing, utilizing multi-angular reflectance measurements in the near infrared and shortwave infrared wavelengths, offers a precise method for estimating leaf water content. We propose and evaluate a new index based on multi-angular reflection, using 256 leaf samples from 10 plant species for calibration and 683 samples for validation. Hyperspectral indices derived from multi-angular spectra were assessed, facilitating efficient leaf water content analysis with minimal time and specific bands required. We investigate the relationship of leaf water content using spectral indices and apply linear and nonlinear regression models to calibration data, resulting in two indices for each indicator. The newly proposed indices, ðR 1 − R 2 Þ∕ðR 1 − R 3 Þ for linear and ðR 1905 − R 1840 Þ∕ðR 1905 − R 1875 Þ for nonlinear, demonstrate high coefficients of determination for leaf water content (>0.94) using multi-angular reflectance measurements. Published spectral indices exhibit weak relationships with our calibration dataset. The proposed leaf water content indices perform well, with an overall root mean square error of 0.0024 ðg∕cm 2 Þ and 0.0026 ðg∕cm 2 Þ for linear and nonlinear indices, respectively, validated by Leaf Optical Properties Experiment, ANGERS, and multiangular datasets. The ðR 1 − R 2 Þ∕ðR 1 − R 3 Þ bands show promise for leaf water content estimation. Future studies should encompass more plant species and field data.

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A Novel Exposed Coal Index Combining Flat Spectral Shape and Low Reflectance

Cited by 5 publications

References 43 publications

Mapping annual dynamics of surface mining disturbances in the northeastern Tibetan Plateau using Landsat imagery and LandTrendr algorithm

Mapping annual dynamics of surface mining disturbances in the northeastern Tibetan Plateau using Landsat imagery and LandTrendr algorithm

A novel index for exposed coal mapping using Landsat imagery

Spectral index for estimating leaf water content across diverse plant species using multiple viewing angles

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