Research on the calculation model of mid-infrared reflectivity of vegetation based on VIIRS data

Liqin He; Huan Su; Xin Jing; Hui Yang; Lei Lan; Yigen Zhong; ZhiFeng Xiao; Lei Yan

doi:10.1117/12.3032215

3 June 2024 Research on the calculation model of mid-infrared reflectivity of vegetation based on VIIRS data

Liqin He, Huan Su, Xin Jing, Hui Yang, Lei Lan, Yigen Zhong, ZhiFeng Xiao, Lei Yan

Proceedings Volume 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024); 1317018 (2024) https://doi.org/10.1117/12.3032215
Event: Third International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 2024, Wuhan, China

Abstract

Based on sensor VIIRS, this article establishes a nonlinear split-window model suitable for calculating vegetation reflectivity of the mid-infrared channel M12 of VIIRS. The model is based on the split-window algorithm and the idea of using two mid-infrared channels to invert the reflectivity of one channel. Analysis shows that the uncertainty caused by various error sources to the model is only 0.82%, so it is believed that this model is suitable for calculating the vegetation reflectivity of mid-infrared channel M12 of VIIRS. This article uses the split-window model to calculate the mid-infrared reflectivity of multiple vegetation research sites in China and its surrounding areas of VIIRS image. The reflectivity of dense vegetation are relatively stable and within a small range, while the reflectivity of low-sparse vegetation are large and have significant differences. These values are consistent with actual data, indicating that the mid-infrared reflectivity obtained by this research model has potential in evaluating large-scale forest volume.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liqin He, Huan Su, Xin Jing, Hui Yang, Lei Lan, Yigen Zhong, ZhiFeng Xiao, and Lei Yan "Research on the calculation model of mid-infrared reflectivity of vegetation based on VIIRS data", Proc. SPIE 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 1317018 (3 June 2024); https://doi.org/10.1117/12.3032215

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KEYWORDS

Reflectivity

Mid infrared

Atmospheric modeling

Vegetation

Solar radiation models

Atmospheric particles

Data modeling

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