Angular Spectral Index Can Reduce the Effect of Specular Reflection on Leaf Chlorophyll Content Estimation

Chlorophyll has been considered as an indicator related to photosynthesis and the vegetative growth process. The advent of high spatial resolution remote sensing provides an efficient way to accurately estimate chlorophyll content in different scales. Due to the combination of the field of view of sensors and varied leaf angles, the measured signals under the high spatial resolution, in fact, are composed of the multi-angular reflection of leaves. Specular reflection, which generally has an additive effect on the spectrum and keeps the shape of the spectrum invariant with the varying viewing angles, dominates the multi-angular reflection of leaves and drastically reduces accuracy of LCC assessment for most spectral indices. In this study, we proposed a novel angular spectral index (ASI) based on multi-angular data, defined by a triangle formed by three wavelengths (550, 720, and 755 nm), to estimate LCC. ASI provided a good and stable LCC estimation at different angles (The RMSE at 0° is 6.98 μg/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, and -40° is 7.05 μg/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>). Furthermore, ASI was tested by the closed-range hyperspectral imaging data of leaves and it demonstrated optimal estimation capability (RRMSE = 20.29%). ASI provides an effective method for accurately estimating LCC by reducing the effect of specular reflection.

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Publication Details

Journal
IEEE Transactions on Geoscience and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/tgrs.2025.3602884
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Angular Spectral Index Can Reduce the Effect of Specular Reflection on Leaf Chlorophyll Content Estimation

P. Wang, Zhongqiu Sun, Shan Lu
IEEE Transactions on Geoscience and Remote Sensing
Remote Sensing in Agriculture
article

Angular Spectral Index Can Reduce the Effect of Specular Reflection on Leaf Chlorophyll Content Estimation

P. Wang, Zhongqiu Sun, Shan Lu
article en

Abstract

Chlorophyll has been considered as an indicator related to photosynthesis and the vegetative growth process. The advent of high spatial resolution remote sensing provides an efficient way to accurately estimate chlorophyll content in different scales. Due to the combination of the field of view of sensors and varied leaf angles, the measured signals under the high spatial resolution, in fact, are composed of the multi-angular reflection of leaves. Specular reflection, which generally has an additive effect on the spectrum and keeps the shape of the spectrum invariant with the varying viewing angles, dominates the multi-angular reflection of leaves and drastically reduces accuracy of LCC assessment for most spectral indices. In this study, we proposed a novel angular spectral index (ASI) based on multi-angular data, defined by a triangle formed by three wavelengths (550, 720, and 755 nm), to estimate LCC. ASI provided a good and stable LCC estimation at different angles (The RMSE at 0° is 6.98 μg/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, and -40° is 7.05 μg/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>). Furthermore, ASI was tested by the closed-range hyperspectral imaging data of leaves and it demonstrated optimal estimation capability (RRMSE = 20.29%). ASI provides an effective method for accurately estimating LCC by reducing the effect of specular reflection.

IEEE Transactions on Geoscience and Remote SensingVol. 63
Northeast Normal University (CN)
Climate action
Openalex Percentile: Top 78%
Remote Sensing in Agriculture
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Angular Spectral Index Can Reduce the Effect of Specular Reflection on Leaf Chlorophyll Content Estimation — P. Wang, Zhongqiu Sun, et al. · IEEE Transactions on Geoscience and Remote Sensing (2025) | TGRS Research Map | TGRS