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Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data
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Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data
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Journal Article
Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data
2025
Overview
Individual inversions of Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) have problems due to the mutual interference between these two vegetation parameters on remote sensing signals. We therefore explore synergetic inversion of these two parameters to improve their inversion accuracy. We selected subtropical forest plantations, where canopy reflectance data were collected using a DJI Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) every month during 2021-2022. Monthly in-situ observations of LAI and Clumping Index (CI) were also made in 23 broadleaf tree plots of dimension 12 m × 12 m. Vegetation Indices (VI) were calculated with the mean reflectance of all pixels at 0.06 m resolution within each sampling plot, and only those VIs with highest sensitivities to LAI or LCC were selected and correlated to LAI and LCC. An empirical model in the form of VI = f(LAI, LCC) was constructed for synergetic inversion of LAI and LCC. For the purpose of comparison, two models VI = f(LAI) and VI = f(LCC) were also constructed and used for the inversions of LAI and LCC, separately. The synergetic inversion model yields R
2
= 0.60 and RMSE = 2.80 cm
2
/cm
2
for LAI and R
2
= 0.45 and RMSE = 32.71 μg/cm
2
for LCC, whereas the separate inversion models result in R
2
= 0.59 and RMSE = 2.82 cm
2
/cm
2
for LAI and R
2
= 0.35 and RMSE = 35.86 μg/cm
2
for LCC. Moreover, we found that the inclusion of VIs containing a red edge band in the synergetic inversion can effectively improve the inversion accuracy. The proposed synergetic inversion method based on multiple VIs would be an effective way to separate the mutual interference between LAI and LCC and improve the accuracy of LCC inversion from remote sensing data.
Publisher
Taylor & Francis,Taylor & Francis Ltd,Department of Geography and Planning,University of Toronto,Toronto,Canada%School of Geographical Sciences,Fujian Normal University,Fuzhou,China,Key Laboratory of Humid Subtropical Eco-Geographical Process,Ministry of Education,Fujian Normal University,Fuzhou,China,School of Geographical Sciences,Fujian Normal University,Fuzhou,China,Key Laboratory of Humid Subtropical Eco-Geographical Process,Ministry of Education,Fujian Normal University,Fuzhou,China%School of Geographical Sciences,Fujian Normal University,Fuzhou,China,Taylor & Francis Group
Subject
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