Comparison of contemporaneous Sentinel-2 and EnMAP data for vegetation index-based estimation of leaf area index and canopy closure of a boreal forest.

IF 3.7 4区 地球科学 Q2 REMOTE SENSING
European Journal of Remote Sensing Pub Date : 2024-11-27 eCollection Date: 2024-01-01 DOI:10.1080/22797254.2024.2432975
Jussi Juola, Aarne Hovi, Miina Rautiainen
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引用次数: 0

Abstract

Data from the new hyperspectral satellite missions such as EnMAP are anticipated to refine leaf area index (LAI) or canopy closure (CC) monitoring in conifer-dominated forest areas. We compared contemporaneous multispectral and hyperspectral satellite images from Sentinel-2 MSI (S2) and EnMAP and assessed whether hyperspectral images offer added value in estimating LAI, effective LAI (LAIeff), and CC in a European boreal forest area. The estimations were performed using univariate and multivariate generalized additive models. The models utilized field measurements of LAI and CC from 38 forest plots and an extensive set of vegetation indices (VIs) derived from the satellite data. The best univariate models for each of the three response variables had small differences between the two sensors, but in general, EnMAP had more well-performing VIs which was reflected in the better multivariate model performances. The best performing multivariate models with the EnMAP data had ~1-6% lower relative RMSEs than S2. Wavelengths near the green, red-edge, and shortwave infrared regions were frequently utilized in estimating LAI, LAIeff, and CC with EnMAP data. Because EnMAP could estimate LAI better, the results suggest that EnMAP may be more useful than multispectral satellite sensors, such as S2, in monitoring biophysical variables of coniferous-dominated forests.

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来源期刊
CiteScore
7.00
自引率
2.50%
发文量
51
审稿时长
>12 weeks
期刊介绍: European Journal of Remote Sensing publishes research papers and review articles related to the use of remote sensing technologies. The Journal welcomes submissions on all applications related to the use of active or passive remote sensing to terrestrial, oceanic, and atmospheric environments. The most common thematic areas covered by the Journal include: -land use/land cover -geology, earth and geoscience -agriculture and forestry -geography and landscape -ecology and environmental science -support to land management -hydrology and water resources -atmosphere and meteorology -oceanography -new sensor systems, missions and software/algorithms -pre processing/calibration -classifications -time series/change analysis -data integration/merging/fusion -image processing and analysis -modelling European Journal of Remote Sensing is a fully open access journal. This means all submitted articles will, if accepted, be available for anyone to read anywhere, at any time, immediately on publication. There are no charges for submission to this journal.
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