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TitleEvaluation of the information content of Medium Resolution Imaging Spectrometer (MERIS) data for regional leaf area index assessment
AuthorCanisius, F; Fernandes, R
SourceRemote Sensing of Environment vol. 119, 2015 p. 301-314,
Alt SeriesNatural Resources Canada, Contribution Series 20170142
Mediapaper; on-line; digital
File formatpdf
NTS30L; 30M; 30N; 31; 32B; 32C; 32D; 40I; 40J; 40O; 40P; 41A; 41B; 41G; 41H; 41I; 41J; 41O; 41P; 42A; 42B
AreaSoutheastern Ontario; Ottawa; Toronto
Lat/Long WENS -85.0000 -74.0000 49.0000 42.0000
Subjectsgeophysics; remote sensing; satellite imagery; vegetation; MERIS; Leaf Area Index (LAI); hyperspectral imagery; vegetation index; red edge; accuracy; algorithms; vegetation indices
Illustrationssatellite imagery; location maps; tables; graphs
ProgramAquifer Assessment & support to mapping, Groundwater Geoscience
AbstractSubstantial research has been conducted to derive Leaf Area Index (LAI), an essential climate variable, from satellite imageries acquired by moderate resolution optical sensors. The Medium Resolution Imaging Spectrometer (MERIS) is unique among such sensors in that it provides relatively high spectral (15 bands) and spatial (~300 m resolution) sampling within visible and near infraredwavelengths. A recent evaluation of four operationalMERIS LAI algorithms found that they did not consistently meet accuracy targets typical of operational requirements.
One explanation for the mixed performance of these algorithms may be that they do not suitably exploit the enhanced spectral sampling ofMERIS.Weexploit this enhanced spectral sampling to estimate several (80) narrowband vegetation indices (VIs) by interpolating MERIS surface reflectance. The interpolation accuracy was evaluated using Hyperion imagery. Regressions were then calibrated between estimated VIs and in-situ LAI over a range of land cover types. The strongest performance (root mean squared errorb0.92 and relative root mean squared errorb0.38) was observed for two selected VIs (the NDVI8 and the CTR) based on both training and validation data. This study demonstrates that MERIS has the information content to meet typical operational performance specifications for LAI retrieval within the 1 unit error margin for the given atmospheric, environmental, soil and plant cover conditions on the day of the overpass and using locally derived relationships. Therefore the development of robust algorithms for retrieving LAI using these VIs is recommended.