| Title | Evaluation of the information content of Medium Resolution Imaging Spectrometer (MERIS) data for regional leaf area index assessment
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| Author | Canisius, F; Fernandes, R |
| Source | Remote Sensing of Environment vol. 119, 2015 p. 301-314, https://doi.org/10.1016/j.rse.2011.10.013 |
| Image |  |
| Year | 2015 |
| Alt Series | Natural Resources Canada, Contribution Series 20170142 |
| Publisher | Elsevier BV |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Province | Ontario |
| NTS | 30L; 30M; 30N; 31; 32B; 32C; 32D; 40I; 40J; 40O; 40P; 41A; 41B; 41G; 41H; 41I; 41J; 41O; 41P; 42A; 42B |
| Area | Southeastern Ontario; Ottawa; Toronto |
| Lat/Long WENS | -85.0000 -74.0000 49.0000 42.0000 |
| Subjects | geophysics; remote sensing; satellite imagery; vegetation; MERIS; Leaf Area Index (LAI) |
| Illustrations | satellite imagery; location maps; tables; graphs |
| Program | Groundwater Geoscience Aquifer Assessment & support to mapping |
| Released | 2012 04 01 |
| Abstract | Substantial 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. |
| GEOSCAN ID | 305348 |
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