Improving the performance of 3-D radiative transfer model FLIGHT to simulate 
optical properties of a tree-grass ecosystem

Melendo-Vega, José Ramón; Martín, M. Pilar; Pacheco-Labrador, Javier; González-Cascón, Rosario; Moreno, Gerardo; Pérez, Fernando; Migliavacca, Mirco; García, Mariano; North, Peter; Riaño, David

doi:10.3390/rs10122061

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Improving the performance of 3-D radiative transfer model FLIGHT to simulate optical properties of a tree-grass ecosystem

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Pacheco-Labrador, Javier
Biosphere-Atmosphere Interactions and Experimentation, Dr. M. Migliavacca, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Migliavacca, Mirco
Biosphere-Atmosphere Interactions and Experimentation, Dr. M. Migliavacca, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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http://dx.doi.org/10.3390/rs10122061
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Citation

Melendo-Vega, J. R., Martín, M. P., Pacheco-Labrador, J., González-Cascón, R., Moreno, G., Pérez, F., et al. (2018). Improving the performance of 3-D radiative transfer model FLIGHT to simulate optical properties of a tree-grass ecosystem. Remote Sensing, 10(12): 2061. doi:10.3390/rs10122061.

Cite as: https://hdl.handle.net/21.11116/0000-0002-B666-E

Abstract

The 3-D Radiative Transfer Model (RTM) FLIGHT can represent scattering in open forest or
savannas featuring underlying bare soils. However, FLIGHT might not be suitable for multilayered
tree-grass ecosystems (TGE), where a grass understory can dominate the reflectance factor (RF)
dynamics due to strong seasonal variability and low tree fractional cover. To address this issue, we
coupled FLIGHT with the 1-D RTM PROSAIL. The model is evaluated against spectral observations
of proximal and remote sensing sensors: the ASD Fieldspec® 3 spectroradiometer, the Airborne
Spectrographic Imager (CASI) and the MultiSpectral Instrument (MSI) onboard Sentinel-2. We tested
the capability of both PROSAIL and PROSAIL+FLIGHT to reproduce the variability of different
phenological stages determined by 16-year time series analysis of Moderate Resolution Imaging
Spectroradiometer-Normalized Difference Vegetation Index (MODIS-NDVI). Then, we combined
concomitant observations of biophysical variables and RF to test the capability of the models to
reproduce observed RF. PROSAIL achieved a Relative Root Mean Square Error (RRMSE) between
6% to 32% at proximal sensing scale. PROSAIL+FLIGHT RRMSE ranged between 7% to 31% at
remote sensing scales. RRMSE increased in periods when large fractions of standing dead material
mixed with emergent green grasses —especially in autumn—; suggesting that the model cannot
represent the spectral features of this material. PROSAIL+FLIGHT improves RF simulation especially
in summer and at mid-high view angles.