doi:10.3390/rs10122061engMelendo-Vega, J.R.Martín, M.P.Pacheco-Labrador, J.González-Cascón, R.Moreno, G.Pérez, F.Migliavacca, M.García, M.North, P.Riaño, D.Improving the performance of 3-D radiative transfer model FLIGHT to simulate optical properties of a tree-grass ecosystemART-2018-109864The 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.2018http://zaguan.unizar.es/record/7697310.3390/rs10122061http://zaguan.unizar.es/record/76973oai:zaguan.unizar.es:76973info:eu-repo/grantAgreement/ES/MINECO/CGL2015-G9095-Rinfo:eu-repo/grantAgreement/ES/MINECO/CGL2012-34383info:eu-repo/grantAgreement/ES/MEC/FPU15-03558Remote Sensing 10, 12 (2018), 2061 [33 pp]byhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccess