## 5 Conclusions  
Estimating canopy pigments and LAI using radiative transfer inversion is challenging in highly heterogeneous environments. This study investigated the ability of LUT-based inversion of AVIRIS-C medium resolution (18 m) hyperspectral images to estimate LAI and biochemical properties (C\({}_{ab}\), Car) of low-foliage open canopies (mean LAI: 0.82 m\({}^{2}\)/m\({}^{2}\), mean CC: 47%) in a woodland savanna. The methodology followed during this study used almost no a priori knowledge, so as to be able to monitor the study site in its entirety over multiple dates and obtain more general results. Various inversion criteria were tested, relying either on spectral intervals or hyperspectral vegetation indices. Results from very different site locations in terms of LAI, canopy cover, and tree structure were consistent and showed good accuracy for LAI and leaf C\({}_{\text{ab}}\) and Car retrieval. These results are further strengthened by the temporal aspect of the estimations, with validation done using both summer and fall data from various years. Estimated LAI and C\({}_{ab}\) also showed a temporal pattern coherent with expected seasonal variations for dates when field data were not available. This study found that the best inversion criterion for LAI estimation is: NDVI (\(\frac{\rho_{0.833\text{$\mu$m}}-\rho_{0.677\text{$\mu$m}}}{\rho_{0.833\text{$ \mu$m}}+\rho_{0.677\text{$\mu$m}}}\)) difference; for leaf C\({}_{ab}\): GM\({}_{\text{$\_94b}}\) (\(\frac{\rho_{0.750\text{$\mu$m}}}{\rho_{0.550\text{$\mu$m}}}\)) difference; and for leaf Car: RMSE between pixel and LUT reflectances over the 0.5-0.55 \(\mu\)m spectral interval. These criteria showed results either comparable to or below previous studies done over denser canopies (RMSE for LAI: 0.22 m\({}^{2}\)/m\({}^{2}\); for C\({}_{ab}\): 5.21 \(\mu\)g/cm\({}^{2}\); for Car: 1.34 \(\mu\)g/cm\({}^{2}\)). Surprisingly, the vegetation indices presenting the best performances were not specifically designed to be soil-adjusted, which could indicate that the modeling within DART was sufficiently realistic when it came to ground reflectance, despite the ground being modeled as a lambertian surface with uniform reflectance. The results demonstrated that medium resolution (18m) hyperspectral imagery, combined with a 3D RTM only using a simple geometric representation of the canopy, is appropriate for the monitoring of vegetation bio-physicochemical parameters for low-LAI open broadleaved forests such as woodland savannas. In the future, the proposed estimation methodology should be further tested on another Californian site (San Joaquin Experimental Range, [[https://www.neonscience.org/field-sites/field-sites-map/SJER](https://www.neonscience.org/field-sites/field-sites-map/SJER)]([https://www.neonscience.org/field-sites/field-sites-map/SJER](https://www.neonscience.org/field-sites/field-sites-map/SJER))) to confirm these results. Estimations should also be extended to leaf water content and leaf mass per area so as to have a complete overview of the forest health over time. Conceptualization, K.A., S.U., and X.B. ; Data curation, T.M., K.A., and M.H. ; Formal analysis, T.M. ; Funding acquisition, K.A., S.U., and X.B. ; Investigation, T.M. ; Methodology, T.M. and K.A. ; Project administration, S.U. and X.B. ; Resources, K.A., M.H., and S.U. ; Software, T.M. ; Supervision, S.U. and X.B. ; Validation, T.M. ; Visualization, T.M. ; Writing--original draft, T.M., K.A., and M.H. ; Writing--review and editing, K.A., M.H., S.U., and X.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the Office Nationale d'Etudes et de Recherches Aerospatiales (ONERA) and by Region Occitanie. The authors are grateful to the CSTARS team for collecting and processing the field data, and to NASA JPL from providing AVIRIS data (NASA grant No. NNX12 AP08G). They also thank [PERSON] and [PERSON] from CESBIO for their insight and help concerning the DART simulations.