@Article{FéretMJBBHCOPSBCNPPSGL:2019:PoLiPh,
               author = "F{\'e}ret, J. B. and Maire, G. le and Jay, S. and Berveiller, D. 
                         and Bendoula, R. and Hmimina, G. and Cheraiet, A. and Oliveira, J. 
                         C. and Ponzoni, Fl{\'a}vio Jorge and Solanki, T. and Boissieu, F. 
                         de and Chave, J. and Nouvellon, Y. and Porcar-Castell, A. and 
                         Proisy, C. and Soudani, K. and Gastellu-Etchegorry, J. P. and 
                         Lef{\'e}vre-Fonollosa, M. J.",
          affiliation = "{Universit{\'e} Montpellier} and CIRAD, UMR ECO\&SOL and {Aix 
                         Marseille Univ} and {University of Paris-Sud} and {Universit{\'e} 
                         Montpellier} and {University of Paris-Sud} and {University of 
                         Paris-Sud} and {Universidade Estadual de Campinas (UNICAMP0} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {University 
                         of Helsinki} and {Universit{\'e} Montpellier} and 
                         {Universit{\'e} Paul Sabatier} and CIRAD, UMR ECO\&SOL and 
                         {University of Helsinki} and {Univ. Montpellier} and {University 
                         of Paris-Sud} and {Centre d'Etudes Spatiales de la Biosph{\`e}re} 
                         and CNES",
                title = "Estimating leaf mass per area and equivalent water thickness based 
                         on leaf optical properties: Potential and limitations of physical 
                         modeling and machine learning",
              journal = "Remote Sensing of Environment",
                 year = "2019",
               volume = "231",
                pages = "e110959",
                month = "Sept.",
             keywords = "Biophysical properties, Leaf spectroscopy, EWT, LMA, Radiative 
                         transfer model, Support vector machine, Vegetation.",
             abstract = "Leaf mass per area (LMA) and leaf equivalent water thickness (EWT) 
                         are key leaf functional traits providing information for many 
                         applications including ecosystem functioning modeling and fire 
                         risk management. In this paper, we investigate two common 
                         conclusions generally made for LMA and EWT estimation based on 
                         leaf optical properties in the near-infrared (NIR) and shortwave 
                         infrared (SWIR) domains: (1) physically-based approaches estimate 
                         EWT accurately and LMA poorly, while (2) statistically-based and 
                         machine learning (ML) methods provide accurate estimates of both 
                         LMA and EWT. Using six experimental datasets including broadleaf 
                         species samples of >150 species collected over tropical, temperate 
                         and boreal ecosystems, we compared the performances of a 
                         physically-based method (PROSPECT model inversion) and a ML 
                         algorithm (support vector machine regression, SVM) to infer EWT 
                         and LMA based on leaf reflectance and transmittance. We assessed 
                         several merit functions to invert PROSPECT based on iterative 
                         optimization and investigated the spectral domain to be used for 
                         optimal estimation of LMA and EWT. We also tested several 
                         strategies to select the training samples used by the SVM, in 
                         order to investigate the generalization ability of the derived 
                         regression models. We evidenced that using spectral information 
                         from 1700 to 2400 nm leads to strong improvement in the estimation 
                         of EWT and LMA when performing a PROSPECT inversion, decreasing 
                         the LMA and EWT estimation errors by 55% and 33%, respectively. 
                         The comparison of various sampling strategies for the training set 
                         used with SVM suggests that regression models show limited 
                         generalization ability, particularly when the regression model is 
                         applied on data fully independent from the training set. Finally, 
                         our results demonstrate that, when using an appropriate spectral 
                         domain, the PROSPECT inversion outperforms SVM trained with 
                         experimental data for the estimation of EWT and LMA. Thus we 
                         recommend that estimation of LMA and EWT based on leaf optical 
                         properties should be physically-based using inversion of 
                         reflectance and transmittance measurements on the 1700 to 2400 nm 
                         spectral range.",
                  doi = "10.1016/j.rse.2018.11.002",
                  url = "http://dx.doi.org/10.1016/j.rse.2018.11.002",
                 issn = "0034-4257",
             language = "en",
           targetfile = "feret_estimating.pdf",
        urlaccessdate = "28 abr. 2024"
}