%0 Journal Article %T Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis %+ Center for Spatial Technologies and Remote Sensing (CSTARS) %+ Univ Alberta, Dept Earth & Atmospher Sci, Earth Observat Syst Lab, Edmonton, AB T6G 2E3, Canada %+ Smithsonian Tropical Research Institute %+ Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA %+ Institut de Physique du Globe de Paris (IPGP) %+ University of California [Davis] (UC Davis) %A Cheng, Tao %A Rivard, Benoit %A Sanchez-Azofeifa, Arturo, G. %A Féret, Jean-Baptiste %A Jacquemoud, Stéphane %A Ustin, Susan %Z National Aeronautics & Space Administration (NASA)NNX09AN51GNNX11AD93GNatural Sciences and Engineering Research Council of Canada Inter-American Institute for Global Change Research (IAI) Collaborative Research Network program Geo 0452325CRN2-021 Tropi-Dry %< avec comité de lecture %@ 0924-2716 %J ISPRS Journal of Photogrammetry and Remote Sensing %I Elsevier %V 87 %P 28-38 %8 2014-01 %D 2014 %R 10.1016/j.isprsjprs.2013.10.009 %K Leaf mass per area %K Dry matter content %K Specific leaf area %K PROSPECT model %K Remote sensing %K Wavelet analysis %Z Sciences of the Universe [physics] %Z Sciences of the Universe [physics]/Astrophysics [astro-ph] %Z Sciences of the Universe [physics]/Astrophysics [astro-ph]/Earth and Planetary Astrophysics [astro-ph.EP]Journal articles %X Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices, namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (NDLMA).Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R-2: 0.51-0.82, p < 0.0001). The best robustness (R-2 = 0.74, RMSE = 18.97 g/m(2) and Bias = 0.12 g/m(2)) was obtained using a combination of two low-scale features (1639 nm, scale 4) and (2133 nm, scale 5), the first being predominantly important. The transferability of the wavelet-based predictive model to the whole measured database was either better than or comparable to those based on spectral indices. Additionally, only the wavelet-based model showed consistent predictive capabilities among the three measured data sets. In comparison, the models based on spectral indices were sensitive to site-specific data sets. Integrating the NDLMA spectral index and the two robust wavelet features improved the LMA prediction. One of the bands used by this spectral index, 1368 nm, was located in a strong atmospheric water absorption region and replacing it with the next available band (1340 nm) led to lower predictive accuracies. However, the two wavelet features were not affected by data quality in the atmospheric absorption regions and therefore showed potential for canopy-level investigations. The wavelet approach provides a different perspective into spectral responses to LMA variation than the traditional spectral indices and holds greater promise for implementation with airborne or space-borne imaging spectroscopy data for mapping canopy foliar dry biomass. (C) 2013 International Society for Photogrammetry and Remote Sensing %G English %L insu-02641557 %U https://insu.hal.science/insu-02641557 %~ INSU %~ UNIV-PARIS7 %~ AFRIQ %~ CNRS %~ IPGP %~ SORBONNE-UNIVERSITE %~ UNIV-PARIS %~ ALLIANCE-SU