Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis | INSTITUT DE PHYSIQUE DU GLOBE DE PARIS

Twitter

Aller au compte twitter

  Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis

Type de publication:

Journal Article

Source:

ISPRS Journal of Photogrammetry and Remote Sensing, Volume 87, p.28-38 (2014)

URL:

http://www.sciencedirect.com/science/article/pii/S0924271613002311

Mots-clés:

Dry matter content, Leaf mass per area, PROSPECT model, Remote sensing, Specific leaf area, Wavelet analysis

Résumé:

<p id="sp0005">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 (ND<sub>LMA</sub>).</p><p id="sp0010">Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (<em>R</em><sup>2</sup>: 0.51–0.82, <em>p&nbsp;</em>&lt;&nbsp;0.0001). The best robustness (<em>R</em><sup>2</sup>&nbsp;=&nbsp;0.74, RMSE&nbsp;=&nbsp;18.97&nbsp;g/m<sup>2</sup> and Bias&nbsp;=&nbsp;0.12&nbsp;g/m<sup>2</sup>) was obtained using a combination of two low-scale features (1639&nbsp;nm, scale 4) and (2133&nbsp;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 ND<sub>LMA</sub> spectral index and the two robust wavelet features improved the LMA prediction. One of the bands used by this spectral index, 1368&nbsp;nm, was located in a strong atmospheric water absorption region and replacing it with the next available band (1340&nbsp;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 spaceborne imaging spectroscopy data for mapping canopy foliar dry biomass.</p>