Evaluation of the Sensitivity of SMOS L-VOD to Forest Above-Ground Biomass at Global Scale

Arnaud Mialon (1) , Nemesio Rodriguez‐fernandez (1) , Maurizio Santoro (2) , Sassan Saatchi (3) , Stéphane Mermoz (1, 4) , Emma Bousquet (1) , Yann H. Kerr (1)

1 CESBIO - Centre d'études spatiales de la biosphère
2 Gamma Remote Sensing and WSL-Birmensdorf
3 JPL - Jet Propulsion Laboratory
4 globeo - globeo

The present study evaluates the L band Vegetation Optical Depth (L-VOD) derived from the Soil Moisture and Ocean Salinity (SMOS) satellite to monitor Above Ground Biomass (AGB) at a global scale. Although SMOS L-VOD has been shown to be a good proxy for AGB in Africa and Tropics, little is known about this relationship at large scale. In this study, we further examine this relationship at a global scale using the latest AGB maps from Saatchi et al. and GlobBiomass computed using data acquired during the SMOS period. We show that at a global scale the L-VOD from SMOS is well-correlated with the AGB estimates from Saatchi et al. and GlobBiomass with the Pearson's correlation coefficients (R) of 0.91 and 0.94 respectively. Although AGB estimates in Africa and the Tropics are well-captured by SMOS L-VOD (R > 0.9), the relationship is less straightforward for the dense forests over the northern latitudes (R = 0.32 and 0.69 with Saatchi et al. and GlobBiomass respectively). This paper gives strong evidence in support of the sensitivity of SMOS L-VOD to AGB estimates at a globale scale, providing an interesting alternative and complement to exisiting sensors for monitoring biomass evolution. These findings can further facilitate research on biomass now that SMOS is providing more than 10 years of data.

Mots clés: L-VOD; AGB (Above Ground Biomass); SMOS (Soil Moisture and Ocean Salinity)

Domaines: Interfaces continentales, environnement

Arnaud Mialon, Nemesio Rodriguez‐fernandez, Maurizio Santoro, Sassan Saatchi, Stéphane Mermoz, et al.. Evaluation of the Sensitivity of SMOS L-VOD to Forest Above-Ground Biomass at Global Scale. Remote Sensing, 2020, 12 (9), pp.1450. ⟨10.3390/rs12091450⟩. ⟨hal-02940874⟩