Earthshine observation of vegetation and implication for life detection on other planets - A review of 2001 - 2006 works

The detection of exolife is one of the goals of very ambitious future space missions that aim to take direct images of Earth-like planets. While associations of simple molecules present in the planet's atmosphere ($O_2$, $O_3$, $CO_2$ etc.) have been identified as possible global biomarkers, we review here the detectability of a signature of life from the planet's surface, i.e. the green vegetation. The vegetation reflectance has indeed a specific spectrum, with a sharp edge around 700 nm, known as the "Vegetation Red Edge" (VRE). Moreover vegetation covers a large surface of emerged lands, from tropical evergreen forest to shrub tundra. Thus considering it as a potential global biomarker is relevant. Earthshine allows to observe the Earth as a distant planet, i.e. without spatial resolution. Since 2001, Earthshine observations have been used by several authors to test and quantify the detectability of the VRE in the Earth spectrum. The egetation spectral signature is detected as a small 'positive shift' of a few percents above the continuum, starting at 700 nm. This signature appears in most spectra, and its strength is correlated with the Earth's phase (visible land versus visible ocean). The observations show that detecting the VRE on Earth requires a photometric relative accuracy of 1% or better. Detecting something equivalent on an Earth-like planet will therefore remain challenging, moreover considering the possibility of mineral artifacts and the question of 'red edge' universality in the Universe.Comment: Invited talk in "Strategies for Life Detection" (ISSI Bern, 24-28 April 2006) to appear in a hardcopy volume of the ISSI Space Science Series, Eds, J. Bada et al., and also in an issue of Space Science Reviews. 13 pages, 8 figures, 1 tabl

Contribuições do solo e dossel em modelo de estimativa de biomassa aérea no Bioma Pampa Soil and canopy contributions in a predictive model of aerial biomass in the Pampa Biome

O objetivo deste trabalho foi avaliar o desempenho preditivo do submodelo espectral do modelo JONG, com a inserção de variáveis espectrais que considerassem a densidade de biomassa do dossel e as contribuições dos diferentes solos subjacentes. Índices calculados pela diferença e razão simples - entre as bandas 4 e 3, 4 e 5, 4 e 7, do sensor orbital ETM+/Landsat 7 - foram sugeridos para representar a contribuição espectral dos solos subjacentes e a influência das diferenças estruturais dos dosséis. A parametrização da componente espectral foi implementada por regressão linear múltipla e, em seguida, foi comparada aos dados de biomassa obtidos em campo. As variáveis espectrais que melhor expressaram as variações da disponibilidade inicial de forragem foram a fração solo (modelo linear de mistura espectral) e a razão entre as bandas 4 e 7. A componente espectral do modelo JONG, com a nova parametrização, apresenta sensibilidade para eliminar as influências do solo e dossel na disponibilidade inicial de biomassa e facilita a interpretação dos resultados, em razão da relação entre as variáveis espectrais selecionadas.<br>The objective of this work was to evaluate the predictive performance of the JONG model's spectral submodel, with the insertion of variables considering contributions of different underlying soils and canopy densities. Indices calculated by subtraction and simple ratio between 4 and 3, 4 and 5, 4 and 7 bands, of Landsat 7/ETM+ sensor - were suggested in order to represent the spectral contribution of the different underlying soils and the influence of canopy structural differences. The spectral component parameterization was implemented by multiple linear regression and, then, it was compared to the biomass data measured in the field. Spectral variables that better describe the variations of initial biomass availability and soil spectral contributions were the soil fraction (spectral mixture linear model), and ratio between 4 and 7 bands. The spectral component of the JONG model, with the new parameterization, showed sensibility in eliminating the canopy and soil influences in the biomass initial availability and, also, improved the interpretation of results due to the relationship between selected spectral variables