Observing terrestrial ecosystems and the carbon cycle from space

• Published in: Global change biology 2015-05, Vol.21 (5), p.1762-1776
• Main Authors: Schimel, David, Pavlick, Ryan, Fisher, Joshua B, Asner, Gregory P, Saatchi, Sassan, Townsend, Philip, Miller, Charles, Frankenberg, Christian, Hibbard, Kathy, Cox, Peter
• Format: Article
• Language: English
• Subjects: arctic; boreal; Carbon; Carbon cycle; Carbon Cycle - physiology; Chlorophyll - analysis; climate; climate feedback; data analysis; diversity; Ecosystem; Ecosystems; fluroescence; global; Lignin - analysis; Models, Theoretical; Nitrogen - analysis; Plants - chemistry; Remote sensing; Satellite Imagery - methods; Satellite Imagery - trends; space; spatial distribution; spectroscopy; Terrestrial; terrestrial ecosystems; tropics; uncertainty
• ISSN: 1354-1013; 1365-2486
• DOI: 10.1111/gcb.12822

Terrestrial ecosystem and carbon cycle feedbacks will significantly impact future climate, but their responses are highly uncertain. Models and tipping point analyses suggest the tropics and arctic/boreal zone carbon–climate feedbacks could be disproportionately large. In situ observations in those regions are sparse, resulting in high uncertainties in carbon fluxes and fluxes. Key parameters controlling ecosystem carbon responses, such as plant traits, are also sparsely observed in the tropics, with the most diverse biome on the planet treated as a single type in models. We analyzed the spatial distribution of in situ data for carbon fluxes, stocks and plant traits globally and also evaluated the potential of remote sensing to observe these quantities. New satellite data products go beyond indices of greenness and can address spatial sampling gaps for specific ecosystem properties and parameters. Because environmental conditions and access limit in situ observations in tropical and arctic/boreal environments, use of space‐based techniques can reduce sampling bias and uncertainty about tipping point feedbacks to climate. To reliably detect change and develop the understanding of ecosystems needed for prediction, significantly, more data are required in critical regions. This need can best be met with a strategic combination of remote and in situ data, with satellite observations providing the dense sampling in space and time required to characterize the heterogeneity of ecosystem structure and function.