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Observing terrestrial ecosystems and the carbon cycle from space.
David Schimel1, Ryan Pavlick, Joshua B Fisher
1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91101, USA.
Global Change Biology
|December 5, 2014
Summary
Understanding terrestrial carbon cycle feedbacks is crucial for climate prediction. Combining remote sensing with in situ data can reduce uncertainties in tropical and arctic/boreal regions, improving climate models.
Area of Science:
- Ecology
- Climate Science
- Remote Sensing
Background:
- Terrestrial ecosystem feedbacks significantly influence future climate, with high uncertainty in responses.
- Tropical and arctic/boreal regions may have disproportionately large carbon-climate feedbacks.
- Sparse in situ data in these critical biomes lead to uncertainties in carbon fluxes and ecosystem parameters.
Purpose of the Study:
- To analyze the spatial distribution of in situ data for carbon fluxes, stocks, and plant traits globally.
- To evaluate the potential of remote sensing to address data gaps in ecosystem properties and parameters.
- To reduce uncertainties in predicting tipping point feedbacks to climate.
Main Methods:
- Global spatial analysis of in situ carbon flux, stock, and plant trait data.
- Assessment of new satellite data products beyond greenness indices.
- Evaluation of remote sensing for observing ecosystem properties in data-sparse regions.
Main Results:
- New satellite data products can address spatial sampling gaps for specific ecosystem properties.
- Space-based techniques can reduce sampling bias and uncertainty in tropical and arctic/boreal environments.
- Significant increases in data are needed in critical regions for reliable change detection.
Conclusions:
- A strategic combination of remote sensing and in situ data is essential for characterizing ecosystem heterogeneity.
- Satellite observations provide dense spatio-temporal sampling crucial for understanding ecosystem structure and function.
- Improved data integration is key to reducing uncertainty in climate change prediction and tipping point analysis.
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