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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-4/W1-2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-301-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-301-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
06 Aug 2022
| 06 Aug 2022
SPECTRAL: AWESOME SPECTRAL INDICES DEPLOYED VIA THE GOOGLE EARTH ENGINE JAVASCRIPT API
D. Montero
Remote Sensing Centre for Earth Systems Research (RSC4Earth), University of Leipzig, Talstraße 35, 04317 Leipzig, Germany
C. Aybar
Department of Geoinformatics (Z GIS), University of Salzburg, 5020 Salzburg, Austria
M. D. Mahecha
Remote Sensing Centre for Earth Systems Research (RSC4Earth), University of Leipzig, Talstraße 35, 04317 Leipzig, Germany
Helmholtz Centre for Environmental Research, Leipzig, 04318 Leipzig, Germany
S. Wieneke
Remote Sensing Centre for Earth Systems Research (RSC4Earth), University of Leipzig, Talstraße 35, 04317 Leipzig, Germany
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Iulia Ilie, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, Sebastian Sippel, Jens-Arne Subke, Matthew Wilkinson, and Miguel D. Mahecha
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Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
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