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Fabio Maselli

As seen in: Color Research & Application, MDPI
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Articles by Fabio Maselli

Use of Remote Sensing and Biogeochemical Modeling to Simulate the Impact of Climatic and Anthropogenic Factors on Forest Carbon Fluxes

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By Marta Chiesi, Luca Fibbi, Silvana Vanucci, Fabio Maselli
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Use of Remote Sensing and Biogeochemical Modelling to Simulate the Impact of Climatic and Anthropogenic Factors on Forest Carbon Fluxes

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By Marta Chiesi, Luca Fibbi, Silvana Vanucci, Fabio Maselli
| Preprints.org
Preprint Communication Version 1 This version is not peer-reviewed Version 1 : Received: 31 December 2023 / Approved: 2 January 2024 / Online: 2 January 2024 (08:36:08 CET) Chiesi, M.; Fibbi, L.; Vanucci, S.; Maselli, F. Use of Remote Sensing and Biogeochemical Modelling to Simulate the Impact of Climatic and Anthropogenic Factors on Forest Carbon Fluxes. Preprints 2024, 2024010057. https://doi.org/10.20944/preprints202401.0057.v1 Chiesi, M.; Fibbi, L.; Vanucci, S.; Maselli, F.
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Optimized application of BIOME-BGC for modeling the daily GPP of natural vegetation over peninsular Spain

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By Sergio Sanchez-Ruiz, Marta Chiesi, Luca Fibbi, Arnaud Carrara, Fabio Maselli, María Amparo Gilabert Verified
| Color Research & Application Verified
A methodology for improving the application of BIOME-BGC in peninsular Spain was developed focusing on the optimization of the rooting depth (z ), which is not available for the study area on a spatially distributed basis. The optimal z was identified by comparing daily gross primary production (GPP) simulations with varying z to GPP estimations from a production efficiency model previously optimized for and validated in the study area.
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