Assimilating Sentinel-2 data in a modified vegetation photosynthesis and respiration model (VPRM) to improve the simulation of croplands CO2 fluxes in Europe
Bazzi, Hassan (Université Paris-Saclay)
Ciais, Philippe (Université Paris-Saclay)
Abbessi, Ezzeddine (Université Paris-Saclay)
Makowski, David (Université Paris-Saclay)
Santaren, Diego (Université Paris-Saclay)
Ceschia, Eric (Université de Toulouse)
Brut, Aurore (Université de Toulouse)
Tallec, Tiphaine (Université de Toulouse)
Buchmann, Nina (ETH Zurich)
Maier, Regine (ETH Zurich)
Acosta, Manuel (Academy of Sciences of the Czech Republic)
Loubet, Benjamin (Université Paris-Saclay)
Buysse, Pauline (Université Paris-Saclay)
Léonard, Joël (Université de Liège)
Bornet, Frédéric (Université de Liège)
Fayad, Ibrahim (Université Paris-Saclay)
Lian, Jinghui (Université Paris-Saclay)
Baghdadi, Nicolas (Université de Montpellier)
Segura Barrero, Ricard (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)
Brümmer, Christian (Thünen Institute of Climate-Smart Agriculture)
Schmidt, Marius (Jülich Research Centre)
Heinesch, Bernard (BioDynE research axis)
Mauder, Matthias (Technische Universität Dresden)
Gruenwald, Thomas (Technische Universität Dresden)

Data:	2024
Descripció:	24 pàg.
Resum:	In Europe, the heterogeneous features of crop systems with majority of small to medium sized agricultural holdings, and diversity of crop rotations, require high-resolution information to estimate cropland Net Ecosystem Exchange (NEE) and its two main components of Gross Ecosystem Exchange (GEE) and the Ecosystem Respiration (RECO). In this context, this paper presents an assimilation of high-resolution Sentinel-2 indices with eddy covariance measurements at selected European cropland flux sites in a new modified version of Vegetation Photosynthesis Respiration Model (VPRM). VRPM is a data-driven model simulating CO2 fluxes previously applied using satellite-derived vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study proposes a modification of the VPRM by including an explicit soil moisture stress function to the GEE and changing the equation of RECO. It also compares the model results driven by S2 indices instead of MODIS. The parameters of the VPRM model are calibrated using eddy-covariance data. All possible parameters optimization scenarios include the use of the initial version vs. the proposed modified VPRM, S2, or MODIS vegetation indices, and finally the choice of calibrating a single set of parameters against observations from all crop types, a set of parameters per crop type, or one set of parameters per site. Then, we focus the analysis on the improvement of the model with distinct parameters for different crop types vs. parameters optimized without distinction of crop types. Our main findings are: (1) the superiority of S2 vegetation indices over MODIS for cropland CO2 fluxes simulations, leading to a root mean squared error (RMSE) for NEE of less than 3. 5 μmolm-2s-1 with S2 compared to 5 μmolm-2s-1 with MODIS (2) better performances of the modified VPRM version leading to a significant improvement of RECO, and (3) better performances when the parameters are optimized per crop-type instead of for all crop types lumped together, with lower RMSE and Akaike information criterion (AIC), despite a larger number of parameters. Associated with the availability of crop-type land cover maps, the use of S2 data and crop-type modified VPRM parameterization presented in this study, provide a step forward for upscaling cropland carbon fluxes at European scale.
Nota:	Unidad de excelencia María de Maeztu CEX2019-000940-M
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Carbon Cycle ; Crop Types ; Data-driven Modelling ; Eddy covariance ; MODIS ; Sentinel-2 ; SDG 15 - Life on Land
Publicat a:	International journal of applied earth observation and geoinformation, Vol. 127 (March 2024) , art. 103666, ISSN 1872-826X

DOI: 10.1016/j.jag.2024.103666

24 p, 16.2 MB

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Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències > Institut de Ciència i Tecnologia Ambientals (ICTA) > Sostenibilitat i Prevenció Ambiental (Sostenipra)
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