Estimation of daily CO2 fluxes and of the components of the carbon budget for winter wheat by the assimilation of Sentinel 2-like remote sensing data into a crop model. Pique, G., Fieuzal, R., Al Bitar, A., Veloso, A., Tallec, T., Brut, A., Ferlicoq, M., Zawilski, B., Dejoux, J., F., Gibrin, H., & Ceschia, E. Geoderma, 376(June):114428, Elsevier, 2020. ![Estimation of daily CO2 fluxes and of the components of the carbon budget for winter wheat by the assimilation of Sentinel 2-like remote sensing data into a crop model [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex Croplands contribute to greenhouse gas emissions but also have the potential to mitigate climate change through soil carbon storage. However, there is a lack of tools based on objective observations for assessing cropland C budgets at the plot scale over large areas. Such tools would allow us to more precisely establish the contribution of an agricultural plot to net CO2 emissions according to the plot management and identify levers for improving the C budget. In this study, we present a diagnostic regional modelling approach, called SAFY-CO2, that assimilates high spatial and temporal resolution (HSTR) optical remote sensing data in a simple crop model and evaluate the performance of this approach in quantifying crop production and the main components of the annual carbon budget for winter wheat. The SAFY-CO2 model simulates daily crop development (biomass, partition to leaves, etc.), the components of net ecosystem CO2 fluxes, and the annual yield and net ecosystem carbon budget (NECB). Multi-temporal green area index (GAI) maps derived from HSTR data from the Formosat-2 and SPOT satellites were used to calibrate the light-use efficiency and phenological parameters of the model. Data from the literature were used to set a priori values for a set of model parameters, and a large dataset of in situ data was used for model validation. This dataset includes 8 years of eddy-covariance net CO2 flux measurements and GAI, biomass and yield data acquired at 2 instrumented sites in southwest France. Biomass and yield data from 16 fields in the study area between 2005 and 2014 were also used for validation. The SAFY-CO2 model is able to reproduce both GAI dynamics (RRMSE = 14%, R2 = 0.97) and biomass production and yield (RRMSE of 27% and 21%, respectively) with high precisions under contrasting climatic, environmental and management conditions. Additionally, the net CO2 flux components estimated by the model generally agreed well with in situ data and presented very good and significant correlations (RMSE of 1.74, 1.13 and 1.29 gC.m−2.d-1 for GPP, Reco and NEE, respectively; R2 of 0.90, 0.75 and 0.85 for GPP, Reco and NEE, respectively) over the 8 studied years. This study also highlights the importance of accounting for post-harvest vegetative events (spontaneous re-growth, weed development and cover crops) for an accurate calculation of the annual net CO2 flux. This approach requires a limited number of input parameters for estimating yield and net CO2 flux components, which is promising for regional/global-scale applications based on Sentinel 2-like data; however, the approach requires plot-scale data concerning organic amendments and straw management (exportation) in animal farming systems to calculate field C budgets.
@article{
title = {Estimation of daily CO2 fluxes and of the components of the carbon budget for winter wheat by the assimilation of Sentinel 2-like remote sensing data into a crop model},
type = {article},
year = {2020},
keywords = {FR_AUR,FR_LAM},
pages = {114428},
volume = {376},
websites = {https://doi.org/10.1016/j.geoderma.2020.114428},
publisher = {Elsevier},
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created = {2021-02-11T10:53:19.100Z},
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last_modified = {2021-03-02T15:39:01.953Z},
read = {false},
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confirmed = {true},
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abstract = {Croplands contribute to greenhouse gas emissions but also have the potential to mitigate climate change through soil carbon storage. However, there is a lack of tools based on objective observations for assessing cropland C budgets at the plot scale over large areas. Such tools would allow us to more precisely establish the contribution of an agricultural plot to net CO2 emissions according to the plot management and identify levers for improving the C budget. In this study, we present a diagnostic regional modelling approach, called SAFY-CO2, that assimilates high spatial and temporal resolution (HSTR) optical remote sensing data in a simple crop model and evaluate the performance of this approach in quantifying crop production and the main components of the annual carbon budget for winter wheat. The SAFY-CO2 model simulates daily crop development (biomass, partition to leaves, etc.), the components of net ecosystem CO2 fluxes, and the annual yield and net ecosystem carbon budget (NECB). Multi-temporal green area index (GAI) maps derived from HSTR data from the Formosat-2 and SPOT satellites were used to calibrate the light-use efficiency and phenological parameters of the model. Data from the literature were used to set a priori values for a set of model parameters, and a large dataset of in situ data was used for model validation. This dataset includes 8 years of eddy-covariance net CO2 flux measurements and GAI, biomass and yield data acquired at 2 instrumented sites in southwest France. Biomass and yield data from 16 fields in the study area between 2005 and 2014 were also used for validation. The SAFY-CO2 model is able to reproduce both GAI dynamics (RRMSE = 14%, R2 = 0.97) and biomass production and yield (RRMSE of 27% and 21%, respectively) with high precisions under contrasting climatic, environmental and management conditions. Additionally, the net CO2 flux components estimated by the model generally agreed well with in situ data and presented very good and significant correlations (RMSE of 1.74, 1.13 and 1.29 gC.m−2.d-1 for GPP, Reco and NEE, respectively; R2 of 0.90, 0.75 and 0.85 for GPP, Reco and NEE, respectively) over the 8 studied years. This study also highlights the importance of accounting for post-harvest vegetative events (spontaneous re-growth, weed development and cover crops) for an accurate calculation of the annual net CO2 flux. This approach requires a limited number of input parameters for estimating yield and net CO2 flux components, which is promising for regional/global-scale applications based on Sentinel 2-like data; however, the approach requires plot-scale data concerning organic amendments and straw management (exportation) in animal farming systems to calculate field C budgets.},
bibtype = {article},
author = {Pique, Gaétan and Fieuzal, Rémy and Al Bitar, Ahmad and Veloso, Amanda and Tallec, Tiphaine and Brut, Aurore and Ferlicoq, Morgan and Zawilski, Bartosz and Dejoux, Jean François and Gibrin, Hervé and Ceschia, Eric},
doi = {10.1016/j.geoderma.2020.114428},
journal = {Geoderma},
number = {June}
}
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Multi-temporal green area index (GAI) maps derived from HSTR data from the Formosat-2 and SPOT satellites were used to calibrate the light-use efficiency and phenological parameters of the model. Data from the literature were used to set a priori values for a set of model parameters, and a large dataset of in situ data was used for model validation. This dataset includes 8 years of eddy-covariance net CO2 flux measurements and GAI, biomass and yield data acquired at 2 instrumented sites in southwest France. Biomass and yield data from 16 fields in the study area between 2005 and 2014 were also used for validation. The SAFY-CO2 model is able to reproduce both GAI dynamics (RRMSE = 14%, R2 = 0.97) and biomass production and yield (RRMSE of 27% and 21%, respectively) with high precisions under contrasting climatic, environmental and management conditions. 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