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Abstract The eddy covariance (EC) method is one of the most widely used approaches to quantify surface‐atmosphere fluxes. However, scaling up from a single EC tower to the landscape remains an open challenge. To address this, we used 63 site years of data to examine simulated annual and growing season sums of carbon fluxes from three paired land‐cover type sites of corn, restored‐prairie, and switchgrass ecosystems. This was also done across the landscape by modeling fluxes using different land‐cover type input data. An artificial neural network (ANN) approach was used to model net ecosystem exchange (NEE), ecosystem respiration (Reco), and gross primary production (GPP) at one paired site using environmental observations from the second site only. With a mean spatial separation of 11 km between paired sites, we were able to model annual sums of NEE,Reco, and GPP with uncertainties of 20%, 22%, and 8%, respectively, relative to observation sums. When considering the growing season only, model uncertainties were 17%, 22%, and 9%, respectively for the three flux terms. We also show that ANN models can estimate sums ofRecoand GPP fluxes without needing the constraint of similar land‐cover‐type, with annual uncertainties of 12% and 10%. These results provide new insights to scaling up observations from one EC site beyond the footprint of the EC tower to multiple land‐cover types across the landscape.
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Partitioning Net Ecosystem Exchange (NEE) of CO 2 Using Solar‐Induced Chlorophyll Fluorescence (SIF)
Abstract Accurate partitioning of net ecosystem exchange (NEE) of CO2to gross primary production (GPP) and ecosystem respiration (Reco) is crucial for understanding carbon cycle dynamics under changing climate. However, it remains as a long‐standing problem in global ecology due to lack of independent constraining information for the two offsetting component fluxes. solar‐induced chlorophyll fluorescence (SIF), a mechanistic proxy for photosynthesis, holds great promise to improve NEE partitioning by constraining GPP. We developed a parsimonious SIF‐based approach for NEE partitioning and examined its performance using synthetic simulations and field measurements. This approach outperforms conventional approaches in reproducing simulated GPP andReco, especially under high vapor pressure deficit. For field measurements, it results in lower daytime GPP andRecothan conventional approaches. This study made the first attempt to demonstrate SIF's potential for improving NEE partitioning accuracy and sets the stage for future efforts to examine its robustness and scalability under real‐world environmental conditions.
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- Award ID(s):
- 1926488
- PAR ID:
- 10374505
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 48
- Issue:
- 4
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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