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Understanding the sequestration of organic carbon (C) in agroecosystems is of primary importance for greenhouse gas (GHG) accounting in managed lands, to reduce the environmental footprint of land use, and inform crediting programs. However, a broader application of precise C accounting is currently constrained by a limited number of direct flux measurements. Aside well-studied ecosystems via the eddy covariance technique (EC), many still bear significant uncertainty. In this study, we propose and evaluate a method for estimating accumulated C stocks in agricultural sites, by assessing the plant aboveground carbon (AGC) throughout two growing seasons using unstaffed aerial vehicles (UAV) and machine learning (ML) regression methods. Then, we used these estimates to assess total plant C, and benchmarked it with CO2 fluxes derived from the eddy covariance method from the ICOS DK-Vng site in Denmark. We utilized a light detection and ranging (LiDAR) sensor onboard a UAV to derive the structural characteristics of crops, and we conducted in parallel destructive field-based measurements of AGC. Then, we designed a ML pipeline to provide estimates of AGC as a supervised regression problem, using the LiDAR-derived point cloud data to extract predictive features and the AGC labels as ground-truth target values. The best performing ML model attained predictions of R2 = 0.71 and R2 = 0.93 at spatial resolutions of 1 m2 and 2 m2, respectively. The C content in the aboveground plant components was assessed via laboratory analysis (46.6 ± 0.3% of C-to-biomass in barley and 47.7 ± 0.3% in wheat), while the belowground components (root allocation and rhizodeposition) were estimated based on a phenology-dependent allometric ratio. The cumulative value of C uptake along the growing season (i.e. net primary productivity) was compared with the difference of C predictions between UAV-LiDAR survey dates, finding an optimal disagreement between methods below ± 9% in two different cereal crops. The plant carbon budget in croplands, determined through UAV-LiDAR and machine learning regression, aligns with the carbon ecosystem uptake estimated through the eddy covariance technique, showcasing comparable results. Thereby, the proposed method also demonstrates the potential to estimate cumulative CO2 fluxes in areas lacking direct eddy covariance measurements. Various experimental setups are evaluated as well as the sources of uncertainty resulting from the sampling design.
Revenga et al. (Tue,) studied this question.