Net ecosystem carbon dioxide (CO2) exchange (NEE) is a key parameter for understanding the terrestrial plant ecosystems, but it is difficult to monitor or predict over large areas at fine temporal resolutions. In this research, we estimated the hourly NEE using a combination of the integrated neural network (NN) model with geostationary satellite imagery to overcome the limitations of existing daily polar orbiting satellite-derived carbon flux products. Two sets of satellite imageries (i.e. the meteorological imager (MI) and geostationary ocean colour imager (GOCI) aboard communication, ocean, and meteorological satellite (COMS)) and CO2 flux data derived from eddy covariance measurements were used to verify the feasibility of applying hourly geostationary satellite imagery with an NN-based approach for estimating NEE at high temporal resolutions. For the NN model, the optimum neuronal architecture was established using an NN with one hidden layer that was trained using the Levenberg–Marquardt back propagation algorithm. The hourly NEE values estimated in test period from the NN model using the combined COMS MI and GOCI imagery and ground measurements as model inputs were compared with the eddy covariance NEE values from the measurement tower, which yielded reliable statistical agreement. The hourly NEE results from the NN model based on COMS MI and GOCI imagery and ground measurement data had the highest accuracy (RMSE = 2.026 μmol m−2 s−2, R = 0.975), while the root mean square error (RMSE) and the regression coefficient (R) generated by the NN model based on satellite imagery as the sole input variable were relatively lower (RMSE = 3.230 μmol m−2 s−2, R = 0.952). Although the simulations for the satellite-only NEE were showed as lower accuracy than the NN model that included all input variables, the hourly variations in NEE also appeared to describe its daily growth and development pattern well, indicating the possibility of deriving hourly-based products from the proposed NN model using geostationary satellite data as inputs.
Bibliographical noteFunding Information:
We thank the Korean Instituted of Ocean Science & Technology (KIOST) for providing GOCI data. This study was supported by Korea Aerospace Research Institute (FR17720). This work was also funded by the Korea Meteorological Administration Research and Development Program under Grant KMIPA 2015-5041 and the Long-term Ecological Research under Changing Global Environment from National Institute of Forest Science, the Research and Development for KMA Weather, Climate, and Earth System Services (NIMS-2016-3100). Finally, we thank all three reviewers and the journal Editor whose comments have improved the clarity of our article.
This work was supported by the Korea Aerospace Research Institute [FR17720]; the Korea Meteorological Administration Research and Development Program [Grant KMIPA 2015-5041]; and the Long-term Ecological Research under Changing Global Environment from National Institute of Forest Science; the Research and Development for KMA Weather, Climate, and Earth System Services [NIMS-2016-3100].
All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)