Synthetic retrieval of hourly net ecosystem exchange using the neural network model with combined MI and GOCI geostationary sensor datasets and ground-based measurements
Article
Article Title | Synthetic retrieval of hourly net ecosystem exchange using the neural network model with combined MI and GOCI |
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ERA Journal ID | 4642 |
Article Category | Article |
Authors | Yeom, Jong-Min (Author), Deo, Ravinesh (Author), Chun, Junghwa (Author), Hong, Jinkyu (Author), Kim, Dong-Su (Author), Han, Kyung-Soo (Author) and Cho, Jaeil (Author) |
Journal Title | International Journal of Remote Sensing |
Journal Citation | 38 (23), pp. 7441-7456 |
Number of Pages | 16 |
Year | 2017 |
Publisher | Taylor & Francis |
Place of Publication | United Kingdom |
ISSN | 0143-1161 |
1366-5901 | |
Digital Object Identifier (DOI) | https://doi.org/10.1080/01431161.2017.1375573 |
Web Address (URL) | http://www.tandfonline.com/doi/full/10.1080/01431161.2017.1375573 |
Abstract | 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 NNmodel 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. |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
370108. Meteorology | |
370105. Atmospheric dynamics | |
410299. Ecological applications not elsewhere classified | |
469999. Other information and computing sciences not elsewhere classified | |
460207. Modelling and simulation | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Korea Aerospace Research Institute, Korea |
School of Agricultural, Computational and Environmental Sciences | |
National Institute of Forest Science, Korea | |
Yonsei University, Korea | |
Korea Meteorological Administration, Korea | |
Pukyong National University, Korea | |
Chonnam National University, Korea | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q46qz/synthetic-retrieval-of-hourly-net-ecosystem-exchange-using-the-neural-network-model-with-combined-mi-and-goci-geostationary-sensor-datasets-and-ground-based-measurements
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