Synthetic retrieval of hourly net ecosystem exchange using the neural network model with combined MI and GOCI geostationary sensor datasets and ground-based measurements

References

• Balas, C. E., M. L. Koc, and R. Tur. 2010. “Artificial Neural Networks Based on Principal Component Analysis, Fuzzy Systems and Fuzzy Neural Networks for Preliminary Design of Rubble Mound Breakwaters.” Applications Ocean Researcher 32: 425–433. doi:10.1016/j.apor.2010.09.005.
   Web of Science ®Google Scholar
• Baldocchi, D. 2003. “Assessing the Eddy Covariance Technique for Evaluating Carbon Dioxide Exchange Rates of Ecosystems: Past, Present and Future.” Global Change Biologic 9: 479–492. doi:10.1046/j.1365-2486.2003.00629.x.
   Web of Science ®Google Scholar
• Baldocchi, D., E. Falge, L. Gu, R. Olson, R. Hollinger, S. Running, P. Anthoni, et al. 2001. “FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities.” Bulletin American Meteorol Social 82: 2415–2434. doi:10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2.
   Web of Science ®Google Scholar
• Baldocchi, D., B. B. Hincks, and T. P. Meyers. 1988. “Measuring Biosphere-Atmosphere Exchanges of Biologically Related Gases with Micrometeorological Methods.” Ecology 69: 1331–1340. doi:10.2307/1941631.
   Web of Science ®Google Scholar
• Bertsekas, D. P., and J. N. Tsitsiklis. 1996. Neuro-Dynamic Programing. Belmont, MA: Athena Scientific.
   Google Scholar
• Bowden, G. J., G. C. Dandy, and H. R. Maier. 2005. “Input Determination for Neural Network Models in Water Resources Application. Part1-Background and Methodology.” Journal of Hydrology 301: 75–92. doi:10.1016/j.jhydrol.2004.06.021.
   Web of Science ®Google Scholar
• Bowden, G. J., H. R. Maier, and G. C. Dandy. 2002. “Optimal Division of Data for Neural Network Models in Water Resources Applications.” Water Resources Research 38: 1–11. doi:10.1029/2001WR000266.
   Web of Science ®Google Scholar
• Canadell, J. G., C. Le Quere, M. R. Raupach, C. B. Field, E. T. Buitenhuis, P. Ciais, T. J. Conway, N. P. Gillett, R. A. Houghton, and G. Marland. 2007. “Contributions to Accelerating Atmospheric CO2 Growth from Economic Activity, Carbon Intensity, and Efficiency of Natural Sinks.” Proceedings in National Academy of Sciences, USA 104: 18866–18870. doi:10.1073/pnas.0702737104.
   PubMed Web of Science ®Google Scholar
• Chen, S., and S. A. Billings. 1992. “Neural Networks for Nonlinear Dynamic System Modeling and Identification.” International Journal Control 56: 319–346. doi:10.1080/00207179208934317.
   Web of Science ®Google Scholar
• Coulibaly, P., F. Anctil, and B. Bobée. 2000. “Daily Reservoir Inflow Forecasting Using Artificial Neural Networks with Stopped Training Approach.” Journal of Hydrology 230: 244–257. doi:10.1016/S0022-1694(00)00214-6.
   Web of Science ®Google Scholar
• Cutler, D. R., T. C. Edwards, K. H. Beard, B. A. Cutler, K. T. Hess, J. Gibson, and J. J. Lawler. 2007. “Random Forests for Classification in Ecology.” Ecology 88: 2783–2792. doi:10.1890/07-0539.1.
   PubMed Web of Science ®Google Scholar
• Falge, E., J. Tenhunen, D. Baldocchi, M. Aubinet, P. Bakwin, P. Berbigier, C. Bernhofer, et al. 2002. “Phase and Amplitude of Ecosystem Carbon Release and Uptake Potential as Derived from FLUXNET Measurements.” Agricultural Forest Meteorol 113: 75–95. doi:10.1016/S0168-1923(02)00103-X.
   Web of Science ®Google Scholar
• Foken, T., and B. Wichura. 1996. “Tools for Quality Assessment of Surface-Based Flux Measurements.” Agricultural and Forest Meteorology 78: 83–105. doi:10.1016/0168-1923(95)02248-1.
   Web of Science ®Google Scholar
• Göckede, M., T. Foken, M. Aubinet, M. Aurela, J. Banza, C. Bernhofer, J. M. Bonnefond, et al. 2008. “Quality Control of CarboEurope Flux Data – Part 1: Coupling Footprint Analyses with Flux Data Quality Assessment to Evaluate Sites in Forest Ecosystems.” Biogeosciences 5: 433–450. doi:10.5194/bg-5-433-2008.
   Web of Science ®Google Scholar
• Green, P. E. 1978. Analyzing Multivariate Data. London: Dryden Press, 519–520.
   Google Scholar
• Hong, J., and J. Kim. 2011. “Impact of the Asian Monsoon Climate on Ecosystem Carbon and Water Exchanges: A Wavelet Analysis and Its Ecosystem Modeling Implication.” Global Change Biologic 17: 1900–1916. doi:10.1111/j.1365-2486.2010.02337.x.
   Web of Science ®Google Scholar
• Hong, J., J. Kim, D. H. Lee, and J. H. Lim. 2008. “Estimation of the Storage and Advection Effects on H2O and CO2 Exchanges in a Hilly KoFlux Forest Catchment.” Water Researcher Research 44: W01426. doi:10.1029/2007WR006408.
   Web of Science ®Google Scholar
• Hong, J., H. Kwon, J. Lim, Y. Byun, J. Lee, and J. Kim. 2009. “Standardization of KoFlux Eddy-Covariance Data Processing.” Korean Journal Agricultural Forest Meteor 11: 19–26.
   Google Scholar
• Jang, J. D., and A. A. Viau. 2004. “Neural Network Estimation of Air Temperature from AVHRR Data.” International Journal Rem Sens 25: 4541–4554. doi:10.1080/01431160310001657533.
   Web of Science ®Google Scholar
• Justice, C. O., J. R. G. Townshend, E. F. Vermote, E. Masuoka, R. E. Wolfe, N. Saleous, D. P. Roy, and J. T. Morisette. 2002. “An Overview of MODIS Land Data Processing and Product Status.” Remote Sensing of Environment 83: 3–15. doi:10.1016/S0034-4257(02)00084-6.
   Web of Science ®Google Scholar
• Kaminsky, E. J., H. Barad, and W. Brown. 1997. “Textural Neural Network and Version Space Classifiers for Remote Sensing.” International Journal of Remote Sensing 18: 741–762. doi:10.1080/014311697218737.
   Web of Science ®Google Scholar
• Lauer, F., and G. Bloch. 2006. “Ho-Kashyap Classifier with Early Stopping for Regularization.” Pattern Recogn Letters 27: 1037–1044. doi:10.1016/j.patrec.2005.12.009.
   Web of Science ®Google Scholar
• Levenberg, K. 1944. “A Method for the Solution of Certain Problems in Least Squares.” Quarterly Applications Mathematical 2: 164–168. doi:10.1090/qam/10666.
   Google Scholar
• Marquardt, D. 1963. “An Algorithm for Least-Squares Estimation of Nonlinear Parameters.” Journal Social Indust Applications Mathematical 11: 431–441. doi:10.1137/0111030.
   Web of Science ®Google Scholar
• Massman, W., and X. Lee. 2002. “Eddy Covariance Flux Corrections and Uncertainties in Long-Term Studies of Carbon and Energy Exchanges.” Agricultural Forest Meteorology 113: 121–144. doi:10.1016/S0168-1923(02)00105-3.
   Web of Science ®Google Scholar
• Masters, T. 1995. Advanced Algorithms for Neural Networks: A C++ Sourcebook. New York: Wiley.
   Google Scholar
• McMillen, R. T. 1988. “An Eddy Correlation Technique with Extended Applicability to Non-Simple Terrain.” Boundary-Layer Meteorology. 43: 231–245. doi:10.1007/BF00128405.
   Web of Science ®Google Scholar
• Moncrieff, J., Y. Malhi, and R. Leuning. 1996. “The Propagation of Errors in Long-Term Measurements of Land–Atmosphere Fluxes of Carbon and Water.” Global Change Biologic 2: 231–240. doi:10.1111/j.1365-2486.1996.tb00075.x.
   Web of Science ®Google Scholar
• Prechelt, L. 1998. “Automatic Early Stopping Using Cross Validation: Quantifying the Criteria.” Neural Network 11: 761–767. doi:10.1016/S0893-6080(98)00010-0.
   PubMed Web of Science ®Google Scholar
• Reitermanova, Z. 2010. “Data Splitting.” WDS’s 10 Proceedings of Contributed Papers Part 1: 31–36.
   Google Scholar
• Şahin, M., Y. Kaya, and M. Uyar. 2013. “Comparison of ANN and MLR Models for Estimating Solar Radiation in Turkey Using NOAA/AVHRR Data.” Advances in Space Research 51: 891–904. doi:10.1016/j.asr.2012.10.010.
   Web of Science ®Google Scholar
• Shim, C., J. Hong, J. Hong, Y. Kim, M. Kang, B. M. Thakuri, Y. Kim, and J. Chun. 2014. “Evaluation of MODIS GPP over a Complex Ecosystem in East Asia: A Case Study at Gwangneung Flux Tower in Korea.” Advancement Space Researcher 54: 2296–2308. doi:10.1016/j.asr.2014.08.031.
   Web of Science ®Google Scholar
• Tang, X., Z. Wang, D. Liu, K. Song, M. Jia, Z. Dong, J. W. Munger, et al. 2012. “Estimating the Net Ecosystem Exchange for the Major Forests in the Northern United States by Integrating MODIS and AmeriFlux Data”. Agricultural Forest Meteorology 156: 75–84. doi:10.1016/j.agrformet.2012.01.003.
   Web of Science ®Google Scholar
• Wallner, G. 2015. “Estimating and evaluating GPP in the Sahel using MSG/SEVIRI and MODIS satellite data.” Student thesis series INES, 352.
   Google Scholar
• Webb, E. K., G. I. Pearman, and R. Leuning. 1980. “Correction of the Flux Measurements for Density Effects Due to Heat and Water Vapour Transfer.” Quarterly Journal Roy Meteorol Social 106: 85–100. doi:10.1002/qj.49710644707.
   Web of Science ®Google Scholar
• Wu, C. L., K. W. Chau, and Y. S. Li. 2009. “Methods to Improve Neural Network Performance in Daily Flows Prediction.” Journal of Hydrology 372: 80–93. doi:10.1016/j.jhydrol.2009.03.038.
   Web of Science ®Google Scholar
• Yeom, J. M., and K. S. Han. 2010. “Improved Estimation of Surface Solar Insolation Using a Neural Network and MTSAT-1R Data.” Computation Geoscience 36: 590–597. doi:10.1016/j.cageo.2009.08.012.
   Web of Science ®Google Scholar
• Yeom, J. M., C. S. Lee, S. J. Park, J. H. Ryu, J. J. Kim, H. C. Kim, and K. S. Han. 2015. “Evapotranspiration in Korea Estimated by Application of a Neural Network to Satellite Images.” Remote Sens Letters 6: 429–438. doi:10.1080/2150704X.2015.1041169.
   Web of Science ®Google Scholar
• Zhang, G. P., and V. L. Berardi. 2001. “Time Series Forecasting with Neural Network Ensembles: An Application for Exchange Rate Prediction.” Journal Operational Researcher Social 52: 652–664. doi:10.1057/palgrave.jors.2601133.
   Web of Science ®Google Scholar
• Zhu, Y., L. Liu, S. Wang, and H. Liu. 2015. “Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images.” Remote Sensing 7: 12192–12214. doi:10.3390/rs70912192.
   Web of Science ®Google Scholar