Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM

References

• Abel, C., S. Horion, T. Tagesson, W. De Keersmaecker, A. W. R. Seddon, A. M. Abdi, and R. Fensholt. 2021. “The Human-Environment Nexus and Vegetation-Rainfall Sensitivity in Tropical Drylands.” Nature Sustainability 4 (1): 25–32. https://doi.org/10.1038/s41893-020-00597-z.
   Web of Science ®Google Scholar
• Baker, I. T., L. Prihodko, A. S. Denning, M. Goulden, S. Miller, and H. R. Da Rocha. 2008. “Seasonal Drought Stress in the Amazon: Reconciling Models and Observations.” Journal of Geophysical Research: Biogeosciences 113. https://doi.org/10.1029/2007JG000644.
   Web of Science ®Google Scholar
• Bengio, Y., P. Simard, and P. Frasconi. 1994. “Learning Long-Term Dependencies with Gradient Descent is Difficult.” IEEE Transactions on Neural Networks 5 (2): 157–166. https://doi.org/10.1109/72.279181.
   PubMed Web of Science ®Google Scholar
• Chen, M., E. K. Melaas, J. M. Gray, M. A. Friedl, and A. D. Richardson. 2016. “A new Seasonal-Deciduous Spring Phenology Submodel in the Community Land Model 4.5: Impacts on Carbon and Water Cycling Under Future Climate Scenarios.” Global Change Biology 22 (11): 3675–3688. https://doi.org/10.1111/gcb.13326.
   PubMed Web of Science ®Google Scholar
• Chen, J. M., G. Mo, J. Pisek, J. Liu, F. Deng, M. Ishizawa, and D. Chan. 2012. “Effects of Foliage Clumping on the Estimation of Global Terrestrial Gross Primary Productivity.” Global Biogeochemical Cycles 26 (1). https://doi.org/10.1029/2010GB003996.
   Google Scholar
• Chen, M., G. R. Willgoose, and P. M. Saco. 2015. “Investigating the Impact of Leaf Area Index Temporal Variability on Soil Moisture Predictions Using Remote Sensing Vegetation Data.” Journal of Hydrology 522: 274–284. https://doi.org/10.1016/j.jhydrol.2014.12.027.
   Web of Science ®Google Scholar
• Dai, W., H. Jin, Y. Zhang, T. Liu, and Z. Zhou. 2019. “Detecting Temporal Changes in the Temperature Sensitivity of Spring Phenology with Global Warming: Application of Machine Learning in Phenological Model.” Agricultural and Forest Meteorology 279: 107702. https://doi.org/10.1016/j.agrformet.2019.107702.
   Web of Science ®Google Scholar
• Dai, Y., X. Zeng, R. E. Dickinson, I. Baker, G. B. Bonan, M. G. Bosilovich, A. S. Denning, et al. 2003. “The Common Land Model.” Bulletin of the American Meteorological Society 84 (84): 1013–1024. https://doi.org/10.1175/BAMS-84-8-1013.
   Google Scholar
• Ding, Y., Y. Zhu, J. Feng, P. Zhang, and Z. Cheng. 2020. “Interpretable Spatio-Temporal Attention LSTM Model for Flood Forecasting.” Neurocomputing 403: 348–359. https://doi.org/10.1016/j.neucom.2020.04.110.
   Web of Science ®Google Scholar
• Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor. 2016. “Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental Design and Organization.” Geoscientific Model Development 9 (5): 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016.
   Web of Science ®Google Scholar
• Fang, H., F. Baret, S. Plummer, and G. Schaepman Strub. 2019. “An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications.” Reviews of Geophysics 57 (3): 739–799. https://doi.org/10.1029/2018RG000608.
   Web of Science ®Google Scholar
• Foley, J. A., I. C. Prentice, N. Ramankutty, S. Levis, D. Pollard, S. Sitch, and A. Haxeltine. 1996. “An Integrated Biosphere Model of Land Surface Processes, Terrestrial Carbon Balance, and Vegetation Dynamics.” Global Biogeochemical Cycles 10 (4): 603–628. https://doi.org/10.1029/96GB02692.
   Web of Science ®Google Scholar
• Ge, H., Z. Yan, W. Yu, and L. Sun. 2019. “An Attention Mechanism Based Convolutional LSTM Network for Video Action Recognition.” Multimedia Tools and Applications 78 (14): 20533–20556. https://doi.org/10.1007/s11042-019-7404-z.
   Web of Science ®Google Scholar
• Gobron, N., and M. M. Verstraete. 2009. “Assessment of the Status of the Development of the Standards for the Terrestrial Essential Climate Variables: Leaf Area Index (LAI).” http://www.fao.org/gtos/doc/ECVs/T11/T11.pdf.
   Google Scholar
• Grant, R. F., A. G. Barr, T. A. Black, H. A. Margolis, A. L. Dunn, J. Metsaranta, S. Wang, J. H. McCaughey, and C. A. Bourque. 2009. “Interannual Variation in net Ecosystem Productivity of Canadian Forests as Affected by Regional Weather Patterns - A Fluxnet-Canada Synthesis.” Agricultural and Forest Meteorology 149 (11): 2022–2039. https://doi.org/10.1016/j.agrformet.2009.07.010.
   Web of Science ®Google Scholar
• Greff, K., R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber. 2016. “LSTM: A Search Space Odyssey.” IEEE Transactions on Neural Networks and Learning Systems 28 (10): 2222–2232. https://doi.org/10.1109/TNNLS.2016.2582924.
   PubMed Web of Science ®Google Scholar
• Gui, Z., Y. Sun, L. Yang, D. Peng, F. Li, H. Wu, C. Guo, W. Guo, and J. Gong. 2021. “LSI-LSTM: An Attention-Aware LSTM for Real-Time Driving Destination Prediction by Considering Location Semantics and Location Importance of Trajectory Points.” Neurocomputing 440: 72–88. https://doi.org/10.1016/j.neucom.2021.01.067.
   Web of Science ®Google Scholar
• Gunlu, A., and S. Bulut. 2023. “Modeling Leaf Area Index Using Time-Series Remote Sensing and Topographic Data in Pure Anatolian Black Pine Stands.” International Journal of Environmental Science and Technology 20 (5): 5471–5490. https://doi.org/10.1007/s13762-022-04552-7.
   PubMed Web of Science ®Google Scholar
• Ham, Y., J. Kim, and J. Luo. 2019. “Deep Learning for Multi-Year ENSO Forecasts.” Nature 573 (7775): 568–572. https://doi.org/10.1038/s41586-019-1559-7.
   PubMed Web of Science ®Google Scholar
• Houborg, R., and M. F. McCabe. 2018. “A Hybrid Training Approach for Leaf Area Index Estimation via Cubist and Random Forests Machine-Learning.” ISPRS Journal of Photogrammetry and Remote Sensing 135: 173–188. https://doi.org/10.1016/j.isprsjprs.2017.10.004.
   Web of Science ®Google Scholar
• Hsieh, W. W. 2009. Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels. Vancouver: Cambridge university press.
   Google Scholar
• Hu, Z., H. Shi, K. Cheng, Y. Wang, S. Piao, Y. Li, L. Zhang, et al. 2018. “Joint Structural and Physiological Control on the Interannual Variation in Productivity in a Temperate Grassland: A Data-Model Comparison” Global Change Biology 24 (7): 2965–2979. https://doi.org/10.1111/gcb.14274.
   PubMed Web of Science ®Google Scholar
• Ji, D., and Y. Dai. 2010. The Common Land Model (CoLM) Technical Guide. Beijing, People’s Republic of China: Beijing Normal University.
   Google Scholar
• Kolby Smith, W., S. C. Reed, C. C. Cleveland, A. P. Ballantyne, W. R. L. Anderegg, W. R. Wieder, Y. Y. Liu, and S. W. Running. 2016. “Large Divergence of Satellite and Earth System Model Estimates of Global Terrestrial CO2 Fertilization” Nature Climate Change 6 (3): 306–310. https://doi.org/10.1038/nclimate2879.
   Web of Science ®Google Scholar
• Krinner, G., N. Viovy, N. de Noblet-Ducoudré, J. Ogée, J. Polcher, P. Friedlingstein, P. Ciais, S. Sitch, and I. C. Prentice. 2005. “A Dynamic Global Vegetation Model for Studies of the Coupled Atmosphere-Biosphere System” Global Biogeochemical Cycles 19 (1): GB1015. https://doi.org/10.1029/2003GB002199.
   Web of Science ®Google Scholar
• Lafont, S., Y. Zhao, J. C. Calvet, P. Peylin, P. Ciais, F. Maignan, and M. Weiss. 2012. “Modelling LAI, Surface Water and Carbon Fluxes at High-Resolution Over France: Comparison of ISBA-A-gs and ORCHIDEE” Biogeosciences (Online) 9 (1): 439–456. https://doi.org/10.5194/bg-9-439-2012.
   Web of Science ®Google Scholar
• Lawrence, D. M., R. A. Fisher, C. D. Koven, K. W. Oleson, S. C. Swenson, G. Bonan, N. Collier, et al. 2019. “The Community Land Model Version 5: Description of new Features, Benchmarking, and Impact of Forcing Uncertainty.” Journal of Advances in Modeling Earth Systems 11 (12): 4245–4287. https://doi.org/10.1029/2018MS001583.
   Web of Science ®Google Scholar
• Li, Q., Y. Zhu, W. Shangguan, X. Wang, L. Li, and F. Yu. 2022. “An Attention-Aware LSTM Model for Soil Moisture and Soil Temperature Prediction.” Geoderma 409: 115651. https://doi.org/10.1016/j.geoderma.2021.115651.
   Web of Science ®Google Scholar
• Liang, S., J. Cheng, K. Jia, B. Jiang, Q. Liu, Z. Xiao, Y. Yao, et al. 2021. “The Global Land Surface Satellite (GLASS) Product Suite.” Bulletin of the American Meteorological Society 102 (2): E323–E337. https://doi.org/10.1175/BAMS-D-18-0341.1.
   Web of Science ®Google Scholar
• Ma, H., and S. Liang. 2022. “Development of the GLASS 250-m Leaf Area Index Product (Version 6) from MODIS Data Using the Bidirectional LSTM Deep Learning Model.” Remote Sensing of Environment 273: 112985. https://doi.org/10.1016/j.rse.2022.112985.
   Web of Science ®Google Scholar
• Myneni, R. B., S. Hoffman, Y. Knyazikhin, J. L. Privette, J. Glassy, Y. Tian, Y. Wang, et al. 2002. “Global Products of Vegetation Leaf Area and Fraction Absorbed PAR from Year one of MODIS Data.” Remote Sensing of Environment 83 (1): 214–231. https://doi.org/10.1016/S0034-4257(02)00074-3.
   Web of Science ®Google Scholar
• Piao, S., Q. Liu, A. Chen, I. A. Janssens, Y. Fu, J. Dai, L. Liu, X. Lian, M. Shen, and X. Zhu. 2019. “Plant Phenology and Global Climate Change: Current Progresses and Challenges.” Global Change Biology 25 (6): 1922–1940. https://doi.org/10.1111/gcb.14619.
   PubMed Web of Science ®Google Scholar
• Reichstein, M., G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and Prabhat. 2019. “Deep Learning and Process Understanding for Data-Driven Earth System Science.” Nature 566 (7743): 195–204. https://doi.org/10.1038/s41586-019-0912-1.
   PubMed Web of Science ®Google Scholar
• Restrepo-Coupe, N., N. M. Levine, B. O. Christoffersen, L. P. Albert, J. Wu, M. H. Costa, D. Galbraith, et al. 2017. “Do Dynamic Global Vegetation Models Capture the Seasonality of Carbon Fluxes in the Amazon Basin? A Data-Model Intercomparison” Global Change Biology 23 (1): 191–208. https://doi.org/10.1111/gcb.13442.
   PubMed Web of Science ®Google Scholar
• Richardson, A. D., R. S. Anderson, M. A. Arain, A. G. Barr, G. Bohrer, G. Chen, J. M. Chen, et al. 2012. “Terrestrial Biosphere Models Need Better Representation of Vegetation Phenology: Results from the North American Carbon Program Site Synthesis.” Global Change Biology 18 (2): 566–584. https://doi.org/10.1111/j.1365-2486.2011.02562.x.
   Web of Science ®Google Scholar
• Schaphoff, S., W. von Bloh, A. Rammig, K. Thonicke, H. Biemans, M. Forkel, D. Gerten, et al. 2018. “LPJmL4 – a Dynamic Global Vegetation Model with Managed Land – Part 1: Model Description” Geoscientific Model Development 11 (4): 1343–1375. https://doi.org/10.5194/gmd-11-1343-2018.
   Web of Science ®Google Scholar
• Sepp, H., and S. Jürgen. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Sezer, O. B., M. U. Gudelek, and A. M. Ozbayoglu. 2020. “Financial Time Series Forecasting with Deep Learning: A Systematic Literature Review: 2005–2019” Applied Soft Computing 90: 106181. https://doi.org/10.1016/j.asoc.2020.106181.
   Web of Science ®Google Scholar
• Stöckli, R., T. Rutishauser, D. Dragoni, J. O＇Keefe, P. E. Thornton, M. Jolly, L. Lu, and A. S. Denning. 2008. “Remote Sensing Data Assimilation for a Prognostic Phenology Model.” Journal of Geophysical Research: Biogeosciences 113 (G4). https://doi.org/10.1029/2008JG000781.
   Web of Science ®Google Scholar
• Thornton, P. E., B. E. Law, H. L. Gholz, K. L. Clark, E. Falge, D. S. Ellsworth, A. H. Goldstein, et al. 2002. “Modeling and Measuring the Effects of Disturbance History and Climate on Carbon and Water Budgets in Evergreen Needleleaf Forests.” Agricultural and Forest Meteorology 113 (1): 185–222. https://doi.org/10.1016/S0168-1923(02)00108-9.
   Web of Science ®Google Scholar
• Tian, H., P. Wang, K. Tansey, D. Han, J. Zhang, S. Zhang, and H. Li. 2021. “A Deep Learning Framework Under Attention Mechanism for Wheat Yield Estimation Using Remotely Sensed Indices in the Guanzhong Plain, PR China.” International Journal of Applied Earth Observation and Geoinformation 102: 102375. https://doi.org/10.1016/j.jag.2021.102375.
   Web of Science ®Google Scholar
• Verger, A., I. Filella, F. Baret, and J. Peñuelas. 2016. “Vegetation Baseline Phenology from Kilometric Global LAI Satellite Products.” Remote Sensing of Environment 178: 1–14. https://doi.org/10.1016/j.rse.2016.02.057.
   Web of Science ®Google Scholar
• Xin, Q., Y. Dai, X. Li, X. Liu, P. Gong, and A. D. Richardson. 2018. “A Steady-State Approximation Approach to Simulate Seasonal Leaf Dynamics of Deciduous Broadleaf Forests via Climate Variables.” Agricultural and Forest Meteorology 249: 44–56. https://doi.org/10.1016/j.agrformet.2017.11.025.
   Web of Science ®Google Scholar
• Xin, Q., X. Zhou, N. Wei, H. Yuan, Z. Ao, and Y. Dai. 2020. “A Semiprognostic Phenology Model for Simulating Multidecadal Dynamics of Global Vegetation Leaf Area Index.” Journal of Advances in Modeling Earth Systems 12 (7). https://doi.org/10.1029/2019MS001935.
   Web of Science ®Google Scholar
• Yuan, H., Y. Dai, Z. Xiao, D. Ji, and W. Shangguan. 2011. “Reprocessing the MODIS Leaf Area Index Products for Land Surface and Climate Modelling.” Remote Sensing of Environment 115 (5): 1171–1187. https://doi.org/10.1016/j.rse.2011.01.001.
   Web of Science ®Google Scholar
• Yuan, Q., H. Shen, T. Li, Z. Li, S. Li, Y. Jiang, H. Xu, et al. 2020. “Deep Learning in Environmental Remote Sensing: Achievements and Challenges.” Remote Sensing of Environment 241: 111716. https://doi.org/10.1016/j.rse.2020.111716.
   Web of Science ®Google Scholar
• Zhang, Z., Q. Xin, and W. Li. 2021. “Machine Learning-Based Modeling of Vegetation Leaf Area Index and Gross Primary Productivity Across North America and Comparison with a Process-Based Model.” Journal of Advances in Modeling Earth Systems 13(10). https://doi.org/10.1029/2021MS002802.
   Web of Science ®Google Scholar
• Zhou, X., Q. Xin, S. Zhang, S. Delzon, and Y. Dai. 2023. “A Prognostic Vegetation Phenology Model to Predict Seasonal Maximum and Time Series of Global Leaf Area Index Using Climate Variables.” Agricultural and Forest Meteorology 342: 109739. https://doi.org/10.1016/j.agrformet.2023.109739.
   Web of Science ®Google Scholar