My research combines advanced data-driven approaches (e.g., GeoAI and causality inference), process-based terrestrial biosphere/Earth system models, and big datasets and techniques of remote sensing, in-situ measurements, geographical information science (GIS), and high-performance computing, to investigate critical processes related to natural greenhouse gas emissions (e.g., CO2 and CH4) and nature-based climate solution, wildfire-human-climate interactions, human-environment interactions, and biosphere-atmosphere interactions of carbon-water-energy fluxes affecting climate change.

Professional Education

  • Researcher, Lawrence Berkeley National Lab, Earth System Modeling (2021)
  • Ph.D., Wuhan University, Remote Sensing and GIS (2021)
  • Bachelor, Wuhan University, Remote Sensing (2016)

Stanford Advisors


  • Hongxu Ma, Kunxiaojia Yuan, Fa Li, Charlotte Leroy, Grigory Bronevetsky. "United States Patent 11668856 Predicting climate conditions based on teleconnections", Jun 6, 2023

All Publications

  • Boreal-Arctic wetland methane emissions modulated by warming and vegetation activity NATURE CLIMATE CHANGE Yuan, K., Li, F., McNicol, G., Chen, M., Hoyt, A., Knox, S., Riley, W. J., Jackson, R., Zhu, Q. 2024
  • Global impacts of vegetation clumping on regulating land surface heat fluxes AGRICULTURAL AND FOREST METEOROLOGY Li, F., Hao, D., Zhu, Q., Yuan, K., Braghiere, R. K., He, L., Luo, X., Wei, S., Riley, W. J., Zeng, Y., Chen, M. 2024; 345
  • Structural complexity biases vegetation greenness measures. Nature ecology & evolution Zeng, Y., Hao, D., Park, T., Zhu, P., Huete, A., Myneni, R., Knyazikhin, Y., Qi, J., Nemani, R. R., Li, F., Huang, J., Gao, Y., Li, B., Ji, F., Kohler, P., Frankenberg, C., Berry, J. A., Chen, M. 2023


    Vegetation 'greenness' characterized by spectral vegetation indices (VIs) is an integrative measure of vegetation leaf abundance, biochemical properties and pigment composition. Surprisingly, satellite observations reveal that several major VIs over the US Corn Belt are higher than those over the Amazon rainforest, despite the forests having a greater leaf area. This contradicting pattern underscores the pressing need to understand the underlying drivers and their impacts to prevent misinterpretations. Here we show that macroscale shadows cast by complex forest structures result in lower greenness measures compared with those cast by structurally simple and homogeneous crops. The shadow-induced contradictory pattern of VIs is inevitable because most Earth-observing satellites do not view the Earth in the solar direction and thus view shadows due to the sun-sensor geometry. The shadow impacts have important implications for the interpretation of VIs and solar-induced chlorophyll fluorescence as measures of global vegetation changes. For instance, a land-conversion process from forests to crops over the Amazon shows notable increases in VIs despite a decrease in leaf area. Our findings highlight the importance of considering shadow impacts to accurately interpret remotely sensed VIs and solar-induced chlorophyll fluorescence for assessing global vegetation and its changes.

    View details for DOI 10.1038/s41559-023-02187-6

    View details for PubMedID 37710041

  • AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics GEOSCIENTIFIC MODEL DEVELOPMENT Li, F., Zhu, Q., Riley, W. J., Zhao, L., Xu, L., Yuan, K., Chen, M., Wu, H., Gui, Z., Gong, J., Randerson, J. T. 2023; 16 (3): 869-884
  • Vegetation clumping modulates global photosynthesis through adjusting canopy light environment. Global change biology Li, F., Hao, D., Zhu, Q., Yuan, K., Braghiere, R. K., He, L., Luo, X., Wei, S., Riley, W. J., Zeng, Y., Chen, M. 2023; 29 (3): 731-746


    The spatial dispersion of photoelements within a vegetation canopy, quantified by the clumping index (CI), directly regulates the within-canopy light environment and photosynthesis rate, but is not commonly implemented in terrestrial biosphere models to estimate the ecosystem carbon cycle. A few global CI products have been developed recently with remote sensing measurements, making it possible to examine the global impacts of CI. This study deployed CI in the radiative transfer scheme of the Community Land Model version 5 (CLM5) and used the revised CLM5 to quantitatively evaluate the extent to which CI can affect canopy absorbed radiation and gross primary production (GPP), and for the first time, considering the uncertainty and seasonal variation of CI with multiple remote sensing products. Compared to the results without considering the CI impact, the revised CLM5 estimated that sunlit canopy absorbed up to 9%-15% and 23%-34% less direct and diffuse radiation, respectively, while shaded canopy absorbed 3%-18% more diffuse radiation across different biome types. The CI impacts on canopy light conditions included changes in canopy light absorption, and sunlit-shaded leaf area fraction related to nitrogen distribution and thus the maximum rate of Rubisco carboxylase activity (Vcmax ), which together decreased photosynthesis in sunlit canopy by 5.9-7.2 PgC year-1 while enhanced photosynthesis by 6.9-8.2 PgC year-1 in shaded canopy. With higher light use efficiency of shaded leaves, shaded canopy increased photosynthesis compensated and exceeded the lost photosynthesis in sunlit canopy, resulting in 1.0 ± 0.12 PgC year-1 net increase in GPP. The uncertainty of GPP due to the different input CI datasets was much larger than that caused by CI seasonal variations, and was up to 50% of the magnitude of GPP interannual variations in the tropical regions. This study highlights the necessity of considering the impacts of CI and its uncertainty in terrestrial biosphere models.

    View details for DOI 10.1111/gcb.16503

    View details for PubMedID 36281563

    View details for PubMedCentralID PMC10100496

  • Causality guided machine learning model on wetland CH4 emissions across global wetlands AGRICULTURAL AND FOREST METEOROLOGY Yuan, K., Zhu, Q., Li, F., Riley, W. J., Torn, M., Chu, H., McNicol, G., Chen, M., Knox, S., Delwiche, K., Wu, H., Baldocchi, D., Ma, H., Desai, A. R., Chen, J., Sachs, T., Ueyama, M., Sonnentag, O., Helbig, M., Tuittila, E., Jurasinski, G., Koebsch, F., Campbell, D., Schmid, H., Lohila, A., Goeckede, M., Nilsson, M. B., Friborg, T., Jansen, J., Zona, D., Euskirchen, E., Ward, E. J., Bohrer, G., Jin, Z., Liu, L., Iwata, H., Goodrich, J., Jackson, R. 2022; 324
  • Wetter California Projected by CMIP6 Models With Observational Constraints Under a High GHG Emission Scenario EARTHS FUTURE Li, F., Zhu, Q., Riley, W. J., Yuan, K., Wu, H., Gui, Z. 2022; 10 (4)
  • Building a machine learning surrogate model for wildfire activities within a global Earth system model GEOSCIENTIFIC MODEL DEVELOPMENT Zhu, Q., Li, F., Riley, W. J., Xu, L., Zhao, L., Yuan, K., Wu, H., Gong, J., Randerson, J. 2022; 15 (5): 1899-1911
  • Uncertain Spatial Pattern of Future Land Use and Land Cover Change and Its Impacts on Terrestrial Carbon Cycle Over the Arctic-Boreal Region of North America EARTHS FUTURE Luo, M., Li, F., Hao, D., Zhu, Q., Dashti, H., Chen, M. 2023; 11 (10)
  • Detection and attribution of long-term and fine-scale changes in spring phenology over urban areas: A case study in New York State INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION Li, L., Li, X., Asrar, G., Zhou, Y., Chen, M., Zeng, Y., Li, X., Li, F., Luo, M., Sapkota, A., Hao, D. 2022; 110
  • Understanding and reducing the uncertainties of land surface energy flux partitioning within CMIP6 land models AGRICULTURAL AND FOREST METEOROLOGY Yuan, K., Zhu, Q., Riley, W. J., Li, F., Wu, H. 2022; 319
  • LSI-LSTM: An attention-aware LSTM for real-time driving destination prediction by considering location semantics and location importance of trajectory points NEUROCOMPUTING Gui, Z., Sun, Y., Yang, L., Peng, D., Li, F., Wu, H., Guo, C., Guo, W., Gong, J. 2021; 440: 72-88
  • Deforestation reshapes land-surface energy-flux partitioning ENVIRONMENTAL RESEARCH LETTERS Yuan, K., Zhu, Q., Zheng, S., Zhao, L., Chen, M., Riley, W. J., Cai, X., Ma, H., Li, F., Wu, H., Chen, L. 2021; 16 (2)
  • Evaluating three evapotranspiration estimates from model of different complexity over China using the ILAMB benchmarking system JOURNAL OF HYDROLOGY Wu, G., Cai, X., Keenan, T. F., Li, S., Luo, X., Fisher, J. B., Cao, R., Li, F., Purdy, A. J., Zhao, W., Sun, X., Hu, Z. 2020; 590
  • A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction. Neurocomputing Li, F., Gui, Z., Zhang, Z., Peng, D., Tian, S., Yuan, K., Sun, Y., Wu, H., Gong, J., Lei, Y. 2020; 403: 153-166


    Prediction of individual mobility is crucial in human mobility related applications. Whereas, existing research on individual mobility prediction mainly focuses on next location prediction and short-term dependencies between traveling locations. Long-term location sequence prediction is of great importance for long-time traffic planning and location advertising, and long-term dependencies exist as individual mobility regularity typically occurs daily and weekly. This paper proposes a novel hierarchical temporal attention-based LSTM encoder-decoder model for individual location sequence prediction. The proposed hierarchical attention mechanism captures both long-term and short-term dependencies underlying in individual longitudinal trajectories, and uncovers frequential and periodical mobility patterns in an interpretable manner by incorporating the calendar cycle of individual travel regularities into location prediction. More specifically, the hierarchical attention consists of local temporal attention to identify highly related locations in each day, and global temporal attention to discern important travel regularities over a week. Experiments on individual trajectory datasets with varying degree of traveling uncertainty demonstrate that our method outperforms four baseline methods on three evaluation metrics. In addition, we explore the interpretability of the proposed model in understanding individual daily, and weekly mobility patterns by visualizing the temporal attention weights and frequent traveling patterns associated with locations.

    View details for DOI 10.1016/j.neucom.2020.03.080

    View details for PubMedID 32501365

    View details for PubMedCentralID PMC7252178

  • A quad-tree-based fast and adaptive Kernel Density Estimation algorithm for heat-map generation INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE Yuan, K., Cheng, X., Gui, Z., Li, F., Wu, H. 2019; 33 (12): 2455-2476
  • Big enterprise registration data imputation: Supporting spatiotemporal analysis of industries in China COMPUTERS ENVIRONMENT AND URBAN SYSTEMS Li, F., Gui, Z., Wu, H., Gong, J., Wang, Y., Tian, S., Zhang, J. 2018; 70: 9-23