Research

The GeoAI Lab organizes its research into four interconnected themes, each powered by our core methodological engine of Geospatial/Remote Sensing, Artificial Intelligence, Machine Learning, and Earth System Modeling.

Research Themes

Research Theme 1

GeoAI for Water Resources & Nutrient Dynamics

Leveraging Remote Sensing, AI, and Process-Based Modeling for Water-Climate-Ecosystem Interactions


This theme focuses on integrating geospatial AI, remote sensing, and Earth system models to study hydrological processes, evapotranspiration, water use efficiency, and nutrient (nitrogen and phosphorus) loading in major watersheds. By combining data-driven and process-based approaches, we aim to improve water sustainability and water quality modeling under climate and land-use change.

Key Subthemes

  • Evapotranspiration Modeling: Remote sensing + AI + process-based model comparisons
  • Water Use Efficiency (WUE): Responses to CO₂ and climate change
  • Nutrient Transport: Nitrogen and phosphorus dynamics in the Chesapeake Bay and Mississippi River Basin

Key Figures



Representative Publications

  • Pan et al. (2020). Comparison of satellite-based and reanalysis ET estimates with field observations — implications for water budget studies. Hydrology and Earth System Sciences (HESS)
  • Pan et al. (2021). Impacts of multiple environmental stressors on nitrogen loading to Chesapeake Bay. JGR-Biogeosciences
  • Bian and Pan et al. (2026) Extreme precipitation reshapes nutrient flows and balance in North America’s largest river basin. Science Advances
  • Bian and Pan et al. (2024). Future nitrogen species dynamics in Chesapeake Bay under climate change. Journal of Hydrology
  • Bian and Pan et al. (2022). Phosphorus export to the Gulf of Mexico under land cover and climate change. Global Biogeochemical Cycles
  • Pan et al. (2018). Terrestrial water-use efficiency in response to CO₂ rise in the 21st century. Int. J. Digital Earth
  • Pan et al. (2015). Responses of global terrestrial evapotranspiration to climate change and increasing atmospheric CO₂ in the 21st century. Earth's Future

Research Theme 2

Carbon Dynamics & Terrestrial Ecosystem Productivity

Modeling Ecosystem Responses to Environmental Change Using GeoAI


This theme addresses the complex interactions between carbon cycling, primary production, and environmental drivers such as temperature, precipitation, and CO₂. Using geospatial modeling and AI, we quantify global and regional carbon fluxes, assess the impacts of climate extremes, and explore spatiotemporal patterns of terrestrial ecosystem production.

Key Subthemes

  • Net Primary Production (NPP): Simulation and observation synthesis across global biomes
  • Carbon-Climate Interactions: Extreme weather impacts on ecosystem carbon fluxes
  • Multi-scale Modeling: Integrating global datasets with AI-enhanced process modeling

Key Figures


Representative Publications

  • Pan et al. (2020). Responses of terrestrial ecosystem carbon fluxes to climate extremes in the conterminous United States. JGR-Biogeosciences
  • Pan et al. (2015). Net primary production of African biomes and its responses to climate change and variability. Ecosystem Health & Sustainability
  • Pan et al. (2015). Responses of global terrestrial NPP to climate extremes across multiple timescales. Journal of Geographical Sciences
  • Pan et al. (2014). Increasing global vegetation browning hidden in overall vegetation greening: insights from time-resolved MODIS data. PLOS ONE
  • Pan et al. (2014). Modeling and inverse estimation of global terrestrial carbon fluxes incorporating nitrogen dynamics. Advances in Meteorology

Research Theme 3

Environmental Change & Public Health

GeoAI for Urban Ecosystems and Human Wellbeing


This theme explores the intersection of environmental conditions and public health, using geospatial AI and remote sensing to analyze the role of vegetation, climate, and urban form on health outcomes. We also examine large-scale trends in global vegetation change and their implications for ecosystem health and resilience.

Key Subthemes

  • Urban Green Space and Health: COVID-19 transmission, vegetation, and urban heat effects
  • Global Vegetation Trends: Detection of large-scale browning and greening patterns
  • GeoHealth: Spatial data science for environmental and public health research

Key Figures


Representative Publications

  • You & Pan (2020). Urban vegetation slows down the spread of coronavirus disease (COVID-19) in the United States. Geophysical Research Letters (GRL)
  • Pan N et al. (2018). Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-series remote sensing data. Remote Sensing of Environment

Research Theme 4

Natural Disasters & Ecosystem Resilience

Monitoring and Modeling the Impact of Disturbances Using GeoAI


This theme focuses on using satellite data and AI-based methods to assess ecosystem vulnerability and resilience in the face of natural disturbances, such as hurricanes, droughts, and extreme weather. Our work helps inform climate adaptation strategies and improve understanding of disturbance recovery processes.

Key Subthemes

  • Forest Resilience: Hurricane impact monitoring and long-term recovery trajectories
  • Disturbance Mapping: Satellite-based detection of environmental stress and damage extent
  • Climate Adaptation: Spatial resilience modeling to support environmental policy planning

Key Figures


Representative Publications

  • Gang and Pan et al. (2020). Forest resilience to hurricane disturbances: Satellite-based monitoring and recovery modeling. Forest Ecology and Management
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