NASA Arctic and Boreal Vulnerability Experiment (ABoVE)

  • Title: Ecophysicological and physical mechanisms linking solar-induced fluorescence (SIF) and vegetation reflectance to Boreal forest productivity
  • Summary: We will link tower based, airborne, and satellite remote sensing data to ground based data on plant pigments and local environmental conditions to map and predict carbon uptake across a range of hydrologic and climate regimes in the northern Boreal forest.
  • Collaborators: Caltech, NASA JPL, UCLA, University of Utah, Bowdoin College
  • Project Duration: 2019-2022

NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs

  • Title: Multi-decadal time series of vegetation chlorophyll fluorescence and derived gross primary production 
  • Summary: Our team is creating a set of observational Solar-Induced fluorescence Earth Science Data Records (ESDRs) which calibrates and blends together independent retrievals from multiple satellites into a consistent, multi-decadal record spanning the period 1996-2020. Toward mapping photosynthesis globally
  • Collaborators: NASA JPL, NASA Goddard Space Flight Center, Caltech
  • Project Duration: 2018-2023

NSF Macrosystems Biology: NEON enabled science 

  • Title: Seasonality of photosynthesis of temperature and boreal conifer forests across North America
  • Summary: Our long-term goal is to understand and predict how conifer photosynthesis responds to environmental change.  The overall objectivefor this application is to quantify spatial and temporal variability in forest photosynthetic capacity of conifer forests across North America. 
  • Collaborators: University of Utah, Caltech, Northern Arizona University, Bowdoin College, University of Nebraska-Lincoln.
  • Project Duration: 2020-2023

American Vineyard Foundation 

  • Title: Developing solar-induced chlorophyll fluorescence as a ground-based and remotely-sensed physiological indicator of grapevine stress under field conditions 
  • Summary: We are linking leaf level and tower based remote sensing measurements to plant photosynthesis across a range of water and temperature conditions to better understand how these tools can be used as a physiological indicator of stress.
  • Collaborators: UC Davis Dept. of Viticulture and Enology
  • Project Duration: 2019-2021


  • Title: Exploiting diurnal cycles to evaluate vegetation responses to heat and drought stress 
  • Summary: Using the new ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), we are trying to understand how plants respond on the diurnal time scale to heatwaves and water stress. 
  • Collaborators: Caltech
  • Project Duration: 2020-2022

USDA Agriculture and Food Research Initiative (AFRI)

  • Title: A Synoptic Approach To Physiological Breeding For Drought Resilience In Bean
  • Summary: We are linking a broad suite of physiological traits (in-situ and optically derived) across a diverse population of 300 common bean and 20 teary bean genotypes with a biophysical model model of photosynthesis to understand the importance of each trait for drought yield resilience under drought. 
  • Collaborators: UC Davis Dept. of Plant Sciences 
  • Project Duration: 2020-2022

Center for Data Science and Artificial Intelligence Research (CeDAR)

  • Title: A model-data fusion approach to quantify and predict the fate of terrestrial carbon in California
  • Summary: Our project will use new satellite data streams and a mechanistic model of natural carbon cycle processes (not anthropogenic) to achieve the following objectives for the state of California: 1) Quantify major terrestrial carbon pools and fluxes using a model-data fusion approach;  2) Determine the environmental controls on changes in terrestrial CO2 uptake and storage; 3) Predict how future climate change will impact natural carbon cycle processes under different scenarios.
  • Collaborators: UC Davis Dept. of Land, Air, Water Resources
  • Project Duration: 2020-2022

Disease detection and yield prediction in strawberry

  • Title: AI-enabled sensors for forecasting yield, ripeness, and disease in strawberry
  • Summary: The long-term objective of the proposed project is to develop AI-enabled sensors for forecasting yield, ripeness, and disease in strawberry. To achieve this objective we envision multiple stages: 1) Pilot investigation of spatio-spectral signals that correlate with yield, time-to-ripeness, and disease loading; 2) Development and deployment of imaging platforms for monitoring and forecasting across the entire growing season; 3) Optimization of sensing systems to reduce cost and complexity, and increase durability for deployment on tractors with partnering growers.
  • Collaborators: UC Davis Dept. of Plant Sciences & Biological & Ag Engineering, USDA-ARS
  • Project Duration: 2020-2021