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. Link to project website
  • 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. Link to project website
  • Website: 
  • 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. Link to program website
  • 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: 2019-2022


  • 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: 2020-2022

Artificial Intelligence Institute for Food Systems (AIFS)

  • Title: Sensing and modeling of lead biochemical and physiological traits, including early vigor
  • Summary: To use AI approaches to quantify, model, and predict relationships between remote sensing observations and in-situ measured leaf quality, vigor, and yield traits. Specific aims: A. Non-destructive and simultaneous sensing of leaf biochemical and physiological traits at field + population scales, to be selected upon in the breeding process. B. Design of mechanistic and AI-enabled models of crop growth and development (in leafy greens) and broadscale crop structural properties (e.g. canopy dimensions, in legumes), with a focus on simulation and prediction of yield and quality – moving towards functional-structural models. AIFS project website
  • Collaborators: UC Davis Dept. of Plant Sciences & Biological & Ag Engineering
  • Project Duration: 2020-2023

NASA Carbon Cycle Science: Crop stress and productivity

  • Title: Integrating Field Measurements and Models to Evaluate Solar Induced Fluorescence as a Predictor of Dryland Crop Productivity
  • Summary: This project, in collaboration with Colorado State University the USDA, will use tower based remote sensing  data and sophisticated numerical models to understand how remote sensing data can be used to predict drylands crop productivity. We will install a scanning spectrometer in corn and wheat systems in Colorado
  • Collaborators: Colorado State University and USDA-ARS
  • Project Duration: 2021-2024

NASA Carbon Cycle Science: Tropical Forest Productivity

  • Title: COSIF: Combining Carbonyl Sulfide and Solar Induced Chlorophyll Fluorescence to scale the carbon cycle of tropical rainforests from leaf to landscape
  • Summary: This project, in collaboration with UCLA and the NASA Jet Propulsion Lab will use field measurements of photosynthesis, tower-based remote sensing data and a data-assimililation framework to better constrain the carbon cycle in across the tropical rainforest, which is currently one of the most difficult ecosystems to characterize carbon uptake via photosynthesis. Field work will take place at La Selva Biological Research Station in Costa Rica.
  • Collaborators: UCLA and NASA Jet Propulsion Laboratory
  • Project Duration: 2021-2024