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: 2020-2023

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

Almond Board of California

  • Title: Remote-controlled evaluation of distribution uniformity and stem water potential: Extending imagery to integrated decision support
  • Summary: The simplest path for achieving this goal on time is by optimizing irrigation application and scheduling through (1) increasing Distribution Uniformity (DU) via improved system testing, maintenance, and application flow metering; and (2) energy-efficient irrigation scheduling using stem water potential (SWP) at key stages (“when to start”, hull-split, “when to end”). The proposed project will address procedural obstacles and knowledge gaps through an aggressive outreach campaign, new integrated irrigation decision support, and user-friendly remote sensing tools to quickly transform imagery to actionable information We will bridge the deep understanding of almond production in public research and extension with sustainability- focused data scientists and developers (Conservation Science Partners) to build open source tools that will allow multiple hardware and software technologies to integrate new DU and SWP algorithms.
  • Collaborators: UC Cooperative Extension (Mallika Nocco, Luke Miliron, Phoebe Gordon)
  • Project Duration: 2021-2025

CalFire Forest Health Program

  • Title: CARDI-C: Carbon Dynamics Investigator for California: An open‐source platform for tracking carbon uptake and storage across California’s forests
  • Summary:  This project will provide estimates of carbon uptake via photosynthesis and carbon storage as aboveground biomass at both a 500m, 8-day and 30m, annual timescale. Further, we will investigate the environmental drivers in carbon uptake using information on weather conditions and use this to predict how the carbon cycle of California’s forest will change going into the future. Ultimately, all of our model projects and forest carbon maps will be made publicly available through an open-source web platform, enabling land managers to understand how forest carbon might change into the future and evaluate current management strategies.
  • Collaborators: The Conservation Fund, NASA JPL, Yurok Tribe
  • Project Duration: 2022-2024

NSF Integrative Biology

  • Title: Integrative Demography: Combining Ecology, Remote Sensing, and Genomics to Understand Population Dynamics
  • Summary: Biodiversity — arising from the persistence of natural populations — provides critical ecosystem services for human life. Successfully predicting population growth or decline depends on identifying key genetic, organismal, and environmental drivers and integrating them into quantitative, predictive models. Here, a team with diverse expertise in quantitative genetics, genomics, ecology, and remote sensing will integrate advances in their fields to develop new and transformative integrative demography (IntDemo) models. These IntDemo models will enable prediction of population dynamics in response to changing environments and as a function of genetic composition; in so doing they will integrate across scales, using individual genotype and life history observations along with local environmental data to predict the emergent property of multi-generational population dynamics. These models will also be used to predict responses to future change and to provide tools to direct the implementation of conservation strategies such as assisted migration or gene flow. 
  • Collaborators: UC Davis Dept. of Plant Biology (Julin Maloof) and Ecology and Evolution (Jenny Gremer)
  • Project Duration: 2021-2025