Radiative transfer modeling platform for simulating chlorophyll fluorescence and leaf temperature across aquatic and terrestrial plants

Overview

This solution addresses a critical gap in remote sensing of plant physiology by extending radiative transfer modeling to aquatic plants, specifically duckweed, alongside terrestrial crops like wheat. Existing models such as DART-Fluspect and SCOPE are constrained to molecular-scale interactions and lack robust parameters for water backgrounds, creating large uncertainties when interpreting Solar-Induced chlorophyll Fluorescence (SIF) and canopy temperature data. The platform enables quantitative, non-contact tracking of photosynthetic and photoprotective pigments, stomatal conductance, leaf water content, and plant productivity across diverse growth environments. By accounting for the contrasting optical and thermal properties of soil versus water substrates, it delivers more accurate physiological insights for both research and operational applications.

Technical specifications

Key features:

• New substrate property parameters simulating SIF and leaf temperature for soil, deep water, and shallow water environments
• Radiative transfer physics-based simulation of plant tissue interactions with electromagnetic waves across multiple wavelengths
• Integration of advanced remote sensing modalities including SIF, hyperspectral imaging, thermal imaging, and radiometry
• Spatial heterogeneity resolution through assimilation of satellite observations into radiation simulations
• Parameter database deliverable for querying within graph-based digital-twin models
• Three-phase validation covering theoretical modeling, continuous diurnal and seasonal data collection, and model parameterization
• Tested across three growth settings: land only, deep water, and land in shallow water

Technology readiness level

The platform is currently in the theoretical modeling and validation phase, building on prior lab expertise in environmental background effects on chlorophyll fluorescence and leaf temperature observations. The research team has established proof-of-concept through previous work demonstrating that remote sensing can rapidly and continuously represent changes in plant physiology without direct contact. Future validation will proceed through three phases: developing new substrate parameters, collecting continuous SIF and leaf temperature data over 7-day diurnal and seasonal cycles for wheat and duckweed, and optimizing the model to deliver a queryable parameter database for digital-twin applications.