Thesis subject
MSc thesis topic: Biochemical and biophysical crop trait estimation through Functional-Structural Plant and Radiative Transfer Modelling
Remote sensing techniques have shown great potential in improving cropping systems by providing insights into plant health, development, and physiological processes. Among these techniques, spectroscopy is particularly valuable, as it enables the retrieval of biophysical and biochemical properties of vegetation through spectral reflectance measurements.
However, the extent to which spectral reflectance captures underlying physiological processes remains uncertain. Understanding these limitations is crucial for advancing remote sensing applications in agriculture. This project aims to integrate Functional-Structural Plant (FSP) modelling with radiative transfer modelling (RTM) by linking a 3D ray-tracing radiative transfer model with the PROSAIL model. This approach will enhance the estimation of biochemical compounds related to photosynthetic traits and canopy structural properties, leveraging the strengths of both 3D ray-tracing and the radiative transfer models used in PROSAIL
Background Functional-Structural Plant (FSP) models simulate plant growth, architecture, and physiological processes, while Radiative Transfer Models (RTMs) describe the interaction between light and plant canopies. By integrating these modelling approaches, it is possible to explore the fundamental constraints of remote sensing-based crop trait estimation. Synthetic datasets generated through these models can help assess the reliability and accuracy of remote sensing inversion techniques. Additionally, if initiated in April 2025, field measurements on crop structure, development, and photosynthesis can complement the in silico experiments for model calibration and validation.
Relevance to research/projects at GRS or other groups
This project aligns with ongoing research at the Centre for Crop Systems Analysis (CSA) and the Laboratory of Geo-information Science and Remote Sensing (GRS). It contributes to efforts in linking 3D radiative transfer modeling with remote sensing observations, particularly using UAV-based hyperspectral data. The study supports advancements in model inversion techniques for plant trait retrieval and enhances the applicability of remote sensing in precision agriculture.
Objectives and Research questions
The primary objective of this thesis is to improve the estimation of crop biochemical and biophysical traits by coupling Functional-Structural Plant (FSP) modelling with radiative transfer models (RTMs), including a 3D ray-tracing approach and PROSAIL. This integrated framework will enable a better understanding of light interactions within the canopy and enhance the accuracy of remote sensing-based plant trait retrievals.
- How does coupling FSP with 3D ray-tracing and PROSAIL improve the estimation of canopy structural and biochemical traits?
- What are the key advantages of using synthetic spectral datasets for evaluating model inversion techniques?
- To what extent do different canopy architectures and physiological properties affect remote sensing-based trait retrievals?
Requirements
- Required: GeoScripting, Remote Sensing, Advance Earth Observation
- Optional: Spatial Modelling and Statistics, Deep Learning
Literature and information
- Jacquemoud, S., & Baret, F. (1990). PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 34(2), 75–91.
- Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P. J., Asner, G. P., François, C., & Ustin, S. L. (2009). PROSPECT + SAIL models: A review of use for vegetation characterization. Remote Sensing of Environment, 113(SUPPL. 1), S56–S66.
- Camino, C., Gonzalez-Dugo, V., Hernandez, P., & Zarco-Tejada, P. J. P. J. (2019). Radiative transfer Vcmax estimation from hyperspectral imagery and SIF retrievals to assess photosynthetic performance in rainfed and irrigated plant phenotyping trials. Remote Sensing of Environment, 231(April), 111186.
Expected reading list before starting the thesis research
- Féret, J. B., Berger, K., de Boissieu, F., & Malenovský, Z. (2021). PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents. Remote Sensing of Environment, 252(October 2020).
- Morales, A., Kottelenberg, D. B., Ernst, A., Vezy, R., & Evers, J. B. (2024). The Virtual Plant Laboratory: a modern plant modeling framework in Julia.
Theme(s): Sensing & measuring; Modelling & visualisation