Kesselring (2025) Remote Sensing of 3D Gas Exchange Variables Across Forests

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Open MIND
• Year: 2025
• Date: 2025-10-09
• Authors: Kesselring, Jasmin
• DOI: 10.5167/uzh-279962

Research Groups

• Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Davos, Switzerland
• Research institutions associated with the Laegern forest near Zurich, Switzerland (e.g., ETH Zurich)

Short Summary

This thesis quantifies the uncertainty in remote sensing estimates of forest evapotranspiration (ET) and gross primary production (GPP) caused by the top-of-canopy (TOC) perspective, revealing that canopy structure and light distribution are primary drivers of this divergence. It highlights the need for multi-sensor integration to improve large-scale gas exchange models.

Objective

• To investigate the magnitude and drivers of uncertainty induced by the top-of-canopy (TOC) perspective of remote sensing in the estimation of evapotranspiration (ET) and gross primary production (GPP) across two structurally different forest ecosystems in Switzerland.

Study Configuration

• Spatial Scale: Two specific forest sites in Switzerland (evergreen coniferous forest in Davos and deciduous broadleaf Laegern forest near Zurich); implications for large-scale and regional monitoring with varying spatial resolutions.
• Temporal Scale: Continuous monitoring, long-term trends, and time series analysis, implying seasonal to multi-year observations.

Methodology and Data

• Models used:
  • Discrete Anisotropic Radiative Transfer (DART) model for 3D radiative transfer simulations.
• Data sources:
  • In situ flux measurements (eddy flux towers for ET and GPP).
  • Vegetation density derived from Global Navigation Satellite System (GNSS) antennas (used to calculate Vegetation Optical Depth - VOD).
  • In situ meteorological variables.
  • Multi-sensor remote sensing data:
    • Satellite-based spectral indices (Sentinel-2).

Main Results

• The top-of-canopy (TOC) perspective of remote sensing introduces significant uncertainty in estimates of evapotranspiration (ET) and gross primary production (GPP) compared to full-canopy derived parameters.
• This uncertainty is primarily driven by the forest canopy structure and the heterogeneous light distribution within the canopy.
• DART model simulations revealed distinct patterns of uncertainty between TOC and full-canopy derived parameters.
• Comparisons of satellite-based (Sentinel-2) spectral indices with in situ full-canopy parameters showed similar structure-induced uncertainty patterns for ET and GPP.
• Time series analysis highlighted limitations of purely TOC remote sensing approaches for capturing dynamic processes such as canopy water content.
• Coarser resolution satellite-based data exhibited a particularly high smoothing effect in structurally heterogeneous canopies, such as the evergreen coniferous forest.
• A clear divergence was demonstrated between gas exchange drivers and proxies associated with the TOC perspective of passive optical remote sensing data and their 3D counterparts.

Contributions

• Quantifies the magnitude and identifies the key drivers (canopy structure, light distribution) of uncertainty in remote sensing-derived ET and GPP estimates due to the top-of-canopy perspective across different forest types.
• Provides insights into the limitations of purely TOC passive optical remote sensing for monitoring dynamic processes and heterogeneous forest structures.
• Emphasizes the critical need for integrating multiple remote sensing approaches, such as LiDAR and microwave technology (e.g., VOD), to enhance the accuracy of large-scale gas exchange models.
• Proposes a potential pathway for developing TOC uncertainty correction methods.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Kesselring2025Remote,
  author = {Kesselring, Jasmin},
  title = {Remote Sensing of 3D Gas Exchange Variables Across Forests},
  journal = {Open MIND},
  year = {2025},
  doi = {10.5167/uzh-279962},
  url = {https://doi.org/10.5167/uzh-279962}
}