Fan et al. (2025) Remote sensing proxies underestimate fire-induced gross primary productivity loss and overestimate recovery in forests

Identification

• Journal: Agricultural and Forest Meteorology
• Year: 2025
• Date: 2025-12-03
• Authors: Xinyi Fan, Qinggaozi Zhu, Yingnan Wei, Ning Yao, Gang Zhao, Qiang Yu, Genghong Wu
• DOI: 10.1016/j.agrformet.2025.110963

Research Groups

• College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
• State Key Laboratory of Soil and Water Conservation and Desertification Control, Institute of Soil and Water Conservation Northwest A&F University, Yangling, Shaanxi, 712100, China
• New South Wales Department of Climate Change, Energy, the Environment and Water, Parramatta, New South Wales, Australia
• College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
• Key Lab of Agricultural Water and Soil Engineering of Education Ministry, Northwest A&F University, Yangling, Shaanxi, 712100, China

Short Summary

This study evaluated five global satellite-based Gross Primary Productivity (GPP) products and three complementary proxies against eddy covariance measurements at ten fire-affected sites, revealing systematic biases that underestimate fire-induced GPP loss and overestimate recovery, particularly in forests.

Objective

• To evaluate the accuracy of satellite-based Gross Primary Productivity (GPP) products and complementary proxies in quantifying fire-induced GPP loss and post-fire recovery across different ecosystems.

Study Configuration

• Spatial Scale: Ten fire-affected sites (five forest sites, five grass/shrub sites) globally.
• Temporal Scale: Multi-year records spanning pre- and post-fire periods.

Methodology and Data

• Models used: BESS GPP (process-based), FLUXCOM GPP (machine learning-based), FluxSat GPP (machine learning-based), GOSIF GPP (derived from reconstructed solar-induced chlorophyll fluorescence, SIF), MODIS GPP (light-use efficiency–based), GOSIF (reconstructed SIF), near-infrared reflectance of vegetation (NIRv), leaf area index (LAI).
• Data sources: Eddy covariance (EC) tower GPP measurements, satellite-based GPP products and proxies.

Main Results

• Satellite proxies generally underestimated fire-induced GPP loss, with forest sites showing the largest discrepancy: EC GPP declined by approximately 94%, while satellite estimates showed 47–88% decline.
• Most satellite products overestimated post-fire carbon gain and underestimated recovery time, often signaling premature recovery in forests.
• Grass and shrub ecosystems exhibited faster GPP rebound and closer agreement with satellite estimates compared to forests.
• BESS GPP and GOSIF better reproduced immediate GPP loss and recovery time, but still underestimated persistent suppression and overestimated cumulative uptake.
• EC data revealed reduced post-fire GPP sensitivity to light, temperature, and vapor pressure deficit in forests, a phenomenon not captured by satellite products.

Contributions

• Provides a comprehensive benchmarking of multiple global satellite-based GPP products and proxies against ground-truth eddy covariance data in fire-affected ecosystems.
• Quantifies systematic biases in current satellite proxies, highlighting their underestimation of GPP loss and overestimation of recovery, especially in forests.
• Emphasizes the challenges in accurately monitoring forest recovery using remote sensing and underscores the need for disturbance-responsive models.
• Reveals that satellite products fail to capture reduced post-fire GPP sensitivity to environmental factors in forests, identifying a critical area for model improvement.

Funding

• Not specified in the provided text.

Citation

@article{Fan2025Remote,
  author = {Fan, Xinyi and Zhu, Qinggaozi and Wei, Yingnan and Yao, Ning and Zhao, Gang and Yu, Qiang and Wu, Genghong},
  title = {Remote sensing proxies underestimate fire-induced gross primary productivity loss and overestimate recovery in forests},
  journal = {Agricultural and Forest Meteorology},
  year = {2025},
  doi = {10.1016/j.agrformet.2025.110963},
  url = {https://doi.org/10.1016/j.agrformet.2025.110963}
}