Ji et al. (2025) Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-11-20
• Authors: Fujiang Ji, Ting Zheng, Alexey N. Shiklomanov, Ruqi Yang, Philip A. Townsend, Fa Li, Dalei Hao, Hamid Dashti, Kyle R. Kovach, Hangkai You, Junxiong Zhou, Min Chen
• DOI: 10.1016/j.rse.2025.115149

Research Groups

• Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, USA
• NASA Goddard Space Flight Center, USA
• Department of Earth System Science, Stanford University, USA
• Atmospheric, Climate, & Earth Sciences Division, Pacific Northwest National Laboratory, USA
• Department of Bioproducts & Biosystems Engineering, University of Minnesota Twin Cities, USA

Short Summary

This study developed a multi-stage framework leveraging PRISMA spaceborne and NEON AOP hyperspectral data to investigate the seasonal dynamics of four key plant traits across diverse U.S. forest types, demonstrating PRISMA's capability to reliably track these traits and identifying their environmental drivers.

Objective

• To investigate the seasonal dynamics of four key plant traits (chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content) across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. using PRISMA spaceborne hyperspectral data, and to identify the primary environmental drivers of their seasonal and spatial variability.

Study Configuration

• Spatial Scale: Eleven NEON sites across diverse forest types and ecoregions in the contiguous U.S.; PRISMA spatial resolution of 30 m.
• Temporal Scale: Seasonal variability; PRISMA revisit time of approximately 29 days.

Methodology and Data

• Models used: Partial least-squares regression (PLSR); Multi-stage framework.
• Data sources:
  • Spaceborne hyperspectral data: PRecursore IperSpettrale della Missione Applicativa (PRISMA)
  • Airborne hyperspectral data: National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP)

Main Results

• PRISMA hyperspectral data reliably tracked seasonal variability in plant traits, achieving overall R² values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4 % to 8.4 % for the four traits.
• Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence.
• Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns.
• Seasonal variability was primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites.
• Spatial variability was primarily driven by soil properties, particularly during the peak growing season, with climatic factors becoming more prominent towards the end of the season at several sites.

Contributions

• Demonstrates the capability of PRISMA and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping.
• Provides valuable insights into the interactions between plant traits and environmental factors.
• Contributes to advancing the understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.
• Developed a multi-stage framework for leveraging spaceborne and airborne hyperspectral data for plant trait mapping.

Funding

• Not available in the provided text.

Citation

@article{Ji2025Tracking,
  author = {Ji, Fujiang and Zheng, Ting and Shiklomanov, Alexey N. and Yang, Ruqi and Townsend, Philip A. and Li, Fa and Hao, Dalei and Dashti, Hamid and Kovach, Kyle R. and You, Hangkai and Zhou, Junxiong and Chen, Min},
  title = {Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115149},
  url = {https://doi.org/10.1016/j.rse.2025.115149}
}