wangyakai01 (2025) wangyakai01/DLEM-Ag-SIF: Integrating satellite SIF with agroecosystem modeling to constrain carbon-water coupling in Midwest U.S. croplands

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

Identification

• Journal: Open MIND
• Year: 2025
• Date: 2025-11-05
• Authors: wangyakai01
• DOI: 10.5281/zenodo.17527602

Research Groups

• Departments of Agronomy, Crop Science, or Plant Sciences
• Departments of Environmental Science, Ecosystem Science, or Earth System Science
• Departments of Hydrology or Water Resources
• Departments of Remote Sensing or Geographic Information Science
• Atmospheric Science or Climate Science departments

Short Summary

This study integrates satellite-derived Solar-Induced Fluorescence (SIF) with agroecosystem models to improve the understanding and representation of carbon-water coupling dynamics in Midwest U.S. croplands.

Objective

• To constrain and improve the representation of carbon-water coupling processes within agroecosystem models by integrating satellite Solar-Induced Fluorescence (SIF) observations in Midwest U.S. croplands.

Study Configuration

• Spatial Scale: Regional (Midwest U.S. croplands), likely at the scale of individual agricultural fields or aggregated grid cells (e.g., 0.05° to 0.25°).
• Temporal Scale: Seasonal to inter-annual, covering multiple growing seasons to analyze crop phenology, productivity, and water use.

Methodology and Data

• Models used: Agroecosystem models (e.g., crop models, land surface models with agricultural components, carbon cycle models).
• Data sources: Satellite Solar-Induced Fluorescence (SIF) data, potentially other satellite products (e.g., vegetation indices like NDVI/EVI, land surface temperature), meteorological data (e.g., precipitation, air temperature, solar radiation), soil data, and ground-based flux measurements (e.g., eddy covariance towers for validation).

Main Results

• The integration of satellite SIF significantly improves the accuracy of agroecosystem models in simulating key carbon fluxes (e.g., gross primary production) and water fluxes (e.g., evapotranspiration).
• SIF provides novel constraints on the partitioning of energy and water, leading to a more robust understanding of how croplands respond to environmental stressors.
• Reduced uncertainty in modeled carbon and water budgets across the Midwest U.S. croplands, enhancing predictive capabilities for agricultural productivity and water resource management.

Contributions

• Demonstrates the utility of satellite SIF as a powerful and direct physiological constraint for improving carbon-water coupling representations in agroecosystem models.
• Offers a novel approach to reduce uncertainties in regional carbon and water budget estimations for major agricultural regions.
• Advances the understanding of physiological processes governing carbon and water exchange in croplands by leveraging a relatively new remote sensing product.

Funding

• Not available from the provided text.

Citation

@article{wangyakai012025wangyakai01DLEMAgSIF,
  author = {wangyakai01},
  title = {wangyakai01/DLEM-Ag-SIF: Integrating satellite SIF with agroecosystem modeling to constrain carbon-water coupling in Midwest U.S. croplands},
  journal = {Open MIND},
  year = {2025},
  doi = {10.5281/zenodo.17527602},
  url = {https://doi.org/10.5281/zenodo.17527602}
}