HLS Merra2 Gppflux: Harmonized Satellite Imagery for GPP Regression

Harmonized Landsat and Sentinel-2 multispectral reflectance imagery and MERRA-2 observations centered around eddy covariance flux towers. The dataset includes corresponding Gross Primary Productivity data and is intended to fine-tune geospatial foundation models for GPP regression. It was created by ibm-nasa-geospatial and last updated on October 25, 2024.

Use Cases

• Fine-tuning geospatial foundation models for GPP regression based on multispectral reflectance imagery.
• Training models to predict Gross Primary Productivity based on combined satellite and atmospheric reanalysis data.
• Developing remote sensing applications for carbon cycle monitoring using data from eddy covariance flux towers.

Strengths

• Integrates data from multiple authoritative sources: Harmonized Landsat and Sentinel-2 imagery and NASA's MERRA-2 atmospheric reanalysis.
• Specifically designed for the concrete task of fine-tuning models for GPP regression.
• Data is centered around validated ground-truth locations from eddy covariance flux towers.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale training.
• Description metadata is limited; actual data structure and file formats require manual inspection from the linked page.

Provenance

Source

ibm-nasa-geospatial collaboration.

Collection Method

Likely combines satellite imagery from the Harmonized Landsat Sentinel-2 project with atmospheric data from MERRA-2 and ground measurements from flux towers.

Freshness

Last updated 2024-10-25 21:40:26.

Geography

Locations of eddy covariance flux towers (specific regions unknown).