GIMMS LAI4g: Global Leaf Area Index from 1982 to 2020

GIMMS LAI4g is a half-monthly, global Leaf Area Index (LAI) dataset for the period 1982 to 2020. The product was generated by researchers from Peking University using a Back Propagation Neural Network and data consolidation method with high-quality Landsat samples. It aims to provide spatiotemporally consistent data for monitoring vegetation dynamics and Earth system modeling.

Use Cases

• Monitoring long-term global vegetation trends based on the 39-year temporal coverage.
• Calibrating or validating Earth system models based on the spatiotemporally consistent LAI estimates.
• Analyzing vegetation response to climate change based on the product's high reported accuracy (R² of 0.95).
• Studying biome-specific vegetation dynamics based on the product's performance across different vegetation biomes.

Strengths

• Covers a 39-year period from 1982 to 2020, providing a long-term record.
• Reported high accuracy with an R² of 0.95, mean absolute error of 0.18 m²/m², and mean absolute percentage error of 15%.
• Uses 3.6 million high-quality global Landsat LAI samples for model development.
• Designed to eliminate effects of satellite orbital drift and sensor degradation, improving temporal consistency.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Peking University

Collection Method

Generated using a Back Propagation Neural Network (BPNN) and data consolidation method, incorporating the PKU GIMMS NDVI product and Landsat samples.

Time Range

1982 to 2020

Geography

Global