LGHAP v2: Global Daily 1-km Gap-free Aerosol Data (2000-2021)

22-year daily gap-free aerosol optical depth (AOD) and PM2.5 concentration grids at 1-km resolution covering global land areas from 2000 to 2021. The dataset was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework integrating satellite, ground monitor, and model data. Data is provided in NetCDF format with Python, MATLAB, R, and IDL codes for reading and visualization.

Use Cases

• Analyzing long-term global trends in aerosol optical depth based on the 22-year daily time series.
• Modeling near-surface PM2.5 concentrations across regions with sparse ground monitors using the described scene-aware ensemble learning method.
• Studying the spatial distribution and variability of air pollutants at a 1-kilometer resolution.
• Validating atmospheric and climate models with gap-free, high-resolution AOD data.

Strengths

• 22-year temporal coverage from 2000 to 2021 provides a long-term record for trend analysis.
• Daily 1-kilometer resolution offers high spatial and temporal detail for global land areas.
• Data is gap-free, addressing a common limitation in remote sensing datasets.
• Integrates multiple data sources including satellites, ground monitors, and numerical models.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

East China Normal University

Collection Method

Integration of multimodal AODs and air quality measurements from diverse satellites, ground monitors, and numerical models using an improved big earth data analytic framework.

Time Range

2000 to 2021

Freshness

Data covers up to 2021; last dataset update date is unknown.

Geography

Global land area