Spatially and temporally continuous LAI datasets based on the mixed pixel decomposition method.

Area of Science:

• Earth and Environmental Sciences
• Remote Sensing
• Ecology

Background:

• Leaf Area Index (LAI) is crucial for assessing plant growth.
• Existing global LAI products from MODIS and AVHRR lack synchronized spatial and temporal resolutions.
• Low-resolution global LAI products obscure regional features due to diverse land cover.

Purpose of the Study:

• To develop an empirical model for generating high spatial and temporal resolution LAI products.
• To create a multi-decade time series of 1-km spatial resolution LAI.
• To integrate AVHRR and MODIS datasets for improved regional LAI estimation.

Main Methods:

• Developed a regression-based model using AVHRR Global Inventory Modelling and Mapping Studies Normalized Difference Vegetation Index (NDVI), MODIS LAI, and land cover data.
• Integrated multi-decade AVHRR and MODIS datasets for different land cover types.
• Evaluated the model using data from 2000 to 2006.

Main Results:

• The developed model demonstrated good consistency between retrieved LAI values from AVHRR NDVI and MODIS LAI.
• The method effectively integrates data from different sensors.
• Achieved improved spatial and temporal resolution for regional LAI estimation.

Conclusions:

• The simple regression-based model provides a viable method for generating high-resolution LAI products.
• This approach is suitable for regions lacking ground-based data.
• The model enhances regional vegetation monitoring capabilities by improving LAI data resolution.