Downscaling of MODIS leaf area index using landsat vegetation index

Several organizations provide satellite Leaf Area Index (LAI) data regularly, at various scales, at high frequency, but at low spatial resolution. This study attempted to enhance the spatial resolution of the MODIS LAI product to the Landsat resolution level. Four climatically diverse sites in Europe and Africa were selected as study areas. Regression analysis was applied between MODIS Enhanced Vegetation Index (EVI) and LAI data. The regression equations were used as input in a downscaling model, along with Landsat EVI images and land-cover maps. The estimated LAI values showed high correlation with field-measured LAI during the dry period. The model validation gave statistically significant results, with correlation coefficient values ranging from relatively low (0.25-0.32), to moderate (0.48-0.64) and high (0.72-0.94). Limited samples per vegetation type, the diversity of species within the same vegetation type, land-use/land-cover changes and saturated EVI values affected the accuracy of the downscaling model.

Automated summary

This study aimed to enhance the spatial resolution of the MODIS LAI product to the Landsat resolution level using a downscaling model. The model was validated in different locations in Europe and Africa and showed high correlation with field-measured LAI values, improving the accuracy of the data.