Downscaling of MODIS leaf area index using landsat vegetation index

Abstract

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.

KEYWORDS:

• LAI
• EVI
• downscaling model
• regression analysis
• high resolution

Acknowledgments

Vegetation type and field measured LAI data were processed within the FP7 EU project MyWater “Merging hydrologic models and Earth observation data for reliable information on water”. MODIS and Landsat images were downloaded from USGS.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Table Annex 1. Detailed list of in-situ measured LAI and corresponding LAI estimated at Landsat spatial resolution for Nestos study area per Day Of Year fieldwork took place. N/A means that due to cloud presence the downscaling model was not able to estimate a LAI value for the specified location.

Paper highlights

• 4 diverse sites with respect to geographic, climatic and vegetative characteristics studied.

• Estimated LAI values showed moderate-high correlation with field LAI data in the majority of cases examined.

• Lowest correlation was observed during the rainy season.

• Downscaling model gave statistically significant results.

• LAI values correlated better with the field measurements during the dry season and for dryer climates.

Additional information

Funding

This work was supported by the Research Committee of the Aristotle University of Thessaloniki (Greece), grant number 90773 “Improvement of the estimation of Leaf Area Index (LAI) at basin scale using satellite images”.