Estimation and validation of leaf area index time series for crops on 5M scale from space

Abstract

Time series of Leaf Area Index (LAI) is of utmost importance for various disciplines of bio- and geosciences where satellite remote sensing makes LAI estimation possible for large areas. Remote sensing LAI, validated against in situ LAI (LAIinsitu), is used as base for calculating LAI for large areas e.g. on catchment scale. Various vegetation indices (NDVI, SAVI, both with and without substituting the red band with red-edge band) were applied for better estimates of LAIrapideye. SAVI (Soil Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index) present same correlation between remote sensing based predicted LAI (LAIrapideye) against LAIinsitu in winter wheat fields. Both NDVI and SAVI with red-edge band showed improved correlation of remote sensing based VI and in situ measurements. Prior to vegetation indices calculation, radiometric normalization was applied to the time series of RapidEye data. To test the impact of radiometric normalization for calculating vegetation indices on a time series of satellite images, pre- and post-radiometric normalization LAIrapideye was compared. More precise and high resolution estimation of LAI for large areas is of vital importance for improving evapotranspiration and soil moisture.