Monitoring crop growth inter-annual variability from MODIS time series: Performance comparison between crop specific green area index and current global leaf area index products

Abstract

Optical remote sensing time series can be used to retrieve biophysical variables indicating crop status, such as leaf area index (LAI) or, more appropriately, green are index (GAI). If these variables are sensible to inter-annual seasonal variations, they can be of great value for crop growth monitoring, especially if they can be coupled with ecophysiological models using data assimilation. This study presents a multi-annual comparison between currently available global LAI products and crop specific GAI retrieved from MODIS 250 m imagery obtained by controlling pixel-target adequacy. This comparison is done over a region in Belgium with fragmented agricultural landscapes. Results indicate that, by assuring a crop specific information and smoothing information using thermal time, the GAI product has a higher sensitivity to the variability of growing conditions that may be encountered across the region, and thus out-performs the other LAI products.