Bastian Siegmann

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In modern agriculture, the spatially differentiated assessment of the leaf area index (LAI) is of utmost importance to allow an adapted field management. Current hyperspectral satellite systems provide information with a high spectral but only a medium spatial resolution. Due to the limited ground sampling distance (GSD), hyperspectral satellite images are(More)
Remote sensing data acquired from satellites are a vital information source for precision agriculture to assess current crop conditions. Field measurements of plant parameters, like the leaf area index (LAI), serve as a crucial basis to validate parameter maps derived from satellite images. Traditionally, in-situ LAI measurements are collected manually.(More)
Remote sensing is a suitable tool for estimating the spatial variability of crop canopy characteristics, such as canopy chlorophyll content (CCC) and green ground cover (GGC%), which are often used for crop productivity analysis and site-specific crop management. Empirical relationships exist between different vegetation indices (VI) and CCC and GGC% that(More)
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