Bastian Siegmann

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The exploration of bio-physical crop parameters is fundamental for the efficiency of smart agriculture. The leaf area index (LAI) is one of the most important crop parameters and serves as a valuable indicator for yield-limiting processes. It contributes to situational awareness ranging from agricultural optimization to global economy. In this paper, we(More)
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)
The efficiency of precision agriculture fundamentally depends on the exploration of bio-physical and bio-chemical plant parameters and the assessment of current crop conditions. The leaf area index (LAI) represents one of the most important crop parameters and is defined as the ratio of foliage area to ground area. It is widely-used in agriculture and(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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