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Comparison of leaf area indices for grasslands within the Alpine upland based on multi-scale satellite data time series and radiation transfer modeling

机译:基于多尺度卫星数据时间序列和辐射传输模型的高山高地草原叶面积指数比较

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In this study, the leaf area index (LAI) of grasslands in the Bavarian Alpine uplands has been derived using inverted radiation transfer modeling (RTM) on original as well as simulated remote sensing data time series. The spatial resolutions of the data sets range from 6.5 to 250 m. While the high resolution data are available for four points in the vegetation period, the medium resolution time series consist of weekly scenes. The aim is to investigate the performance of the inverse RTM when applied to satellite data of different spatial and temporal resolutions. Further, we determine the adequate resolutions of remote sensing data for LAI retrieval in a heterogeneous landscape. All results were validated using in situ measurements. While the algorithm proves to be generally applicable in this challenging landscape on different scales, retrieval accuracy increases with higher spatial resolution. Satellite images with a spatial resolution up to 20 m are identified as a good compromise between accurate results and spatial detail. The 250 m resolution LAI time series on the other hand provides valuable information on the phenology and sudden LAI reductions caused by harvest, which are not captured by the high spatial resolution time series with few scenes.
机译:在这项研究中,巴伐利亚阿尔卑斯山高地草原的叶面积指数(LAI)已使用原始和模拟遥感数据时间序列上的反向辐射传输建模(RTM)得出。数据集的空间分辨率范围从6.5到250 m。尽管可以在植被期的四个点获得高分辨率数据,但中等分辨率时间序列包含每周一次的场景。目的是研究反RTM在应用于具有不同时空分辨率的卫星数据时的性能。此外,我们为异质景观中的LAI检索确定了足够的遥感数据分辨率。所有结果均使用原位测量验证。尽管该算法已被证明可普遍适用于不同规模的这一具有挑战性的领域,但随着空间分辨率的提高,检索精度也会提高。空间分辨率高达20 m的卫星图像被认为是准确结果与空间细节之间的良好折衷。另一方面,250 m分辨率的LAI时间序列提供了有关物候和收获导致的LAI突然减少的有价值的信息,而很少有场景的高空间分辨率时间序列无法捕获这些信息。

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