Abstract Urban areas and their vertical characteristics have a manifold and far-reaching impact on our environment. However, openly accessible information at high spatial resolution is still missing at large for complete countries or regions. In this study, we combined Sentinel-1A/B and Sentinel-2A/B time series to map building heights for entire Germany on a 10 m grid resolving built-up structures in rural and urban contexts. We utilized information from the spectral/polarization, temporal and spatial dimensions by combining band-wise temporal aggregation statistics with morphological metrics. We trained machine learning regression models with highly accurate building height information from several 3D building models. The novelty of this method lies in the very fine resolution yet large spatial extent to which it can be applied, as well as in the use of building shadows in optical imagery. Results indicate that both radar-only and optical-only models can be used to predict building height, but the synergistic combination of both data sources leads to superior results. When testing the model against independent datasets, very consistent performance was achieved (frequency-weighted RMSE of 2.9 m to 3.5 m), which suggests that the prediction of the most frequently occurring buildings was robust. The average building height varies considerably across Germany with lower buildings in Eastern and South-Eastern Germany and taller ones along the highly urbanized areas in Western Germany. We emphasize the straightforward applicability of this approach on the national scale. It mostly relies on freely available satellite imagery and open source software, which potentially permit frequent update cycles and cost-effective mapping that may be relevant for a plethora of different applications, e.g. physical analysis of structural features or mapping society's resource usage.

中文翻译：

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使用 Sentinel-1 和 Sentinel-2 时间序列的国家尺度建筑高度映射

摘要 城市地区及其垂直特征对我们的环境产生多方面而深远的影响。然而，对于完整的国家或地区来说，高空间分辨率的可公开访问的信息仍然普遍缺失。在这项研究中，我们结合 Sentinel-1A/B 和 Sentinel-2A/B 时间序列，在 10 m 网格上绘制整个德国的建筑高度，解析农村和城市环境中的建筑结构。我们通过将带状时间聚合统计与形态指标相结合，利用了来自光谱/极化、时间和空间维度的信息。我们使用来自多个 3D 建筑模型的高度准确的建筑高度信息来训练机器学习回归模型。这种方法的新颖之处在于它可以应用的非常精细的分辨率和大的空间范围，以及在光学图像中使用建筑阴影。结果表明，仅雷达模型和仅光学模型均可用于预测建筑物高度，但两种数据源的协同组合可产生出色的结果。在针对独立数据集测试模型时，实现了非常一致的性能（频率加权 RMSE 为 2.9 m 到 3.5 m），这表明对最频繁出现的建筑物的预测是可靠的。德国各地的平均建筑高度差异很大，德国东部和东南部的建筑较低，而德国西部高度城市化的地区建筑较高。我们强调这种方法在全国范围内的直接适用性。它主要依赖于免费提供的卫星图像和开源软件，