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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Conditioning Water Stages From Satellite Imagery on Uncertain Data Points
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Conditioning Water Stages From Satellite Imagery on Uncertain Data Points

机译：在不确定的数据点上根据卫星图像调节水位

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Observed spatially distributed water stages with uncertainty are of considerable importance for flood modeling and management purposes but are difficult to collect in the field during a flood event. Synthetic aperture radar (SAR) remote sensing offers an inviting alternative to provide this kind of data. A straightforward technique to derive water stages from a single SAR flood image is to extract heights from a digital elevation model at the flood boundaries. Schumann have presented a regression modeling approach as an improvement to this simple technique. However, regression modeling associated with their model may restrict output to mapping purposes rather than extend it to integration with other data or models. This letter introduces an inviting alternative that conducts statistical analysis on river cross-sectional data points, thereby allowing uncertainty assessment of remote-sensing-derived water stages without any regression modeling constraint. This renders remote-sensing data fit for, e.g., flood inundation model evaluation with uncertainty in observations and data assimilation studies, where (linear) “transformation,” i.e., modeling, to observed data should be minimal.
机译：观测到的具有不确定性的空间分布水位对于洪水建模和管理目的非常重要，但在洪水事件期间很难在野外收集。合成孔径雷达（SAR）遥感提供了一种诱人的替代方法来提供此类数据。从单个SAR洪水图像中得出水位的直接方法是从洪水边界的数字高程模型中提取高度。 Schumann提出了一种回归建模方法，作为对该简单技术的改进。但是，与其模型关联的回归建模可能会将输出限制为映射目的，而不是将其扩展为与其他数据或模型集成。这封信介绍了一个诱人的替代方法，该方法可以对河流横截面数据点进行统计分析，从而可以对遥感衍生的水位进行不确定性评估，而无需任何回归建模约束。这使得遥感数据适合于例如洪水淹没模型评估，但在观测和数据同化研究中具有不确定性，其中对观测数据的（线性）“转换”即建模应最小。

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