An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data

文献类型： 外文期刊

第一作者： Zhang, Wei

作者： Zhang, Wei;Qin, Zhihao;Zhang, Wei;Li, Ainong;Jin, Huaan;Bian, Jinhu;Zhang, Zhengjian;Lei, Guangbin;Huang, Chengquan

作者机构：

关键词： data fusion;linear spectral mixing model;multi-resolution segmentation;sliding window;temporal weight

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2013 年 5 卷 10 期

页码：

收录情况： SCI

摘要： Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving the ability of monitoring rapid landscape changes at fine scales (e.g., 30 m). One approach to acquire them is by fusing observations from sensors with different characteristics (e.g., Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS)). The existing data fusion algorithms, such as the Spatial and Temporal Data Fusion Model (STDFM), have achieved some significant progress in this field. This paper puts forward an Enhanced Spatial and Temporal Data Fusion Model (ESTDFM) based on the STDFM algorithm, by introducing a patch-based ISODATA classification method, the sliding window technology, and the temporal-weight concept. Time-series ETM+ and MODIS surface reflectance are used as test data for comparing the two algorithms. Results show that the prediction ability of the ESTDFM algorithm has been significantly improved, and is even more satisfactory in the near-infrared band (the contrasting average absolute difference [AAD]: 0.0167 vs. 0.0265). The enhanced algorithm will support subsequent research on monitoring land surface dynamic changes at finer scales.

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