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Updated: Jun 29, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
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Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

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基于变化和基于形状的数据融合方法在精细分辨率的陆地表面现象学监测中与Landsat和Sentinel-2数据的比较

Caiqun Wang1, Tao He1, Dan-Xia Song2

  • 1Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.

The Science of the total environment
|March 28, 2024
PubMed
概括

重建精细分辨率的陆地表面现象学 (LSP) 对农业和气候研究至关重要. 时空形状匹配模型 (SSMM) 和灵活的时空数据融合 (FSDAF) 方法显示了与观测的最佳一致性,在准确性方面表现优于其他方法.

关键词:
基于变化的数据融合.数据质量数据质量数据质量协调的Landsat和哨兵-2号卫星陆地表面的现象学基于形状的数据融合.空间异质性 空间异质性

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