LC-TSRNet: A Land-Cover-Guided Two-Stage Network for Annual MODIS Surface Reflectance Time Series Reconstruction

Key points

• Most existing methods rely on cloud-free observations from optical time series or introduce SAR time series as auxiliary information.1
• To address these issues, this study proposes LC-TSRNet for seamless reconstruction of annual MODIS SR products.1
• In the first stage, an annual land cover map is introduced as prior knowledge, and CSPM is designed by leveraging the spectral similarity within identical land cover types, thereby providing prior spectral constraints for missing regions.1
• In the second stage, to address large-area, persistent, and overlapping gaps in MODIS imagery, SMRM is designed to adaptively exploit and integrate multi-scale spatiotemporal contextual information.1

Sources (1)

• [1]LC-TSRNet: A Land-Cover-Guided Two-Stage Network for Annual MODIS Surface Reflectance Time Series Reconstruction
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 08:56 AM

  MODIS surface reflectance (SR) products often suffer from data gaps due to cloud contamination and sensor malfunctions, limiting their applications in long-term and large-scale studies.

  Most existing methods rely on cloud-free observations from optical time series or introduce SAR time series as auxiliary information.

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