Time-Scale Extension Algorithm of MUSES LAI Products Using Image Super-Resolution

Presenting our TEA algorithm at IGARSS 2025, Brisbane
🏆 Recipient of IEEE GRSS Student Travel Grant (1,550 USD)

This study demonstrated that the Time-Scale Extension Algorithm (TEA) can successfully extend the time span of the 1km MUSES LAI product back to 1981. By leveraging deep learning-based image super-resolution, the TEA achieved superior performance compared to traditional bicubic interpolation, providing a more consistent and high-quality long-term LAI dataset.