Combining Filling and Fusion Strategies for Generating Synthetic Daily Landsat Time Series Image on Google Earth Engine

Landsat satellites have provided high-quality Earth observations for more than 40 years, which significantly benefits much research on agriculture, environment, ecology, and so on. However, its low temporal resolution (16-day) and disturbances such as cloud contamination, prevent its usage in some scenarios. Therefore, reconstructing the Landsat image series is always an important topic. Currently, the approaches for reconstructing that are massive, which mainly include the filling-based methods and fusion-based ones. However, their scalability and applicability in large-scale applications are limited. To address this problem, based on the Google Earth engine (GEE), which is a powerful cloud platform, this article introduces a GEE-based fusion and filling model (GFFM) for generating high-quality synthetic Landsat surface reflectance time series data. This model adopts a pixel-wise regression technique to fuse the Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data, providing a synthetic image series first. Then, a harmonic analysis is used to densify the Landsat image series. Finally, we utilize a Bayesian model average (BMA) as a weight function to integrate and adjust the previously obtained data to acquire the final seamless image series. We compare the proposed GFFM with some state-of-the-art fusion and filling approaches on various datasets. The experimental results demonstrate that the GFFM not only outperforms these fusion and filling approaches on different datasets, but also shows more robustness in cases of less and cloudy input data.