Fusing Landsat 8 and Sentinel-2 data for 10-m dense time-series imagery using a degradation-term constrained deep network

Abstract

Dense medium-resolution imagery is essential for fine-scale time-series applications. The combined use of Landsat 8 and Sentinel-2 can derive 10-m time-series imagery at a nominal temporal resolution of ∼ 2.9 days. Specifically, Landsat images can be downscaled to a 10-m resolution by fusing them with temporally adjacent Sentinel-2 images. Current approaches simply use a linear model or a shallow network that is insufficient to obtain the complex mapping between inputs and outputs, and they rarely consider the temporal variation issue, especially for scenes experiencing land cover changes. Facing these limitations, we proposed a degradation-term constrained spatiotemporal fusion network (DSTFN). Technically, a deep network architecture incorporating residual dense blocks and attention mechanism modules is adopted to enhance the feature representation and extraction. A degradation constraint term is embedded into the loss function to maximize the use of the input coarse-resolution image and improve the capability of predicting change. A series of experiments based on two new datasets indicate that DSTFN achieves the best quantitative scores in every test and is thus effective and robust. In 20 resolution-degraded tests, on average, DSTFN decreases the mean relative error by 0.85%–5.35% and increases the peak signal-to-noise ratio 0.97–6.23 relative to baseline approaches. The tests featuring diverse temporal dynamics also confirm the strong generalization ability of DSTFN to deal with land cover change. The proposed network can be used to produce 10-m dense time-series imagery and shows great promise for a variety of time-series analyses and applications. The test materials are expected to be employed as standard datasets for future model assessment.