A New Spatial–Temporal Depthwise Separable Convolutional Fusion Network for Generating Landsat 8-Day Surface Reflectance Time Series over Forest Regions

Abstract

Landsat has provided the longest fine resolution data archive of Earth’s environment since 1972; however, one of the challenges in using Landsat data for various applications is its frequent large data gaps and heavy cloud contaminations. One pressing research topic is to generate the regular time series by integrating coarse-resolution satellite data through data fusion techniques. This study presents a novel spatiotemporal fusion (STF) method based on a depthwise separable convolutional neural network (DSC), namely, STFDSC, to generate Landsat-surface reflectance time series at 8-day intervals by fusing Landsat 30 m with high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m surface reflectance data. The STFDSC method consists of three main stages: feature extraction, feature fusion and prediction. Features were first extracted from Landsat and MODIS surface reflectance changes, and the extracted multilevel features were then stacked and fused. Both low-level and middle-level features that were generally ignored in convolutional neural network (CNN)-based fusion models were included in STFDSC to avoid key information loss and thus ensure high prediction accuracy. The prediction stage generated a Landsat residual image and is combined with original Landsat data to obtain predictions of Landsat imagery at the target date. The performance of STFDSC was evaluated in the Greater Khingan Mountains (GKM) in Northeast China and the Ziwuling (ZWL) forest region in Northwest China. A comparison of STFDSC with four published fusion methods, including two classic fusion methods (FSDAF, ESTARFM) and two machine learning methods (EDCSTFN and STFNET), was also carried out. The results showed that STFDSC made stable and more accurate predictions of Landsat surface reflectance than other methods in both the GKM and ZWL regions. The root-mean-square-errors (RMSEs) of TM bands 2, 3, 4, and 7 were 0.0046, 0.0038, 0.0143, and 0.0055 in GKM, respectively, and 0.0246, 0.0176, 0.0280, and 0.0141 in ZWL, respectively; it can be potentially used for generating the global surface reflectance and other high-level land products.