Abstract
The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth’s surface. However, missing observations (i.e., gaps) caused by clouds and cloud shadows, orbit and sensing geometry, and sensor issues have broadly limited the development of Landsat time-series applications. Due to the large area and temporal and spatial irregularity of time-series gaps, it is difficult to find an efficient and highly precise method to fill them. The Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) method has been proposed and delivered good performance in filling large-area gaps of single-date Landsat images. However, it can be less practical for a time series longer than one year due to the lack of mechanics that exclude dissimilar data in time series (e.g., different phenology or changes in land cover). To solve this problem, this study proposes a new gap-filling method, Spectral Temporal Information for Missing Data Reconstruction (STIMDR), and examines its performance in Landsat reflectance time series. Two groups of experiments, including 2000 × 2000 pixel Landsat single-date images and Landsat time series acquired from four sites (Kenya, Finland, Germany, and China), were performed to test the new method. We simulated artificial gaps to evaluate predicted pixel values with real observations. Quantitative and qualitative evaluations of gap-filled images through comparisons with other state-of-the-art methods confirmed the more robust and accurate performance of the proposed method. In addition, the proposed method was also able to fill gaps contaminated by extreme cloud cover for a period (e.g., winter in high-latitude areas). A down-stream task of random forest supervised classification through both gap-filled simulated datasets and the original valid datasets verified that STIMDR-generated products are relevant to the user community for land cover applications.
Highlights
Time series has become the dominant form of remote sensing data for monitoring changes on the land surface [1,2]
Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) was developed to recover large-area gaps in Landsat images [18,50]
Eight gap-filled results are compared in Figure 5, and their pixel-based root mean square error (RMSE) values are displayed in Supplementary Figure S3
Summary
Time series has become the dominant form of remote sensing data for monitoring changes on the land surface [1,2]. Since the launch of the first Landsat sensor in 1972, Landsat satellites have observed changes in the land surface from space and have made the longest medium-resolution time series available for free [3,4]. Landsat time-series applications are limited by missing observations (i.e., gaps), which continue to be a major obstacle, when a dense time series is Remote Sens. 2022, 14, 172 required [10] This has stimulated research to reconstruct missing values and to produce gap-free imagery [10]. Reconstructing gap-free Landsat images in a time series is often more challenging than reconstructing single-date images for additional considerations of time series features
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