Fusing or filling: Which strategy can better reconstruct high-quality fine-resolution satellite time series?

Abstract

High-quality fine-resolution satellite time series data are important for monitoring land surface dynamics in heterogeneous areas. However, the quality of raw satellite time series is affected by clouds and the revisit frequency. Currently, there are two major strategies to reconstruct high-quality fine-resolution time series: the interpolation of the missing pixels using auxiliary data from the same satellite (known as filling) and the fusion of fine-resolution and coarse-resolution images (known as fusing). These two strategies use different principles and input data to reach the same goal, but which one is superior in different scenarios is not known. Therefore, this study fills this research gap by comparing two representative methods from filling and fusing: the Neighborhood Similar Pixel Interpolator (NSPI) for filling and the Flexible Spatiotemporal DAta Fusion (FSDAF) for fusing. The potential factors affecting the accuracy of the two methods were investigated using two simulated experiments. The results show that (1) the accuracy of both methods decreases with the time interval between the image to be reconstructed and the auxiliary image; (2) NSPI is generally better than FSDAF for reconstructing images with small cloud patches but this superiority is insignificant in homogeneous areas; (3) the accuracy of NSPI significantly decreases with cloud size, and NSPI is worse than FSDAF for reconstructing images with large clouds; and (4) the performance of FSDAF is significantly affected by the scale difference between the fine- and coarse-resolution images, especially for heterogeneous areas. The findings of this study can help users select the appropriate method to reconstruct satellite time series for their specific applications.