On the Generation of Gapless and Seamless Daily Surface Reflectance Data

The land surface reflectance data are indispensable to generate many other land products. Global land surface reflectance data have been routinely produced from remote sensing sensors aboard different satellite platforms. However, the original data, especially the daily data, suffer from a large number of spatial gaps, which result from atmospheric contamination and instrument deficiencies. This seriously limits their further applications. Many composite products with less spatial gaps have been generated to solve the above problem, but they easily sacrifice their temporal resolutions of original data. Even worse, they cannot be directly implemented in realistic applications because of the noise and composite seams. This paper proposes a temporal-spatial reconstruction method (TSRM) to generate daily gapless and seamless land surface reflectance data. The TSRM integrates both temporal and spatial information for recovering different land cover types using three processing steps. First, spatial gaps are coarsely filled with multiyear weighted average (Step1). After that, all the gaps that are not filled in the first step are interpolated by using harmonic analysis of time series with true value constraint (Step2). Finally, the reconstructed results in the last step are seamlessly processed using the Poisson image editing method, and the seamless daily reflectance data set is generated (Step3). The Moderate Resolution Imaging Spectroradiometer reflectance data set (MOD09GA and MYD09GA) on two testing areas is selected to verify the performance of the proposed TSRM. Experimental results show that the TSRM has good performance with regard to maintaining the temporal and spatial integrity of the daily land surface reflectance data. Results on different testing sites also demonstrate that the TSRM preserves spectral integrity with clear seasonal trends for each spectral band.