Evaluating a spatiotemporal shape-matching model for the generation of synthetic high spatiotemporal resolution time series of multiple satellite data

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Time series of high spatiotemporal resolution satellite data are required to monitor land surface biophysical properties and their seasonal and inter-annual dynamics at sub-field to field scales. To generate such time series, various algorithms have been developed to fuse infrequent cloud-free Landsat observations with daily Moderate Resolution Imaging Spectroradiometer (MODIS) observations. An early and widely used Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was developed based on the assumptions that observations from the two sensors are consistent in terms of spatial aggregation and temporal variation. These assumptions are not always valid in the real world, especially in a complex heterogeneous region. In this study, we investigated a Spatiotemporal Shape-Matching Model (SSMM) to generate synthetic time series of high spatiotemporal resolution satellite data. The SSMM, which is conceptually different from the image pair-based STARFM and STARFM-like approaches, makes full use of all spatiotemporally matched fine and coarse resolution data in an entire time series to establish a temporally uniformed fusion model for a given fine resolution pixel. The SSMM assumes that the temporal shapes of both the fine and coarse resolution time series are similar, but their magnitudes and phenological phases could differ largely even for the same vegetation type. This study assessed the capability of the SSMM to generate high spatiotemporal resolution time series of two-band Enhanced Vegetation Index (EVI2). Specifically, we generated the synthetic 30 m time series using the SSMM and STARFM algorithms in the northeastern United States based on 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) data and 30 m Landsat 8 and Sentinel-2 observations. We then evaluated the SSMM-derived 30 m time series across 15 land cover types and various degrees of heterogeneity. The result indicates that the SSMM is able to effectively generate synthetic time series in all different land cover types, which has advantages over the STARFM approach. Although the SSMM performance is degraded in heterogeneous regions, it can explain 82–91% of variations in 30 m EVI2 time series and produce constant root mean square error (0.053–0.056) across various levels of heterogeneity. Moreover, the SSMM can explain 87%-93% and 69%-87% of variation in 30 m EVI2 time series, respectively, with the models established using more than 10 pairs and 4–10 pairs of fine and coarse resolution observations. This suggests that the SSMM is also capable of generating high spatiotemporal resolution time series using historical Landsat time series with limited cloud-free observations, which are critical for studying vegetation dynamics and monitoring crop conditions.