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

Authors: ZHANG, X. - South Dakota State University; Gao, Feng; WANG, J. - South Dakota State University; YE, Y. - South Dakota State University

Submitted to: International Journal of Applied Earth Observation and Geoinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/12/2021
Publication Date: 9/16/2021
Citation: Zhang, X., Gao, F.N., Wang, J., Ye, Y. 2021. Evaluating a spatiotemporal shape-matching model for the generation of synthetic high spatiotemporal resolution time series of multiple satellite data. International Journal of Applied Earth Observation and Geoinformation. 104:102545. https://doi.org/10.1016/j.jag.2021.102545.
DOI: https://doi.org/10.1016/j.jag.2021.102545

Interpretive Summary: Remote sensing data at high spatial and temporal resolution are required for monitoring crop progress and conditions at the field scale. Data fusion approaches have been developed to fuse remote sensing images from different sensors for generating frequent observations at a high spatial resolution. However, these approaches are challenging to apply in highly heterogeneous areas when the prediction dates are far in the future. This paper presents a novel method to reconstruct the daily 30-m two-band Enhanced Vegetation Index (EVI2) using a Spatiotemporal Shape-Matching Model (SSMM). The SSMM approach was applied in the northeastern United States using 500-m Visible Infrared Imaging Radiometer Suite (VIIRS) data and 30-m Landsat 8 and Sentinel-2 images. Results show that SSMM can effectively generate EVI2 time series for different land cover types and mostly outperform the image pair-based data fusion algorithm. The resulting 30- m EVI2 time-series data would enable accurate crop monitoring at the sub-field to field scale.

Technical Abstract: Time series of high spatiotemporal resolution satellite data has been widely expected to monitor land surface biophysical properties and their seasonal and inter-annul dynamics at field scales. To generate such time series, various algorithms, including one of the most widely used Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), have been developed to fusion infrequent cloud-free Landsat observations with daily Moderate Resolution Imaging Spectroradiometer (MODIS) observations, which are 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 over a complex heterogeneous region. Therefore, this study investigated a Spatiotemporal ShapeMatching Model (SSMM) to generate synthetic time series of high spatiotemporal resolution satellite data. The SSMM, which is conceptually different from the 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. Although the SSMM performance is relatively worse 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 over 69% of variation in 30 m EVI2 time series, respectively, with the model established using more than 10 pairs and as less as 4 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.