Spatiotemporal fusion method to simultaneously generate full-length normalized difference vegetation index time series (SSFIT)

Abstract High spatiotemporal resolution normalized difference vegetation index (NDVI) time-series imagery is required for monitoring vegetation dynamics with dense observations and spatial details, and in recent years, many spatiotemporal data fusion methods have been proposed to fulfill this need. However, strict data requirements and inappropriate modeling strategies often limit their performance, particularly under poor conditions of available input data. In this study, we proposed a Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series (SSFIT) with a high spatial resolution and frequent coverage. The utilization of temporal information across sensors contributes to its two distinct features: (1) no cloud-free high-spatial-resolution image is required and (2) high-spatial-resolution images on multiple prediction dates are generated at the same time. Comparison experiments were conducted by simulating ideal and challenging conditions of input time-series data in two characteristic areas, and the proposed methods were also compared with four typical methods: The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), and Improved FSDAF. The results demonstrate that SSFIT yields a better overall prediction accuracy and efficiency under ideal input conditions (average root mean square error of 0.1037 and 0.0713, average correlation coefficient of 0.9180 and 0.7875, and computation time of 109 and 120 s, computed in the two test areas) and is more robust against the decrease in available input data under challenging conditions. SSFIT is thus expected to be extended to various remote sensing products and support applications for monitoring land surface dynamics.