A Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series

Keywords: Normalized Difference Vegetation Index (NDVI), Spatiotemporal data fusion, High spatial and temporal resolution, Time series reconstruction
Session Type: Virtual Paper
Day: Wednesday
Session Start / End Time: 4/7/2021 08:00 AM (Pacific Time (US & Canada)) - 4/7/2021 10:20 AM (Pacific Time (US & Canada))
Room: Virtual 29

Abstract

High spatiotemporal resolution NDVI time-series imagery is demanded for monitoring vegetation dynamics with dense observations and spatial details, and in recent years many spatiotemporal data fusion methods have been proposed to fulfill the need. However, strict data requirements and inappropriate modeling strategies often limit their performances especially 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 high spatial resolution and frequent coverage. The utilization of temporal information across sensors contributes to its two distinct features: (1) no cloud-free high resolution image is required, (2) high spatial resolution images on multiple prediction dates are generated at the same time. Comparison experiments were conducted under ideal and challenging conditions of input time-series data in two characteristic areas, and the proposed methods were compared four typical methods, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), Fit-FC, and Improved FSDAF (IFSDAF). The results demonstrate that SSFIT yields better overall prediction accuracy and efficiency in the ideal input conditions (average root mean square error: 0.1045 and 0.0816; average correlation coefficient: 0.9189 and 0.7635; computation time: 109 s and 120 s; computed in the two test areas), and is more robust against the decrease of available input data. SSFIT is also expected to extend to various satellite products and support applications for monitoring land surface dynamics.