Comparison of Spatiotemporal Fusion Models: A Review

Comparison of Spatiotemporal Fusion Models: A Review

Blog Article

Simultaneously capturing spatial and temporal dynamics is always a challenge for the remote sensing community.Spatiotemporal fusion has gained wide interest in various applications for its superiority in integrating both fine Fan Shop - Oilers - Clothing spatial resolution and frequent temporal coverage.Though many advances have been made in spatiotemporal fusion model development and applications in the past decade, a unified comparison among existing fusion models is still limited.In this research, we classify the models into three categories: transformation-based, reconstruction-based, and learning-based models.

The objective of this study is to (i) compare four fusion models (STARFM, ESTARFM, ISTAFM, and SPSTFM) under a one Landsat-MODIS (L-M) Air Intake Systems pair prediction mode and two L-M pair prediction mode using time-series datasets from the Coleambally irrigation area and Poyang Lake wetland; (ii) quantitatively assess prediction accuracy considering spatiotemporal comparability, landscape heterogeneity, and model parameter selection; and (iii) discuss the advantages and disadvantages of the three categories of spatiotemporal fusion models.