To accurately describe dynamic vegetation changes, high temporal and spectral resolution data are urgently required. Optical images contain rich spectral information but are limited by poor weather conditions and cloud contamination. Conversely, synthetic-aperture radar (SAR) is effective under all weather conditions but contains insufficient spectral information to recognize certain vegetation changes. Conditional adversarial networks (cGANs) can be adopted to transform SAR images (Sentinel-1) into optical images (Landsat8), which exploits the advantages of both optical and SAR images. As the features of SAR and optical remote sensing data play a decisive role in the translation process, this study explores the quantitative impact of edge information and polarization (VV, VH, VV&VH) on the peak signal-to-noise ratio, structural similarity index measure, correlation coefficient (r), and root mean squared error. The addition of edge information improves the structural similarity between generated and real images. Moreover, using the VH and VV&VH polarization modes as the input provides the cGANs with more effective information and results in better image quality. The optimal polarization mode with the addition of edge information is VV&VH, whereas that without edge information is VV. Near-infrared and short-wave infrared bands in the generated image exhibit higher accuracy (r > 0.8) than visible light bands. The conclusions of this study could serve as an important reference for selecting cGANs input features, and as a potential reference for the applications of cGANs to the SAR-to-optical translation of other multi-source remote sensing data.

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High-frequency disturbance forest ecosystems undergo complex and frequent changes at various spatiotemporal scales owing to natural and anthropogenic factors. Effectively capturing the characteristics of these spatiotemporal changes from satellite image time series is a powerful and practical means for determining their causes and predicting their trends. Herein, we combined the spatiotemporal cube and vegetation indices to develop the improved spatiotemporal cube (IST-cube) model. We used this to acquire the spatiotemporal dynamics of forest ecosystems from 1987 to 2020 in the study area and then classified it into four spatiotemporal scales. The results showed that the cube-core only exists in the increasing IST-cubes, which are distributed in residential areas and forests. The length of the IST-cube implies the duration of triggers. Human activities result in long-term small-scope IST-cubes, and the impact in the vicinity of residential areas is increasing while there is no change within. Meteorological disasters cause short-term, large scope, and irregular impacts. Land use type change causes short-term small scope IST-cubes and a regular impact. Overall, we report the robustness and strength of the IST-cube model in capturing spatiotemporal changes in forest ecosystems, providing a novel method to examine complex changes in forest ecosystems via remote sensing.