## 2 Study Area and Data
### Data  
#### 2.2.1 Remote-Sensing Data  
Remote-sensing data employed in this study were acquired from the Sentinel-1 and Sentinel-2 satellite constellations via the Google Earth Engine (GEE) platform. Sentinel-2 acquisitions comprised spectral bands B2 (blue), B3 (green), B4 (red), B5 (red-edge) and B8 (near-infrared), whereas Sentinel-1 data consisted of dual-polarization SAR backscatter (VV and VH). Given that the study area (Hunan Province) experiences frequent cloud cover and precipitation--particularly during the rainy season (June-August)--the temporal availability of Sentinel-2 imagery is markedly constrained. We therefore retrieved all imagery acquired between October 2021 and October 2022, and, following a cloud-mask threshold of <70% and general quality screening in GEE, retained only those scenes meeting these criteria; no attempt was made to enforce a uniform monthly composite. As illustrated in Figure 2c, the count of usable Sentinel-2 scenes varies by month (e.g., six scenes in May, but virtually none in July and December), resulting in an intentionally irregular time series that reflects true observational conditions. In contrast, Sentinel-1 acquisitions were available consistently each month; we generated median composites on a monthly basis to produce a continuous SAR time series (Figure 2d).  
Figure 1: (**a**) Geographical location of Hunan Province, China. (**b**) Land use type map of Hunan Province.  
For Sentinel-2 preprocessing, cloud and shadow pixels were first excluded using the QA60 band. In months with multiple valid scenes, a per-pixel median composite was computed to further mitigate cloud artefacts. Any residual data gaps in the composite images were filled via localized interpolation, and the resulting series underwent temporal smoothing using a Savitzky-Golay filter. Crucially, no artificial interpolation or gap-filling was applied across the overall temporal dimension, thereby preserving the genuine discontinuities and non-aligned structure inherent to real-world remote-sensing observations.  
Due to the inherent characteristics of Sentinel-1 data (with a resolution of 10 m), particularly the speckle noise commonly found in Synthetic Aperture Radar (SAR) images, this noise interferes with the accurate representation of surface information. To address this, we applied multi-look processing and the Lee filter for speckle filtering [26]. These two methods work together to effectively reduce the noise impact and enhance image detail clarity [27]. Considering that the topographic variations in the study area might interfere with radar signals, we performed terrain correction using a Digital Elevation Model (DEM). This step eliminates distortions caused by terrain, ensuring the geometric accuracy of the data. Finally, the data were logarithmically converted to the dB scale. We chose to generate the final Sentinel-1 time-series images on a monthly basis. Specifically, for each month, we used median compositing to compute the median value for each pixel from all available Sentinel-1 images of that month. The advantages of this approach are twofold: first, it further suppresses residual noise; second, it generates stable monthly feature representations, ensuring spatial seamlessness of the data. As a result, the data for each month not only reflects true surface changes but also aligns with the crop growth cycle.  
Figure 2: (a) Sentinel-2 time-series data for some regions after data processing. (b) Sentinel-1 time-series data for some regions. (c) Sentinel-2 image count. (d) Sentinel-1 image count.  
To address coverage gaps caused by orbital limitations, we employed a least squares fitting method to fill missing values and applied the SG filter to smooth the time series, ensuring temporal continuity. In the end, a total of 12 Sentinel-1 time-series images were constructed.