## 3 Data and Methods
### Data Collection  
#### 3.1.1 Satellite Data  
We obtained PlanetScope, Sentinel-1, and Sentinel-2 imagery through Google Earth Engine (GEE) [77]. We obtained multi-temporal composites of atmospherically corrected PlanetScope surface reflectance imagery from the NICFI (Norway's International Climate & Forests Initiative) satellite data program's tropical forest monitoring monthly basemaps ([[https://university.planet.com/page/nicfi](https://university.planet.com/page/nicfi)]([https://university.planet.com/page/nicfi](https://university.planet.com/page/nicfi)); accessed on 16 February 2024) available from GEE [78]. The PlanetScope NICFI basemaps undergo a series of preprocessing steps, including atmospheric correction, cloud masking, mosaicking, and seam line removal. The dataset comprises four bands--Red, Green, Blue, and Near-Infrared--with a spatial resolution of 4.77 m. We selected six basemaps from the dry season with low cloud cover (June to November) in 2023 and further generated an annual composite image using medoid composition, which selects a real pixel from the image collection that minimizes the distance to all others, similar to a median but ensuring existing spectral values.  
The Sentinel-2 data used in this study is sourced from the L1C (orthorectified top-of-atmosphere reflectance) dataset on GEE. Sentinel-2 consists of 13 spectral bands, including four visible and near-infrared bands at 10 m resolution, six red-edge and shortwave infrared bands at 20 m resolution, and three atmospheric bands at 60 m resolution. A single Sentinel-2 satellite has a revisit time of 10 days, which is reduced to 5 days when both satellites in the constellation are operational. To ensure temporal consistency, we first selected all images with <5% cloud cover within the study area from June to November 2023, resulting in a total of 325 images. These filtered images were atmospherically corrected using the  
Figure 3: Flowchart of the research methodology.  
SIAC method [79], and medoid composites were generated to produce annual Sentinel-2 composite images. We extracted ten bands from the dataset, excluding Aerosols, Water Vapor, and Cirrus bands, for subsequent classification tasks. To ensure consistent spatial resolution, all bands were resampled using cubic convolution to 4.77 m to align with the NICFI basemap pixels.  
We selected Sentinel-1 10 m resolution Ground Range Detected (GRD) images from GEE, covering the period from June to November 2023, resulting in a total of 59 images. Sentinel-1 had a global revisit time of 12 days during the study period. The GRD images were radiometrically calibrated and orthorectified, with terrain correction applied using SRTM30. This study utilized two different polarization modes under the Interferometric Wide Swath (IW) mode: single co-polarization vertical transmit/receive (VV) and dual-band co-polarization vertical transmit and horizontal receive (VH). Sentinel-1's IW mode provides a spatial resolution of approximately 5 m \(\times\) 20 m for single polarization and 10 m \(\times\) 20 m for dual polarization. In GEE, the GRD images are standardized to a 10 m spatial resolution for consistency with Sentinel-2. To reduce speckle noise inherent in SAR (Synthetic Aperture) images, we applied a 3 \(\times\) 3 Refined Lee speckle filter [80]. This filter computes local statistics and selectively applies smoothing based on gradient and directional information, ensuring that image edges and details are preserved. Subsequently, medoid composites were generated for the VV and VH polarization images to create annual images, which were then resampled using cubic convolution to 4.77 m to align with the NICFI basemap pixels.