## 2 Materials and Methods
### Methodology
#### 2.3.1 Deep Learning Model Selection and Sample Creation  
The deep learning-based SR method involves training a model with pairs of low-resolution (LR) and high-resolution (HR) remote sensing images to learn the relationship between them. Then the trained model is used to input LR images and produce reconstructed SR images. As mentioned in Section 1, the three mainstream networks for SR reconstruction are CNN, ResNet, and GAN. The early SRCNN model, although simple in structure, required interpolation of LR images, thereby increasing computational complexity [26; 27]. SRResNet introduced residual learning and deeper network layers, enhancing  
Figure 4: Workflow of the annual land cover mapping process.  
multi-scale image processing capabilities [28]. However, using the MSE loss function in these networks resulted in overly smooth images lacking detail and realism. Moreover, SR methods based on CNN and ResNet tend to be sensitive to noise and artifacts in input images and require separate model training for different scale super-resolution tasks, limiting their generalization and application scope. In contrast, SRGAN employs a ResNet as the generator and enhances the naturalness and quality of the generated images by introducing GAN and perceptual loss, effectively avoiding excessive smoothness [29; 30]. Through adversarial training, the network focuses more on the overall quality and authenticity of the generated images, reducing the impact of noise and artifacts and improving the model's generalization ability for super-resolution tasks at various scales. Consequently, the SRGAN was used as the training model to improve the perceptual quality and detail the representation and generalization performance of reconstructed images.  
Additionally, [PERSON] found that GANs produce the best results when upscaling images four times in resolution [30]. Therefore, this study employed an iterative super-resolution method to enhance the resolution of 5.5 km AVHRR data to 250 m. Initially, the 5.5 km AVHRR data were resampled to 4 km using nearest neighbor interpolation, while the 500 m MOD09A1 data were resampled to 1 km. Subsequently, two deep learning models were established: SRmodel_a1 between the 4 km AVHRR NDVI and 1 km MOD09A1 NDVI, and SRmodel_a2 between the super-resolved 1 km NDVI and 250 m MOD09Q1 NDVI. The SRGAN models for upscaling the 5.5 km AVHRR NDVI to 250 m were established by employing the time series AVHRR and MODIS NDVI data with the same year (2001) as input while the early time series AVHRR NDVI data (1982-1990) were subsequently upscaled to 250 m through the above models.  
To facilitate the input of the preprocessed time series NDVI data into the SR network for training, this study employed the Orfeo ToolBox meets TensorFlow (OTBTF) tool to create training datasets consisting of LR and HR image pairs. This tool, developed based on the remote sensing image processing library Orfeo ToolBox [31] and the high-performance numerical computation library TensorFlow [32], can be applied to deep networks without being constrained by image size and offers high computational efficiency regardless of hardware configuration [33]. The specific process for creating image pairs is as follows:  
1. Set the size of the LR data blocks to 32 \(\times\) 32 and the HR data blocks to 128 \(\times\) 128. Use QGIS to create a square grid over the original data range based on these dimensions
2. Use QGIS to create the centroids of the square grid
3. Input the centroid file and the time series NDVI LR and HR images into the patch extraction program of OTBTF tool to obtain the LR-HR image patches (Figures 5 and 6).  
Figure 5: The process of making SR model training dataset (LR-HR image pairs). (**a**) Creating a square grid; (**b**) creating centroids of the grids.