## 2 Materials and Methods
### Satellite Image Data and Preprocessing  
Landsat 9, launched in September 2021 as part of the joint NASA-USGS Landsat program, carries two primary remote sensing instruments: the Operational Land Imager 2 (OLI-2) and the Thermal Infrared Sensor 2 (TIRS-2). The OLI-2 sensor captures data across nine spectral bands, including visible (coastal aerosol: 0.43-0.45 \(\upmu\)m, blue: 0.45-0.51 \(\upmu\)m, green: 0.53-0.59 \(\upmu\)m, red: 0.64-0.67 \(\upmu\)m), near-infrared (0.85-0.88 \(\upmu\)m), shortwave infrared (SWIR1: 1.57-1.65 \(\upmu\)m, SWIR2: 2.11-2.29 \(\upmu\)m), panchromatic (0.50-0.68 \(\upmu\)m), and cirrus (1.36-1.38 \(\upmu\)m) wavelengths, with a spatial resolution of 30 m for multispectral bands and 15 m for the panchromatic band. The TIRS-2 instrument operates in two thermal infrared bands (10.6-11.19 \(\upmu\)m and 11.50-12.51 \(\upmu\)m) with a spatial resolution of 100 m, resampled to 30 m to match the multispectral data. This sensor configuration enables comprehensive land surface monitoring with improved radiometric performance and signal-to-noise ratios compared to previous Landsat missions, facilitating enhanced capabilities for environmental monitoring, agricultural assessment, and climate change research applications. The Landsat 9 OLI-2/TIRS-2 satellite remote sensing images used in this study were sourced from the Landsat 9 Surface Reflectance Tier 2 dataset in GEE. All surface reflectance data in this dataset underwent atmospheric correction. The remote sensing images include five visible and near-infrared (VNIR) bands, as well as two shortwave infrared (SWIR) bands, which were orthorectified to obtain surface reflectance data. Additionally, one thermal infrared (TIR) band was orthorectified to obtain surface temperature data. Additionally, this dataset includes intermediate bands used for calculating Surface Temperature (ST) products, as well as Quality Assurance (QA) band data. Based on this image dataset, the study used GEE combined with machine learning algorithms to select remote sensing images of 4 typical periods from a total of 66 Landsat 9 images taken between January and December 2022, which were suitable for evaluating the cultivated land quality of Shuanghe Farm. The specific selection process was as follows. First, remote sensing images of the target area were cropped using the vector map of Shuanghe Farm. Then, pixel sampling of the remote sensing images was conducted based on the cultivated land quality distribution vector map of Shuanghe Farm. Due to the limitations of the GEE platform, the sampling resolution could not be set too small. Therefore, in this study, the sampling resolution was set to 290, allowing for the extraction of sampled pixels and the corresponding cultivated land grade values for the target area. Finally, we established a random forest classification model on the GEE platform. This model was based on the SR1 to SR7 band data of the sampled pixels in the target area and the cultivated land quality grade data. As a result, the model produced performance prediction data for the cultivated land quality grades for all Landsat 9 remote sensing images of Shuanghe Farm in 2022. Data with significant cloud cover or missing areas were excluded, and 21 remote sensing images from different periods were selected. These images were used for cultivated land quality evaluation using the random forest classification model in GEE. The model's performance was assessed by the classification accuracy of Grades 1 to 6, overall accuracy, and the Kappa coefficient. The remote sensing images corresponding to the highest classification accuracy, overall accuracy, and Kappa coefficient for each grade were selected as the remote sensing images of typical periods suitable for evaluating cultivated land quality at Shuanghe Farm. Ultimately, remote sensing images of four typical periods were selected, as detailed in Table 1.  
A foundational step in our proposed deep learning framework is the selection of optimal temporal windows from which to extract raw spectral reflectance data.