## 1 Introduction
In recent years, the rapid development of satellite remote sensing technology has provided a more convenient and cost-effective means of monitoring the spatial distribution of abandoned farmland [24], precisely identifying the temporal and spatial evolution patterns of farmland abandonment, and effectively overcoming the limitations of the two methods mentioned earlier. Currently, methods for extracting abandoned farmland based on remote sensing technology can generally be divided into overlay analysis, time series analysis, and supervised classification. Among these, the overlay analysis method is widely used in farmland management due to its simplicity and efficiency. Comparing farmland data from two or more time points and analyzing the changes in farmland patches over time can accurately extract information on abandoned farmland. For instance, [PERSON] et al. overlaid two periods of farmland vector layers, excluded patches related to land conversion to forest and forest projects, and assessed the abandoned farmland status in typical counties of Chongqing. The accuracy of abandoned farmland patch extraction reached 85.3% [25]. However, the overlay analysis method heavily relies on land use status survey data from government departments. In the study area of Qiaotou Town, discrepancies in the scale of agricultural surveys (e.g., 1:10,000 vs. 1:50,000) may introduce biases in the results of abandoned farmland extraction. These biases are influenced by local conditions, such as the fragmentation of farmland and the complexity of land use patterns, and, thus, cannot be generalized to other regions. The time series analysis method identifies abandoned farmland by examining the spectral sequence changes of remote sensing images over time and the differences compared to normal farmland. For example, [PERSON] et al. used MODIS time series data to map the distribution of wastelands in Central and Eastern Europe from 2004 to 2006, estimating the area of abandoned land [26]. [PERSON] et al. conducted research in Huizhou, Haifeng, and Lufeng counties in Guangdong Province, developing extraction methods based on interpretation symbols, Normalized Difference Vegetation Index (NDVI) time series features, and spectral characteristics for land classification [27]. The results indicated that the NDVI time series-based extraction method was the most suitable. Compared to the overlay analysis method, it can distinguish between seasonal and perennial abandoned farmland. However, time series analysis requires a substantial amount of remote sensing data, and the low resolution of MODIS data tends to result in fragmented abandoned farmland patches, which may lead to classification errors and affect extraction accuracy [28]. In tropical regions of South China, land types are complex, and the land patches are relatively fragmented. Additionally, The frequent occurrence of cloud cover and rainy weather makes it difficult to obtain cloud-free remote sensing images over extended time periods. The machine learning method based on supervised classification has significant advantages in farmland extraction, abandoned farmland identification, and other related studies, including high accuracy, flexibility, and interpretability. By reasonably selecting features and classifiers and integrating multi-source data, the classification performance can be further improved. These methods include the CART decision tree [29], Random Forest (RF), Support Vector Machine (SVM) [30; 31], and Artificial Neural Network (ANN) [32]. Random Forest (RF) is an ensemble learning method based on decision trees, introduced by [PERSON] et al. in 2001 [33]. It is preferred for its high classification accuracy, fast prediction speed, and ability to effectively handle multi-source remote sensing data. For example, [PERSON] et al. combined MODIS NDVI time series products with Random Forest classification to monitor abandoned farmland across Europe, achieving an accuracy of 90.1% [24]. [PERSON] et al.