## 1 Introduction  
Farmland abandonment is one of the key aspects of land use and land cover (LULC) research [1, 2]. The phenomenon of farmland abandonment in China has become increasingly serious due to accelerated urbanization, industrialization, land pollution, the loss of labor, and other anthropogenic and natural factors [3, 4, 5]. The aggravation of the phenomenon of farmland abandonment harms ecological safety, food security, and agricultural stability [6, 7, 8]. Therefore, the detection of abandoned farmland is of great significance for ecological and environmental protection, food security, and agricultural stability. Minachi County, Sammenxia City, Henan Province, a typical agricultural county in China, is rich in farmland resources. However, due to insufficient governmental management, farmland abandonment is common and is detrimental to food production and farmland resources [9]. Therefore, there is an urgent need to strengthen the government's management and supervision of the phenomenon of farmland abandonment. Farmland abandonment is defined as a period of time in which there is no human cultivation activity on farmland. Some studies consider that no cultivation activity in a single season can be defined as abandonment [10], but most studies define it as farmland with no cultivation activity for two consecutive years or more, distinguishing between seasonal and permanent abandonment based on duration [11; 12]. The traditional method used for monitoring abandoned farmland is field surveys [13; 14], which require a considerable amount of human and material resources and are unsuitable for large-scale, high-precision, or long-term projects [15]. With the popularization of remote-sensing technology, the interpretation and analysis of remote-sensing images have become a common and effective method for monitoring farmland abandonment [16; 17]. [PERSON] et al. used the MODIS Normalized Difference Vegetation Index (NDVI) series to identify abandoned farmland using SVM [16]. [PERSON] et al. used the CART decision tree for extraction and explored the spatial distribution pattern of abandoned farmland [12]. [PERSON] et al. and [PERSON] et al. identified abandonment through random forest modeling based on changes in the vegetation index (VI), achieving good accuracy [18; 19]. Most of these methods were based on multi-temporal remote-sensing image interpretation, which can extract abandoned farmland data and distinguish between seasonal and permanent abandonment. However, due to the phenomena of \"different spectrums with the same object\" and the \"same spectrums with the same object\", the extraction results are easily interfered with by shrubs and weeds, and when the number of images increases, the cumulative error also increases, resulting in a loss of accuracy. The NDVI is one of the important parameters reflecting the growth of crops. The change in the NDVI between the growing and non-growing stages of artificial crops is more obvious than that of natural vegetation; thus, according to this principle, we can distinguish abandoned farmland well. [PERSON] et al. and [PERSON] et al. extracted abandoned farmland information based on an NDVI time series analysis and obtained highly accurate results [20; 21]. [PERSON] et al. used block-level vector-boundary-based time series analysis to extract data regarding abandoned farmland in cloudy and rainy areas [22]. [PERSON] et al. plotted the change curves of multi-VI time series and combined them with harmonic analysis for abandonment extraction [23]; this kind of method, based on time series analysis, can alleviate the influence of shrubs and weeds but cannot distinguish between seasonal abandonment and permanent abandonment. To address the shortcomings of the above two methods, [PERSON] et al. combined decision trees based on time series analysis. Their method weakened the impact of weeds and shrubs in time series analysis and distinguished between seasonal and permanent abandonment [24].