); [EMAIL_ADDRESS] (Y.Z);  
ZDepartment of Land Resources Management, School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Y.Z. );  
ZDepartmentand insufficient awareness of its protection [4; 5; 6]. Northeast China's black soil region is one of the three major black soil regions in the world and also serves as a primary base for commercial grain supply in China [7; 8]. In Northeast China, state-owned farms manage vast areas of cultivated land and play a crucial role in agricultural production [9; 10]. At the same time, the Chinese government has increasingly emphasized the protection and sustainable utilization of black soil region cultivated land in recent years, introducing a series of policy documents and laws to safeguard these resources [11; 12]. State-owned farms, managed by the government, naturally bear the responsibility for protecting black soil region resources and ensuring the sustainable development and utilization of black soil region cultivated land. To protect black soil region cultivated land effectively, it is essential to understand its current condition. Evaluating the quality of cultivated land serves as the foundation for state-owned farms to scientifically manage, effectively protect, and precisely enhance the quality of cultivated land. Therefore, state-owned farms have an urgent need for cultivated land quality assessment at the farm scale. By understanding the distribution of cultivated land quality, state-owned farms can adopt more targeted measures to improve and enhance the quality of the land. However, traditional methods of evaluating cultivated land quality primarily rely on soil sampling, laboratory analysis, expert surveys, scoring, data modeling, and GIS, which require considerable time, labor, and financial resources [13; 14; 15]. Therefore, utilizing satellite remote sensing to quickly and conveniently assess the quality of cultivated land has great practical significance. Currently, research on cultivated land quality assessment based on remote sensing primarily focuses on multi-source data fusion. For example, studies by [PERSON] et al. [16], [PERSON] et al. [17], [PERSON] et al. [18], and [PERSON] et al. [19] have conducted cultivated land quality assessments by integrating satellite remote sensing indicators with data such as topography and meteorological information. [PERSON] et al. [20] conducted cultivated land quality assessments by combining soil indicators from field surveys, such as organic matter and pH values, with remote sensing indicators like vegetation index. These studies gather data from various online sources as indicators for land quality evaluation, replacing part or all of the soil's physical and chemical indicators traditionally collected manually. A new system of indicators is then constructed to assess the quality of the cultivated land. Some studies have used traditional mathematical models to assess cultivated land quality based on remote sensing indicator systems. For example, [PERSON] et al. [21] used the index method to calculate land quality grades. Other researchers have applied machine learning methods around remote sensing-based indicator systems. [PERSON] et al. [22] established a land quality indicator system based on satellite-derived indicators using the Pressure-State-Response (PSR) framework proposed by [PERSON] and [PERSON] [23] and assessed land quality using the GA-BPNN model. [PERSON] et al. [24] evaluated land quality from multi-source data using the RNN algorithm. Although these studies provide valuable insights, they require the collection and processing of multiple remote sensing datasets before the evaluation process can begin. Additionally, many studies still rely on traditional modeling methods for the use of remote sensing indicators.