## 1 Introduction  
To meet the growing global demand for food in the short term, conventional intensive agricultural practices have been employed; however, these practices often compromise long-term sustainability and contribute to land degradation [1]. Two prevalent strategies in conventional agriculture to boost crop yields include (1) expanding arable land through the conversion of existing land cover, which typically results in habitat loss, and (2) enhancing yields on already cultivated land by employing improved agricultural techniques, which frequently involve increased application of fertilizers and pesticides [2]. This approach can lead to contamination from heavy metals and persistent organic pollutants [3]. To promote sustainable agricultural practices, the Food and Agriculture Organization (FAO) of the United Nations established land evaluation principles [4] and five standardized suitability classes [5], which have been widely accepted by the scientific community. The assessment of cropland suitability levels serves as a foundational element for developing adaptive strategies in land management, particularly in areas susceptible to environmental stress [6] and drought [7]. Spatial approaches for assessing cropland suitability have increasingly utilized geographic information systems (GISs), particularly the combination of GIS-based multicriteria analysis with the analytic hierarchy process (AHP), which is recognized as a leading technique for this purpose [8; 9; 10]. An analysis of research articles indexed in the Web of Science Core Collection from 2000 to 2021 reveals that 82% of approaches employed GIS-based multicriteria analysis integrated with AHP [2]. The purpose of utilizing GIS-based multicriteria analysis in prior research has been to assess the suitability of cropland within specific geographical regions. The selection of abiotic factors for modeling cropland suitability involved a synthesis of existing literature and insights from domain experts [8; 11]. These abiotic factors are typically organized into three primary categories: climate, soil, and topography, which significantly influence cropland suitability [12]. While many studies concentrated on these particular groups, the number of criteria varied considerably based on crop type and location. While expert knowledge can enhance suitability modeling, it may also introduce biases and inefficiencies [13]. Beyond agriculture, applications of this type of analysis extend into forestry [14], ecology [9], and urban vegetation studies [15]. Nevertheless, inherent limitations hinder its effectiveness as the demand for accuracy and widespread applicability grows. Key issues include susceptibility to subjectivity, operational complexity, and challenges in handling extensive datasets [16]. Practically, it lacks a direct method for accuracy evaluation without comprehensive yield data at the parcel level, often relies on generalized criteria (like the mean annual climate), and necessitates subjective weight assignments that primarily depend on expert input [2]. The utilization of machine learning techniques has effectively mitigated limitations in forecasting chemical and physical soil properties, enhancing traditional methodologies. Machine learning has facilitated the processing of large datasets and the amalgamation of diverse data types while establishing intricate nonlinear connections between training datasets and independent variables [17]. Unlike the manual and subjective determination of weights for various abiotic factors in suitability assessments, machine learning offers a fully automated and objective mechanism for assessing feature significance (Figure 1). This approach also eliminates the need for standardization and subjective weighting, thereby reducing the potential for bias in GIS-based multicriteria evaluations [2]. To assess cropland suitability, prior investigations have suggested remote sensing methodologies that are globally accessible and applicable in contrast to crop yield statistics. [PERSON] et al.