## 1 Introduction  
Looking into the rules, processes, and mechanisms that control how crops' spatial and temporal patterns change over time is a very new area of research that is receiving a lot of attention in the fields of sustainability science and global change [1; 2]. To fully consider and create food security policies and climate change response plans at different regional levels, it is important to have a complete understanding of how agricultural land has been used in the past, how it is being used now, and how it will be used in the future. A good understanding of this is especially important for figuring out the spatial and temporal dynamics that affect where crops grow, as well as for studying the carbon and nitrogen cycles on land, how water is used, and soil erosion. Maize, as one of the primary agricultural commodities in China, plays a crucial role in ensuring food security and livelihood stability within the country. Specifically, in the Ningxia Hui Autonomous Region, maize holds significant importance as it accounts for over 60% of the total grain output [3]. The grass and cattle business greatly benefits from its crucial contribution to augmenting farmers' income and facilitating high-quality development. Utilizing satellite remote sensing technology to better understand the spatial and temporal distributions of maize and keep an eye on its growth makes it easier for local agricultural departments to quickly obtain information about crop growth. This holds immense importance for the implementation of refined production management and the assessment of agricultural yields at a regional level. The conventional approach for determining the maize planting area is based on statistical reporting and local measurements [4]. However, this method is highly vulnerable to human interference and suffers from slow data updating, making it challenging to capture the internal variability characteristics of administrative units. A significant reliance on labor, decreased effectiveness, and a constrained range of activities set the phenomenon apart. The practice of large-scale maize growing has faced challenges in meeting the development demands of modern maize production and management due to rising labor costs and limitations in information collection for such crops [5; 6]. The local administration conducts an annual assessment of the cultivated area, employing household visits, field measurements, and traditional instruments like leather rulers. In recent years, the advancement of remote sensing technology has facilitated the utilization of remote sensing for the acquisition of agricultural data. Consequently, agricultural remote sensing has emerged as the primary method for gathering crop-related information. Satellite remote sensing images have a lot of information in them, cover a lot of ground, obtain data quickly, have a lot of spectral bands, and have a lot of spectral information. They have been used a lot in research to help identify plant types and keep an eye on large areas of vegetation. Additionally, these images offer an efficient means of acquiring data on maize planting areas [7]. Recently, researchers from all over the world and in higher education institutions have been working hard on precise target-feature extraction in order to meet the higher accuracy standards needed in real-world applications. Notably, the integration of hyperspectral technology and machine learning methods has yielded substantial advancements in this field. The classification methods of support vector machine (SVM), Maximum Likelihood Classification (MLC), random forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT) have been demonstrated to effectively distinguish various plant types in remote sensing data [8; 9]. [PERSON] et al. [10] employed the random forest and support vector machine classification methods to categorize seven distinct classes, including sugarcane, maize, bare land, and buildings.