## 1 Introduction  
Satellite-based Remote Sensing (RS) data is now a fundamental aspect of cartography. RS satellites are used to collect these types of Earth Observation (EO) data from space. The growth of satellite technologies and RS systems has led to the creation of programmes that analyse RS data. These are known as RS and Geographic Information Systems (GIS). GIS is a technology that integrates hardware, software, and data to generate graphical representations of RS data and do spatial analysis. Today, GIS is an essential and widely employed tool for the agricultural sector. These technologies specify new and improved analysis and evaluation methods, especially in agriculture. As the data have a geographic component, they can represent practically anything imaginable. Locally and globally, GIS applications play an essential role in agriculture. GIS has become a more valuable resource over the past decade as it has assisted farmers in improving production, reducing expenses, and providing an efficient tool for managing land resources [1]. Complex spatial analyses for agriculture can compare variables such as soil type, wind direction, rainfall, slope, aspect, terrain, or elevation to reduce flooding, drought, erosion, and disease risks to improve crop management, site suitability, and drainage planning. Using Unmanned Aerial Vehicle (UAV) data to identify areas of need and the underlying causes of food shortage, as well as land use and primary food crop statistics, GIS also contributes to global food security. Providing the necessary food supply to support continuing population increase has become a growing challenge for today's executive organisations. It is anticipated that agricultural production will have to double by 2050 to fulfil future food demands. GIS is utilised not just for real-time analysis but also for comparing previous data for those objectives above. Satellite visuals can be used to evaluate previous agricultural land use trends. This can assist in predicting and planning arable lands required to maintain future populations [2]. Planning for the food security of a region's or even a country's population places a significant emphasis on determining the efficiency of food production. As the foundation of many emerging economies, agriculture contributes significantly to the Gross Domestic Product (GDP). Therefore, the ability to acquire somewhat accurate yield estimations prior to harvest is crucial, so appropriate interventions can be implemented if poor yields are anticipated. Winter wheat is one of the main staples in human consumption worldwide, as it has high nutritional value, multi-purpose usage, and affordability. Adding to its convenient, cost-efficient planting and irrigation, winter wheat has become one of the most important crops globally [3]. Therefore, yield estimation of winter wheat is an important topic for food security. In 2022, wheat accounted for 1 million 736.1 thousand tons (54.9 percent) of cereals and legumes produced in Azerbaijan. In addition, winter wheat also accounts for the largest share of total winter crop production at 62 percent [4]. As a result, advancement of the crop yield prediction is highly important for winter wheat, which can help organisations and individuals to manage and plan agriculture [5]. The estimation of productivity is crucial to economic progress [6]. These projections warn decision-makers about the possibility of crop yield losses and enable prompt importing and exporting decisions. As can be seen, yield estimation methods are a crucial component of these processes. Obtaining yield estimations with GIS is a crucial application for reducing costs and automating processes. There are two standard traditional techniques for calculating crop yield: empirical-statistical and crop growth models [7]. These techniques are derived from field reporting. The first is the classic method used in many regions, which is based on traditional data collection procedures for crops and yield estimation based on field reports from the ground [8].