## 1 Introduction
Some empirical-statistical models consider long-term crop yields and factors that influence crop production in a certain region. The yield of the crop is then linked to the effective parameter using an empirical equation, and the coefficient of each factor is determined [9]. Nowadays, the crop yield is estimated using these coefficients. Each collection of empirical models ties several parameters to crop yield. Models of crop growth anticipate crop production as a function of the complicated interaction between many physiological processes and the environment. These models predict the potential for biomass output using a daily crop growth simulator. There are significant operational challenges associated with these approaches, including the need for too many ground factors, proper data gathering, and expensive costs. RS techniques concurrently eliminate the drawbacks of conventional procedures [10]. 1970 marked the beginning of crop yield estimation techniques based on satellite imagery and the use of satellite imagery in agriculture for EO. Previously, these technologies were not commonly used since satellite photos were costly and lacked spatial resolution. By eliminating the mentioned disadvantages, high spatial and spectral resolution images have been produced since 1990, and many studies have employed these techniques. Analysis of RS data alone or in conjunction with other ancillary data (such as soil moisture) permits the estimation of crop productivity prior to harvest [11]. The capability of RS to offer information on crop status and health is a significant factor in estimating potential crop production. In a recent study conducted in China for the crop growth model of the simple algorithm for yield estimate (SAFY) of wheat, researchers incorporated the leaf area collected from Sentinel-2 remote sensing data. Several SAFY parameters, including the Emergence Date (D0), the Effective Light energy utilization rate (ELUE), etc. are fine-tuned using the Shuffled Complex Evolution Optimization with Principal Component Analysis (SPUCI) optimization method. This crop growth model was developed using multiple data including RS of a certain time period [12]. Crop yield growth models are usually developed using machine learning technologies and require a significant time period for a machine to observe the trends and reports [13]. These RS data-based analyses make the applicability of many methods possible. For instance, in a previous study conducted in Turkiye, the relationship between soil quality data such as productivity and salinity was proven by employing GIS and RS technologies [14]. The findings demonstrated that rising Electrical Conductivity (EC) values resulted in lower cotton and wheat yields. Crop yields decreased linearly when salinity rose beyond the threshold for cotton and wheat. According to the research, RS and GIS techniques are practical approaches for predicting agricultural productivity. In a study carried out by [PERSON] and [PERSON] in 2020, Landsat 8 earth imagery visuals from 2017, 2018, and 2019 were used in order to map the RS-based NDVI, LAI, and NDWI on grape yields to determine their correlation with grape yield estimation [15]. Different stages of growth were observed, and different methods were used to develop yield estimation models based on an automatic model using RS-based time-series imagery. The results indicated that of all the vegetation indices, NDVI had the highest correlation with yield prediction. According to the results obtained, we can conclude that vegetation indices, especially NDVI can be used for forecasting yields. Time series based on satellite imagery can achieve reliable results on yield prediction models. A study conducted for determining suitable landmass for agricultural purposes in Egypt used the MEDALUS methodology for developing the Soil Quality Index (SQI) formula. The equation was changed to be adjustable so that it may take into account various soil characteristics that can have an impact on soil quality and hence alter the outcome of Soil Quality (SQ) [16].