## 4 Results and Discussion  
Two Sentinel 2 mosaic raster datasets containing green field and bare land imageries, also the SRTM DEM raster data were used to calculate several indices for this study.Green field imagery was used for calculating NDVI and LAI. Results are shown in Figure 4a,b. By using Sentinel 2 raster data, several SQ indices were calculated as shown in Figure 4e-h. SRTM data is used for calculating SGI (Figure 4d). The index results obtained from the calculations with various intervals are shown below. In this study, NDVI results were obtained in the range of \(\sim\)0 to \(\sim\)1. Usually, NDVI values range from \(-\)1 to 1 [22]. As seen in Figure 4a, the NDVI values of AOI generally show a high trend. LAI index usually ranges between \(\sim\)0 and 10 [25]. Near 0 values indicate bare ground, while green leaves per area increase as the index value rises to \(\sim\)10. As shown in Figure 4b, the green area of the AOI in this research was obtained in the range of \(\sim\)\(-\)0.3 to \(\sim\)7. Negative results can be considered as 0. \(\rm{K_{C}}\) index is calculated from the LAI, thus, its' values start from 0.08 [23]. The slope gradient is obtained from 0 to ~5 percent in this study. Usually, it ranges between 0 to 100. Variations between 0 to 3 indicate little or no slope, while 4 to 9 are accepted as gentle slopes [42]. The coefficient for the STI usually ranges between \(-1\) (very poor) to 1 (excellent) [32]. Band 10 with a 1373.5 nm wavelength and Band 11 with a 1613.7 nm wavelength were obtained by correcting Sentinel 2 Level 1 data using the SNAP ESA Sen2 Cor algorithm. As shown in Figure 4e, soil texture for the AOI in this study has moderate values. Figure 4f shows high concentrations of saline soils for the AOI, as values above 0.2 indicate salinity in bare soils. Values between 0 to 0.19 show low or no salinity, while the maximum salinity value for this index is 1 [39]. As a result of calculations for determining total inorganic carbon compounds, we obtained moderate to low values in AOI as shown in Figure 4g as this index ranges from \(-1\) (low) to 1 (high) [37]. The NDMI values range from \(-1\) (low) to 1 (high), where the negative values correspond to low, while the positive values indicate high water/drainage content [35]. As shown in Figure 4h, soil moisture for the AOI in this study has low to moderate values. Following the results shown in Figure 4, soil quality indices were later included in SQI calculation approaches. For this, indices multiplied with each other and were later calculated by the sixth root. Using the Data plotting algorithm in QGIS, the correlation between the SQI and NDVI values has been visualized. As shown in Figure 5, a non-linear scattered positive correlation between NDVI and SQI has been detected. According to the results, the SQI rate varied between 0 to 1. Considerately, the Total Yield was calculated using the values shown in Figure 6. The yield gradient obtained using the MEDALUS method by multiplying SQI, NDVI, and the Kc indices is shown below. To estimate the Total Yield of the given AOI, the mean value of the Yield gradient rate was multiplied by the total pixel count. Thus, unique value index reports were obtained with the total pixel count of 3637 using QGIS processing tools. Actual data reported by the official sources were 1103.2 tonnes. The indications are shown in Table 1.  
Figure 5: Correlation between NDVI and SQI. On crop production, [PERSON]'s approach accounted for the effects of crop coefficient, photosynthesis, and SQ based on the MEDALUS method. According to numerous study sources, these two models have been applied with all parameters derived from satellite data. This method is also applicable to a large-scale dataset. Nevertheless, the rate of validity is highly dependent on field productivity [19]. Using indices such as SQI, Kc, and NDVI, the YGI formula was calculated and the Mean Value was multiplied by the area to estimate Total Yield in tonnes, as shown in Formula (2).