## 3 Methodology
### [PERSON]'s Approach to Yield Estimation
#### 3.1.3 Soil Quality Index
According to current statistics, the average soil depth in agricultural areas should range between 20 and 30 centimetres [33]. The soil depth varies from 0 to 20 centimetres across the entirety of Azerbaijan [34]. These estimates indicate roughly 15 to 20 cm in agricultural regions [34]. The Soil Depth Index (SDI) was accepted as 0.2 m in our computation based on open-source information about the AOI [34]. The Normalized Difference Moisture Index (NDMI) has supplied information on forest health and is particularly effective for detecting damaged forests and forest recovery following epidemics (individual stages of degradation and recovery mode). NDMI is determined as the ratio between the difference and the sum of the refracted radiations in the NIR and Shortwave infrared (SWIR) areas to determine the crop or soil water stress level. The interpretation of NDMI's absolute value enables the rapid identification of farm or field regions with water stress issues. NDMI is straightforward to interpret: its values range from \(-1\) to \(1\), and each number corresponds to a distinct agricultural condition, irrespective of the crop. NDMI is computed with the following formula [35]:  
\[\text{NDMI}=\frac{\text{NIR}-\text{SWIR1}}{\text{NIR}+\text{SWIR1}} \tag{9}\]  
Total Inorganic Carbon Index  
Calcite is the most stable polymorph of calcium carbonate, a carbonate mineral (CaCO\({}_{3}\)). It is a prevalent inorganic mineral, particularly as a component of limestone. Calcite has a hardness of 3 based on scratch resistance on the Mohs scale of mineral hardness. Large calcite crystals are utilised in optical equipment, and calcite-rich limestone has several applications [36]. According to research for estimating inorganic carbon rate in water bodies [37], the Total Inorganic Carbon Index (TICI) is calculated using Coloration Index (CoI) on MODIS data:  
\[\text{CoI}=\rho_{555}-(\rho_{443}+\frac{555-443}{670-443}*(\rho_{670}-\rho_{44 3})) \tag{10}\]  
\[\text{TICI}=0.4579\text{CoI}-0.0006 \tag{11}\]  
This approach is alterable with the Sentinel-2 data. Thus, TICI becomes:  
\[\text{TICI}=0.4579\rho_{559.8}-(\rho_{443}+\frac{559.8-442.7}{664.6-442.7}*( \rho_{664.6}-\rho_{442.7}))-0.0006 \tag{12}\]  
Salinity Index  
In dry and semi-arid places where precipitation exceeds evaporation, salinization of the soil is one of the most prevalent land degradation processes. Under such climatic conditions, soil-soluble salts build and degrade soil characteristics, causing a loss in soil production. Soil salinization is the enrichment of soil with soluble salts that provide information on the soil's response to the salt. Soil salinity in irrigated areas is becoming a severe problem for agriculture. Globally significant land areas have decreased in value and production due to the salinization of their soils [38]. Salinity is widespread in irrigated soils as a result of the accumulation of soluble salts from the continual use of irrigation waters containing high or moderate concentrations of dissolved salts [39]. The combination of RS and GIS is utilised to characterise various salt-affected soil data. The most used technique for calculating salinity index is the calculation of distinct index and ratio imagery utilising infrared and visible spectral bands. In general, dry soil has a higher reflectivity, and no red-zone absorption was considered in the construction of vegetation indices. Satellite photos and false colour combinations are visually interpreted to identify places damaged by salt. RS and GIS tools are used to examine digital RS data. The formula for the SI has been constructed for the Sentinel 2 products using Blue, Green and Red bands with \(\rho\)492.4, \(\rho\)559.8, and \(\rho\)664.6 wavelengths [19; 38; 39]. \[\text{SI}=\frac{\text{Green}*\text{Red}}{\text{Blue}} \tag{13}\]  
During the dry season, white or bluish-white salt crusts on the surface are excellent signals for the detection and correlation of saltiness.