## Materials
### Data Description  
The precipitation values used to calculate PCI were obtained from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) database [67]. CHIRPS is a widely used precipitation dataset for climate data analysis and research. It contains global precipitation estimates from 1981 to the present, available on daily, 10-day, and monthly bases. The dataset is produced by combining two main data sources: satellite-based estimates and ground station data. CHIRPS uses precipitation data provided by the U.S. National Oceanic and Atmospheric Administration Climate Prediction Center and enhances satellite estimates by incorporating precipitation data from thousands of ground stations worldwide [68]. Due to the discrete nature of precipitation data, there can be challenges in obtaining precipitation from remote sensing systems, which necessitates research on their correlation with ground observations. Studies conducted specifically for Turkiye have shown that monthly CHIRPS data have a high correlation with ground observations [69]. In addition, CHIRPS data used in drought studies specific to Turkiye have been verified to produce satisfactory results by [PERSON] et al. [70] and [PERSON] [71]. Land surface temperature (LST) and NDVI were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) database for the calculation of TCI and VCI. In addition, NDWI was also similarly obtained from MODIS. Launched in 1999 on the Terra satellite and in 2002 on the Aqua satellite, both satellites scan the entire Earth's surface every 1 to 2 days, collecting data in 36 spectral bands. The instrument's detectors collect data at three different spatial resolutions: 250 m, 500 m, and 1000 m. MODIS bands 31 (centered at 11,030 \(\upmu\)m) and 32 (12,020 \(\upmu\)m) are thermal infrared bands used in LST calculations [72]. NDVI, an indicator widely used to monitor vegetation health and density, is obtained using MODIS Band 1 (red: 645 nm) and Band 2 (NIR: 858 nm). Finally, NDWI values are obtained using MODIS Band 2 (NIR: 858 nm) and Band 5 (SWIR: 1240 nm). Figure 1: (**a**) The study area in Türkiye, (**b**) districts in the study area, (**c**) a digital elevation map, and (**d**) cropland areas. In previous studies, researchers have used soil moisture (SM) data from various databases, including the Global Land Data Assimilation System (GLDAS) [73], Advanced Microwave Scanning Radiometer for EOS [74], Famine Early Warning Systems Network Land Data Assimilation System [75], ERA5-Land [76], and Soil Moisture and Ocean Salinity [77]. In this study, SM values were obtained using the mHM, a physically-based high-resolution distributed hydrologic model used to simulate hydrological processes in watersheds at different scales. The mHM divides watersheds into small cells and performs calculations for each cell separately, allowing for more realistic results by accounting for spatial heterogeneity. The model is based on physical principles and simulates hydrological processes including evaporation, transpiration, runoff, and groundwater flow [78]. While the model is designed to predict surface runoff using inputs such as precipitation, potential ET, and mean air temperature, it also outputs SM at various depths, including 0-50 cm. In this study, these SM values were used to calculate the SMCI. Gross primary productivity (GPP) values were obtained from the Penman-Monteith-Leuning Evapotranspiration V2 (PMLV2) dataset [79]. The Penman-Monteith-Leuning (PML) model was first developed by [PERSON] et al. [80], who introduced a biophysical model for surface conductance based on the Penman-Monteith (PM) equation [81]. Since then, two improved versions of the PML model have been developed: PML-V1 and PML-V2. PML-V2, as described by [PERSON] et al. [82], uses a biophysical canopy conductance model to integrate evapotranspiration (ET) with GPP. To simplify the model, only key parameters of physiological importance for stomatal response and assimilation are retained.