Multi-Source Geospatial Assessment of Meteorological and Vegetation-Based Drought Indicators in Sinop

Abstract

Agricultural drought has become an increasingly significant environmental challenge due to climate variability, influencing agricultural production, ecosystem functioning, and regional water security. Accurate drought assessment therefore requires the integration of meteorological observations with remotely sensed vegetation information to capture both climatic forcing and ecological responses. This study presents an integrated geospatial framework for evaluating the performance of meteorological and satellite-derived vegetation indicators in characterizing agricultural drought conditions across Sinop Province, located in the northern Black Sea region of Türkiye. Meteorological variables, including precipitation, air temperature, and potential evapotranspiration, were obtained from the CHIRPS and NASA POWER databases, while vegetation dynamics were derived from MODIS MOD13Q1 satellite imagery. Four meteorological drought indicators—Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Agricultural Standardized Precipitation Index (aSPI), and Effective Reconnaissance Drought Index (eRDI) were evaluated alongside six spectral vegetation indices, namely NDVI, EVI, RDVI, TVI, SAVI, and OSAVI. Geospatial processing, statistical analysis, correlation assessment, and temporal comparisons were conducted within a GIS environment to determine the relative effectiveness of each indicator under humid coastal climatic conditions. The results demonstrated strong consistency between meteorological drought indices and vegetation responses, confirming the influence of climatic variability on vegetation dynamics throughout the study area. Among the evaluated meteorological indicators, eRDI provided the most comprehensive representation of agricultural drought by simultaneously incorporating effective precipitation and atmospheric evaporative demand. Within the satellite-based indicators, NDVI exhibited the highest capability for detecting vegetation stress and showed the strongest agreement with meteorological drought conditions. Comparative analyses further revealed that integrating climatic and remotely sensed datasets substantially improved drought characterization compared with the use of individual indicators. The proposed framework offers a reliable and transferable approach for agricultural drought monitoring in humid coastal environments. The findings contribute to improved drought early-warning systems, evidence-based agricultural management, water-resource planning, and climate adaptation strategies for Sinop Province and other regions with comparable environmental characteristics.