GI products are influenced by both the geographical features of the environment in which they are grown and their socioeconomic characteristics. GI products carry not only the geographical features of the product but also the cultural heritage passed down through generations in the region. In this respect, making GI products resilient to climate change is also important in terms of carrying cultural heritage to future generations. Furthermore, many studies have shown that GI products are an important tool in strengthening local communities in terms of employment and combating poverty, as they are a source of income for the regional economy (Belletti et al., 2017).

GI is considered in two categories. The first category is origin, and the second is geographical indication. Origin means that all stages of the product’s production take place within a designated area. Geographical indication, on the other hand, requires that while the product is produced within a designated area, its processing stages may also take place outside that area. Both categories reveal that the identity of a GI product is inherently linked to the region’s ecological and cultural fabric. In GI research, the notion of terroir is fundamental terroir is basically referred to as the unique combination between geomorphology, climate, soil characteristics, landscape arrangement, altitude, and human factor that affects product particularity as a whole. Low differences in these elements affect product quality (Van Leeuwen, 2022).

Current literature shows an increasing trend in the study of the impacts of climate change on Agricultural GI products. Studies show that there have been changes in the optimal areas of production defined by an increase in temperature and a decline in precipitation for the European wine, olive, and cheese sectors, as well as for the Chinese citrus, rice, and apple GI sectors (Hannah et al., 2013). The findings demonstrate the high vulnerability of GI products to negative climate change impacts.

Turkey is one of the greatest European countries in terms of agricultural diversity, biodiversity, and agriecological potential. The region around Antalya and Korkuteli is home to many varieties of fruit plants because of the significantly large elevation gradient in that region coupled with the regional flora. The Korkuteli Karyağdı pear is the main focus of this case study because it is one of the representatives of the agrifood products that is an important part of that region’s system and retains close socioeconomic links to it. The product under study receives positive support from its own resistance as well as its environmental biodiversity characteristic (Gül and Kart, 2024).

The Mediterranean Basin is one of the regions most affected by climate change. Decreasing rainfall, rising air temperatures, decreasing soil moisture, increased evaporation, and prolonged drought periods put the region and its geographically indicated products at high risk (Lionello et al., 2014; Chmielewski and Rötzer, 2001). The fact that the locations of geographically indicated products are determined and documented eliminates the option of changing the location of the products. This increasing vulnerability rate and positional stability are characteristics that increase the vulnerability of GI products (Henry, 2023).

Geographic Information Systems (GIS), Remote Sensing (RS), and Earth Observation (EO) technologies are widely used in stress studies related to agricultural drought. Vegetation indices (e.g., NDVI, VCI) are used for assessing vegetation health and productivity, LST helps with thermal anomaly detection, ET and SM data sources can define hydrological anomalies, whereas rainfall anomalies can be mapped using CHIRPS precipitation products (Tucker, 1979; Funk et al., 2015). No single variable can represent the dynamics of drought occurrence; hence, the multiple index approach offers an overall performance methodological framework that combines the capabilities of multiple indices for a better evaluation (AghaKouchak et al., 2015; Hao and Singh, 2015). GEE represents an important platform that supports the efficient examination of space time dynamics over large scales and has become widely used for agricultural monitoring and space related modeling researches (Gorelick et al., 2017).

Although the literature on environmental risks impacting agricultural GI products is relatively limited, existing studies predominantly focus on conservation policies, branding strategies and socioeconomic dimensions (Bowen, 2010; Ilbery and Kneafsey, 2000). The use of EO based methodologies in GI vulnerability has not been widely utilized in current studies. The use of EO technology with a spatial approach in this field makes a significant contribution to the subject of this study.

This research contributes to the field by developing a CDRI for the Korkuteli Karyağdı Pear using a multi sensor EO approach. CHIRPS precipitation, ET (water loss), soil moisture (moisture deficit), LST and air temperature (thermal stress), and NDVI, VCI (vegetation vitality) were integrated within the GEE environment using a unified normalization and modeling workflow. The variables were normalized between 0 and 1, and secondary indices MDI, HTI and VDI were derived. These indices were then aggregated to produce the CDRI drought risk maps. The resulting spatial risk patterns provide actionable, evidence based insights for decision makers seeking to strengthen climate resilience strategies for agricultural GI products.

This research uses an integrated methodology framework incorporating satellite imagery, climate data, and ground observations from the GEE platform to estimate the risk of drought in the Korkuteli Karyağdı pear growing region due to climate change. The results cover the period from 2001 to 2023. Open source resources such as CHIRPS, MODIS, and ERA5 Land were considered due to their long term accessibility and public availability.

The data was created in a layered structure. All layers were created to share a common projection. The time interval was created to be a common time interval for all data layers. Normalization was performed to ensure comparability of the data layers. This process scales the data between 0 and 1. After this process, HTI, MDI, VDI, and MDI were obtained. The CDRI was derived using an equally weighted formula of these three findings.

This study proposes a methodology suggesting that a methodology based solely on meteorological data is insufficient for determining the drought risk of agricultural GI crops; therefore, other parameters related to GI crops, such as vegetation characteristics and thermo hydrological criteria, should also be considered.

This study utilized ground observation data covering the period 2001–2023. The findings indicate that the three main components of drought decreased vegetation vitality, increased thermal stress, and rainfall deficit are spatially concentrated within the Korkuteli Karyağdı Pear production area. Although the drought components appeared at different intensity levels, the risks were generally found to converge on the southern and southeastern slopes. This result supports the applicability of a multicomponent drought model. The study utilized ground observation data from 2001 to 2023. Spatial distribution risks were obtained for the production areas of the GI crop, which is the subject of this study, as decreased vegetation health, increased thermal stress levels, and decreased rainfall yield. Drought was presented at different levels. It was determined that the southern and southeastern slopes of the study area have high risk regions. The applicability of a multicomponent drought model is demonstrated by the results obtained in this study.

Below, the spatial outputs of NDVI, VCI, LST, AT, ET, SM, MDI, and the composite CDRI generated by integrating these numerical outputs are presented. The results reveal that the study area is exposed to multicomponent drought pressure.

In examining drought stress factors, this research attempts to identify how these factors are distributed across the Korkuteli Karyağdı Pear Protected GI and determine the quantitative sensitivity of the GI crop to climate change. Results indicate that vegetation stress and hydro thermal and meteorological stresses cooccur in the same areas, making direct contributions to environmental conditions. Negative values of NDVI and VCI indicate reduced vegetation health, while elevated land surface temperatures and AT suggest an increased thermal load. High evapotranspiration rates and reduced ground storey moisture suggest an increase in hydro stress. The irregular rainfall distribution in the MDI dataset reflects more severe dry spells.

The CDRI, constructed based on these multi sensor indicators, reveals a spatially well dispersed pattern of medium and high risk areas, especially along the southern and western slopes. The fact that stress factors lineup in places underlines how sensitive the Karyağdı Pear is to changes in conditions due to very narrow tolerance to environmental changes. Increased water and heat stress are indeed detrimental to fruit development, the structure of tissues, flavor, and the proper setting of fruits by flowers, ultimately lowering the quality of the final product. Such findings are consistent with the patterns of vulnerability to climate change in other GI certified crops around the world, hence indicating similar risks of a climatic origin facing the Korkuteli GI variety.

The use of the GEE allowed the integration of EO data over the years in order to analyze the processes of drought without having dependence on a single index. The multi index approach allowed seeing that the meteorological aspect does not fully represent the process of agricultural drought; it is necessary to introduce the vegetation and hydro thermal aspect in order to have a more defined picture. The territorial organization of the CDRI allows decision makers access to an easy map of the areas that concentrate high risks in order to plan the irrigation of the areas, the reviving of the orchards, the adaptation of the cultivars.

Despite its contributions, the study also has limitations. Satellite based datasets, while consistent over broad regions, may not fully represent microclimatic variability within small orchard parcels. The spatial resolution of MODIS and ERA5 restricts the detailed detection of fine scale heterogeneity. The use of annual composites may mask short term extreme events and phenologically critical stress periods. Fixed threshold values employed in CDRI classification may limit the ability to detect alternative risk distributions. Furthermore, assigning equal weights to all components may not fully capture the true physiological sensitivity of the crop. The lack of terrestrial measurement data in this study creates limitations in verifying satellite images, which are a remote sensing data source. These limitations were addressed by developing a solution using multiple data and multiple index approaches. The findings of this study reveal that meteorological parameters alone are insufficient in agricultural drought studies. It was determined that the multicriteria approach models presented by AghaKouchak et al. (2015), Rhee et al. (2010), and Zhang et al. (2017) are the closest to accurate models in determining drought risk levels. They highlighted the importance of multiple data and multiple index approaches in drought modeling in areas with heterogeneous microclimates. The intense stress findings obtained on the southern slopes of the district are consistent with drought trends revealed in similar regional studies (Lionello et al., 2014).

Studies on the relationship between climate change and terroir sensitivity of agricultural GI products such as olive varieties, fruits, and wine grapes reveal common findings in this study (Hannah et al., 2013; Bowen, 2010; Henry, 2023). An increase in negative impacts due to climate change in GI product regions is a common finding.

The results obtained from this study confirm the biophysical mechanisms revealed by previous remote sensing techniques. The findings indicate accelerated evaporation due to increasing surface temperature (Otkin et al., 2014). Furthermore, increasing temperature negatively impacts soil moisture and leads to a decrease in NDVI values (Kogan, 1995). Additionally, VCI anomalies demonstrate the persistence of long term negative impacts on plant health (Zhang et al., 2017). These findings demonstrate that CDRI is an important metric in stress detection.

Although globally validated satellite datasets (MODIS, CHIRPS, ERA5 Land) were used, the absence of ground based measurements remains a limitation for independent verification of the index components. To mitigate this constraint, internal cross validation among EO variables (NDVI, VCI consistency, LST, TEMP coherence, ET, SM hydrological complementarity, and CHIRPS precipitation anomalies) was employed, a widely accepted approach in drought monitoring when field data are unavailable. Nevertheless, the study acknowledges that future work should integrate UAV thermography, in situ soil moisture sensors, and phenological field observations to externally validate and further refine the CDRI model at orchard scale.

PDSI is a widely used tool to monitor large scale drought events but is rather focused on precipitation and evapotranspiration only. The proposed CDRI incorporates VDI, HTI, and rainfall deficit into a multidimensional system. This provides it with an advantage to assess risks which affect agricultural systems only. The ability to map drought sensitivity at the neighborhood scale and provide no meteorological variables to be included in the model enhances its capability to analyze risks too.

Despite these acknowledged limits, the study indicates the production zone is edging toward greater vulnerability as droughts intensify. The overlapping pressure from vegetation stress, heat, and weather conditions suggests that both yield and quality are likely to suffer more as climate pressures persist. Better water management, testing of heat tolerant varieties, boosting of soil organic matter, and adopting microclimate focused orchard practices are some of the key highlights in terms of urgent calls for adaptation. In the near future, the model will be improved by the integration of higher resolution satellite data, Unmanned Aerial Vehicle (UAV) observations, on the ground measurements, and tracking of phenology. The addition of climate projections will also give further support to the development of long term risk scenarios.

In the research, the drought related stress areas are identified and matched in the Korkuteli Karyağdı Pear cultivation region, and the vulnerability level of the area against the impact of climate change is determined. From the observations, the areas of stress due to vegetation, hydro thermal stress, and meteorological stress have overlapped, and the region is facing drought stress. The reduced values of NDVI and VCI indicate reduced vegetation health, while the increase in LST and AT values shows an increase in the surface load and air load, respectively. Additionally, the increase in evapotranspiration and the reduced value of SM indicate stronger water stress, while the irregular values of MDI indicate drought stress is increasing in the region due to irregular rainfalls.

While the PDSI relies primarily on long term precipitation and evapotranspiration balance to characterize meteorological drought, the CDRI proposed in this study integrates vegetation stress indicators (NDVI, VCI), hydro thermal stress components (LST, AT, ET, SM), and precipitation anomalies within a multidimensional EO framework. Therefore, whereas PDSI is limited to a broad scale, atmosphere focused perspective, CDRI incorporates ground surface ecological responses detectable at finer spatial scales, enabling a more sensitive and plant specific assessment based on openly accessible EO datasets.

It is noticed in the CDRI surface that medium to high risk areas are widening, and the harshest drought bears upon the southern part of the district. Overlapping stressors in these zones make the Karyağdı Pear which lives in a pretty narrow ecological window particularly sensitive to shifting environmental conditions. Water scarcity and increased heat stress negatively affect tissue development, fruit growth, flavor, and flowering processes, leading to a decrease in quality. The study reveals these negative effects, and the similar situations observed in other regions of the world demonstrate the study’s consistency with other regions. This study highlights a global, not just regional, impact.

Obtaining CDRI at the neighborhood level revealed that Bayat, Kargın, Küçükköy, Kargın, Esenyurt, and Yazır have the highest risk. This risk is influenced by low NDVI, high surface temperatures, and dry soil areas. Kargalık, Akyar, Uzunoluk, İmrahor, Kiremitli, and Yeni neighborhoods emerged as medium risk areas. Tatköy, Aşağıpazar, and Karşıyaka neighborhoods were found to be in the low risk group. Establishing neighborhood level CDRI significantly contributes to the preparation of drought mitigation action plans. It provides valuable assistance to implementers in carrying out drought mitigation activities such as regional shading, soil moisture conservation activities, and smart irrigation system designs in high risk areas.

The GEE platform used in this study enables the use of open source data and also allows for the creation of a coding structure that is easily adaptable for common use. By employing an m indexing strategy, it effectively integrates drought due to weather, water, and heat, as well as plant stress. It proves that through the combination of EO data for hydrometeorology, and plant level information, better drought estimates can be obtained than through conventional meteorology inputs for drought estimation. The spatial data of CDRI maps help for better decision making regarding drought mitigation, tracing, and agricultural planning for agricultural growth. This data is of immense use for orchard management and plant variety selection, agricultural economic planning, and irrigation network design.

There are some mentioned constraints, which are largely centered on image resolution and techniques. There are several future suggestions regarding how more advanced image resolution and techniques for decision support may improve this proposed approach for agricultural systems centered on GI. The process and data involved are open source, so updates for this approach can be submitted for revision as new information becomes available, supporting overall sustainability.

Although recent studies have progressed from mere meteorological factors, they have increasingly focused on crop suitability modeling, changes in plant phenology, and bioclimatic analysis. The originality of this study lies in its holistic approach, considering the plant, meteorological, and hydro thermal characteristics of agricultural GI products together. This feature allows it to obtain results that are as close to accurate as possible. Furthermore, the fact that the datasets consist of open source data, making them economical and easily accessible, strengthens the study’s sustainability aspect. The updating and open source nature of the dataset used in the study are significant original features.