The status of water resources adversely affected by climate change, hence drought, is a major concern in agriculture, water resources management, and human use. Drought is defined as a natural event that negatively affects land and water resources, and hydrological equilibrium is disrupted due to precipitations falling below normal levels (Benson et al., 1997). It is possible to classify drought as meteorological, agricultural, and hydrological drought. Meteorological drought is the decrease, which occurs according to the long-year averages, in precipitation. On the other hand, agricultural drought is based on the amount of water available in the root zone of the plant. In terms of agriculture, the periods in which the amount of water to meet the water needs of plants in the soil is not present are defined as arid. Precipitation, plant water consumption, and soil characteristics can be shown as the main factors for agricultural drought. Hydrological drought refers to the decrease in surface and groundwater resources due to the lack of long-term precipitation. Even long after the end of the meteorological drought, hydrological drought may exist (Di̇nç et al., 2016). Drought risk management constitutes a vital part of water-resource management policies and strategies. National drought policies have an essential role in managing drought risk (Meza et al., 2020, Vogt et al., 2018, Wilhite et al., 2014). It is necessary to prepare drought management plans to mitigate the effects of drought depending on the legislation of the country and by considering the specific drought characteristics and effects of the basin (EuropeanCommission, 2007). Elements of the drought management plan include knowing the characteristics of the river basin, investigation of historical drought events in the basin, evaluation of the possible risk(s), determination of indicators and threshold values for drought analysis, creation of a program of measures to reduce the effects of drought, and establishment of the early warning system and organizational structure (GWP, 2015, Pérez-Blanco & Gómez, 2014). Drought risk management includes the following stages: hazard, impact assessment, and affectability, early warning system, including drought monitoring and forecasting, and preparedness and harm reduction (Wilhite, 2000). Drought early warning systems typically aim to monitor, evaluate, and present information on climate, hydrological features, water supply conditions, and trends (Funk & Shukla, 2020). The goal here is to provide early information before or during the onset of drought within a drought risk management plan to mitigate the potential impacts. Since drought is a slow-starting and progressive hydrological event, monitoring and analyzing drought is of great importance. Monitoring and analysis of drought are done employing various indicators and indices. These indicators and indices help characterize drought by providing information on the severity, location, duration, and timing of drought to determine, classify, and monitor drought conditions. Some indicators and indices can also be used to validate indicators of drought data that have been modelled, remotely detected, or assimilated into the model. The geographic information systems have made it possible to overlap, map, and compare different indicators and indices thanks to the power of evolving computational and imaging techniques (Svoboda & Fuchs, 2017).
Indicators are variables or parameters used to describe drought conditions. In general, drought indicators include the variables summarized in Table 1.
The index values initiate or terminate the implementation stages of a drought management plan. Therefore, drought management plans should be formed based on index values (Svoboda & Fuchs, 2017). The indicators and indices in the “Handbook of Drought Indicators and Indices”, published by the World Meteorological Organization (WMO) in partnership with the Global Water Partnership (GWP) in the context of the Integrated Drought Management Programme (IDMP), are classified into five main categories according to their characteristics. These are meteorology, soil moisture, hydrology, remote sensing, and compounded or modelled.
Two of the most practical and widely used indices are NDVI and VCI. Quiring and Ganesh (2009) were applied the VCI index to 254 Texas counties during 18 growing-seasons and found a good correlation between this index and many frequently used meteorological drought indices (Quiring & Ganesh, 2010). Interannual variations of NDVI were investigated and their relationships with temperature and precipitation variables and human activity in China between 1982 and 1999 (Piao et al., 2003). Variability of the NDVI over Botswana was worked by Nicholson and Farrar (1994) during 1982-1987 (Nicholson & Farrar, 1994). Shad et al. (2017) were pointed out that NDVI and VCI indices concerning MODIS sensors can be a good alternative for estimating the drought concerning meteorological indices for Isfahan (Shad et al., 2017). Chodhary et al. (2015) used NDVI and VCI indices to investigate drought effects on corn cultivation (Choudhary et al., 2015). Indices are especially vital in examining regions with sporadic or insufficient measuring stations and estimating drought (Klisch & Atzberger, 2016, Nanzad et al., 2019, Rezaei Moghaddam et al., 2014).
Several studies mentioned that NDVI and VCI indices are useful methods to detect drought and make a prediction. The main aim of the study was to answer the following questions based on remote sensing technologies. Advances in space technologies and computer systems have brought along a broader and more efficient use of remote sensing technologies and geographic information systems (GIS). The ability to easily transfer various geospatial data to the GIS environment with images taken from space via satellites has increased the possibilities for analysis of issues such as natural resource management, land use and land cover, environmental and ecological analysis, disaster risk assessment, and meteorological, hydrological and agricultural applications. Remote sensing technologies, especially satellite products, are used effectively and extensively in various hydrological applications for various regions of the world (Kundu et al., 2020). Jafari et al. (2020) compared satellite products with field measurements for drought monitoring for Iran’s southern part (Jafari et al., 2020). Shojaei and Rahimzadegan (2020) improved a comprehensive drought index for the west of Iran (Shojaei & Rahimzadegan, 2020). A precipitation-based drought study for Iran was done by Mahmoudi et al (Mahmoudi et al., 2019). Vegetation and soil moisture, which can be obtained by remote sensing, are data sources commonly used in drought studies (Drisya et al., 2018, Wang et al., 2020, Zhu et al., 2018). High-resolution vegetation change information provided both temporal and spatial by vegetation indices (e.g., NDVI), can contribute to drought-related research without requiring additional information on drought. Vegetation indices are preferred because they are easy to use, and they do not require any assumptions and/or additional information other than themselves (Bulut & Yilmaz, 2016). Vegetation indices can be determined by remote sensing methods and have a wide range of applications because the green vegetation gives high reflectivity values in the near-infrared region of the electromagnetic spectrum (Gökdemir, 2002). Most of the satellite sensors measure red and near-infrared light waves reflected from the land surface. Using mathematical formulas, raw satellite data related to these light waves are converted into vegetation indices. Vegetation indices describe the greenness (relative density and health status) of the plant for each cell in the satellite image. Not all vegetation indices perceive greenness in vegetation directly by measuring rays at visible and near-infrared wavelengths, some can indirectly perceive the change in vegetation. The water content in the plant allows the plant to perform less temperature swing in the day than the soil, thus, using the knowledge of temperature change throughout the day, those indices reach the knowledge of vegetation change (Hatfield & Prueger, 2015). Because such indices are sensitive to vegetation, they can provide important information about the drought experienced in the basin. Various indices are used for this purpose in different geographical regions of the world in the literature. Main indices were developed on remote sensing data that find wide usage areas in the literature, especially in determining drought e.g. Enhanced Vegetation Index (EVI) (Brede et al., 2015, Jiao et al., 2016, Khusfi et al., 2020), Evaporative Stress Index (ESI) (Anderson et al., 2016, Nguyen et al., 2019, 2020), Normalized Difference Vegetation Index (NDVI) (Solangi et al., 2019, Tsiros et al., 2004, Zaw et al., 2020), Temperature Condition Index (TCI) (Rahman, 2019, Tsiros et al., 2004), Vegetation Condition Index (VCI) (Abraham et al., 2018, Baniya et al., 2019, Gebrehiwot et al., 2016), Vegetation Drought Response Index (VegDRI) (Tadesse et al., 2017), Vegetation Health Index (VHI) (Bento et al., 2018, Masitoh & Rusydi, 2019), Water Requirement Satisfaction Index (WRSI) (Masupha & Moeletsi, 2020), Normalized Difference Water Index (NDWI) (Amalo et al., 2018), Land Surface Water Index (LSWI) (Chandrasekar et al., 2010, 2011).
The authors were motivated to get answers to the following questions during the work.
- Is it possible to apply NDVI and VCI indices to the Seyhan Basin?
- Are the results that NDVI and VCI indices will produce in the Seyhan Basin reasonable?
- Is there a drought problem in the Seyhan Basin, and how is it progressing?
- What is the frequency of the drought in the basin?
In this study, the remote sensing method was used, and drought analysis was performed for the Seyhan Basin with NDVI and VCI vegetation indices.