Spatiotemporal monitoring of droughts in Iran using remote-sensing indices

This study spatially monitored drought in Iran using drought indicators. Four drought indicators measured from 2016 to 2020 were used: temperature condition index (TCI), vegetation condition index (VCI), vegetation health index (VHI), and precipitation condition index (PCI). Moreover, a standardized precipitation index (SPI) was prepared using rainfall measurements from 1989 to 2019. The TCI revealed that most of Iran was classified as having “severe drought” in 2020. The highest value of VCI showed for northern Iran, which belongs to the class without drought. The VHI indicated that vegetation stress increased over the study period throughout the region, and areas of severe and moderate drought reached their greatest extents in the aforementioned years. Significant droughts occurred in central, eastern, and southeastern Iran, and mild droughts occurred in northern Iran. The PCI indicated that rainfall amounts have diminished in most of the country over the period of study. The 30-year SPI showed that northern Iran received fine rain and the region has parts that can be classified as either extremely wet or very wet. However, most of the country was extremely dry and severely dry. The analysis of the VHI index for agricultural plants showed that 27.71% of Iran's agricultural regions, including the provinces of Razavi Khorasan, Hamadan, and Khozestan, experienced “critical drought” conditions. The study provides guidance for the selection of the most useful drought-monitoring indicators and can enable a more thorough understanding of drought in arid and semiarid regions.


Introduction
Drought patterns are studied by researchers in ecology, hydrology, meteorology, geology, and agriculture. Short-term drought occurs in areas where rainfall is below normal, and long-term droughts occur when rainfall decreases over long periods (e.g., seasons or years) (Mishra and Singh 2010). Drought impacts water supplies, environmental conditions, cultivation, and public health (Kamali et al. 2017;Park et al. 2017;Guo et al. 2019). Recently, droughts have been increasing due to increasing water consumption, diminishing freshwater supplies, and global heating (Liu et al. 2016;Nam et al. 2015;Guo et al. 2019). Prolonged droughts pose severe environmental and economic problems (Boken 2009). Drought has spread to most of the world over the last four decades. Various levels of severity, temporal and spatial extents, and effects of droughts have been observed (FAO 2020).
Understanding vegetation health in response to annual and seasonal precipitation changes is essential for drought-impact mitigation (Moussa Kourouma et al. 2021). Drought surveillance is critical for drought risk assessment and can improve food security (Moussa Kourouma et al. 2021). Two main approaches can be taken to assess drought impacts: assessment of climate trends from data gathered at meteorological stations and assessing remote-sensing (RS)-derived indices. A disadvantage of monitoring drought by analyzing weather data is the limited spatial detail of information due to the sparse distribution of meteorological stations, and it undermines the strength of indices developed from these data (Magno et al. 2014). RS techniques, however, have proven to be reliable for spatial investigation of drought patterns.
In remote areas, terrestrial observations provide insufficient spatial and temporal information about drought (Andreadis et al. 2005;AghaKouchak et al. 2015). The benefit of RS data is that they are coherent and homogeneous, provide global coverage, and can be changed for different scales of analysis (AghaKouchak and Nakhjiri 2012; Thavorntam et al. 2015;Agutu et al. 2017;Rhee and Im 2017). Several indicators have been developed to assess drought conditions for various applications (Tian et al. 2020). Some that are based on the RS data include: the normalized difference vegetation index (NDVI), the precipitation condition index (PCI), the vegetation condition index (VCI), the temperature condition index (TCI), and the soil moisture condition index (SMCI). A single drought indicator cannot provide enough information to fully understand the development of droughts as drought is a complex process (Wardlow et al. 2012;Tian et al. 2018;Guo et al. 2019). Therefore, a selection of drought indicators must be combined to gain a more complete picture (Guo et al. 2019).
One cause of drought is the lack of precipitation (McVicar and Jupp 1998). Precipitation is a fundamental component of defining drought, especially when examining agricultural droughts (Udelhoven et al. 2009). PCI is a drought assessment tool based on rainfall measurements. The Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) database, a meteorological station-based record of observations from 1981 to the present, can reveal general precipitation patterns. But combining TCI and VCI yields a composite index that has a strong correlation with rainfall and may provide "early-warning" information that may be useful for drought forecasting (Quiring and Ganesh 2010;Jain et al. 2010;Sruthi and Aslam 2015). Du et al. (2013), however, showed that TCI alone has limited value for assessment of soil moisture. But the combination of VCI and TCI produces a vegetation health index (VHI) that can be used to accurately monitor drought through vegetation.
This study tests the effectiveness of RS indicators for monitoring and assessment of drought. A large catalog of satellite images from various space agencies and various types of geographic processing tools are employed to derive several indices. First, NDVI and land surface temperatures (LST) were calculated from RS data. The VCI and TCI indices were extracted from a time series of maps using NDVI and LST. The quality of vegetation relative to the ideal vegetation condition is indicated by VCI, but vegetation health, which is based on the interactions of vegetation with atmospheric temperature, is assessed using VHI, an indicator of the degree of drought-related stress on the vegetation. TCI, VCI, VHI, PCI, and SPI were determined for the entirety of Iran to assess the temporal and spatial changes in drought conditions. The innovations of this study are twofold. First, this comprehensive survey of drought in Iran from 2016 to 2020 in Google Earth Engine using Sentinel-2, Landsat-8, and CHIRPS data was conducted to evaluate four drought-related indices and their capacity to consistently identify drought conditions. Rainfall records from all meteorological stations and monitors (rain gauges, and synoptic and climatological stations) in Iran from their installation through 2019 were examined over three periods to establish SPI. Second, critical drought was examined by assessing the spatial distributions of agricultural lands classified as having high and very high VHI in 2020.

Study area
Iran is located between 25° and 40° N and 44° and 64° E, covering an area of approximately 1,648,000 km 2 (Kaboli et al. 2021). Iran is the world's 17th largest country in terms of area. Drought is a prominent feature of Iran, as it commonly occurs in more than twothirds of country due in part to the encirclement of the Iranian Plateau by mountains and because it is located in the arid belt of the sub-tropics. Nationally, the annual average temperature in summer is 38 °C. Northern Iran (adjacent to the Caspian Sea) experiences temperatures below 0 °C during the winter and is humid for the rest of the year (Shahabfar and Eitzinger 2013). The average summer temperature in the north rarely exceeds 29 °C (Shahabfar and Eitzinger 2013; Koohi et al. 2021). In the northern Alborz mountains, rainfall exceeds 1000 mm, and the Alborz and Zagros mountains receives precipitation amounts between 600 and 800 mm and between 500 and 600 mm, respectively (Koohi et al. 2021). In central Iran, however, annual precipitation decreases significantly. There are areas in central Iran that has received no rainfall for several years (Rahimi et al. 2013). According to De Martonne's definitions, Iran has extra-arid, arid, and semiarid regions. The slopes of the Alborz and Zagros mountains are semiarid (Rahimi et al. 2013). A prolonged period of low precipitation and improper water management have generated devastating droughts in Iran (Jafari et al. 2020). According to reports, $520 million of Iranian farm production was lost due to drought conditions in 2001 (Iranian Ministry of Energy 2018).

Remotely sensed data and drought indices
Remotely sensed data from Sentinel-2 and Landsat-8 were used to develop several drought indices. Sentinel-2 and Landsat-8 satellites provide valuable environmental data, especially LST. Google Earth Engine (GEE), a cloud-based architecture designed for use in geoscience research, is comprised of satellite images from several space agencies and geoprocessing tools from the public domain (Sazib et al. 2018;Rahaman and Venkatesh 2020;Khan and Gilani 2021). GEE is cost-effective and provides rapid processing of spatial data (Rahaman and Venkatesh 2020) using JavaScript and Python streamline. GEE allows users to access and analyze satellite and geographic data related to 40 years of Earth images. The main advantage of GEE is the easy access to satellite data archives and high-performance computing resources to process huge geospatial and remotely sensed datasets that are processed and periodically updated. It is easy to obtain results using GEE due to its advanced algorithms and lack of need for programming by the user. Another advantage is the variety of satellite data available to users, especially Landsat-8 and Sentinel-2 (Tavakkoli Piralilou et al. 2022). This platform has been widely used in the field of soil mapping (Gorelick et al. 2017), natural hazard susceptibility Venkatappa et al. 2021;Notti et al. 2022), and cropland mapping (Lobell et al. 2015).
This study evaluates drought using RS indicators found in the GEE data catalog. The goal of this study was to determine the spatial and temporal patterns of drought in Iran over the 5-year period from 2016 to 2020. There is a significant need for detailed drought-monitoring information, particularly for sustainable development. The importance of these issues and the progress of RS technologies toward providing data with better temporal and spatial resolutions has motivated us to study drought mapping in Iran. The goal of this research was to use the data with the highest temporal and spatial resolution. For this reason, we used Landsat-8 and Sentinel-2, which are freely available (and free) and have suitable spatial resolutions.
In the last 5 years (2016-2020), Iran has continuously experienced rains below the longterm average and precipitation has decreased over this period. SPI was calculated over a long period using the precipitation measurements of Iranian stations (Fig. 1). VCI and TCI were combined to create VHI. PCI was also calculated using the CHIRPS precipitation dataset (https:// data. chc. ucsb. edu/ produ cts/ CHIRPS-2.0/). This global database covers a period of more than 30 years (from 1981 to the present). The database combines station and satellite-gathered precipitation data to track and monitor drought trends in a networked time-series precipitation database. Data were extracted from CHIRPS version 2.0 at a resolution of 0.25° × 0.25°. PCI was calculated using monthly precipitation totals. TCI, VCI, VHI, and PCI were mapped. The index values were classified as extreme drought (< 10), severe drought (11-20), moderate drought (21-30), slight drought (31-40), and no drought (> 40) . SPI was calculated using monthly precipitation amounts. McKee et al. (1993) classified the SPI into seven classes, including extremely dry (≤ 2), severely dry (− 1.5 to − 1.99), moderately dry (− 1.0 to − 1.49), near normal (− 0.99 to 0.99), moderately wet (1.0-1.49), very wet (1.5-1.99) and extremely wet (≥ 2.0).

Land surface temperature (LST)
LST provides a comprehensive physical reflection of the land surface and can reveal changes to climates (Wang and Lu 2005). Changing climate conditions, particularly increasing temperatures, can severely damage vegetation. Therefore, LST is of critical value for drought evaluation. LST was estimated using a split-window algorithm that is based on the difference in radiation temperature of two adjacent thermal bands. This method is important because it is more accurate than other approaches. The split-window algorithm uses multiple multispectral and thermal sensors to calculate land surface diffusion (Sobrino et al. 2004). There is no standard for determining LST from Landsat-8 imagery; coefficients were employed to simulate different atmospheric and surface conditions (Table 1). LST was calculated by replacing the coefficients in equation 1 (Sobrino et al. 2004): where TB 10 and TB 11 are brightness temperatures of bands 10 and 11, C 1 -C 6 are coefficients of split-window algorithm, W is the water vapor column, Δm is the surface diffusion differences, and m is average land surface diffusion capacity. The procedure followed six steps (Sobrino et al. 2004).
Step 1. The spectral irradiation of bands 10 and 11 and bands 2-5 of OLI sensors was estimated using Eq. 2: where L is the spectral radiation above the atmosphere, DN max is difference between the maximum and minimum sensor calibration, and L max and L min are the maximum and minimum spectral radiation of the respective bands.
Step 2. The brightness temperature of bands 10 and 11 was estimated from the sensor-recorded radiant temperature. Thermal conversion constants are thermal bands and were calculated using Eq. 3: Step 3. NDVI was obtained from bands 4 and 5 of the OLI sensor (Eq. 4): NDVI ranges from − 1 to + 1.
Step 4. As green vegetation is a valuable for FVC estimation, two spectral components of an RS image, bare soil and green vegetation, are nonlinearly combined. The spectral properties of these two elements are usually estimated in different ways. The most common FVC relationship is (Eq. 5): where NDVI Max and NDVI Min are areas with and without vegetation or the maximum and minimum of NDVI.
Step 5. The land surface diffusion capacity from bare and vegetated areas using thermal bands 10 and 11 was calculated as expressed in Eq. 6: LSE is land surface diffusion capacity, S is the soil release, Vegetation is the plant propagation, and FVC is the vegetation deficit.
Step 6. The land surface diffusion capacity for each thermal band, the difference between the bands, and the average were calculated (Eq. 7 and 8) (Sobrino et al. 2004): (1) LST = TB 10 + C 1 TB 10 − TB 11 + C 2 TB 10 − TB 11 NDVI is a function of the effects of rainfall, soil moisture, and agricultural activities on vegetation in a region. NDVI is an important indicator of drought. It is a plant health and plant density index introduced by Tucker (1989). The near-infrared (R NIR ) and red (R Red ) bands were used to determine NDVI using Eq. 9:

VCI
NDVI decreases in locations with stressed vegetation. Vegetation growth was quantified with VCI (Liu and Kogan 1996). The layers were normalized to eliminate the effects of scattered short-term climate noise using long-term ecological signals. VCI was calculated by Eq. 10 : where VCI i is for a particular year i, NDVI i for the same year i, and NDVI min and NDVI max are the minimum and maximum NDVI for the period of analysis. VCI ranges from 0 (severe drought) to 1 (ideal moisture conditions) (Yulistya et al. 2019).

TCI
If cloudy conditions continue for more than three weeks during the rainy season, weekly NDVI levels decrease, which causes misidentification of drought and water stress. TCI was proposed by Kogan (1995) to eliminate the effects of cloud pollution on vegetation assessment by analysis of satellite imagery (Unganai and Kogan 1998). Clear skies are associated with higher surface temperatures via reradiation (infrared) from the surface. Drought is tied to surface temperature. High LST indicates drought and low LST indicates conditions of water abundancy during growing seasons (Singh et al. 2003). Higher temperatures in the surface soil increase water stress in plants (Kogan 1995). TCI is calculated using Eq. 11: where TCI i is the index of a specific temperature in a particular year, LST i is the measure for the same year i, and LST max and LST min are the maximum and minimum LST values during the statistical period.

VHI
VHI was obtained using VCI and TCI which are related to agricultural drought (Eq. 12). TCI and VCI reflect temperature and vegetation conditions. Kogan (1995) showed that the TCI complements the VCI to monitor drought. Used together, they provide a reliable tool for this purpose (Sun et al. 2020).
where r 1 and r 2 are the weights of VCI and TCI. VHI considers VCI and TCI equal in importance due to the uncertainty of humidity and temperature during the life cycle of plants. VHI is classified into five drought categories.

PCI
USGS and CHC researchers sponsored by USAID, NASA, and NOAA have been developing methods for mapping rainfall since 1999, particularly for areas with sparse data. CHIRPS was created to produce complete, reliable, and up-to-date datasets for several early-warning purposes (e.g., trends and seasonal drought), collaborating with researchers at the USGS Earth Resources Observation and Science Center (EROS). PCI, like VHI, ranges over five classes from drought-free to severe drought (Wang et al. 2019). PCI is calculated by Eq. 13: where PRC max and PRC min are maximum and minimum precipitation amounts in a given month (Ali et al. 2019).

SPI
SPI is a standardized indicator of meteorological drought introduced by McKee et al. (1993). The index is often used to assess drought conditions (Mishra and Singh 2010;Abeysingha and Rajapaksha 2020) and is the most suitable index for mapping drought conditions because it is simple to calculate, it uses available rainfall data, it can be calculated over any time scale, and the results can be compared spatially. SPI is calculated by Eq. 14: where x i is a month's i rainfall at a station, x m is average rainfall, and σ standard deviation (McKee et al. 1993).

Land use/land cover
A 10-m resolution map of Earth's land cover was produced by ESRI in 2020. That map was the source of data used to create a map of land use/land cover in Iran (Fig. 2). ESRI applied deep-learning algorithms to Sentinel-2 imagery to create eight classes: water, trees, grass, flooded vegetation, crops, scrub/shrubs, built areas, and bare land (https:// livin gatlas. arcgis. com/ landc over/). Cropland in each of the provinces of Iran was selected from the map of Iran. The portions of agricultural land classified as having high and very high VHI were highlighted to focus on the areas of critical drought.

Results and discussion
Three indices-VCI, TCI, and VHI-were calculated using NDVI and LST, and the indices were categorized to map drought severity (Figs. 3, 4, 5). PCI and SPI were also extracted using PRC and the mean monthly and average measures of rainfall and they were mapped (Figs. 6, 7). The proportions of Iran classified as having severe drought based on TCI was 33.70% in 2016, 31.25% in 2017, 32.47% in 2018, 35.74% in 2019, and 41.05% in 2020 (Table 2). Severe drought based on TCI accounted for the largest portion of Iran each year. The extent of severe drought increased each year and was greatest in 2020. The regions of Iran experiencing the highest temperatures were located in the southeast, southwest, and center of Iran (Fig. 3). TCI increased from west to east and from north to south across Iran over the study period. TCI is strongly affected by elevation (TCI is inversely related to elevation) and latitude in Iran, but it is influenced by topography as well. The region experiencing the least severe droughts during the study period was in the northwest, along Iran's northern border and in the Zagros Mountains, and in the northeast. VCI tends toward a zero value in the driest months. Low VCI values in successive time periods indicate a continuation of drought conditions. Though the vegetation across most of Iran has been experiencing damaging conditions, the vegetation of northern Iran has persisted through slight or no drought during the study period. While Singh et al. (2003) showed that NDVI is essential for vegetation and growth analysis, it has mainly been used to monitor drought (Rhee et al. 2010). But NDVI alone does not indicate drought severity (Kogan et al. 2013;Kogan et al. 2016). Gidey et al. (2018) showed that multi-period NDVI surveillance supported by VCI and TCI values can improve drought forecasting. They  (2020) also demonstrate that VCI is, in fact, a better indicator than NDVI for assessing drought because it reveals vegetation vitality relative to the best and worst conditions during the same seasons in different years. Because VCI is a reliable drought indicator over extensive areas (as in this study), it can be used to focus drought assessments (Bajgiran et al. 2008) (Fig. 4). Quring and Ganesh (2010) showed that VCI can also be used to assess agricultural drought. It is necessary to exercise caution when using VCI for drought monitoring, VHI can be used to track agricultural drought. VHI integrates temperature and vegetation indices to more accurately assess drought (Kogan 1995). VHI value classification indicated that severe drought was the class covering the largest proportion of the country over the study period (38.59% in 2016, 38.08% in 2017, 36.36% in 2018, 40.83% in 2019, and 40.67% in 2020) ( Fig. 5; Table 3). A spatiotemporal analysis of VHI reveals that vegetation stress increased over the study period and severe and moderate drought designations covered most of the country. The most severe droughts occurred in the central, eastern, and southeastern regions, while the northern half of Iran experienced the shortest and least intense droughts. Extreme drought based on PCI values covered the largest portion of the study area (63.42% in 2016, 50.50% in 2017, 58.59% in 2018, 51.10% in 2019, and 58.50% in 2020) ( Fig. 6; Table 4). From 2016 to 2020, most of Iran received little precipitation. PCI indicated that average annual rainfall amounts occurred in northern and northwestern Iran. PCI seems to be the index that is most closely aligned with meteorological station data in the rainy regions along the Caspian Sea, and in the Iranian provinces of Western and Eastern

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Azerbaijan. Low rainfall is one of the main causes of droughts, so it is not surprising that PCI indicates droughts occurred in dry provinces (Zhang and Jia 2013;Han et al. 2020a). SPI was analyzed over the longest (30 years) period. To calculate SPI, station data, including synoptic, rain gauge-derived, and climatological, were grouped and analyzed by length of record from 1989 to 2019. There are 404 stations that provide at least 10 years of data, so the stations were group as follows: 105 stations had a 30-year statistical record ; 153 stations had a 20-year statistical record (1999-2019); and 298 stations had a 10-year statistical record (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019).
SPI values are annual data points that can reveal the anomalies that have occurred over the period of record. The "extremely dry" SPI category covered the largest area of Iran (Figs. 7, 8). Only the northernmost parts of Iran received at least adequate amounts of precipitation and this was indicated by an "extremely wet" classification. The highest numbers of very severe droughts in Iran occurred in the driest regions. Moreover, SPI indicated that 38.41-45.11% of the study area had extremely dry conditions over various periods. The highest percentage of the area was observed for the extremely dry class (45.11%) during the statistical period from 2009 to 2019 (Fig. 8). The smallest portion of the area (0.30%) from 1999 to 2019 was classified as extremely wet. Areas of extreme drought increased in extent between 2009 and 2019 (Fig. 7). Western Iran experienced near-normal conditions between 1999 and 2019, but between 2009 and 2019, these areas were classified as moderately dry (Fig. 8).

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The relationship between land use/land cover classes and drought severity was examined (Table 5). In 2020, agriculture covered 48,050.66 square kilometers for critical drought class of the 173,402.3 square kilometers agricultural area in Iran. About 27.71% of the lands classed as agricultural experienced critical drought conditions (i.e., both severe and extreme droughts) ( Table 6). The provinces of Razavi Khorasan, Hamadan, Khozestan, Markazi, Kordestan, Fars, Zanjan, Lorestan, and West Azerbaijan have the largest amounts of agricultural land experiencing critical droughts in Iran. The provinces of Gilan, Mazandarn, Kohgiluyeh and Boyer-Ahmad, Golestan, Alborz, Ardabil, and North Khorasan have the smallest amounts of agricultural land experiencing critical drought conditions during the study period. Researchers have explored the application of land cover classification to drought conditions (i.e., global climate change and sustainable development) (Eskandari et al. 2020;Phiri et al. 2020;Ebrahimy et al. 2021). 1 3

The advantages of using different parameters for drought studies
Each parameter-precipitation, LST, and vegetation condition-contributes to the improving the identification of drought conditions and severity, but their contributions are also a function of other parameters like climate classes and vegetation types. Han et al. (2020b) reported that vegetation health is affected by other factors besides drought, and there is a time lag after drought when the health of vegetation becomes apparent. This may be because vegetation-based indices are weakly correlated with rainfall and temperature patterns even though precipitation is the most important factor in drought (Rhee et al. 2010). When dehydrated, some plants are stressed by higher temperatures and this is what is indicated by drought severity. Zhang et al. (2018) demonstrated that rising temperatures have negative effects on vegetation particularly during planting season when water demand for growth increases. This is supported by our results. Agricultural and meteorological droughts are, of course, directly related (Araste et al. 2017). In general, the results of this study show that the employment of a diverse set of satellite-derived environmental measures is effective for regional analysis of droughts and their severity in Iran. Due to its impacts, drought monitoring is critical, especially in arid and semiarid regions. A diverse set of drought indicators can be combined to improve the accuracy of severity assessments. But finding the most suitable indicators that comprehensively represent dimensions of drought is the challenge. A single indicator is insufficient for regional drought monitoring, so integration of these tools are needed for regional and national spatiotemporal assessments of drought. Since single-indicator drought assessment and analysis does not allow managers to manage comprehensively, it is crucial to develop more robust indices that meaningfully combine multiple indicators (Hon'ble et al. 2019). Drought indices that combine vegetation conditions and LST will perform better.

Comparison to other studies
Drought studies in Iran have illustrated the extent of droughts during recent years. Raziei et al. (2009), for example, reported that ten of thirty-one Iranian provinces experienced prolonged droughts between 1998 and 2001. Golian et al. (2015) analyzed meteorological and agricultural droughts and concluded that severe droughts occurred in nearly every region of Iran between 1998and 2001. Yazdani and Haghsheno (2008 found that 17 drought seasons occurred in Iran during the last 50 years, the worst occurred in 1999, historically causing the greatest damage to the agricultural economy and water supplies. Tabari et al. (2011) studied time-series data regarding rainfall and drought intensity in eastern Iran and concluded that the region had become more arid between 1966 and 2005. Modarres et al. (2016) examined the trends of Iran's droughts and floods from 1950 to 2010 and found that droughts fluctuated in intensity during that time. Severe droughts in the 1990s and in 2008 (Tabari et al. 2013;USDA 2008;Alborzi et al. 2018;Ghadami et al. 2020;Khazaei et al. 2019;Maghrebi et al. 2020) significantly impacted most of the country. Recent droughts have dried up streams, lagoons, aqueducts, and wells. They have decreased groundwater supplies, caused erosion, degraded environments, desertified regions, generated dust storms, and generally made life more difficult for villagers (Mirnezami et al. 2018;Mirzaei et al. 2019;Nodefarahani et al. 2020; OCHA (Office for the Coordination of Humanitarian Affairs), 2000; OCHA (Office for the Coordination of Humanitarian Affairs), 2001). This study demonstrated the severity of the droughts that Iran has experienced between 2016 and 2020.

3 4 Conclusion
Droughts can cause great economic and environmental damage. Iran experiences significant economic and social impacts every year. Drought monitoring needs to be perfected and the formulation of reliable multivariate drought indicators is essential. This assessment of drought in Iran combined Sentinel-2 and Landsat-8 satellite data with rainfall data from an assortment of meteorological stations. The spatial distribution of drought severity was projected from RS-derived indicators. Four drought indices-TCI, VCI, VHI, and PCIwere used to determine the spatial extents of droughts in Iran between 2016 and 2020, and SPI was calculated using rainfall measurements from 1989 to 2019. VHI analysis indicated that most of the country had been affected by severe droughts. Agricultural provinces that experienced critical drought conditions in 2020 were identified. SPI showed that most of the region was extremely dry during the study period and that extremely dry conditions prevailed during the three periods (defined by the length of station records). Though local and regional drought monitoring in Iran has attracted attention from scholars, there are still gaps in the scholarship about the country. This study has demonstrated a practical assessment of drought in Iran using a more extensive dataset.