Satellite-based Drought Indices Research Articles

Spatio-temporal assessment of agricultural drought using remote sensing and ground-based data indices in the Northern Ethiopian Highland

Study regionNorthern Ethiopian highland, North Wollo, Ethiopia Study focusDrought is one of the most serious environmental and socioeconomic problems of Sub-Sahara Africa's countries. The study aims to assess and evaluate the agricultural drought occurrence and its severity by using remote sensing-based multivariate indices to provide real-time spatial-temporal information for drought monitoring. Satellite datasets from Landsat images with a spatial resolution of 30 m were processed and rescaled to compute vegetation and temperature indices and derive drought status indices. The temporal and spatial variability of drought were detected and mapped by using satellite-based drought indices and validated by available in-situ data. New hydrological insights for the regionThe study revealed that the area was exposed to extreme drought in 1984, 2009, and 2014. On the contrary, no longer extreme drought dominated in 1989, 1994, 1999, and 2004. Moreover, the relationships between various indices have been examined by using a linear regression model. It has been detected that there is a significant strong relationship between NDVI and LST (R2 /p = 0.93/0.00), VCI and TCI (R2 /p = 0.71/0.00), VHI and SPI (R2/p = 0.85/0.00). Therefore, the spatial extent and temporal assessment of drought-related information are important for decision-making in the field of agriculture.

Quantifying land degradation and desertification in semi-arid regions through environmental monitoring and assessment (A case study of the Imiter area, Toudgha River catchment, Morocco)

This study focuses on the spatiotemporal drought analysis in the driest region of southeastern Morocco, specifically the Imiter region within the eastern Anti Atlas region. The region has witnessed various climate change events in recent years, necessitating a comprehensive investigation of the relationship between climate and environmental conditions. To achieve this, Landsat 7, 8 and 9 satellite data were utilized for drought analysis. Additionally, satellite-based drought indices including the Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI), were employed to assess the extent of drought evolution. Over a period of two decades (from 1998 to 2022), 24 annual spatial maps were generated for each index to identify the most affected areas. The analysis of NDVI and VCI from 1998 to 2011 revealed significant year-to-year variability, attributed to various factors such as declining piezometric levels, deforestation and mining activities. These findings contribute to a better understanding of drought dynamics in the study area and highlight the importance of monitoring environmental conditions for effective drought management and mitigation strategies.

Drought Monitoring from Fengyun Satellite Series: A Comparative Analysis with Meteorological-Drought Composite Index (MCI)

Drought is a complex natural hazard that affects various regions of the world, causing significant economic and environmental losses. Accurate and timely monitoring and forecasting of drought conditions are essential for mitigating their impacts and enhancing resilience. Satellite-based drought indices have the advantage of providing spatially continuous and consistent information on drought severity and extent. A new drought product was developed from the thermal infrared observations of the Fengyun (FY) series of satellites. We proposed a data fusion algorithm to combine multiple FY satellites, including FY-2F, FY-2G, and FY-4A, to create a long time series of a land surface temperature (LST) data set without systematic bias. An FY drought index (FYDI) is then derived by coupling the long-term LST data set with the surface–atmospheric energy exchange model at 4 km spatial resolution over China from 2013 to present. The performance and reliability of the new FYDI product are evaluated in this study by comparing it with the Meteorological-drought Composite Index (MCI), one of the authoritative drought monitoring indices used in the Chinese meteorological services. The main objectives of this paper are: (1) to evaluate the performance of the FYDI in capturing the spatiotemporal patterns of drought events over China; (2) to quantitively analyze the consistency between the FYDI and MCI products; and (3) to explore the advantages and limitations of the FYDI for drought monitoring and assessment. The preliminary results show that the FYDI product has good agreement with the MCI, indicating that the FYDI can effectively identify the occurrence, duration, severity, and frequency of drought events over China. These two products have a strong correlation in terms of drought detection, with a correlation coefficient of approximately 0.7. The FYDI was found to be particularly effective in the regions where ground observation is scarce, with the capability of reflecting the spatial heterogeneity and variability of drought patterns more clearly. Overall, the FYDI can be a useful measure for operational drought monitoring and early warning, complementing the existing ground-based MCI drought indices.

A Modified Temperature Vegetation Dryness Index (mTVDI) for Agricultural Drought Assessment Based on MODIS Data: A Case Study in Northeast China

Satellite-based drought indices have been shown to be effective and convenient in detecting drought conditions. The temperature vegetation dryness index (TVDI) is one of the most frequently used drought indices; however, it is not suitable for areas with high fractional vegetation cover (FVC). In this study, a modified temperature vegetation dryness index (mTVDI) was constructed by using the multispectral vegetation dryness index (MVDI) proposed by a PROSAIL simulation and water stress experiments which was based on the theory of the TVDI and utilized MODIS data. Compared with the TVDI, the mTVDI presents a more triangular feature space, as well as obviously increased R2 values for dry and wet edges (from 0.37–0.90 to 0.53–0.91 for dry edges and from 0.00–0.77 to 0.24–0.80 for wet edges). The mTVDI was evaluated using standardized precipitation evapotranspiration indices (SPEIs), precipitation, potential evapotranspiration (PET), and the crop water deficit index (CWDI), and the results confirmed that the mTVDI can better reflect the actual spatial changes, compared to the TVDI, under high FVC, as well as presenting an increased Pearson correlation coefficient (by 0.06–0.10) when compared with SPEIs. Moreover, the good performance of the mTVDI in major drought events indicates its reliability and accuracy for drought monitoring. Overall, the mTVDI is a reliable and accurate satellite-based dryness index suitable for high FVC conditions.

Performance of Drought Indices in Assessing Rice Yield in North Korea and South Korea under the Different Agricultural Systems

Drought affects a region’s economy intensively and its severity is based on the level of infrastructure present in the affected region. Therefore, it is important not only to reflect on the conventional environmental properties of drought, but also on the infrastructure of the target region for adequate assessment and mitigation. Various drought indices are available to interpret the distinctive meteorological, agricultural, and hydrological characteristics of droughts. However, these drought indices do not consider the effective assessment of damage of drought impact. In this study, we evaluated the applicability of satellite-based drought indices over North Korea and South Korea, which have substantially different agricultural infrastructure systems to understand their characteristics. We compared satellite-based drought indices to in situ-based drought indices, standardized precipitation index (SPI), and rice yield over the Korean Peninsula. Moderate resolution imaging spectroradiometer (MODIS), tropical rainfall measuring mission (TRMM), and global land data assimilation system (GLDAS) data from 2001 to 2018 were used to calculate drought indices. The correlations of the indices in terms of monitoring meteorological and agricultural droughts in rice showed opposite correlation patterns between the two countries. The difference in the prevailing agricultural systems including irrigation resulted in different impacts of drought. Vegetation condition index (VCI) and evaporative stress index (ESI) are best suited to assess agricultural drought under well-irrigated regions as in South Korea. In contrast, most of the drought indices except for temperature condition index (TCI) are suitable for regions with poor agricultural infrastructure as in North Korea.

Evaluation of Drought in the North of Thailand using Meteorological and Satellite-Based Drought Indices

This research attempts to evaluate drought impact on vegetation dynamics using various drought indices including the vegetation condition index (VCI), the temperature condition index (TCI), the vegetation health index (VHI) and The standardized precipitation index (SPI). Monthly VCI, TCI and VHI values were obtained from multi-temporal Terra/MODIS products from 2000 to 2017. Temporal and spatial scales of drought occurrences were detected from seasonal variations and drought severity maps generated from these drought indices. The correlation analysis was analyzed to identify the dominant factors that control vegetation variability. The results found that VHI was better to detect vegetation health in the mountainous areas at higher altitudes and lower temperatures. VCI and SPI captured drought occurrences for paddy fields in the southern part of the region where there was lower rainfall and vegetation cover. The result can be applied for crop and drought early warnings in the region.

Drought monitoring based on a new combined remote sensing index across the transitional area between humid and arid regions in China

As the most frequent and longest-lasting climate-related disasters, droughts are complex processes caused by hydrological imbalance, and have significant effects on ecosystem function and socioeconomic development. However, most conventional satellite-based drought indices and combined drought indices are mechanically constrained by the fact that they were developed and evaluated for a specific climate or critical ecological region, especially in China. A comprehensive drought index should be developed for extensive application in different climate-hydrology regions. To address this problem, an optimised principal component analysis method (PCA-RF) was proposed to support drought assessment across the transitional area between the humid and arid regions in central China, including the Loess Plateau (LP) and Qinling Mountains (QL). This method eliminated the causal relationships of non-linear changes for multi-aspect parameters such as land surface temperature, precipitation and soil moisture, and was used in developing a new index named “Meteorological drought index by PCA-RF methods (PMDI)”. The results showed that PMDI had higher correlations and lower root mean square error than other drought indices (established using the empirical-weights method) with the 1-month standardized precipitation evapotranspiration index (SPEI-1). That means PMDI performed well in meteorological drought monitoring. Additionally, the normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) were utilised to evaluate PMDI effectiveness in detecting vegetation response to drought, which represents agricultural drought. The results indicated that correlations between PMDI and vegetation indices (VIs) were stronger when severe or extreme droughts occurred, and that their relationships were affected by the high spatiotemporal heterogeneity in vegetation change over the LP and QL. Overall, PMDI was shown here to be a very suitable index for monitoring meteorological drought (short-term) and has effective action in capturing the response of vegetation signals to agriculture drought events (long-term). Given the excellent performance of PMDI in diverse climate-hydrology conditions of our study regions, we concluded that the PMDI has considerable potential as a drought monitoring methodology on a larger spatial scale.

Assessment of agricultural drought using MODIS derived FAO's agriculture stress index system (ASIS) over the Iran croplands

In Iran, the drought is one of the costliest natural disasters, which has devastating consequences for the food security of agricultural households. Drought monitoring characteristics are important for better understanding of the drought phases in mitigation planning. Various satellite-based drought indices and systems have been developed and applied at both the regional and global scales. Recently the remotely sensed agricultural stress index system (ASIS) based on imagery from the Advanced Very High Resolution Radiometer (AVHRR-NOAA) and Meteorological Operational Satellite (METOP) by the Global Information and Early Warning System (GIEWS) of FAO developed. It shows considerable potential for drought monitoring at the global scale. Vegetation Health Index (VHI), start and end of the crop season (SOS-EOS) are the main inputs of ASI S. While the GIEWS models use the METOP-AVHRR images (1984 at present), in this study, an attempt has been made to retrieve key ASI inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS) to evaluate the ability of the MODIS ASIS for characterizing agricultural drought severity and explore the impacts of drought on crop production during growing season. Comparing national and sub-national wheat and barley yields with the ASIS drought maps, demonstrated that the proposed approach could identify major historical droughts over the observed period (2002-2015) in Iran. We detected that the extreme severe drought occurred during the year 2007-2008 crop season, affecting approximately 64% of crop land.

Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output

Drought forecasting is essential for effectively managing drought-related damage and providing relevant drought information to decision-makers so they can make appropriate decisions in response to drought. Although there have been great efforts in drought-forecasting research, drought forecasting on a short-term scale (up to two weeks) is still difficult. In this research, drought-forecasting models on a short-term scale (8 days) were developed considering the temporal patterns of satellite-based drought indices and numerical model outputs through the synergistic use of convolutional long short term memory (ConvLSTM) and random forest (RF) approaches over a part of East Asia. Two widely used drought indices—Scaled Drought Condition Index (SDCI) and Standardized Precipitation Index (SPI)—were used as target variables. Through the combination of temporal patterns and the upcoming weather conditions (numerical model outputs), the overall performances of drought-forecasting models (ConvLSTM and RF combined) produced competitive results in terms of r (0.90 and 0.93 for validation SDCI and SPI, respectively) and nRMSE (0.11 and 0.08 for validation of SDCI and SPI, respectively). Furthermore, our short-term drought-forecasting model can be effective regardless of drought intensification or alleviation. The proposed drought-forecasting model can be operationally used, providing useful information on upcoming drought conditions with high resolution (0.05°).

Recent ENSO influence on East African drought during rainy seasons through the synergistic use of satellite and reanalysis data

This study identified a relationship between the El Niño-Southern Oscillation (ENSO) and East African drought during the two rainy seasons (i.e., short rain from October to December and long rain from March to May). ENSO shows a positive relationship with the East African short rain during the entire period analyzed (1949–2016). Meanwhile, a statistically significant relationship between ENSO and East African long rain appears only in a recent period (2000–2016), which is unprecedented in the past 50 years before the 2000s. The strengthened interannual relationship between ENSO and East African long rain is associated with distinguished Indian Ocean Walker cell in boreal spring, implying that their relationship could be affected by either multidecadal natural variability or anthropogenic forcing. Various satellite-based drought indices which consider vegetation health, land surface temperature, evapotranspiration, and precipitation with 1 km spatial resolution showed a robust relationship between ENSO and East African drought in the recent period (2000–2016) during the both rainy seasons. In the case studies of June 2005, August 2007, and November 2010, the anomalous wet condition in East Africa during the mature phase of El Nino became dry as La Nina developed in the following year, thereby a lagged response was observed in vegetation-related drought indices and long-term meteorological drought indices. Satellite-based high resolution (1 km) drought indices often showed heterogeneous drought patterns under the same drought condition from reanalysis data at coarse resolution (2.5°), indicating the importance of spatiotemporally continuous high-resolution measurements for drought monitoring in East Africa. Consequently, the synergetic use of high resolution satellite observations and reanalysis data is crucial to provide the effective monitoring, assessment, and seasonal outlook of East African drought.

Evaluating the utility of various drought indices to monitor meteorological drought in Tropical Dry Forests.

Even though existing remote-sensing-based drought indices are widely used in many different types of ecosystems, their utility has not been widely assessed in tropical dry forests (TDFs). The aim of this study is to evaluate the performance of three remote-sensing-based drought indices, the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI), for meteorological drought monitoring in TDFs using the moderate-resolution imaging spectroradiometer (MODIS) products. The correlation between the VCI, TCI, and VHI and multiple time scales of the Standardized Precipitation Index (SPI) (1, 3, 6, 9, 12, 15, 18, 21, 24months) for each month (January to December) and each season (dry season, dry-to-wet season, wet season and wet-to-dry season) were conducted using the Pearson correlation analysis. We also correlated year-to-year changes of satellite-based drought indices with the changes of the in situ annual SPI (A_SPI) which provides annual information on the mean meteorological drought. The analysis reveals that the ability of these remote-sensing-based drought indices for meteorological drought monitoring varies with timing, and the TCI outperforms the VCI and VHI in terms of seasonal and annual scale. These remote-sensing indices performed well in monitoring meteorological drought in the dry season, poorly in the in the dry-to-wet season, and moderately in the wet season. The TCI performed best in monitoring meteorological drought in the wet-to-dry period, followed by VHI, whereas the VCI performed worst. All of these remote-sensing-based drought indices failed to detect drought in May during the green-up period and in September, October, and November when the water content in the root regions was abundant. Our results indicate that the evapotranspiration of TDFs is more sensitive than canopy greenness to detect meteorological drought. Results from this study increase the ability to provide real-time drought monitoring and early warnings of drought in TDFs.

Multiple indices based drought analysis by using long term climatic variables over a part of Koel river basin, India

The present study demonstrates changes in vegetation pattern and climatic variability in past years in the parts of Koel basin in Jharkhand state of India by considering the spatial climatic variability, NDVI anomaly and satellite based drought indices viz Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI). Least square statistical method has been used for assessment of long term climatic fluctuation of major four climatic parameters viz maximum temperature of summer season, minimum temperature of winter season, rainfall of monsoon season, and solar radiation of Rabi and kharif season. Analysis of climate extremes has been done for 26 locations in the study area and then interpolated spatially in Geographical Information System platform. Long term NDVI anomaly shows adverse effect of climate extreme in past 20 years in the study area. The climatic variability exhibits that average maximum temperature during 1979–2017 fluctuates with an increase of 0.50–0.81 °C contrary to a decrease of 0.32–0.15 °C in various parts of study area. Similarly rainfall fluctuates with a decrease of 26–90 mm contrary to an increase of 19–230 mm. Drought prone zones as delineated from spatial overlaying map of VCI, TCI and VHI indicated value from 23 to 55. Major part of the study area severely affected by drought facing water scarcity and mediocre vegetation condition. These areas need proper planning and soil moisture management to overcome the recurrent drought conditions perceived in upcoming years.

Development of Satellite-based Drought Indices for Assessing Wildfire Risk

Development of Satellite-based Drought Indices for Assessing Wildfire Risk

위성기반 가뭄지수의 상호비교를 통한 한반도 농업가뭄 분석

위성기반 가뭄지수의 상호비교를 통한 한반도 농업가뭄 분석

Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia

Rapidly developing droughts, including flash droughts, have frequently occurred throughout East Asia in recent years, causing significant damage to agricultural ecosystems. Although many drought monitoring and warning systems have been developed in recent decades, the short-term prediction of droughts (within 10 days) is still challenging. This study has developed drought prediction models for a short-period of time (one pentad) using remote-sensing data and climate variability indices over East Asia (20°–50°N, 90°–150°E) through random forest machine learning. Satellite-based drought indices were calculated using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture, Tropical Rainfall Measuring Mission (TRMM) precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and normalized difference vegetation index (NDVI). The real-time multivariate (RMM) Madden–Julian oscillation (MJO) indices were used because the MJO is a short timescale climate variability and has important implications for droughts in East Asia. The validation results show that those drought prediction models with the MJO variables (r ~ 0.7 on average) outperformed the original models without the MJO variables (r ~ 0.4 on average). The predicted drought index maps showed similar spatial distribution to actual drought index maps. In particular, the MJO-based models captured sudden changes in drought conditions well, from normal/wet to dry or dry to normal/wet. Since the developed models can produce drought prediction maps at high resolution (5 km) for a very short timescale (one pentad), they are expected to provide decision makers with more accurate information on rapidly changing drought conditions.

Comparison of meteorological and satellite-based drought indices as yield predictors of Spanish cereals

In the context of global warming, as drought episodes become increasingly frequent, it is crucial to accurately measure the impacts of droughts on the overall performance of agrosystems. This study aims to compare the effectiveness of meteorological drought indices against satellite-based agronomical drought indices as crop yield explanatory factors in statistical models calibrated at a local scale. The analysis is conducted in Spain using a spatially detailed, 12-year (2003–2015) dataset on crop yields, including different types of cereals. Yields and drought indices were spatially aggregated at the agricultural district level. The Standardised Precipitation Index (SPI), computed at different temporal aggregation levels, and two satellite-based drought indices, the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI), were used to characterise the dynamics of drought severity conditions in the study area. Models resting on satellite-based indices showed higher performance in explaining yield levels as well as yield anomalies for all the crops evaluated. In particular, VCI/TCI models of winter wheat and barley were able to explain 70% and 40% of annual crop yield level and crop yield anomaly variability, respectively. We also observed gains in explanatory power when models for climate zones (instead of models at the national scale) were considered. All the results were cross-validated on subsamples of the whole dataset and on models fitted to individual agricultural districts and their predictive accuracy was assessed with a real-time forecasting exercise. Results from this study highlight the potential for including satellite-based drought indices in agricultural decision support systems (e.g. agricultural drought early warning systems, crop yield forecasting models or water resource management tools) complementing meteorological drought indices derived from precipitation grids.

Evaluation of multi-sensor satellite data for monitoring different drought impacts

Drought is a natural disaster that significantly affects human life; therefore, precise monitoring and prediction is necessary to minimize drought damage. Conventional drought monitoring is based predominantly on ground observation stations; however, satellite imagery can be used to overcome the disadvantages of existing monitoring methods and has the advantage of monitoring wide areas. In this research, we assess the applicability of drought monitoring based on satellite imagery, focusing on historic droughts in 2001 and 2014, which caused major agricultural and hydrological issues in South Korea. To assess the applicability and accuracy of the drought index, drought impact areas in the study years were investigated, and spatiotemporal comparative analyses between the calculated drought index and previously affected areas were conducted. For drought monitoring based on satellite imagery, we used hydro-meteorological factors such as precipitation, land surface temperature, vegetation, and evapotranspiration, and applied remote sensing data from various sensors. We verified the effectiveness of using precipitation data for meteorological drought monitoring, vegetation and land surface temperature data for agricultural drought monitoring, and evapotranspiration data for hydrological drought monitoring. Moreover, we confirmed that the Standard Precipitation Index (SPI) can be indirectly applied to agricultural or hydrological drought monitoring by determining the temporal correlation between SPI, calculated for various time scales, and satellite-based drought indices.

Assessment of drought conditions using HJ-1A/1B data: a case study of Potohar region, Pakistan

Drought is a natural disaster which causes global damages and affects people. In this work, a comparative study of different drought indices such as Normalized Difference Vegetation Index (NDVI), Deviation NDVI (DevNDVI), Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) from HJ-1A/1B multispectral data is discussed. These indices have shown potential to detect the drought severity. The objective of this study is to monitor drought in the Potohar region using HJ-1A/1B satellite data, from late November to April during 2009–2014. Additionally, results obtained from satellite data have been verified using ground-based rainfall and crop yield data. The results concluded that the Potohar region faced drought condition in 2010, which is further verified by ground data. Furthermore, NDVI and VCI in this region are found more effective than other drought indices. On the basis of validation of individual drought index with crop yield data, each index is assigned weight accordingly. Moreover, the combination of indices has the ability to detect time periods where drought is affecting the yield production. Regular monitoring and mapping of satellite-based drought indices would play an important role in predicting drought conditions.

The Microwave Temperature Vegetation Drought Index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010)

Satellite-based drought indices have been proved to be effective and convenient in detecting drought conditions at regional and global scales. However, most current drought indices are based on the visible/near infrared/thermal remote sensing, which might be influenced greatly by cloud, atmospheric water content and rain-fall. Microwave sensors can overcome the shortages of visible/near infrared/thermal remote sensing and show to be another important approach for drought monitoring due to its all-weather working advantages. But to date, the application of microwave vegetation drought indices in drought monitoring has not been thoroughly investigated. Here, for the first time we constructed a microwave derived Temperature Vegetation Drought Index (TVDI) - MTVDI based on the theory of optical TVDI using the brightness temperatures (Tb) from the Advanced Microwave Scanning Radiometer (AMSR‐E) onboard Aqua satellite. Firstly, we built a new land surface temperature (Ts) inversion model based on the AMSR‐E 18.7GHz horizontal, 23.8GHz and 89.0GHz vertical polarized Tb, and then developed the Microwave Normalized Difference Vegetation Index (MNDVI) from the AMSR‐E 23.8GHz Microwave Polarization Difference Index (MPDI). After that, we constructed three versions of MTVDI: original MTVDI using Ts and MNDVI; Imp‐MTVDI (Improved MTVDI) using the Ts‐Tair (the difference between land surface temperature and air temperature) to replace the Ts; and NonL‐MTVDI (Nonlinear MTVDI) using nonlinear equation to fit the dry and wet edges, respectively. Finally, we used precipitation, soil moisture (SM) and P/PET (the ratio of precipitation to potential evapotranspiration) to validate the performances of MTVDI, Imp‐MTVDI, NonL‐MTVDI, MODIS derived TVDI and iTVDI (improved TVDI). The time-series drought assessments across China from 2003 to 2010 indicated that the trends of the proposed MTVDI showed the most negative correlations with the variations of precipitation, P/PET and SM, and showed best performances of significance test in most regions of China. Moreover, the MTVDI could better separate the drought levels in different degrees than MODIS-derived TVDI. However, the proposed MTVDI still has some uncertainties in regions widely covered by desert, Gobi and large water surfaces. In addition, this paper mainly focuses on large spatial scale and long term drought monitoring and only uses satellite data for model validation. Further studies are needed to develop a higher spatial- and temporal-resolution MTVDI for short-term and small spatial-scale drought monitoring.

An evaluation of satellite-based drought indices on a regional scale

We compared conventional and satellite-based drought indices from drought vulnerable sites in South Korea during 2004–2013. Satellite-based drought indices, the energy-based water deficit index (EWDI), and the standalone Moderate Resolution Imaging Spectroradiometer (MODIS)-based evaporative stress index (stMOD_ESI) were evaluated using MODIS imagery to assess its capability to analyse the complex topography of the Korean peninsula. Of the drought indices examined, the EWDI and stMOD_ESI were accurate when capturing moderate drought conditions, compared to the observed precipitation-based conventional drought indices (standardized precipitation index (SPI-3) and Palmer drought severity index (PDSI)). In addition, the satellite-basedsoil moisture index (SSMI) developed from the Advanced Microwave Scanning Radiometer (AMSR-E) and Advanced Scatterometer (ASCAT) soil moisture products were reasonably correlated with the EWDI and stMOD_ESI. These results suggest that the satellite-based drought indices (EWDI and stMOD_ESI) may be applicable on a regional scale.