To assess vegetation drought, it is important to understand the relationship between climate and vegetation and to accurately measure the response of vegetation activity to meteorological drought. In this study, we used the vegetation health index (VHI) to investigate the propagation time and time-lag of vegetation response to different meteorological drought indices, including the standardized precipitation index (SPI), evaporative demand drought index (EDDI), standardized precipitation–evapotranspiration index (SPEI), and copula-based joint drought index (CJDI). Using correlation analyses of meteorological drought indices with different time-scales and time-lags and VHIs with different weights, we determined which meteorological drought indices and their corresponding time-scales and time-lags best represent the effects of meteorological drought on vegetation activity on the Korean Peninsula. We also evaluated the relative roles of normalized difference vegetation index (NDVI) and land surface temperature (LST) in quantifying vegetation response to meteorological drought. The meteorological drought index for monitoring vegetation response to meteorological drought on the Korean Peninsula was best applied using EDDI in January–May and SPEI in June–December. Vegetation health was dominated by LST in January–September, with a higher impact of NDVI in November–December. We expect these results to provide useful information for vegetation drought monitoring.

  • Quantifying vegetation response to meteorological drought based on correlation analysis.

  • Identifying meteorological drought indices that are most closely related to vegetation activity.

  • Estimating the propagation time and time-lag of meteorological drought on vegetation drought.

  • Evaluating the relative contribution of NDVI and LST to VHI.

Meteorological drought is an extreme climatic phenomenon characterized by below-normal precipitation and high evapotranspiration over months to years (Won et al. 2021) and is a major natural hazard that can have direct and indirect devastating impacts on ecological (Gampe et al. 2021), agricultural (Lesk et al. 2021), environmental (Chiang et al. 2021), energy (Watson et al. 2022), and economic (Naumann et al. 2021) sectors. In addition, droughts are expected to become increasingly severe as climate change intensifies (Kim et al. 2011a).

The types of drought are broadly categorized into meteorological drought due to lack of precipitation and/or excess evapotranspiration (Mohammadi 2023), vegetation drought or agricultural drought due to lack of soil moisture (Kim et al. 2011b; Ejaz et al. 2023), hydrological drought caused by decreased river flows, reservoir inflows, and groundwater (Lin et al. 2023), and socioeconomic drought caused by the gap between water demand and supply (Lee et al. 2022). In general, a sustained meteorological drought caused by insufficient precipitation and/or excessive evapotranspiration leading to a lack of soil moisture results in a vegetation drought or agricultural drought (Kim et al. 2008), and a hydrological drought occurs when it leads to a decrease in runoff (Jung et al. 2022). In other words, most droughts in hydrological, groundwater, vegetation, agricultural, and socioeconomic environments originate from meteorological droughts (Guo et al. 2019; Han et al. 2019; Jung et al. 2022; Won et al. 2022b), and this process of transmission from one type of drought to another is called drought propagation (Xu et al. 2021).

The types of drought covered in this study are divided into two main types: meteorological drought and vegetation drought. Meteorological drought identification is usually performed using drought indices. However, the same type of drought can be identified differently depending on the drought index applied. In this study, we categorized three aspects of meteorological drought that affect vegetation health. The first is meteorological drought stress due to lack of moisture supply from the atmosphere, and we introduced the standardized precipitation index (SPI) (McKee et al. 1993) as a meteorological drought index to express it quantitatively. The SPI has been widely used as an index for meteorological drought monitoring and analysis (Kim et al. 2011c; Won et al. 2020b). The second is meteorological drought stress due to increased moisture demand from the atmosphere, and evaporative demand drought index (EDDI) (Hobbins et al. 2016) was used as a meteorological drought index to explain this. In recent years, evapotranspiration has received increasing attention in the field of drought monitoring, with studies focusing on the importance of this aspect of atmospheric water demand (Won et al. 2018; Won & Kim 2020; Seo et al. 2022). The third is meteorological drought stress, which considers both the lack of moisture supply and the increased demand for moisture from the atmosphere. Meteorological drought indices such as precipitation-evapotranspiration index (SPEI) (Vicente-Serrano et al. 2010) and copula-based joint drought index (CJDI) (Won et al. 2020a) have been applied to quantitatively represent this, and there are also studies that consider both the moisture supply side and the moisture demand side of the atmosphere (Won et al. 2020c; Ejaz et al. 2023). In addition to the three aspects of meteorological drought indices used in this study, there are also studies using the Pedj drought index (PDI), which considers the effects of precipitation and temperature changes (Lashkari et al. 2021).

The vegetation health index (VHI) (Kogan 2001) is a vegetation index that comprehensively monitors the effects of water stress and temperature stress on vegetation and is a widely used remote sensing-based vegetation index (Won et al. 2021, 2022a; Won & Kim 2023; Zeng et al. 2023). In this study, VHI was applied to quantitatively express the vegetation health of the Korean Peninsula. The VHI is based on two assumptions; (1) the VHI defines poor vegetation health as lower normalized difference vegetation index (NDVI) and higher land surface temperature (LST); and (2) in the absence of prior knowledge of the relative contribution of NDVI and LST to vegetation health, the contribution of the two factors is assumed to be equal. The first assumption of the VHI has been confirmed in several studies, and the contribution of NDVI and LST to vegetation health has been found to vary with location, climatic environment, and vegetation type (Bento et al. 2018; Won et al. 2020c).

The VHI is calculated as the average of the two factors when there is no prior knowledge of the vegetation condition index (VCI) and temperature condition index (TCI) contributions (Ejaz et al. 2023). However, the contribution of VCI and TCI varies by region and period. Additionally, when applying a meteorological drought index to relate a relationship with the VHI, the relationship with the VHI varies depending on not only the meteorological drought index but also the time-scale applied to the calculation of the meteorological drought index. There may also be a time-lag effect between meteorological drought indices and VHI. Recently, many studies have investigated the relationship between meteorological drought indices and VHI (Zeng et al. 2022, 2023; Weng et al. 2023). In line with this research trend, it is necessary to examine the relationship between vegetation and meteorological drought on the Korean Peninsula using various meteorological drought indices and VHIs with various weights applied. In exploring the effects of meteorological drought on vegetation activity on the Korean Peninsula, the objectives of this study can be summarized as seeking answers to two questions: (1) Which meteorological drought indices are most closely related to vegetation activity, and what are their corresponding time-scales and time-lags? (2) What is the relative contribution of NDVI and LST in quantifying the vegetation stress experienced by meteorological drought? For this purpose, a correlation analysis is performed between different meteorological drought indices calculated with different lags and time-scales and VHI calculated with different weights. In other words, we want to determine the meteorological drought index (including its corresponding time-scale and time-lag) that shows the strongest relationship between the meteorological drought index and the VHI, and the weights of the VCI and TCI that comprise the VHI.

Data and study region

The NDVI used in this study is MOD13C2 from the Moderate Resolution Imaging Spectroradiometer (MODIS) family of products available from the Land Processes Distributed Active Archive Center (,https://lpdaac.usgs.gov). MOD11C3 was used for LST and MOD16A2 for potential evapotranspiration (PET). Precipitation was obtained from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) provided by the Climate Hazards Center (,https://chc.ucsb.edu). NDVI, LST, and CHIRPS have a monthly temporal resolution and a spatial resolution of 0.05°, while PET has an 8-day temporal resolution and a 500-m spatial resolution. To bias-correct the satellite data, we used monthly precipitation, daily maximum temperature, daily minimum temperature, daily average wind speed, and daily relative humidity from 56 Automated Synoptic Observing System (ASOS) sites operated by the Korea Meteorological Administration (https://data.kma.go.kr). Figure 1 shows the location of the 56 ASOS sites used. The spatial mean values of monthly precipitation, monthly potential evaporation, LST, and NDVI for the study area are shown in Figure S1 in the Supplementary Material. It can be seen that precipitation shows a good agreement between satellite and ground observations, while PET is relatively less so. Therefore, in order to more accurately represent climatic conditions in ungauged regions, it is necessary to use satellite data with spatial characteristics for bias correction. Here, PET (ASOS) was calculated using the Penman–Monteith method, which uses the daily minimum and maximum temperatures, daily mean wind speed, and daily mean dew point temperature (Allen et al. 1998).
Figure 1

Location of meteorological site.

Figure 1

Location of meteorological site.

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The study area was selected from 39.975°N to 33.025°N, 124.475°E to 131.125°E to cover the Korean Peninsula (see Figure 1). The applied data period is from January 2001 to December 2021.

Merging ground and satellite observations

Conditional merging (CM) is a method to estimate the value of weather data in unmeasured areas by using the spatial characteristics of satellite data (Vishnu et al. 2022). It is a technique that synthesizes ground observation data assumed to be true and satellite data containing spatial information. In other words, the error caused by applying the spatial distribution of ground observation climate data can be estimated from satellite data containing spatial information, so the error can be corrected by using the spatial characteristics of satellite data.

Figure S2 in the Supplementary Material shows the results of the bias correction using the CM method on a monthly basis, which shows the spatially averaged values of monthly precipitation and monthly PET for the study area. These findings indicate that the accuracy of satellite-derived meteorological data is limited and that bias correction is necessary to improve the utility of satellite meteorological data.

Meteorological drought index

In this study, four meteorological drought indices (SPI, EDDI, SPEI, and CJDI) were applied to analyze meteorological drought from various aspects. SPI, which is widely used as an index for meteorological drought monitoring and analysis, is calculated from observed precipitation data over a user-defined cumulative period (McKee et al. 1993). In other words, SPI is a drought index developed from the perspective that drought is caused by insufficient moisture supply from the atmosphere, and it is calculated using moving average monthly precipitation over various time-scales. The EDDI is a drought index developed from the perspective that drought is caused by an excessive demand for moisture from the atmosphere, and it uses PET to identify drought (Hobbins et al. 2016). EDDI is calculated using moving average PET time series over different time-scales, but a positive value indicates extreme drought; therefore, EDDI is applied with a negative sign in this study to facilitate comparison with other drought indices. SPEI is a drought index that can assess the amount of available water resources by improving SPI, which is calculated using precipitation data only (Vicente-Serrano et al. 2010). In other words, SPEI is an index that monitors drought by the difference between the moisture supply side and the moisture demand side of the atmosphere, which is calculated using a moving average time series of precipitation-PET time series. CJDI is a drought index that combines SPI, which considers the moisture supply side of the atmosphere, and EDDI, which considers the moisture demand side and is calculated using a copula (Won et al. 2020a). In this study, SPI and EDDI time series corresponding to the optimal time-scale of SPI and EDDI selected for each pixel were used to calculate CJDI. Negative values of the drought index indicate more severe drought conditions. The drought classification for the drought index is shown in Table 1 (Ionita et al. 2016). More detailed explanations of the meteorological drought index are provided in Text S1 in the Supplementary Material.

Table 1

Drought classification of meteorological drought indices

MDI valueDrought category
−1 to −0.5 Weak drought 
−1.5 to −1 Normal drought 
−2 to −1.5 Severe drought 
−2 or less Extreme drought 
MDI valueDrought category
−1 to −0.5 Weak drought 
−1.5 to −1 Normal drought 
−2 to −1.5 Severe drought 
−2 or less Extreme drought 

Vegetation index

The VCI, an index that incorporates information on the visible and near-infrared portions of the electromagnetic spectrum, is calculated from the NDVI for the purpose of detecting changes in meteorological factors affecting vegetation and is calculated using the following equation (Kogan 1997).
formula
(1)

In the above expression, is the observed NDVI at each pixel, and and are the minimum and maximum observed NDVI at each pixel in the corresponding month over the entire period of data (2001–2021).

TCI by thermal infrared is calculated primarily on the basis of LST, which is calculated using the following equation (Kogan 1997).
formula
(2)

In the above expression, is the LST observed at each pixel, and and are the minimum and maximum values of the LST observed at each pixel in the month over the entire period of data.

The VHI is an index that combines the VCI, which accounts for water-induced vegetation stress, and the TCI, which accounts for temperature-induced vegetation stress, and is calculated using the following equation.
formula
(3)

In the above equation, is the weight that determines the contribution of VCI and TCI depending on the environment of the study area, and it is customarily set to 0.5 (Kogan 1997). Instead, in this study, 21 weights were set in 0.05 intervals from 0 to 1 (: 0, 0.05, 0.10, …, 0.90, 0.95, 1) to explore the differences in the environment of the study area, and the correlation with the meteorological drought index was examined. The VHI calculated by applying the that shows the greatest correlation with the meteorological drought index is defined as VHIopt. The lower the VHI, the more unhealthy the vegetation. The classification of drought severity using VHI is shown in Table 2.

Table 2

Drought classification of vegetation health index (VHI) (Kogan 2001)

VHI valueDrought category
30 < VHI ≤ 40 Weak drought 
20 < VHI ≤ 30 Normal drought 
10 < VHI ≤ 20 Severe drought 
VHI ≤ 10 Extreme drought 
VHI valueDrought category
30 < VHI ≤ 40 Weak drought 
20 < VHI ≤ 30 Normal drought 
10 < VHI ≤ 20 Severe drought 
VHI ≤ 10 Extreme drought 

Correlation between meteorological drought indices and vegetation indices

Several studies used the Pearson correlation coefficient (PPC) (Pearson 1895) to analyze the correlation between meteorological drought indices and vegetation indices (Zeng et al. 2022; Shi et al. 2023; Weng et al. 2023). PPC, which measures the linear relationship between two variables, assumes that both variables are normally distributed. However, it is not clear whether VHI follows a normal distribution. Therefore, we used the Spearman rank correlation coefficient (SRCC) (Spearman 1961), which does not assume a normal distribution. The statistical significance of the correlations was determined using the -value, which is shown in Figure S3 in the Supplementary Material. In this study, the correlation analysis between SPI (or EDDI or SPEI) for time-scales from 1 to 12 months and time-lags from 0 to 4 months and VHI (with 21 weights) was performed for each month using SRCC. Correlations between CJDI, calculated as a time series corresponding to the best time-scale of SPI and EDDI selected for each pixel, and VHI were also performed, i.e. 46,620 correlations were calculated for one pixel (12 time-scales × 5 lags × 12 months × 3 meteorological drought indices × 21 VHI weights + 1 time-scale × 5 lags × 12 months × CJDI × 21 VHI weights = 46,620). For illustrative purposes, a description of the results of the analysis using the combination of EDDI and VHI (weight 0.15) is included in the manuscript (Figures 24) and in the Supplementary Material (Figure S4). This does not mean that the EDDI-VHI (0.15) combination is the most correlated. In fact, the most correlated combination varied widely from month to month and pixel to pixel.

Figure S4 in the Supplementary Material is a spatial distribution of the correlation between EDDI and VHI (0.15). The results are shown with a time-lag that represents the maximum correlation coefficient value per pixel. The correlation pattern between EDDI and VHI (0.15) shows spatial and seasonal variations, with higher correlations at relatively shorter time-scales. At shorter time-scales, positive correlations were found in most regions regardless of season, with the exception of some southeastern regions. In the North, on the other hand, the positive correlation is consistent across different time-scales, mostly in all seasons. More detailed explanations are provided in Text S2 in the Supplementary Material.

Figure 2 shows the spatial distribution of the maximum correlation between EDDI and VHI (0.15). For each pixel, the time-lag and time-scale representing the maximum correlation were applied. Positive correlations were consistently found in most regions except for some inland areas in the southeast. Across the Korean Peninsula, the range of strong positive correlations increases in January–April, but conversely, the range of strong positive correlations decreases from May. In particular, April shows relatively high positive correlations across the peninsula compared to other months. However, in November–December, some southeastern regions show a negative correlation. In other words, the more severe the meteorological drought in spring, the worse the vegetation health, which is somewhat mitigated in late autumn and winter.
Figure 2

Spatial distribution of maximum correlation for EDDI-VHI (0.15) combination.

Figure 2

Spatial distribution of maximum correlation for EDDI-VHI (0.15) combination.

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Time-lags in meteorological drought indices and vegetation indices

The time-lag between meteorological drought indices and vegetation indices can reveal the time-lag of vegetation response to meteorological drought. However, the optimal time-lag between meteorological drought indices and vegetation indices is not the same in space and time (Zhong et al. 2021). In this study, we examined the frequency distribution of correlations between meteorological drought indices and vegetation indices as a function of time-lag. The time-lag was identified as corresponding to the strongest relationship between the meteorological drought index and the vegetation index at each pixel.

Figure 3 shows the spatial distribution of the maximum correlation between EDDI and VHI (0.15), focusing on representative months (January, April, July, and October) and representative time-scales (1, 4, 8, and 12 months). In general, the strength and extent of the correlation vary with the time-lag, but overall, at shorter lags, a strong positive correlation is found across most of the Korean Peninsula. For lag-0 in April, except for some coastal areas, the correlation is also high at longer time-scales compared to other seasons. For lag-0 in January and October, the longer time-scale is characterized by a gradually widening range of negative correlations, mainly in some southeastern regions.
Figure 3

Distribution of the maximum correlation of the EDDI-VHI (0.15) combination for time-lag and time-scale.

Figure 3

Distribution of the maximum correlation of the EDDI-VHI (0.15) combination for time-lag and time-scale.

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Propagation time from meteorological drought to vegetation drought

Propagation time refers to the length of time from the onset of meteorological drought to the onset of vegetation drought, which can be simplified as a mathematical link between meteorological drought indices at different time-scales and vegetation indices (Xu et al. 2021). Meteorological drought shows different correlations with VHI depending on the applied time-scale, even when the same meteorological drought index is used, which means that applying meteorological drought indices of different time-scales rather than a single time-scale can better represent the actual environmental conditions.

Figure 4 shows the number of pixels in the time-scale corresponding to the maximum correlation value between EDDI and VHI (0.15). The horizontal axis represents time-scales from 1 to 12 months, and the vertical axis is the number of pixels corresponding to the optimal time-scale. We can see that the optimal time-scales of EDDI for VHI (0.15) are mostly short time-scales (i.e., 1 month). The 1-month time-scale is particularly prevalent in January–May and September, while the relatively longer time-scales are mainly found in the summer months.
Figure 4

Optimal time-scale for EDDI-VHI (0.15) combination.

Figure 4

Optimal time-scale for EDDI-VHI (0.15) combination.

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Relative importance of VCI and TCI

The relative importance of VCI and TCI to VHI was assessed based on the correlation between meteorological drought indices (SPI, EDDI, SPEI, CJDI) and VHI with 21 weighting parameters. The relative contribution of VCI and TCI was identified as corresponding to the strongest relationship between the meteorological drought indices and VHI per pixel. A higher weighting indicates a greater contribution of VCI to VHI, while a lower weighting indicates a greater contribution of TCI to VHI.

Figure 5 is a box-plot of the distribution of the optimal weighting for the EDDI-VHI combination. From January to April, the number of pixels with a high contribution from TCI is increasing, with the third quartile slowly decreasing, but from August to November, the range of the interquartile range (IQR) shows a shift in the direction of a high contribution from VCI. In January–September, there are more pixels with a high contribution from TCI, but not in October–December. Following the same procedure, the distribution of the optimal weighting for the SPI (or SPEI or CJDI)-VHI combination is shown in Figure S5 in the Supplementary Material. Figure S6 in the Supplementary Material shows the spatial distribution of the optimal weighting for the EDDI-VHI combination. Following the same procedure, the spatial distribution of the optimal weighting for the SPI (or SPEI or CJDI)-VHI combination is shown in Figures S7–S9 in the Supplementary Material.
Figure 5

Distribution of optimal weighting for EDDI-VHI combinations.

Figure 5

Distribution of optimal weighting for EDDI-VHI combinations.

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Figure S10 in the Supplementary Material shows the spatial average correlation of EDDI-VHI for different s. The weight of the highest spatiotemporal average correlation for each period is designated as the optimal , and the VHI calculated by applying the optimal is defined as VHIopt. In other words, the optimal VHI (i.e., VHIopt) of EDDI-VHI is as follows: (1) January–September: VHI (0.15); (2) October: VHI (0.55); (3) November–December: VHI (0.7).

Selecting a final index combination for vegetation drought monitoring

The final combination of indices for vegetation drought monitoring was evaluated on a spatially averaged basis based on correlation. The spatially averaged correlations between the meteorological drought indices (SPI, EDDI, SPEI, CJDI) for different time-scales and time-lags and the VHI for the 21 weights were highly variable. Monthly spatial mean correlations between meteorological drought indices and VHI were analyzed to determine the optimal combination of indices for vegetation drought monitoring.

Figure S11 in the Supplementary Material shows the spatial average correlation of SPI (or EDDI or SPEI or CJDI)-VHI. In other words, the monthly optimal meteorological drought index MDIopt is as follows: (1) January–May: EDDI; (2) June–December: SPEI. Figure 6 shows the spatial average correlation and spatiotemporal average correlation between MDIopt and VHI. The VHIopt of MDIopt-VHI was selected using the same procedure as described in Figure S10 in the Supplementary Material, and the VHIopt of MDIopt-VHI is as follows: (1) January–September: VHI (0.1); (2) October: VHI (0.5); (3) November–December: VHI (0.85). This means that the final index combinations are as follows: (1) January–May: EDDI-VHI (0.1); (2) June–September: SPEI-VHI (0.1); (3) October: SPEI-VHI (0.5); (4) November–December: SPEI-VHI (0.85).
Figure 6

Spatial average correlation and spatiotemporal average correlation of MDIopt-VHI.

Figure 6

Spatial average correlation and spatiotemporal average correlation of MDIopt-VHI.

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Evaluating the performance of optimal meteorological drought index-vegetation index combinations

The selection of the optimal meteorological drought index and vegetation index is important when monitoring vegetation drought or assessing its severity, which is essential to improve the applicability of VHI in vegetation drought detection. Bento et al. (2018, 2020) and Zeng et al. (2022) are similar to our study in terms of using the strongest correlation between vegetation indices and meteorological drought indices, and they identified the optimal weighting as corresponding to the strongest relationship between SPEI or self-calibrated Palmer Drought Severity Index (sc-PDSI) and VHI. However, there is a need for a comparative analysis of correlations between different meteorological drought indices with different time-scales and time-lags. In this study, the monthly MDIopt and VHIopt were determined by correlation analysis between SPI, EDDI, SPEI, and CJDI with multiple time-scales and different time-lags and VHI with different weighting s, and the performance of the final selected index combination was evaluated.

Figure 7 compares the spatial mean correlation of VHIopt with the choice of meteorological drought indices. To evaluate the performance of VHIopt on the choice of meteorological drought indices, all four meteorological drought indices applied in this study were introduced as a comparison. In Figure 7, the horizontal line represents the IQR of the correlation coefficient for the SPEI-VHIopt combination, and the vertical line represents the IQR of the correlation coefficient for the SPI (or EDDI or CJDI)-VHIopt combination (spring: green, summer: blue, autumn: brown, winter: gray). The horizontal and vertical lines intersect at the median of each correlation coefficient IQR. If the intersection points are above the 1:1 diagonal (black line), it means that the correlation of the SPI (or EDDI or CJDI)-VHIopt combination is higher than the correlation of the SPEI-VHIopt combination, and conversely, if they are below, it means that the correlation of the SPEI-VHIopt combination is higher. SPEI has a higher correlation with VHIopt than SPI or CJDI (see Figure 7(a) and 7(c)). However, the correlations of SPEI and EDDI with VHIopt are less clear-cut (see Figure 7(b)). In January–May and September, the EDDI-VHIopt correlation is relatively higher, but conversely, the SPEI-VHIopt correlation is relatively higher in June–August and October–December.
Figure 7

Spatial average correlation with VHIopt for different choices of meteorological drought indices.

Figure 7

Spatial average correlation with VHIopt for different choices of meteorological drought indices.

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Figure 8 shows a comparison of the spatial mean correlation with MDIopt as a function of the weight of the vegetation index. In general, the relative contribution of VCI and TCI to vegetation health is applied by averaging the two indices (VHIori) without prior knowledge. Therefore, VHIori and VHIopt were selected for comparison to evaluate the performance of MDIopt on the weighting of vegetation indices. For MDIopt, EDDI (January–May) and SPEI (June–December) were applied. The horizontal and vertical lines in Figure 8 represent the IQRs of the correlation coefficients for the MDIopt-VHIopt and MDIopt-VHIori combinations, respectively. If the intersection of the horizontal and vertical lines is below the 1:1 diagonal, it means that the correlation of the MDIopt-VHIopt combination is higher than the correlation of the MDIopt-VHIori combination, and conversely, if it is above, it means that the correlation of the MDIopt-VHIori combination is higher. For October, the optimal weighting for VHIopt was chosen to be exactly the same as the for VHIori, so the spatial averages for that month are exactly aligned on a 1:1 diagonal. For all other months except October, we find that MDIopt-VHIopt has a higher correlation than MDIopt-VHIori, meaning that the correlation between MDIopt and VHI is improved when using VHIopt instead of VHIori.
Figure 8

Spatial average correlation with MDIopt as a function of the weight of the vegetation index.

Figure 8

Spatial average correlation with MDIopt as a function of the weight of the vegetation index.

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Our results showed the existence of monthly differences in vegetation response to meteorological drought (see Figure S11 in the Supplementary Material). This suggests that vegetation response to meteorological drought events should be analyzed separately by month (Zhong et al. 2021). A study across Europe also reported a strong tendency for vegetation health to deteriorate with increasing meteorological drought severity, especially in March (Bento et al. 2018). In a study of arid regions around the world, the relative frequency of significantly correlated pixel counts in the Northern Hemisphere was generally higher in the spring and summer months, while the relative frequency of significant correlations in the Southern Hemisphere was generally higher in the summer and autumn months (Bento et al. 2020). A study has also been reported on seasonal differences in how vegetation is affected by different meteorological droughts, each caused by a lack of precipitation and an increase in evaporative demand, centered on the Korean Peninsula (Won et al. 2021). What the aforementioned studies have in common is that they all used a commonly used VHI weight of 0.5. However, we were able to provide appropriate VHI weights on a pixel-by-pixel and month-by-month basis to more accurately examine the health of the Korean Peninsula's vegetation. In addition, we were able to investigate the response of vegetation to various causes of meteorological drought through the application of meteorological drought indices that consider the moisture supply and/or moisture demand aspects of the atmosphere. This study provides information on which meteorological drought index and which vegetation index are more appropriate to use on a monthly basis for vegetation management across the Korean Peninsula.

Time-lags

In general, the strength and extent of the correlation vary with time-lag (Zhong et al. 2021). To examine whether the Korean Peninsula vegetation responds to meteorological droughts immediately or with a time-lag, we examined the monthly variation in the time-lag corresponding to the strongest relationship between MDIopt and VHIopt.

Table S1 in the Supplementary Material shows the relative percentage of MDIopt's time-lag corresponding to the maximum correlation for each pixel. We can see that 0-month has the highest percentage of MDIopt's time-lag, and the second highest is 1-month. More detailed explanations are provided in Text S3 in the Supplementary Material. In summary, except for July–September, the time-lag of meteorological drought on vegetation was mostly 1 month or less, indicating that the Korean Peninsula vegetation generally responds quickly to the effects of meteorological drought. However, it can also be recognized that there are relatively more areas of vegetation that respond slowly to the effects of meteorological drought in July–September.

Propagation time

The strength of the correlation varies with time-scale as well as time-lag (Zhong et al. 2021). We examined the monthly variation in propagation time corresponding to the strongest relationship between MDIopt and VHIopt to determine the changing impact of short- and long-term meteorological variability on Korean Peninsula vegetation.

Figure S12 in the Supplementary Material shows the relative proportion of MDIopt's time-scales corresponding to the maximum correlation per pixel. The optimal time-scale for MDIopt is 1 month with the highest percentage and the second highest is 2 months. More detailed explanations are provided in Text S3 in the Supplementary Material. In summary, except for summer, the propagation time of meteorological drought on vegetation was 3 months or less, indicating that the Korean Peninsula vegetation is more sensitive to short-term meteorological drought than to the effects of long-term accumulated meteorological drought. However, it can be recognized that there are relatively more areas of vegetation affected by long-term accumulated meteorological drought in summer.

Relationship between meteorological drought and VHI

Finally, we compared two vegetation index time series (VHIori and VHIopt) and two meteorological drought index time series (SPI6 and MDIopt) for a specific time period as an example. VHIori was selected for comparison because it is applied without prior knowledge of the VCI and TCI contributions of vegetation health, and SPI6 (SPI calculated with a time-scale of 6 months) was selected for comparison because it is currently used by the Korea Meteorological Administration for drought forecasting and warning purposes.

Figure S13(a) in the Supplementary Material shows the spatial distribution of SPI6 and VHIori from February to May 2004, and Figure S13(b) shows the spatial distribution of MDIopt and VHIopt from February to May 2004. Since Figure S13 is for February–May, a weighting = 0.1 was applied for VHIopt and a weighting = 0.5 was applied for VHIori. SPI6 and VHIori do not show any relationship between meteorological drought and subsequent decline in vegetation health. While SPI6 shows meteorological drought occurring in March in some areas and dissipating in May, vegetation health as represented by VHIori was already deteriorating in February, and we can see that vegetation stress increased across the country in April when meteorological drought entered the dissipation phase. On the other hand, MDIopt shows a spatial pattern that is relatively consistent with VHIopt. MDIopt and VHIopt provide a good representation of the impact of the meteorological drought of February to April 2004 on vegetation health. In particular, it can be seen that in May 2004, when the meteorological drought had entered the dissipation phase, the remaining meteorological drought on the east coast was still affecting vegetation health in the same region.

Several studies have investigated the relationship between meteorological drought indices and vegetation indices using the strongest correlation (Zhong et al. 2021; Shi et al. 2023; Weng et al. 2023). Figure S13 shows that selecting the meteorological drought index and vegetation index using the strongest correlation between meteorological drought and VHI better represents the vegetation response to meteorological drought. These examples provide evidence that the results of this study can provide useful information for vegetation drought monitoring. Investigating vegetation stress caused by various meteorological drought conditions can be used to establish measures to prepare for climate change impacts on vegetation health and can also help in sustainable forest management. This research can be linked to the following goals among the sustainable development goals proposed by the United Nations (https://sdgs.un.org/goals): (1) Goal 13: Take urgent action to combat climate change and its impacts, and (2) Goal 15: Protect, restore, and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.

To assess vegetation drought, it is important to first understand the relationship between meteorological conditions and vegetation status and to accurately measure the response of vegetation activities to meteorological drought. In this study, we applied SPI, EDDI, SPEI, and CJDI to quantitatively express meteorological drought in various aspects, and introduced VHI, a widely used vegetation index, to monitor vegetation health. As a first step to find out how to best monitor the condition of vegetation affected by meteorological drought on the Korean Peninsula, we wanted to investigate which meteorological drought index to use and how to weigh NDVI and LST when calculating VHI.

In this study, we analyzed the meteorological drought index on a monthly basis to monitor the response of vegetation to meteorological drought. Through this study, we investigated how the effects of atmospheric moisture supply and moisture demand contribute to the deterioration of vegetation health. These results may provide useful information for formulating effective mitigation measures to counteract the deterioration of vegetation health on the Korean Peninsula due to meteorological drought. We investigated how water stress and temperature stress contribute to vegetation health deterioration on a monthly basis through the introduction of various weighted VHIs. In this study, we improved the effectiveness of vegetation drought detection using VHI, thus improving the reliability and applicability of VHI.

The results of this study suggest that when monitoring the condition of vegetation affected by meteorological drought, it is better to selectively use meteorological drought indices with seasonal characteristics rather than using a single meteorological drought index, and if possible, vegetation indices should also consider seasonal characteristics. In this study, we examined the optimal meteorological drought index and the optimal vegetation index for each season as a first step toward finding out how to best monitor the condition of vegetation affected by meteorological drought on the Korean Peninsula. While the selection of a seasonal optimal index is important, the selection of a spatial optimal index will also be an important decision for future vegetation drought monitoring, as shown in part by the results of this study. Topography, land use, and hydro-climatic characteristics will also play an important role in the selection of meteorological drought indices and vegetation indices. This is left for future research.

This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Aquatic Ecosystem Conservation Research Program (or Project), funded by Korea Ministry of Environment (MOE) (2022003050007).

H. J. and S. K. conceptualized the whole article; H. J. and S. K. developed the methodology; H. J. and J. W. developed the software; H. J. and S. K. validated the data; H. J. and S. K. rendered support in formal analysis; S. K. and S. K. investigated the data; S. K. brought the resources; J. W. and S. K. rendered support in data curation; H. J. and S. K. prepared the original draft; J. W. and S. K. wrote the review and edited the article; J. W. visualized the data; S. K. supervised the article; S. K. administered the project; S. K. rendered support in funding acquisition.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Allen
R.
,
Pereira
L.
,
Raes
D.
&
Smith
M.
1998
Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements – FAO Irrigation and Drainage Paper 56
.
FAO
,
Rome
,
Italy
.
Bento
V.
,
Gouveia
C.
,
DaCamara
C.
&
Trigo
I.
2018
A climatological assessment of drought impact on vegetation health index
.
Agricultural and Forest Meteorology
259
,
286
295
.
https://doi.org/10.1016/j.agrformet.2018.05.014
.
Bento
V.
,
Gouveia
C.
,
DaCamara
C.
,
Libonati
R.
&
Trigo
I.
2020
The roles of NDVI and land surface temperature when using the vegetation health index over dry regions
.
Global and Planetary Change
190
,
103198
.
https://doi.org/10.1016/j.gloplacha.2020.103198
.
Chiang
F.
,
Mazdiyasni
O.
&
AghaKouchak
A.
2021
Evidence of anthropogenic impacts on global drought frequency, duration, and intensity
.
Nature Communications
12
,
2754
.
http://doi.org/10.1038/s41467-021-22314-w
.
Climate Hazards Center
.
Available from: https://chc.ucsb.edu (accessed 27 July 2022)
.
Ejaz
N.
,
Bahrawi
J.
,
Alghamdi
K.
,
Rahman
K.
&
Shang
S.
2023
Drought monitoring using landsat derived indices and Google Earth engine platform: A case study from Al-Lith Watershed, Kingdom of Saudi Arabia
.
Remote Sensing
15
,
984
.
https://doi.org/10.3390/rs15040984
.
Gampe
D.
,
Zscheischler
J.
,
Reichstein
M.
,
O'Sullivan
M.
,
Smith
W.
,
Sitch
S.
&
Buermann
W.
2021
Increasing impact of warm droughts on northern ecosystem productivity over recent decades
.
Nature Climate Change
11
,
772
779
.
http://doi.org/10.1038/s41558-021-01112-8
.
Guo
Y.
,
Huang
S.
,
Huang
Q.
,
Wang
H.
,
Fang
W.
,
Yang
Y.
&
Wang
L.
2019
Assessing socioeconomic drought based on an improved multivariate standardized reliability and resilience index
.
Journal of Hydrology
568
,
904
918
.
http://doi.org/10.1016/j.jhydrol.2018.11.055
.
Han
Z.
,
Huang
S.
,
Huang
Q.
,
Leng
G.
,
Wang
H.
,
Bai
Q.
,
Zhao
J.
,
Ma
L.
,
Wang
L.
&
Du
M.
2019
Propagation dynamics from meteorological to groundwater drought and their possible influence factors
.
Journal of Hydrology
578
,
124102
.
http://doi.org/10.1016/j.jhydrol.2019.124102
.
Hobbins
M.
,
Wood
A.
,
McEvoy
D.
,
Huntington
J.
,
Morton
C.
,
Anderson
M.
&
Hain
C.
2016
The evaporative demand drought index. Part I: Linking drought evolution to variations in evaporative demand
.
Journal of Hydrometeorology
17
,
1745
1761
.
https://doi.org/10.1175/JHM-D-15-0121.1
.
Ionita
M.
,
Scholz
P.
&
Chelcea
S.
2016
Assessment of droughts in Romania using the standardized precipitation index
.
Natural Hazards
81
,
1483
1498
.
https://doi.org/10.1007/s11069-015-2141-8
.
Jung
H.
,
Won
J.
,
Kang
S.
&
Kim
S.
2022
Characterization of the propagation of meteorological drought using the copula model
.
Water
14
,
3293
.
https://doi.org/10.3390/w14203293
.
Kim
S.
,
Han
S.
&
Kavvas
M.
2008
Analytical derivation of steady-state soil water probability density function coupled with simple stochastic point rainfall model
.
ASCE Journal of Hydrologic Engineering
13
,
1069
1077
.
https://doi.org/10.1061/(ASCE)1084-0699(2008)13:11(1069)
.
Kim
E.
,
Choi
H.
,
Park
M.
,
Cho
S.
&
Kim
S.
2011a
The effect of climate change on Korean drought occurrences using a stochastic soil water balance model
.
Scientific Research and Essays
6
,
2771
2783
.
https://doi.org/10.5897/SRE11.198
.
Kim
S.
,
Han
S.
&
Kim
E.
2011b
Stochastic modelling of soil water and plant water stress using cumulant expansion theory
.
Ecohydrology
4
,
94
105
.
https://doi.org/10.1002/eco.127
.
Kim
S.
,
Kim
B.
,
Ahn
T.
&
Kim
H.
2011c
Spatio temporal characterization of Korean drought using severity-area-duration curve analysis
.
Water and Environment Journal
25
,
22
30
.
https://doi.org/10.1111/j.1747-6593.2009.00184.x
.
Kogan
F.
1997
Global drought watch from space
.
Bulletin of the American Meteorological Society
78
,
621
636
.
https://doi.org/10.1175/1520-0477(1997)078%3c0621:GDWFS%3e2.0.CO;2
.
Kogan
F.
2001
Operational space technology for global vegetation assessment
.
Bulletin of the American Meteorological Society
82
,
1949
1964
.
https://doi.org/10.1175/1520-0477(2001)082 < 1949:OSTFGV > 2.3.CO;2
.
Land Processes Distributed Active Archive Center
.
Available from: https://lpdaac.usgs.gov (accessed 10 July 2022)
.
Lashkari
A.
,
Irannezhad
M.
,
Zare
H.
&
Labzovskii
L.
2021
Assessing long-term spatio-temporal variability in humidity and drought in Iran using Pedj Drought Index (PDI)
.
Journal of Arid Environments
185
,
104336
.
https://doi.org/10.1016/j.jaridenv.2020.104336
.
Lee
J.
,
Hong
E.
,
Kim
J.
,
Jang
W.
,
Jung
C.
&
Kim
S.
2022
Evaluation of agricultural drought in South Korea using socio-economic drought information
.
International Journal of Disaster Risk Reduction
74
,
102936
.
https://doi.org/10.1016/j.ijdrr.2022.102936
.
Lesk
C.
,
Coffel
E.
,
Winter
J.
,
Ray
D.
,
Zscheischler
J.
,
Seneviratne
S.
&
Horton
R.
2021
Stronger temperature–moisture couplings exacerbate the impact of climate warming on global crop yields
.
Nature Food
2
,
683
691
.
http://doi.org/10.1038/s43016-021-00341-6
.
Lin
Q.
,
Wu
Z.
,
Zhang
Y.
,
Peng
T.
,
Chang
W.
&
Guo
J.
2023
Propagation from meteorological to hydrological drought and its application to drought prediction in the Xijiang River basin, South China
.
Journal of Hydrology
617
,
128889
.
https://doi.org/10.1016/j.jhydrol.2022.128889
.
McKee
T.
,
Doesken
N.
&
Kleist
J.
1993
The relationship of drought frequency and duration to time scales
. In:
Proceedings of the Eighth Conference on Applied Climatology
,
Anaheim, CA, USA
.
Naumann
G.
,
Cammalleri
C.
,
Mentaschi
L.
&
Feyen
L.
2021
Increased economic drought impacts in Europe with anthropogenic warming
.
Nature Climate Change
11
,
485
491
.
http://doi.org/10.1038/s41558-021-01044-3
.
Open MET Data Portal
.
Available from: https://data.kma.go.kr (accessed 2 August 2022)
.
Pearson
K.
1895
VII. Note on regression and inheritance in the case of two parents
.
Proceedings of the Royal Society of London
58
,
240
242
.
Seo
J.
,
Won
J.
,
Choi
J.
,
Lee
J.
&
Kim
S.
2022
A copula model to identify the risk of river water temperature stress for meteorological drought
.
Journal of Environmental Management
311
,
114861
.
https://doi.org/10.1016/j.jenvman.2022.114861
.
Shi
X.
,
Chen
F.
,
Shi
M.
,
Ding
H.
&
Li
Y.
2023
Construction and application of optimized comprehensive drought index based on lag time: A case study in the middle reaches of Yellow River Basin, China
.
Science of the Total Environment
857
,
159692
.
https://doi.org/10.1016/j.scitotenv.2022.159692
.
Spearman
C.
1961
The Proof and Measurement of Association Between Two Things
.
https://doi.org/10.1037/11491-005
.
United Nations’ sustainable development goals (SDGs)
.
Available from: https://sdgs.un.org/goals (accessed 3 October 2023)
.
Vicente-Serrano
S.
,
Beguería
S.
&
López-Moreno
J.
2010
A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index
.
Journal of Climate
23
,
1696
1718
.
http://dx.doi.org/10.1175/2009JCLI2909.1
.
Vishnu
C.
,
Oommen
T.
,
Chatterjee
S.
&
Sajinkumar
K.
2022
Challenges of modeling rainfall triggered landslides in a data-sparse region: A case study from the Western Ghats, India
.
Geosystems and Geoenvironment
1
,
100060
.
https://doi.org/10.1016/j.geogeo.2022.100060
.
Watson
P.
,
Spaulding
A.
,
Koukoula
M.
&
Anagnostou
E.
2022
Improved quantitative prediction of power outages caused by extreme weather events
.
Weather and Climate Extremes
37
,
100487
.
http://doi.org/10.1016/j.wace.2022.100487
.
Weng
Z.
,
Niu
J.
,
Guan
H.
&
Kang
S.
2023
Three-dimensional linkage between meteorological drought and vegetation drought across China
.
Science of the Total Environment
859
,
160300
.
https://doi.org/10.1016/j.scitotenv.2022.160300
.
Won
J.
&
Kim
S.
2020
Future drought analysis using SPI and EDDI to consider climate change in South Korea
.
Water Supply
20
,
3266
3280
.
https://doi.org/10.2166/ws.2020.209
.
Won
J.
,
Jang
S.
,
Kim
K.
&
Kim
S.
2018
Applicability of the evaporative demand drought index
.
Journal of the Korean Society of Hazard Mitigation
18
,
431
442
.
https://doi.org/10.9798/KOSHAM.2018.18.6.431
.
Won
J.
,
Choi
J.
,
Lee
O.
&
Kim
S.
2020a
Copula-based joint drought index using SPI and EDDI and its application to climate change
.
Science of the Total Environment
744
,
140701
.
https://doi.org/10.1016/j.scitotenv.2020.140701
.
Won
J.
,
Choi
J.
,
Lee
O.
,
Seo
J.
&
Kim
S.
2020c
Weighting coefficient estimation of vegetation health index for ecological drought analysis
.
Journal of Wetlands Research
22
,
275
285
.
https://doi.org/10.17663/JWR.2020.22.4.275
.
Won
J.
,
Seo
J.
,
Lee
J.
,
Lee
O.
&
Kim
S.
2021
Vegetation drought vulnerability mapping using a copula model of vegetation index and meteorological drought index
.
Remote Sensing
13
,
5103
.
https://doi.org/10.3390/rs13245103
.
Won
J.
,
Jung
H.
,
Kang
S.
&
Kim
S.
2022a
Monitoring the ecological drought condition of vegetation during meteorological drought using remote sensing data
.
Korean Journal of Remote Sensing
38
,
887
899
.
https://doi.org/10.7780/KJRS.2022.38.5.3.7
.
Won
J.
,
Seo
J.
&
Kim
S.
2022b
A copula model integrating atmospheric moisture demand and supply for vegetation vulnerability mapping
.
Science of the Total Environment
812
,
151464
.
http://doi.org/10.1016/j.scitotenv.2021.151464
.
Xu
Y.
,
Zhang
X.
,
Hao
Z.
,
Singh
V.
&
Hao
F.
2021
Characterization of agricultural drought propagation over China based on bivariate probabilistic quantification
.
Journal of Hydrology
598
,
126194
.
https://doi.org/10.1016/j.jhydrol.2021.126194
.
Zeng
J.
,
Zhang
R.
,
Qu
Y.
,
Bento
V.
,
Zhou
T.
,
Lin
Y.
,
Wu
X.
,
Qi
J.
,
Shui
W.
&
Wang
Q.
2022
Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018
.
Weather and Climate Extremes
35
,
100412
.
https://doi.org/10.1016/j.wace.2022.100412
.
Zeng
J.
,
Zhou
T.
,
Qu
Y.
,
Bento
V. A.
,
Qi
J.
,
Xu
Y.
,
Li
Y.
&
Wang
Q.
2023
An improved global vegetation health index dataset in detecting vegetation drought
.
Scientific Data
10
,
338
.
https://doi.org/10.1038/s41597-023-02255-3
.
Zhong
S.
,
Sun
Z.
&
Di
L.
2021
Characteristics of vegetation response to drought in the CONUS based on long-term remote sensing and meteorological data
.
Ecological Indicators
127
,
107767
.
https://doi.org/10.1016/j.ecolind.2021.107767
.
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