Analysis of severe droughts in Taiwan using vegetation indices from geostationary satellite observations

Analysis of severe droughts in Taiwan using vegetation indices from geostationary satellite observations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Analysis of severe droughts in Taiwan using vegetation indices from geostationary satellite observations Chien-Ben Chou, Min-Chuan Weng, Huei-Ping Huang, Tzu-Ying Yeh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8054799/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract One of the emerging concerns of global change is the increasing frequency and intensity of severe droughts. Recently, two record-breaking droughts occurred consecutively in the spring of 2021 and 2023 across Taiwan. Motivated by the need to monitor such extreme events, this study analyzes vegetation indices from Himawari-8/9 satellite observations. The key indices, including Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI), are computed for subregions of Taiwan to allow an accurate characterization of drought conditions across heterogeneous landscapes. Taking advantage of the high temporal resolution of the data, daily-updated time series of the indices are analyzed to form the framework for the detection of early warning signals. As a local application, the methodology is used to analyze the effect of drought on irrigated rice fields. The result reveals overall unhealthy conditions of the crops, even though the growth of the crops was temporarily sustained through the drought by irrigation. NDVI Drought Himawari-8/9 time series analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Severe droughts impact human lives through degradations in water resources, agricultural and economic activities, even the ecological system. In recent decades, the interplay between global change and severe droughts has received significant attention (Xu et al., 2019 , Ault, 2020 , Mukherjee et al., 2018 ). Liao and Zhuang ( 2015 ) found that droughts result in a 10% decrease in gross primary production (GPP) globally in 2001–2010, based on satellite observations. The most severe drought events can lead to a collapse or strict restriction of domestic water supplies. Well-known examples include the 2021-23 event over the Murray-Darling Basin in southeast Australia (Leblanc et al. 2009 ), and the 1994 event in Japan, one of the most severe in the country’s history (Lee et al., 2012 ). The interruption of the ecosystem by severe droughts is also widely documented (e.g., Xiao et al. 2009 ). A related concern is the increasing frequency and/or intensity of wildfires due to droughts, e.g., as studied by Crockett and Westerling ( 2018 ) for western United States in the 21st century. Traditionally, droughts are classified into four types: meteorological, agricultural, hydrological, and socioeconomic (Wilhite & Glantz, 1985 ). A meteorological drought is characterized by the deficit in precipitation. A hydrological drought occurs when the shortage of rainfall significantly lowers the levels of river, reservoir, and groundwater. If a meteorological drought occurs during the critical periods of the growing season, it may develop into an agricultural drought (Nicholas and Roundy, 2017 ). A socioeconomic drought refers to the case when water shortage leads to a significant disruption of economic activities and services. The newer notion of a flash drought was proposed in the early 2000s (Svoboda et al., 2002) and has recently gained increasing attention (Otkin et al., 2018 ). A flash drought is characterized by its rapid development, due to the combined influences of multiple meteorological factors (e.g., high wind speed, high temperature) that help intensify the event. Recently, a novel definition of ecological drought has been proposed for some 21st-century droughts. It is characterized by an increase in the duration and spatial extent of the drought due to an explosive increase in the demand of water by human activities (Crausbay et al., 2017).The disaster associated with an ecological drought is exacerbated by the failure of adaptation by humans. With the given background and motivation, this study aims to explore the strategies for monitoring severe droughts. Our subjects of study are two major 21st-century drought events that occurred in Taiwan within just 3 years of period. The event in Spring 2021 covered almost the whole area of Taiwan, while the one in 2023 affected the southern and western parts of Taiwan. Both are among the most extreme events in the past 100 years of record. Both belong to the class of flash droughts due to their rapid intensification driven by rainfall deficit, strong wind, sunny skies, and topographic effects. Some of those details for the 2021 event were analyzed by Chou et al. ( 2022 ). For this class of flash droughts, an early warning would have allowed a better preparation to reduce the damage. To detect the early deterioration of vegetation conditions, the desired data to use should cover the target area continuously with a high resolution in time. Observations made by geostationary satellites are ideal for this purpose. Our previous study (Chou et al. 2022 ) first explored this strategy by using the data from the Himawari-8 satellite. It demonstrated the potential of using the satellite-based vegetation indices for monitoring the 2021 drought event and the associated hydrological cycle. In this paper, we significantly expand the previous study by analyzing both the 2021 and 2023 events in further detail, using more data from the Himawari-8/9 platforms. Two refinements of the methodology are considered. First, the vegetation indices are analyzed for individual sub-regions, allowing for a more accurate characterization of the drought conditions across heterogeneous terrains and land covers. Secondly, the previous analysis of monthly-mean indices is refined to “daily running mean” indices to sharpen the detection of early warning signals. Future applications of the methodology to real-time monitoring of droughts are also discussed 2 Data and Method The key analysis of droughts uses a set of satellite-based indices that characterize the meteorological and vegetation conditions at the surface over Taiwan. The usefulness of such indices has been explored in many studies in related context (e.g., Mukherjee et al., 2018 , Zaimes et al., 2019 ). The observational data from Himawari-8/9 for 2016–2024 are used. Himawari-8/9 includes16 channels with variance spatial resolution of 0.5,1 km for visible, near-infrared and 2 km for infrared channels. Due to the increased number of channels, Himawari-8/9 is able to retrieve multiple products for environmental monitoring (Bessho et al., 2016 ). The indices are computed from clear-sky data, with the clear pixels retained by cloud mask. The time series and anomaly map are analyzed. The tools for computing and analyzing vegetation indices are based on programs written in Interactive Data Language (IDL). Land surface temperature is a standard output of the Clavr-x pre-processing package, which we directly use in this study. The formulas and relevant background for the indices are summarized below. 2.1 The NDVI index The normalized difference vegetation index (NDVI) has been designed to monitor plant development from space (Tucker, 1979 ). The formula for NDVI is given as $$\:\text{N}\text{D}\text{V}\text{I}=\frac{{\rho\:}_{NIR}-{\rho\:}_{red}}{{\rho\:}_{NIR}+{\rho\:}_{red}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ In the equation, ρ NIR and ρ red represent the reflectance values of near-infrared and visible red light, respectively. For healthy plants, reflectance is higher at the near-infrared frequencies due to the cellular structure of the leaves, and lower at the red-light frequencies due to absorption by dense chlorophyll content in the leaves. Therefore, a high NDVI value implies that vegetation is healthy. The value of NDVI is between 0 and 1 for vegetation. It is slightly positive for rock or bare soil, and negative for water and cloud. 2.2 The VCI index Spatial variations of NDVI may arise from the differences in weather conditions and geographic factors (nonuniform topography and land-surface cover). When using the NDVI data to estimate the impacts of weather conditions on vegetation, it is desirable to remove the spatial variability of NDVI due to geographic factors. To achieve this, Kogan ( 1990 ) and Kogan ( 1995 ) defined the vegetation condition index (VCI). It normalizes NDVI by scaling it between the absolute maximum and the absolute minimum of the historical records at a given location. It is assumed that NDVI reaches the maximum under the best weather conditions when the plants can efficiently use the geographic resources. In contrast, NDVI reaches the minimum under the worst weather conditions when the plants cannot use the geographic resources. The formula of VCI is $$\:{VCI}_{p}=\:\:100\:\left(\frac{{NDVI}_{p}-{NDVI}_{min}}{{NDVI}_{max}-{NDVI}_{min}}\right)\:\:\:\:\:\:\:\:\:\:\:\left(2\right)\:$$ In the equation, NDVI p is the averaged value of NDVI over a designated period (which can be a week, a month, or a season). NDVI min and NDVI max are the minimum and maximum values from the long-term observations. By definition, the value of VCI is between 0 and 100. A higher value indicates that the plants are under favorable weather conditions. A lower value indicates the opposite. 2.3 The TCI index The land surface temperature (LST) derived from remote sensing data can be used to measure water stress for the plants. When soil moisture is high in the root zone, evapotranspiration helps cool the land surface. When soil moisture reaches an exhaustive level, the effect of evapotranspiration diminishes which leads to a higher LST. Similar to VCI, the temperature condition index (TCI) uses surface temperature instead of NDVI to quantify the relation between temperature and the health of plants. As defined by Kogan ( 1995 ) and García-León et al. (2019), it can be expressed as In the equation, LST p is the average of LST over a designated period, LST min and LST max refer to the minimum and maximum values from long-term observations. Similar to VCI, the range of TCI is from 0 to 100. A higher value means the plants are under less stress. 2.4 The VHI index The linear combination of VCI and TCI can be used to define the vegetation health index (VHI), as proposed by Kogan ( 1995 ). Its general formula is: $$\:{\:\:\:\:VHI}_{p}={\alpha\:}\text{*}{\:VCI}_{p}+\left(1-{\alpha\:}\right)*{\:TCI}_{p}\:\:\:\:\:\:\:\:\:\left(4\right)$$ In the equation, α is a constant between 0 and 1. It determines the weight of VCI p and TCI p in their contribution to VHI p . As the relative impact of moisture and temperature on vegetation health is still being investigated (Zeng et al., 2023 ), we choose to follow a popular selection of α = 0.5 (Karnieli et al., 2010 ). When using this index to monitor droughts, it is suitable to apply it to areas with a negative correlation between LST and NDVI. In such areas, the primary factor limiting vegetation growth is water, not energy (Karnieli et al., 2010 ). This concept has been affirmed by a previous study (Anderson et al., 2013 ) based on historical records in the United States. It demonstrated that VHI is an efficient index for monitoring droughts, except in the cold season and at high latitudes. Under those conditions, there is limited energy to facilitate the growth of plants. 3 Results The vegetation indices are used to retrospectively monitor two severe drought events over Taiwan in the spring of 2021 and 2023. The monthly anomalies of NDVI from January to June are used to quantify the intensity and spatial coverage of both drought events. The results are shown in Figs. 1 and 2 . Throughout the event, the 2021 drought covered nearly the entire territory, except the northwestern corner of the island. The 2023 drought event was initiated in the western part of the island. As it developed, the disaster eventually covered much of the southwestern part of the island. At the end of April, it also expanded into a strip in the northwestern part of the island. Both droughts diminished in May of the respective year after the arrival of the seasonal Meiyu front, which restored precipitation to its normal level. The time series of the monthly-mean NDVI for the entire island region are shown in Fig. 3 (a). The red and yellow lines represent 2021 and 2023, respectively. The thick blue line is the 9-year average from 2016 to 2024. It is accompanied by a pair of light-blue lines that indicate one standard deviation from the mean value. Since the 2021 event covered the whole island while the 2023 event only covered the southwestern part of it, the signal for drought is relatively weak in the yellow line (for 2023). In contrast, the red line reveals significant drought conditions in March and April, 2021. It shows not only the lower value in March and April but also a steep decreasing gradient at the onset of drought. In real time, this could have served as a useful early warning signal. To focus on the rapid development, Fig. 3 (b) shows the “daily running mean” time series, with the value of each day calculated from the mean of the preceding 30 days. This time series helps highlight the rapid change in NDVI. For example, a particularly steep decreasing gradient can be seen at around day 60 of the year (near the end of February). To sharpen the detection of the 2023 drought, the time series is re-calculated using the data from only the southwestern part of the island. (The region is shown in the inset of Fig. 4 (a).) The updated monthly-mean and daily (running mean) time series for the subregion are shown in Figs. 4 (a) and (b). By restricting to a particular region, the yellow line in Fig. 4 (a) clearly shows a steep decreasing gradient from March to April. In the daily time series in Fig. 4 (b), a particularly sharp decline can be identified as early as around day 90 of the year. For both springtime drought events, the daily updated time series show promise for enhancing the early detection of drought conditions. To put the idea into actual practice, the technique could be further refined by monitoring each subregion or sub-category of land-cover type, etc. In the following, we further expand the regional monitoring scheme to multiple subregions of the island. The subdivision is determined based on the latitude and elevation of the subregions. A total of 12 subregions (shown in the inset of each panel in Fig. 5 ) are considered. The analysis of daily time series is repeated for each of the subregions. The results are summarized in Fig. 5 . Having the key indices for the subregions allows a more accurate characterization of the evolution of drought across heterogeneous geographical landscapes. For example, Figs. 5 (d) and (g) pinpoint two regions in southwestern Taiwan that were severely affected by the 2023 drought. This new scheme regional analysis will help sharpen future detection of early warning signals for drought. We next analyze the VCI and VHI indices, which serve the purpose of removing the dependence of spatial variability of NDVI from the effect of geographic resources. The time series of VCI and VHI of 2021 and 2023 are shown in Fig. 6 . Figure 6 (a) and (b) show the indices averaged over the whole island, while Figs. 6 (c) and (d) show those averaged over southwestern Taiwan. The results affirm that VCI tracks the evolution of drought more clearly than NDVI. When averaged over the whole island, both VCI and NDVI accurately represent the drought event in February-April 2021 (as shown in Fig. 6 (a) and 3(a)). For the regional footprint of drought in western Taiwan for both 2021 and 2023 events, VCI reveals a clearer signal compared to NDVI (see Fig. 6 (c) and 4(a)). The superior performance of VCI is particularly notable for the 2023 drought over southwestern Taiwan. The VHI index (Fig. 6 (b)) also shows improvement (but not as dramatic as VCI) over NDVI in sharpening the drought signal. For future applications, we could explore the possibility of giving a greater weight to VCI (and a smaller weight to TCI) in defining the VHI. As a conceptual application of our methodology, the regional analysis is used to assess the impact of drought on local agricultural productivity. We select three locations dominated by irrigated rice fields in Chishang Township, Xingang Township, and Zhushan Village. The locations of the three sites of rice paddies are shown in Fig. 7 (a). The vegetation indices for the irrigated rice paddy in Chishang Township are shown in Fig. 7 (b). It is found that irrigation temporarily supported the growth of rice even during the drought. (The two peaks in the time series correspond to two rice cropping seasons per year.) Nevertheless, the time series of 2021 still falls one standard deviation below the mean. It implies that the drought did significantly impact the overall crop yield even with the support by irrigation. The primary rice-producing region in Taiwan is the Chianan Plain in the southwestern part of the island, where the other two irrigated rice fields in Xingang Township and Zhushan Village are located. The time series of the vegetation indices for the two sites are shown in Fig. 7 (c) and (d). It is found that the Spring droughts in both 2021 and 2023 caused NDVI values during the first harvest season to fall below the average, particularly in 2023 as shown by the yellow line. During the second harvest season, the 2023 drought was more severe and prolonged than the one in 2021 (cf. Figure 5 (g)), its effect is shown by the yellow lines in Fig. 7 (c) and (d). The unusual low values of the indices during this period is reflected in a reduction in vegetation due to field fallowing of the rice paddies. This further demonstrates that while irrigation can support crop growth through a drought, it is not enough to sustain a normal level of productivity under the overall drought conditions. The case study for the rice fields illustrates the usefulness of the vegetation indices from Himawari-8/9 satellite data. We expect future applications of this type of analysis to customized sites for farmers. 4 Summary Satellite-based indices of NDVI, VCI, and VHI as derived from the Himawari-8/9 data are used to retrospectively monitor two severe drought events in Taiwan in 2021 and 2023. The results show that the daily-updated time series of the vegetation indices usefully capture the rapid evolution of drought conditions. Using this approach, several early signals were identified for the 2021 and 2023 events. The results also demonstrate that VCI is more efficient indicators than NDVI for detecting the early signals. Comparing the results of VHI and VCI, it is found that the water limitation is more important than the energy limitation on vegetation health. We suggest expanding this analysis to other indices in future work. This study also establishes the framework for enhanced monitoring of droughts by computing daily-updated time series of the key indices for subregions divided by their geographical characteristics. We expect this scheme to help sharpen regional drought detection across the complex terrains of Taiwan. As a further application, our analysis is also used for a case study for the impact of drought on irrigated rice fields. The result shows that while irrigation helped push the growth of rice through a drought, the overall annual yield still decreased significantly under the overall drought conditions. Declarations Competing interests The authors declare that they have no competing interests. Author Contribution CHien-Ben Chou : Conceptualization, Software,prepareing figure 1-7, Writing – Original Draft.Min-Chuan Weng : Data Curation, Software.Huei-Ping Huang : Conceptualization, Writing – Review & Editing.Tzu-Ying Yeh : Data Curation, Writing – Review & Editing (assistance).Yu-Cheng Chang : Review & Editing (assistance).All authors reviewed the manuscript Acknowledgements Some preliminary results of this study were presented at the ACEER 2025 Conference. The authors appreciate the feedback received at the conference. The first author would like to thank Dr. Dong-Hong Wu of the Ministry of Agriculture for valuable discussions. References Anderson MC, Hain C, Otkin J, Zhan X, Mo K, Svoboda M, Wardlow B, Pimstein A (2013) An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with U.S. Drought Monitor classifications. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8054799","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":543930066,"identity":"5d2102d5-cc03-47d0-adf2-ea90be46a44f","order_by":0,"name":"Chien-Ben 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17:46:34","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64064,"visible":true,"origin":"","legend":"","description":"","filename":"50607d9296ec4024a2b64e4d7d48a0211structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8054799/v1/ee16da5c57a028f400c50e37.xml"},{"id":96534907,"identity":"5bbd9724-58b2-457d-ba12-00f9791c4285","added_by":"auto","created_at":"2025-11-22 17:46:33","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69878,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8054799/v1/e48d4ec008fbde89fd596bc2.html"},{"id":96604868,"identity":"31589a25-33e9-4298-ac33-2cc62c069401","added_by":"auto","created_at":"2025-11-24 09:15:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":525715,"visible":true,"origin":"","legend":"\u003cp\u003eThe anomaly of the percentage of NDVI over Taiwan from January to June 2021 (from (a–f)). The right color bar displays the value of the anomaly.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8054799/v1/b9dbb00af50ae993d8cb7cde.png"},{"id":96534891,"identity":"dac9fbe3-e3ca-4215-a9c6-c36f20a1b372","added_by":"auto","created_at":"2025-11-22 17:46:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":511039,"visible":true,"origin":"","legend":"\u003cp\u003eThe anomaly of the percentage of NDVI over Taiwan from January to June 2023 (from (a–f)). The right color bar displays the value of the anomaly.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8054799/v1/0ea5ef619fc4ef1a98d1def3.png"},{"id":96534890,"identity":"cda21086-de3f-4d65-9736-6301c6cce19a","added_by":"auto","created_at":"2025-11-22 17:46:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":230084,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The NDVI time series of monthly-mean indices stepping through the 12-month annual cycle (January to December, as marked on the abscissa). The blue line and the accompanying pair of light-blue lines are the mean and +/− one standard deviation, derived from the data for 2016–2024. The red line is for 2021, and the yellow line is for 2023. (b) Same as (a), but for the “daily running mean” time series with the NDVI value of each day calculated from the mean of the previous 30 days.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8054799/v1/fda2336f9992eee06c237653.png"},{"id":96534894,"identity":"942fb743-3ded-4b99-828b-869e16a860e2","added_by":"auto","created_at":"2025-11-22 17:46:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":235955,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The NDVI time series of monthly-mean indices and variables stepping through the 12-month annual cycle (January to December, as marked on the abscissa (b) The NDVI time series with the value of each day calculated from the mean of the previous 30 days. The calculation used only the data for the northwestern region (as shown in the inset in panel (a)) of Taiwan.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8054799/v1/463664d633b757f4156e2cc3.png"},{"id":96534901,"identity":"63542c5f-c200-4ec2-889e-009a29d10bb1","added_by":"auto","created_at":"2025-11-22 17:46:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":258032,"visible":true,"origin":"","legend":"\u003cp\u003eDaily time series in the same format as Figure 4(b) but for multiple sub-regions (shown in the inset of each panel) of Taiwan based on latitude and elevation. The insets in the left column represent four sub-regions in the western plain (elevation below 500 meters). The middle column represents the mountain area (elevation above 500 meters). The right column represents the eastern plain.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8054799/v1/5a193b51921e39c9b45bc4a7.png"},{"id":96604702,"identity":"562272ca-ea4e-440b-a0c3-e5d044db177f","added_by":"auto","created_at":"2025-11-24 09:14:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":186061,"visible":true,"origin":"","legend":"\u003cp\u003e(a) VCI and (b) VHI, time series of monthly mean indices stepping through the 12-month annual cycle (January to December, as marked on the abscissa.) The value of the index is calculated from the full Taiwan area. (c) to (d) are the same as (a) to (b) but with the index calculated for only the western area of Taiwan (shown in the inset of Figure 4(a)).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8054799/v1/f892b12152f43de7b97c7380.png"},{"id":96534903,"identity":"b5bc8dc4-2ca9-409d-9654-0304c00e0517","added_by":"auto","created_at":"2025-11-22 17:46:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":525299,"visible":true,"origin":"","legend":"\u003cp\u003eThe NDVI time series value of each day calculated from the mean of the previous 30 days, (a) positions of three irrigation paddy (b)irrigation rice paddy in Chishang township. (c) irrigation rice paddy in Xingang Township, (d) irrigation rice paddy in Zhushan village\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8054799/v1/a79a2e1575312325387fd53f.png"},{"id":100920394,"identity":"861b2f9a-9330-4a5d-9e3e-2976f20ecd7d","added_by":"auto","created_at":"2026-01-22 19:54:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2511593,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8054799/v1/17db6435-b80b-4cf0-b855-3c662cd77089.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of severe droughts in Taiwan using vegetation indices from geostationary satellite observations","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSevere droughts impact human lives through degradations in water resources, agricultural and economic activities, even the ecological system. In recent decades, the interplay between global change and severe droughts has received significant attention (Xu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Ault, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Mukherjee et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Liao and Zhuang (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that droughts result in a 10% decrease in gross primary production (GPP) globally in 2001\u0026ndash;2010, based on satellite observations. The most severe drought events can lead to a collapse or strict restriction of domestic water supplies. Well-known examples include the 2021-23 event over the Murray-Darling Basin in southeast Australia (Leblanc et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and the 1994 event in Japan, one of the most severe in the country\u0026rsquo;s history (Lee et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The interruption of the ecosystem by severe droughts is also widely documented (e.g., Xiao et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). A related concern is the increasing frequency and/or intensity of wildfires due to droughts, e.g., as studied by Crockett and Westerling (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for western United States in the 21st century.\u003c/p\u003e\u003cp\u003eTraditionally, droughts are classified into four types: meteorological, agricultural, hydrological, and socioeconomic (Wilhite \u0026amp; Glantz, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). A meteorological drought is characterized by the deficit in precipitation. A hydrological drought occurs when the shortage of rainfall significantly lowers the levels of river, reservoir, and groundwater. If a meteorological drought occurs during the critical periods of the growing season, it may develop into an agricultural drought (Nicholas and Roundy, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A socioeconomic drought refers to the case when water shortage leads to a significant disruption of economic activities and services. The newer notion of a flash drought was proposed in the early 2000s (Svoboda et al., 2002) and has recently gained increasing attention (Otkin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A flash drought is characterized by its rapid development, due to the combined influences of multiple meteorological factors (e.g., high wind speed, high temperature) that help intensify the event. Recently, a novel definition of ecological drought has been proposed for some 21st-century droughts. It is characterized by an increase in the duration and spatial extent of the drought due to an explosive increase in the demand of water by human activities (Crausbay et al., 2017).The disaster associated with an ecological drought is exacerbated by the failure of adaptation by humans.\u003c/p\u003e\u003cp\u003eWith the given background and motivation, this study aims to explore the strategies for monitoring severe droughts. Our subjects of study are two major 21st-century drought events that occurred in Taiwan within just 3 years of period. The event in Spring 2021 covered almost the whole area of Taiwan, while the one in 2023 affected the southern and western parts of Taiwan. Both are among the most extreme events in the past 100 years of record. Both belong to the class of flash droughts due to their rapid intensification driven by rainfall deficit, strong wind, sunny skies, and topographic effects. Some of those details for the 2021 event were analyzed by Chou et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For this class of flash droughts, an early warning would have allowed a better preparation to reduce the damage. To detect the early deterioration of vegetation conditions, the desired data to use should cover the target area continuously with a high resolution in time. Observations made by geostationary satellites are ideal for this purpose. Our previous study (Chou et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) first explored this strategy by using the data from the Himawari-8 satellite. It demonstrated the potential of using the satellite-based vegetation indices for monitoring the 2021 drought event and the associated hydrological cycle. In this paper, we significantly expand the previous study by analyzing both the 2021 and 2023 events in further detail, using more data from the Himawari-8/9 platforms. Two refinements of the methodology are considered. First, the vegetation indices are analyzed for individual sub-regions, allowing for a more accurate characterization of the drought conditions across heterogeneous terrains and land covers. Secondly, the previous analysis of monthly-mean indices is refined to \u0026ldquo;daily running mean\u0026rdquo; indices to sharpen the detection of early warning signals. Future applications of the methodology to real-time monitoring of droughts are also discussed\u003c/p\u003e"},{"header":"2 Data and Method","content":"\u003cp\u003eThe key analysis of droughts uses a set of satellite-based indices that characterize the meteorological and vegetation conditions at the surface over Taiwan. The usefulness of such indices has been explored in many studies in related context (e.g., Mukherjee et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Zaimes et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The observational data from Himawari-8/9 for 2016\u0026ndash;2024 are used. Himawari-8/9 includes16 channels with variance spatial resolution of 0.5,1 km for visible, near-infrared and 2 km for infrared channels. Due to the increased number of channels, Himawari-8/9 is able to retrieve multiple products for environmental monitoring (Bessho et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The indices are computed from clear-sky data, with the clear pixels retained by cloud mask. The time series and anomaly map are analyzed. The tools for computing and analyzing vegetation indices are based on programs written in Interactive Data Language (IDL). Land surface temperature is a standard output of the Clavr-x pre-processing package, which we directly use in this study. The formulas and relevant background for the indices are summarized below.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 The NDVI index\u003c/h2\u003e\u003cp\u003eThe normalized difference vegetation index (NDVI) has been designed to monitor plant development from space (Tucker, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). The formula for NDVI is given as\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{N}\\text{D}\\text{V}\\text{I}=\\frac{{\\rho\\:}_{NIR}-{\\rho\\:}_{red}}{{\\rho\\:}_{NIR}+{\\rho\\:}_{red}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the equation, ρ\u003csub\u003eNIR\u003c/sub\u003e and ρ\u003csub\u003ered\u003c/sub\u003e represent the reflectance values of near-infrared and visible red light, respectively. For healthy plants, reflectance is higher at the near-infrared frequencies due to the cellular structure of the leaves, and lower at the red-light frequencies due to absorption by dense chlorophyll content in the leaves. Therefore, a high NDVI value implies that vegetation is healthy. The value of NDVI is between 0 and 1 for vegetation. It is slightly positive for rock or bare soil, and negative for water and cloud.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 The VCI index\u003c/h2\u003e\u003cp\u003eSpatial variations of NDVI may arise from the differences in weather conditions and geographic factors (nonuniform topography and land-surface cover). When using the NDVI data to estimate the impacts of weather conditions on vegetation, it is desirable to remove the spatial variability of NDVI due to geographic factors. To achieve this, Kogan (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) and Kogan (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) defined the vegetation condition index (VCI). It normalizes NDVI by scaling it between the absolute maximum and the absolute minimum of the historical records at a given location. It is assumed that NDVI reaches the maximum under the best weather conditions when the plants can efficiently use the geographic resources. In contrast, NDVI reaches the minimum under the worst weather conditions when the plants cannot use the geographic resources. The formula of VCI is\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{VCI}_{p}=\\:\\:100\\:\\left(\\frac{{NDVI}_{p}-{NDVI}_{min}}{{NDVI}_{max}-{NDVI}_{min}}\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the equation, \u003cem\u003eNDVI\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e is the averaged value of NDVI over a designated period (which can be a week, a month, or a season). \u003cem\u003eNDVI\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eNDVI\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e are the minimum and maximum values from the long-term observations. By definition, the value of VCI is between 0 and 100. A higher value indicates that the plants are under favorable weather conditions. A lower value indicates the opposite.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 The TCI index\u003c/h2\u003e\u003cp\u003eThe land surface temperature (LST) derived from remote sensing data can be used to measure water stress for the plants. When soil moisture is high in the root zone, evapotranspiration helps cool the land surface. When soil moisture reaches an exhaustive level, the effect of evapotranspiration diminishes which leads to a higher LST. Similar to VCI, the temperature condition index (TCI) uses surface temperature instead of NDVI to quantify the relation between temperature and the health of plants. As defined by Kogan (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and Garc\u0026iacute;a-Le\u0026oacute;n et al. (2019), it can be expressed as\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"375\" height=\"82\"\u003e\u003c/p\u003e\u003cp\u003eIn the equation, \u003cem\u003eLST\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e is the average of \u003cem\u003eLST\u003c/em\u003e over a designated period, \u003cem\u003eLST\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eLST\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e refer to the minimum and maximum values from long-term observations. Similar to VCI, the range of TCI is from 0 to 100. A higher value means the plants are under less stress.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 The VHI index\u003c/h2\u003e\u003cp\u003eThe linear combination of VCI and TCI can be used to define the vegetation health index (VHI), as proposed by Kogan (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Its general formula is:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{\\:\\:\\:\\:VHI}_{p}={\\alpha\\:}\\text{*}{\\:VCI}_{p}+\\left(1-{\\alpha\\:}\\right)*{\\:TCI}_{p}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the equation, α is a constant between 0 and 1. It determines the weight of \u003cem\u003eVCI\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eTCI\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e in their contribution to \u003cem\u003eVHI\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e. As the relative impact of moisture and temperature on vegetation health is still being investigated (Zeng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we choose to follow a popular selection of α\u0026thinsp;=\u0026thinsp;0.5 (Karnieli et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). When using this index to monitor droughts, it is suitable to apply it to areas with a negative correlation between LST and NDVI. In such areas, the primary factor limiting vegetation growth is water, not energy (Karnieli et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This concept has been affirmed by a previous study (Anderson et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) based on historical records in the United States. It demonstrated that VHI is an efficient index for monitoring droughts, except in the cold season and at high latitudes. Under those conditions, there is limited energy to facilitate the growth of plants.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eThe vegetation indices are used to retrospectively monitor two severe drought events over Taiwan in the spring of 2021 and 2023. The monthly anomalies of NDVI from January to June are used to quantify the intensity and spatial coverage of both drought events. The results are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Throughout the event, the 2021 drought covered nearly the entire territory, except the northwestern corner of the island. The 2023 drought event was initiated in the western part of the island. As it developed, the disaster eventually covered much of the southwestern part of the island. At the end of April, it also expanded into a strip in the northwestern part of the island. Both droughts diminished in May of the respective year after the arrival of the seasonal Meiyu front, which restored precipitation to its normal level.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe time series of the monthly-mean NDVI for the entire island region are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a). The red and yellow lines represent 2021 and 2023, respectively. The thick blue line is the 9-year average from 2016 to 2024. It is accompanied by a pair of light-blue lines that indicate one standard deviation from the mean value. Since the 2021 event covered the whole island while the 2023 event only covered the southwestern part of it, the signal for drought is relatively weak in the yellow line (for 2023). In contrast, the red line reveals significant drought conditions in March and April, 2021. It shows not only the lower value in March and April but also a steep decreasing gradient at the onset of drought. In real time, this could have served as a useful early warning signal. To focus on the rapid development, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(b) shows the \u0026ldquo;daily running mean\u0026rdquo; time series, with the value of each day calculated from the mean of the preceding 30 days. This time series helps highlight the rapid change in NDVI. For example, a particularly steep decreasing gradient can be seen at around day 60 of the year (near the end of February).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo sharpen the detection of the 2023 drought, the time series is re-calculated using the data from only the southwestern part of the island. (The region is shown in the inset of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a).) The updated monthly-mean and daily (running mean) time series for the subregion are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a) and (b). By restricting to a particular region, the yellow line in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a) clearly shows a steep decreasing gradient from March to April. In the daily time series in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(b), a particularly sharp decline can be identified as early as around day 90 of the year. For both springtime drought events, the daily updated time series show promise for enhancing the early detection of drought conditions. To put the idea into actual practice, the technique could be further refined by monitoring each subregion or sub-category of land-cover type, etc.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the following, we further expand the regional monitoring scheme to multiple subregions of the island. The subdivision is determined based on the latitude and elevation of the subregions. A total of 12 subregions (shown in the inset of each panel in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) are considered. The analysis of daily time series is repeated for each of the subregions. The results are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Having the key indices for the subregions allows a more accurate characterization of the evolution of drought across heterogeneous geographical landscapes. For example, Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(d) and (g) pinpoint two regions in southwestern Taiwan that were severely affected by the 2023 drought. This new scheme regional analysis will help sharpen future detection of early warning signals for drought.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe next analyze the VCI and VHI indices, which serve the purpose of removing the dependence of spatial variability of NDVI from the effect of geographic resources. The time series of VCI and VHI of 2021 and 2023 are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (a) and (b) show the indices averaged over the whole island, while Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (c) and (d) show those averaged over southwestern Taiwan. The results affirm that VCI tracks the evolution of drought more clearly than NDVI. When averaged over the whole island, both VCI and NDVI accurately represent the drought event in February-April 2021 (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(a) and 3(a)). For the regional footprint of drought in western Taiwan for both 2021 and 2023 events, VCI reveals a clearer signal compared to NDVI (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(c) and 4(a)). The superior performance of VCI is particularly notable for the 2023 drought over southwestern Taiwan. The VHI index (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(b)) also shows improvement (but not as dramatic as VCI) over NDVI in sharpening the drought signal. For future applications, we could explore the possibility of giving a greater weight to VCI (and a smaller weight to TCI) in defining the VHI.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs a conceptual application of our methodology, the regional analysis is used to assess the impact of drought on local agricultural productivity. We select three locations dominated by irrigated rice fields in Chishang Township, Xingang Township, and Zhushan Village. The locations of the three sites of rice paddies are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(a). The vegetation indices for the irrigated rice paddy in Chishang Township are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(b). It is found that irrigation temporarily supported the growth of rice even during the drought. (The two peaks in the time series correspond to two rice cropping seasons per year.) Nevertheless, the time series of 2021 still falls one standard deviation below the mean. It implies that the drought did significantly impact the overall crop yield even with the support by irrigation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe primary rice-producing region in Taiwan is the Chianan Plain in the southwestern part of the island, where the other two irrigated rice fields in Xingang Township and Zhushan Village are located. The time series of the vegetation indices for the two sites are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(c) and (d). It is found that the Spring droughts in both 2021 and 2023 caused NDVI values during the first harvest season to fall below the average, particularly in 2023 as shown by the yellow line. During the second harvest season, the 2023 drought was more severe and prolonged than the one in 2021 (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(g)), its effect is shown by the yellow lines in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(c) and (d). The unusual low values of the indices during this period is reflected in a reduction in vegetation due to field fallowing of the rice paddies. This further demonstrates that while irrigation can support crop growth through a drought, it is not enough to sustain a normal level of productivity under the overall drought conditions. The case study for the rice fields illustrates the usefulness of the vegetation indices from Himawari-8/9 satellite data. We expect future applications of this type of analysis to customized sites for farmers.\u003c/p\u003e"},{"header":"4 Summary","content":"\u003cp\u003eSatellite-based indices of NDVI, VCI, and VHI as derived from the Himawari-8/9 data are used to retrospectively monitor two severe drought events in Taiwan in 2021 and 2023. The results show that the daily-updated time series of the vegetation indices usefully capture the rapid evolution of drought conditions. Using this approach, several early signals were identified for the 2021 and 2023 events. The results also demonstrate that VCI is more efficient indicators than NDVI for detecting the early signals. Comparing the results of VHI and VCI, it is found that the water limitation is more important than the energy limitation on vegetation health. We suggest expanding this analysis to other indices in future work.\u003c/p\u003e\u003cp\u003eThis study also establishes the framework for enhanced monitoring of droughts by computing daily-updated time series of the key indices for subregions divided by their geographical characteristics. We expect this scheme to help sharpen regional drought detection across the complex terrains of Taiwan. As a further application, our analysis is also used for a case study for the impact of drought on irrigated rice fields. The result shows that while irrigation helped push the growth of rice through a drought, the overall annual yield still decreased significantly under the overall drought conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCHien-Ben Chou : Conceptualization, Software,prepareing figure 1-7, Writing \u0026ndash; Original Draft.Min-Chuan Weng : Data Curation, Software.Huei-Ping Huang : Conceptualization, Writing \u0026ndash; Review \u0026amp; Editing.Tzu-Ying Yeh : Data Curation, Writing \u0026ndash; Review \u0026amp; Editing (assistance).Yu-Cheng Chang : Review \u0026amp; Editing (assistance).All authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eSome preliminary results of this study were presented at the ACEER 2025 Conference. The authors appreciate the feedback received at the conference. The first author would like to thank Dr. Dong-Hong Wu of the Ministry of Agriculture for valuable discussions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnderson MC, Hain C, Otkin J, Zhan X, Mo K, Svoboda M, Wardlow B, Pimstein A (2013) An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with U.S. Drought Monitor classifications. J Hydrometeor 14:1035\u0026ndash;1056. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JHM-D-12-0140.1\u003c/span\u003e\u003cspan address=\"10.1175/JHM-D-12-0140.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAult TR (2020) On the essentials of drought in a changing climate. 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Sci Data 10:338. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41597-023-02191-3\u003c/span\u003e\u003cspan address=\"10.1038/s41597-023-02191-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NDVI, Drought, Himawari-8/9, time series analysis","lastPublishedDoi":"10.21203/rs.3.rs-8054799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8054799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOne of the emerging concerns of global change is the increasing frequency and intensity of severe droughts. Recently, two record-breaking droughts occurred consecutively in the spring of 2021 and 2023 across Taiwan. Motivated by the need to monitor such extreme events, this study analyzes vegetation indices from Himawari-8/9 satellite observations. The key indices, including Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI), are computed for subregions of Taiwan to allow an accurate characterization of drought conditions across heterogeneous landscapes. Taking advantage of the high temporal resolution of the data, daily-updated time series of the indices are analyzed to form the framework for the detection of early warning signals. As a local application, the methodology is used to analyze the effect of drought on irrigated rice fields. The result reveals overall unhealthy conditions of the crops, even though the growth of the crops was temporarily sustained through the drought by irrigation.\u003c/p\u003e","manuscriptTitle":"Analysis of severe droughts in Taiwan using vegetation indices from geostationary satellite observations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-22 17:46:28","doi":"10.21203/rs.3.rs-8054799/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1bcad24-ac90-4ca5-9f7b-21447de61f68","owner":[],"postedDate":"November 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-22T19:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-22 17:46:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8054799","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8054799","identity":"rs-8054799","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}