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Drought, as an extreme climatic event, is considered one of the most severe natural disasters worldwide. In Northeast Asia, the frequency and intensity of drought have been exacerbated by climate change, causing significant negative impacts on the region's socioeconomic conditions and agricultural production. This study analyzed the spatiotemporal evolution and trends in drought in transboundary river basins in Northeast Asia from 1990 to 2020, using meteorological station data and remote sensing data. The Standardized Precipitation Evapotranspiration Index (SPEI) and Vegetation Condition Index (VCI) were employed to assess drought characteristics, and a comprehensive analysis of the SPEI and VCI indices was conducted to evaluate drought severity under different land cover types. The results indicate that (1) in the past two decades, both the SPEI and VCI indices have shown an increasing trend in the basin, with moderate and mild droughts being predominant. (2) High and extreme droughts mainly occur in forest areas, accounting for 17.91% and 10.76%, respectively, followed by farmland. [ABSTRACT FROM AUTHOR]

Droughts have always been one of the most dangerous hazards for civilizations, especially when they impact the headwaters of a watershed, as their effects can spread downstream. In this context, observed droughts (1981–2015) and projected droughts (2016–2100) were assessed in Candarave, the headwaters of the Locumba basin. Regarding observed droughts, SPI-3 and SPEI-3 detected seven extreme droughts (1983, 1992, 1996, 1998, 2010, 2011, and 2012), with the most intense occurring in 1992 and 1998. SPI-6 and SPEI-6 identified the same extreme drought events, highlighting 1992 as the most intense. Additionally, it was concluded that the VCI also detected the droughts identified by the SPEI; however, a more detailed analysis of its use is necessary due to the limited availability of suitable satellite images in the area. On the other hand, a high-resolution dataset of climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) under the SSP3-7.0 scenario was used to project future droughts. Of the models in that dataset, CanESM5, IPSL–CM6A–LR, and UKESM1–0–LL did not perform well in the study area. SPI and SPEI projected more than ten episodes of extreme drought, indicating that extreme droughts will become more frequent, severe, and intense in the last 30 years of this century. [ABSTRACT FROM AUTHOR]

Drought presents a substantial threat to both ecological and agricultural systems. Agriculture in Iraq is predicated on precipitation, which is a major contributor to the likelihood of drought resulting from even marginal fluctuations in precipitation. Furthermore, research suggests that Iraq suffers an approximate annual loss of 100,000 acres of arable land due to drought. NDVI and VCI, two significant indices, were utilized in this research to assess and monitor the severity of the drought in the northern region of Wasit province in Iraq. For the period from 1993 to 2023, drought intensity maps were generated utilizing NDVI-based VCI and the Geographic Information System (GIS), an extremely effective spatial data management instrument. NDVI results evidenced that the vegetation cover area was the highest in 1993 and 1998 and declined until it reached the lowest levels in 2023. The vegetation area was concentrated in the southwest parts. In contrast, VCI results demonstrated the extreme drought through the years from 2003 to 2023, which can be attributed to higher temperatures, evaporation, and lower amounts of rainfall. Throughout the thirty-year analysis period, extreme drought conditions were prevalent, especially in the last two decades. Furthermore, this drought should prompt the government to implement preventative measures to avert it. Implementing soil and water conservation measures, such as the establishment of percolation basins, contour bunds, and check dams, can also enhance drought management. [ABSTRACT FROM AUTHOR]

Drought is a multifaceted hazard, influenced directly or indirectly by factors such as precipitation, evapo‐transpiration, soil moisture content and groundwater levels. Drought dynamics is fundamentally linked to hydrological processes, as droughts directly impact water availability, soil moisture and overall hydrological balance. Given the global climate change and consequent increasing frequency of extreme events, assessing the vulnerability to drought stress in underprivileged areas has become highly relevant. In this context, drought characterization has become crucial for understanding the complex patterns of droughts and its impacts on agriculture, ecosystems and communities, thereby contributing to food security, environmental sustainability and disaster preparedness. The major goal of this study was to evaluate the onset, duration, frequency and extent of agricultural drought using satellite‐based datasets. The hyper‐temporal Système Pour l'Observation de la Terre (SPOT) VEGETATION (VGT) Normalized Difference Vegetation Index (NDVI) data were used in this study to quantify the drought related stress on vegetation cover. The spatial extent of drought and its pattern were estimated using the long‐term Vegetation Condition Index (VCI) derived from hyper‐temporal SPOT‐VGT NDVI datasets. Notably, strong agreement was found between the estimations of onset and duration, particularly during severe drought years such as 2002 and 2008. The study reveals that more over 95% of the total area encountered severe crop stress before the second week of July in 1999, 2002 and 2008, which are identified as severe drought years in the region. The frequency maps effectively captured the occurrence of drought events over a sixteen‐year span, highlighting distinct drought episodes in the study area and underscoring the significant prevalence of drought stress across a considerable area. The high r2 (0.61) value indicates a significant level of shared variability between VCI and yield of major crops, demonstrating their consistent alignment in capturing vegetation health and crop yield anomalies. The comprehensive delineation of drought patterns in the Bundelkhand region revealed by this study is potential for future preparedness efforts, enabling strategic planning and facilitating the implementation of sustainable policy interventions. [ABSTRACT FROM AUTHOR]

This study uses remote sensing data from 2005 to 2023 to analyze the spatiotemporal pattern of agricultural drought severity and hotspots in Somaliland. The Vegetation Condition Index (VCI), derived from MODIS satellite imagery, was employed to assess drought conditions, while CHIRPS rainfall data provided insights into precipitation patterns. Results revealed significant temporal and spatial variability in drought severity across Somaliland. VCI trends indicated cyclical patterns of vegetation health, with severe stress observed from 2015 to 2018, followed by recovery from 2019 to 2021. A strong positive correlation between VCI and rainfall was observed, with correlation coefficients ranging from 0.5638 in Saaxil to 0.7701 in Togdheer. Drought severity classification identified Sool and Togdheer as the most critically affected regions, with 90% and 85% of their areas under extreme drought conditions, respectively. Saaxil exhibited the lowest percentage of extreme drought at 35%. Temporal analysis of NDVI deviations confirmed prolonged vegetation stress from 2015 to 2018, with notable improvement in 2020 and 2021. The findings underscore Somaliland's vulnerability to recurrent droughts, emphasizing the urgent need for targeted interventions and adaptive management strategies to enhance resilience in this semi-arid region. [ABSTRACT FROM AUTHOR]

A new approach to an automatable fiber impregnation and consolidation process for the manufacturing of fiber-reinforced composite parts is presented in this article. Therefore, a vacuum chamber sealing machine classically used in food packaging is modified for this approach—Vacuum Chamber Infusion (VCI). Dry fiber placement (DFP) preforms, made from 30 k carbon fiber tape, with different layer amounts and fiber orientations, are infused with the VCI and with the state-of-the-art process—Vacuum Assisted Process (VAP)—as the reference. VCI uses a closed system that is evacuated once, while VAP uses a permanently evacuated open system. Since process management greatly influences material properties, the mechanical properties, void content, and fiber volume fraction (FVF) are analyzed. In addition, the study aims to identify how the complexity of a resin infusion process can be reduced, the automation potential can be increased, and the number of consumables can be reduced. Comparable material characteristics and a reduction in consumables, setup complexity, and manufacturing time by a factor of four could be approved for VCI. A void content of less than 2% is measured for both processes and an FVF of 39% for VCI and 45% for VAP is achieved. [ABSTRACT FROM AUTHOR]

The practical applications of satellite data in comprehending the changing environment must take in account quantitative awareness of the uncertainty across different satellite products. However, prior drought research efforts in Ethiopia paid less attention to evaluating satellite products although drought has led to agricultural failures in the lowlands of Ethiopia. This study aimed to evaluate the spatial-temporal distribution of agricultural drought in the lowlands of the Bale zone throughout the crop growing season (March to May) from 2012 to 2022. The dataset utilized in this study was chosen by assessing the performance of enhanced MODerate resolution Imaging Spectroradiometer (eMODIS) and enhanced Visible Infrared Imaging Radiometer Suite (eVIIRS) Normalized difference vegetation index (NDVI) in comparison to observed gridded rainfall, employing the simple linear regression model. The assessment of agricultural drought was conducted using the NDVI anomaly and the vegetation condition index (VCI). The eMODIS exhibited a coefficient of determination of 0.45 with a p-value of 0.02, whereas the eVIIRS had a coefficient of determination of 0.47 with a p-value of 0.01. Thus, eVIIRS NDVI was selected as the best dataset for evaluating agricultural drought in the research site. The findings indicated that in both 2012 and 2022, there were periods of agricultural drought. During these periods, severe to extreme drought conditions were seen in 7.6 % to 54.98 % of the study area, encompassing both lowland and midland regions. The biggest influence was detected in the northern, middle, and southern regions of the research area. Severe and moderate drought dominated study area as depicted by NDVI anomaly while extreme and severe as seen from VCI. Whereas the years 2014 and 2020 were the wettest. The study implies that eVIIRS NDVI might be an alternate approach to give empirical information that would aid stakeholders in limiting the consequences of agricultural drought on farming activities.

Climate variability poses a significant threat to smallholder farmers in the Gwayi catchment, Zimbabwe. This study investigated the spatiotemporal dynamics of agrometeorological droughts from 1990 to 2020 using the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). The triangulation of these indices provided a comprehensive understanding of drought dynamics. Severe to extreme drought events were identified in 1995, 1998, 2005, 2016, 2018, and 2020, while moderate droughts were consistently detected across all indices. Favourable conditions were observed in 2000 and 2006. A strong positive correlation (r = 0.88) between VHI and VCI suggests their similar effectiveness in reflecting vegetation health. In contrast, moderate correlations between VHI and SPI (r = 0.30), VHI and SPEI (r = 0.39), VCI and SPI (r = 0.28), and VCI and SPEI (r = 0.40) indicate that these indices capture complementary information, reflecting not only precipitation deficits but also the impacts of temperature, soil moisture, and land cover changes. The SPI and SPEI displayed a strong positive correlation (r = 0.78), aligning in their sensitivity to precipitation and evapotranspiration. Spatial analysis revealed a pronounced north-south drought gradient, with the southwestern region being particularly vulnerable, underscoring the need for targeted interventions.

Delay in vaccination schedule may result in increased risk of vaccine-preventable outbreaks. Lack of parental confidence in vaccines and sociodemographic factors could be related to those delays. Vaccine Confidence Index (VCI) is an indicator to estimate confidence in vaccines, composed of simple dimensions about it and evaluated using a 5-point Likert scale. Objectives of this study, conducted in the Region of Murcia, Spain, were to determine which sociodemographic factors were associated with delayed administration in measles, mumps and rubella (MMR) vaccine, both first and second doses, and to evaluate if there were differences in parental vaccine confidence of children who were delayed on MMR vaccine schedule compare to vaccinated on time. The 2019–2020 (first dose) and 2016–2017 (second dose) cohorts were divided into four categories: vaccinated on time, slightly delayed, moderately delayed and highly delayed. Online questionnaire was sent to their parents, followed by a telephone questionnaire for non-responders. Regarding MMR first dose, a significant association was found between delay and birth order and intellectual/motor delay of children. Parental educational status, the main source of vaccine information and the social networks more frequently used by parents were also associated with delay. Suffering motor/intellectual delay and the obstacles to vaccination process derived from caring for other siblings were associated with delay in second MMR dose. The VCI decreased significantly in first dose group as delay increases but did not change in the second one. Confidence in vaccines and sociodemographic factors, respectively, were associated with delaying MMR first and second dose.

Atmospheric pollution by anthropogenic emissions leads to an increase in the content of aerosols in the air. This causes distortions in the Earth remote sensing data in the visible ranges. Thus, the use of vegetation indices, in particular, the normalized differential vegetation index (NDVI), is difficult. In this regard, interest in the use of satellite images obtained in the short-wave infrared bands SWIR1 and SWIR2 has recently increased. Unlike NDVI, when calculating the AFRI-1600 and AFRI-2100 indices, the values of these bands are taken into account (instead of visible red). Therefore, these indices are called aerosol-free. The article addresses the issues of the NDVI and AFRI indices. The studies have been carried out in three regions located in the south-west of Azerbaijan. The initial data has been taken from 2000 and 2021 images obtained using the Landsat-5 and Landsat-8 satellites, respectively. The scanners of these satellites have ranges of 1600 and 2100 μm. The study has taken place in several stages. At the first stage, the AFRI-1600 indices have been calculated and the areas with high aerosol content for the indicated years have been identified. By studying the dynamics of the amount of aerosols, two types of sites have been identified. Type 1 sites are those where the aerosol content was high in 2000, but dropped in 2021. In type 2 sites, a high value of this indicator was not previously recorded, but was noted in 2021. The NDVI index value has been obtained for both categories, and the classification of the site coverage has been carried out. The results of superimposing index maps on each other have shown that an increase in aerosol content corresponds to a decrease in vegetation density, and vice versa, a decrease in aerosols corresponds to an increase in this indicator. Thus, the increased aerosol content has a negative effect on the condition of forest cover. This is confirmed by the VCI (vegetation condition index) maps.

Drought is a climatic phenomenon that can adversely affect the environment and human activities. Currently, agricultural drought has become a concern in countries where agriculture largely depends on rainfall and is highly vulnerable to climatic variability. The study of drought requires the availability of hydroclimatic and remote sensing data. Based on these data, researchers have proposed several indices (SPI, SPEI, PDSI, VCI, VHI, etc.) that allow the monitoring of the spatiotemporal evolution of drought. This study focuses on remote sensing data to illustrate agricultural drought in the Sebou watershed from 2010 to 2023. We used the NDVI derived from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite images to calculate the Vegetation Condition Index (VCI) during the crop growth season. The analysis of the VCI over 14 years shows that the Sebou watershed has undergone droughts of varying severity, with a notable increase in drought events from 2016 to 2023. In 2016, approximately 50% of the basin experienced extreme agricultural drought. Thus, we found that the VCI is a valuable indicator that reflects the vegetation's response to the rainfall deficit recorded in recent years.

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Uncertainty and uneven distribution of monsoonal rainfall and its consequences on crop production is a matter of serious concern in India, specifically, in the Indo-Gangetic plain region. In this study, drought patterns were investigated through standardised precipitation index (SPI) of varying timescales, using the India Meteorological Department (IMD) precipitation data (1901–2021). We analysed the spatio-temporal pattern of different drought characteristics (frequency, duration, severity, intensity) of the Indian Gangetic basin using run theory. The bivariate copula method has been incorporated to combine two drought properties (severity and duration). Copula integrates multivariate distribution and considers the dependency rate among the variables. The five most widely used copulas from various copula families, elliptical (normal, t-copula) and Archimedean (Clayton, Gumbel, Frank), were estimated for modelling, and the best fit copula was selected. The study revealed that seasonal drought is more frequent and intense in the Upper and Middle Gangetic Plain, whereas annual drought is quite scattered in nature. It is worthy to mention that downward drought trends were observed in this agricultural belts significantly after 1965; specifically, in the Upper, Middle, and Trans Gangetic Plain regions. With increasing drought duration and severity, the drought return period raised, but the frequency decreased gradually. Most of the droughts characterised by less duration and severity occurred with a return period below 10 years for the whole region. The major 100 + years return period droughts were to be found after 1960 and their frequencies were significantly higher after 2000. The most recent remarkable droughts with more than 100 years of return occurred during 2008–2011 and 2016–2018 in the Upper and Middle Gangetic plains, whereas in the Lower Gangetic plain, a hundred-year return period drought was occurred during 2010–2013. This study provides agroclimatic-zones-wise significant information of drought characteristics and its nature of occurrence in the Indian Ganga Basin. The results enhance the understanding of drought management and formulation of adaptive strategies to mitigate the adverse impact of droughts. [ABSTRACT FROM AUTHOR]

The study examines the morpho-physiological differences in leaf characteristics between two species of C4 plants: sorghum and maize. The research was conducted in field conditions where plants were rainfed. Both species different varieties were cultivated at two distinct sites that exhibited variations in soil texture and drought stress incidence according to the vegetation condition index (VCI). Samples were collected during various growth stages to analyze the relative water content (RWC), proline levels, and stomatal density. Sorghum plants displayed higher RWC, proline levels, and stomatal density than maize plants. In sorghum, the biochemical traits, such as the proline content, may play a more critical role in withstanding water-limited conditions than in maize in our experiment. Under the same water restriction period, sorghum showed higher RWC levels. Sorghum plants reduced stomatal density under more water-limited conditions, which proves its plasticity. Additionally, early maturation played a crucial role in both species. The early sorghum variety KWS Kallisto and maize variety Walterinio KWS had a more stable yield at both sites. Nevertheless, the highest yields were found in the later varieties, KWS Hannibal and KWS Inteligens. The higher proline levels and the relative water content are drought-tolerant mechanisms and may be used to indicate drought intensity in field conditions. Our findings spotlight the influence of genetic diversity and genotype-environment interactions in determining crop responses to drought stress, providing valuable information for future breeding programs to enhance drought tolerance in crops. [ABSTRACT FROM AUTHOR]

Purpose: The velamentous cord insertion is a rare pathology in which the umbilical blood vessels branch before reaching the placenta; by varying its structure, the cord becomes prone to spontaneous internal ruptures. This pathology is an obstetric emergency, so its early diagnosis is essential. Methods and results: We present a 27-year-old pregnant woman who attends an antenatal check-up for a routine third-trimester examination. Ultrasound reveals grade I polyhydramnios and suggestive findings of a trivascular umbilical cord with velamentous insertion 35 mm from the nearest placental border. The ultrasound diagnosis allowed a term delivery by elective cesarean section, avoiding severe complications of the maternal–fetal binomial. Conclusion: Velamentous cord insertion can and should have an early prenatal diagnosis, even from the second trimester, through imaging techniques such as transabdominal ultrasound or color Doppler. Early detection and appropriate peripartum management will highly reduce complications during labor. [ABSTRACT FROM AUTHOR]

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. HIGHLIGHTS 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.;

In the semi-arid Marrakech-Safi (MS) region of southwest Morocco, climate change has amplified drought occurrences, posing significant threats to water resources and agriculture. A comprehensive understanding of drought patterns is imperative to manage these risks and enhance resilience effectively. Precipitation from 18 pluviometer stations was employed to analyze meteorological drought using the standardized precipitation index (SPI) from 1980 to 2018. Additionally, agricultural drought is quantified using three remote sensing-driven indices: vegetation condition index (VCI), temperature conditions index (TCI), and vegetation health index (VHI) from 2000 to 2018. These indices are correlated with SPI to evaluate their performance and gauge vegetation sensitivity to meteorological drought. The Mann–Kendall test assesses trends in drought events and their severity. The results demonstrated that SPI, VCI, TCI, and VHI experienced alternating dry and wet periods with an overall upward trend, especially in mountainous areas, plateau zones, and Haouz plain of the MS region. The correlation analysis establishes a significant relationship between remote sensing-based indices and SPI-6, with mean correlation coefficients exceeding 0.6. The findings underscore the importance of considering multiple time scales to comprehensively assess climate's impact on vegetation. Seasonal drought trends analysis indicates no significant negative trends in winter and spring but positive trends in autumn. HIGHLIGHTS Use the SPI to study meteorological droughts between 1980 and 2018.; Use Mann–Kendall and Sen's slope estimators to analyze seasonal and yearly SPI trends.; Spatio-temporal characteristics of agricultural droughts using the VCI, TCI, and VHI drought indices from 2000 to 2018.; Assess and analyze the correlations of SPI (3, 6, and 12 months) with VCI and VHI.;

Dust is one of the major environmental problems that make health and financial losses in arid and semi-arid regions. As a country located in the Middle East, Iran has been affected by these natural hazards and every year dust storms occur in different parts of Iran causing huge amounts of damage. Dust storms have increased due to recent droughts and internal Sand and Dust Storm (SDS) hotspots in the Middle East. Interactions between the atmosphere and the earth's surface, such as the effect of vegetation or soil moisture and rainfall intensity in an area, can be important in determining the convective activity of the atmosphere (Lee et al., 2016). Since dust emission and wind speed depend on factors such as soil type, soil moisture, and vegetation (Prospero et al., 2002), it is important to study the relationship between the amount and type of vegetation and drought with dust events. Gradually, as the warm season approaches, the pattern of dust formation is intensified by north, and northwest winds and the wind field channeling due to convergent topography, especially over Mesopotamia. . The lowest values of the VCI in the studied region are observed in the countries of Iraq, Syria, and Saudi Arabia; while the seasonal changes of VCI over Iran are not significant. Nevertheless, in the southeast of Iran, the highest values are related to the summer season, which can be due to the rainfall caused by the activity of the Indian monsoon in this region.

Whole brain irradiation (WBI), also known as whole brain radiation therapy (WBRT), is a well-established treatment for multiple brain metastases and as a preventive measure to reduce the risk of recurrence after surgical removal of a cerebral metastasis. However, WBI has been found to lead to a gradual decline in neurocognitive function in approximately 50% of patients who survive the treatment, significantly impacting their overall quality of life. Recent preclinical investigations have shed light on the underlying mechanisms of this adverse effect, revealing a complex cerebrovascular injury that involves the induction of cellular senescence in various components of the neurovascular unit, including endothelial cells. The emergence of cellular senescence following WBI has been implicated in the disruption of the blood-brain barrier and impairment of neurovascular coupling responses following irradiation. Building upon these findings, the present study aims to test the hypothesis that WBI-induced endothelial injury promotes endothelial dysfunction, which mimics the aging phenotype. To investigate this hypothesis, we employed a clinically relevant fractionated WBI protocol (5 Gy twice weekly for 4 weeks) on young mice. Both the WBI-treated and control mice were fitted with a cranial window, enabling the assessment of microvascular endothelial function. In order to evaluate the endothelium-dependent, NO-mediated cerebral blood flow (CBF) responses, we topically administered acetylcholine and ATP, and measured the resulting changes using laser Doppler flowmetry. We found that the increases in regional CBF induced by acetylcholine and ATP were significantly diminished in mice subjected to WBI. These findings provide additional preclinical evidence supporting the notion that WBI induces dysfunction in cerebrovascular endothelial cells, which in turn likely contributes to the detrimental long-term effects of the treatment. This endothelial dysfunction resembles an accelerated aging phenotype in the cerebrovascular system and is likely causally linked to the development of cognitive impairment. By integrating these findings with our previous results, we have deepened our understanding of the lasting consequences of WBI. Moreover, our study underscores the critical role of cerebromicrovascular health in safeguarding cognitive function over the long term. This enhanced understanding highlights the importance of prioritizing cerebromicrovascular health in the context of preserving cognitive abilities. [ABSTRACT FROM AUTHOR]

Drought assessment using drought indices has been widely carried out for drought monitoring. Remote sensing-based indices use remotely sensed data to map drought conditions in a particular area or region. Therefore, the objective of the present study is to make a study on drought risk based on the calculation of an indicator from biophysical parameters extracted from NOAA/AVHRR satellite data, namely TCI and VCI, to obtain a better understanding of the differentiation between each index, and their application for drought monitoring in the High Atlas of Marrakech on the Chchaoua Morocco watershed during 1980-2020. Landsat oli7 and8 data were used to construct the indices. The result showed that each index proved to be a useful, fast, sufficient, and inexpensive tool for drought monitoring. However, each index has its differences. The TCI was found to be drought sensitive during the dry season or in months when high temperatures occurred. While VCI detected drought more sensitively in the rainy season as well (December-January-February to May) than TCI and VCI. Meanwhile, VCI, including the improved TCI, combined two indicators to better understand drought occurrence. These indices were calculated using GIS, QGis, ArcGis satellite imagery scenes, and Landsat. After a comparative study of these years, from 1984 to 2020, the evolution of the VCI and TCI was highlighted. [ABSTRACT FROM AUTHOR]

Abstract Drought is a key challenge for third-world countries whose economies are based on agriculture. Also, lack of rainfall plays a major role in accelerating climate change. Drought is resulting in a decline in vegetation greenness and plant mortality specifically in the semi-arid region. In this study, satellite-based drought assessment was used to characterize drought and its impact on forests in the semi-arid regions of Ethiopia. Drought severity and forest cover change were detected over the period between 2014 and 2022 Using geographic information systems and remote sensing techniques. Vegetation condition index (VCI) and Normalized difference vegetation index (NDVI) were used to detect drought severity in the present study. Results of this study showed that 2017, 2015, 2022 and 2019 were the most drought years. The forest resource is significantly decreased during severe drought years. The Minimum dense forest was observed in 2017 and 2015 over a spatial extent of 193 ha and 217 ha respectively during drought years. To evaluate how precipitation deficit affects forest function, simple linear regression is performed. Results of this linear regression “R2 = 0.49 and P = 0.05” indicated that nearly 50% of forest greenness is influenced by precipitation deficit. This study suggested that remote sensing indices are very crucial to characterize drought patterns at a small “scale”, particularly in the assessment of drought impact on forest resources. Article Highlights Satellite indices like NDVI and VCI help to monitor the drought impact on forest cover. When severe drought is occurred along with precipitation deficit, the forest cover is decreased. Remote sensing data provide clear information for decision-making regarding drought's impact on the forest.

Climate change, vegetation dynamics, human activities and forest management influence the occurrence of fires. This study investigated the spatio-temporal variability of the Vegetation Condition Index (VCI) and its influence on fire occurrence in three different land use types in Mount Kenya Forest Reserve and National Park (MKFRNP): National Park (NP), Forest Stations (FS) and Farmlands (FL). The study used MODIS satellite data to obtain the Normalized Difference Vegetation Index (NDVI), the VCI, the number of fires and the burnt area. The specific objectives of this research were (i) to examine the spatio-temporal variability of VCI, fire occurrence and burnt area in MKFRNP from 2003 to 2018 and (ii) to explore the relationship between VCI, fire occurrence and burnt area in different areas of the MKFRNP (NP, FS and FL). The findings show that even though fires occur throughout the year in MKFRNP, most of the fires occur during dry seasons. The relationship between spatio-temporal fire occurrence and VCI distribution is different for each land use type. In the FL, the probability of fire ignition and the number of fires per month was more or less the same irrespective of the VCI because of the traditional use of fire as a land management tool. However, the probability of fire ignition and the number of fires per month is high in the NP and FS when the VCI is below 50% (drought), especially in the dry seasons, when and where the impact of meteorological conditions and climate have much more impact than human activities. In addition to the efforts already made by communities, KFS and KWS in the fire fighting and monitoring system, satellite data can be useful to acquire accurate and timely information on the VCI and the likely spatio-temporal occurrence of fires in order to be prepared in the most fire-prone periods and improve fire management, the planning of resources and fire suppression activities in MKFRNP. [ABSTRACT FROM AUTHOR]

Drought is a reoccurring slow-onset hazard event and has a tremendous impact on agricultural production and ecosystem health. Mainland Southeast Asia is a major rainfed crop-producing area of the world, and this region is increasingly vulnerable to drought hazards. However, the monitoring and characterization of agricultural and vegetative droughts in this region remains understudied. This paper presented the first comprehensive analysis of spatiotemporal characteristics and trends of agricultural and vegetative droughts across the region using the Vegetation Condition Index (VCI) based on the reconstructed MODIS-based vegetation time-series data between 2000 and 2021. This study also developed an approach for identifying severe drought years using annual extreme pixels. Results showed that vegetation-based drought characteristics and trends varied in space and time across the region. Central Myanmar suffered from the most frequent droughts (nearly 60%), but prolonged events were less common. By contrast, the Lower Mekong area suffered from frequent and prolonged drought conditions. Most of the recent severe droughts were observed in Cambodia, and this area is characterized by a drying trend. Regionally, the severe drought years were detected in 2000, 2004, 2005, 2010, 2016, 2019, and 2020, and they were commonly found in Central Myanmar, Thailand, and Cambodia. The analysis of drought years indicated that the temporal change pattern of drought has shifted from the northern regions (e.g., Central Myanmar) in 2010 to the Lower Mekong region in recent years. The findings of this study provide valuable information for drought early warning management and agricultural planning across mainland Southeast Asia.

Abstract In the Agriculture sector, the farmers need a reliable estimation for pre-harvest crop yield prediction to decide their import-export policies. The present work aims to assess the impact of remote sensing-based derived products with Climate data on the accuracy of a prediction model for the sugarcane yield. The regression method was used to develop an empirical model based on VCI, Historical Sugarcane Yield, and Climatic Parameters of 75 districts of six major sugar-producing states of India. The MOD13Q1 product of MODIS on Board Terra Satellite at 16-day intervals was accessed during the growing season of sugarcane crop with 36 meteorological parameters for experimentation. The accuracy of the model was evaluated using R2, Root Mean square Metric (RMSE), Mean Absolute Error (MAE), and mean square error (MSE). The preliminary results concluded that the proposed methodology achieved the highest accuracy with (R2 =0.95, MAE=5.18, MSE=34.5, RMSE=5.87). The conclusion of the study highlighted that the coefficient of determination can be improved significantly by incorporating maximum and minimum temperature parameters with Remote sensing derived vegetation indices for the sugarcane yield.

Drought is a complex natural hazard which can create significant impacts on society and environment. Given that this phenomenon varies across space and changes over time dependent on various factors (e.g., physical conditions and human activities), the available of spatiotemporal drought data enables a better monitoring and assessment of drought severity This study introduced the integrated multivariate drought index (iMDI) data, a new regional drought index, at 1 km spatial and monthly temporal resolutions for the Vietnamese Central Highlands over a 20-years period. The iMDI was developed recently which is a combination of vegetation condition index (VCI), the temperature condition index (TCI), and the evaporative stress index (ESI) based on the feature of scaling algorithms (i.e., normalisations and standardisation). The data were processed using the median values of MODIS time-series imagery obtained from the Google Earth Engine (GEE) platform. The iMDI datasets are available for monthly and annual drought monitoring between 2001 and 2020. Additionally, the datasets of VCI, TCI, and ESI were provided so that users can apply for their own purposes even though these data can directly obtain from GEE or other sources. Users, especially those without technical expertise, can reap the advantages of having open access to iDMI data. By doing so, they can reduce their expenses and the time required to process data. As such, this accessibility can promote the use of data for diverse applications, such as evaluating the impact of droughts on the environment and human activities and monitoring droughts regionally.

Drought is a multifaceted natural hazard that has an impact on various aspects like; agricultural, hydrological, ecological and socio-economic systems. Drought has been a predominant concern for farmers in Gamo Zone over the last decades; hence, monitoring drought is important for soil conservation, water planning and management to mitigate impacts on agriculture in the Zone. The vegetation indices normal difference vegetation index and vegetation condition index are popular since they are based only on satellite images. The present study attempts to monitoring spatiotemporal distribution of agricultural droughts and its association with land surface temperature, precipitation and soil moisture from the period 2000–2020 using remote sensing application method in Gamo Zone. The vegetation condition index (VCI) and Normal difference vegetation index (NDVI) were used to evaluate spatial and temporal distribution of agricultural drought in Gamo Zone. The Pearson correlation method was used to identify Normal difference vegetation index association with land surface temperature, soil moisture and precipitation. The results discovered that severe drought to very severe drought was identified in 2000, 2002, 2004, 2008, 2009 and 2015 with the area coverage 39.7%, 28.8%, 33.4%, 24.5%, 61.3% and 23.0%, respectively. Similarly, the findings show that slight to mild droughts have a great chance of occurrence in Gamo Zone. The data also demonstrate that normal difference vegetation index has a strong relationship with land surface temperature (R = − 0.95), precipitation (R = 0.65) and soil moisture (R = 0.85), indicating that normal difference vegetation index is more sensitive to land surface temperature than soil moisture and precipitation, although soil moisture has a positive relationship with precipitation (R = 0.60). Therefore, this study revealed that normal difference vegetation index and vegetation condition index indices are suitable for agricultural drought monitoring and they are strongly associated with precipitation, soil moisture and land surface temperature. This study shall be helpful for decision makers to take the necessary measures by considering the drought risk maps for early warning system and future drought management plans. [ABSTRACT FROM AUTHOR]

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

Evapotranspiration (ET) is crucial to the management of water supplies and the functioning of numerous terrestrial ecosystems. To understand and propose planning strategies for water-resource and crop management, it is critical to examine the geo-temporal patterns of ET in drought-prone areas such as the Upper Orange River Basin (UORB) in South Africa. While information on ET changes is computed from directly observed parameters, capturing it through remote sensing is inexpensive, consistent, and feasible at different space–time scales. Here, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived spectral indices within Google Earth Engine (GEE) to analyze and characterize patterns of ET over the UORB from 2003 to 2021, in association with various climatic parameters. Our results show spatially consistent ET patterns with the Vegetation Condition Index (VCI), with lower values in the west, increasing toward the eastern section of the basin, over the Lesotho highlands. We noted that the UORB faced significant variability in ET and VCI during pronounced drought episodes. The random forests (RF) model identified precipitation, temperature, Standardized Precipitation Index (SPI)-6, Palmer Drought Severity Index (PDSI), and VCI as variables of high importance for ET variability, while the wavelet analysis confirmed the coherence connectivity between these variables with periodicities ranging from eight to 32 months, suggesting a strong causal influence on ET, except for PDSI, that showed an erratic relationship. Based on the sequential Mann–Kendall test, we concluded that evapotranspiration has exhibited a statistically downward trend since 2011, which was particularly pronounced during the dry periods in 2015–2016, 2019, and 2021. Our study also confirmed the high capacity of the GEE and MODIS-derived indices in mapping consistent geo-temporal ET patterns. [ABSTRACT FROM AUTHOR]

Over time, drought affects all regions of Morocco, especially in the arid climate region, which has negative consequences on agriculture, economic and environmental. The present study aims to describe the intensity of drought in Morocco and more specifically their impact on the distribution of vegetation. Spatial and temporal remote sensing data are used to monitor drought in the Doukkala region of Morocco, using a set of Landsat images, including Landsat 5 (ETM), and Landsat 7 (ETM+) captured during the period 1964-2014. This was determined based on remote sensing parameters: temperature condition index (TCI), vegetation condition index (VCI) and vegetation health index (VHI). The Normalized Difference Vegetation Index (NDVI) was determined for the years 1966, 1984, 1988, 2000 2006 and 2009, in order to identify the vegetation categories and quantify the vegetation density in the Doukkala region. The NDVI obtained was analyzed using the SPI (Normalized Precipitation Index) based on the rainfall data of the years 1966, 1984, 1988, 2000 2006 and 2009. The results obtained showed that the correlation between NDVI and SPI indicated negative values or less than 1. The calculation of VHI showed low values (VHI < 40%) in one part of the studied area that indicate severe to extreme drought conditions, while in the other part the VHI showed high values (VHI > 40%), which mainly reflect favorable conditions for crop development (no drought). The results of this study can be used for monitoring and evaluation of the drought for sustainable management of the area. [ABSTRACT FROM AUTHOR]

The devastating socioeconomic impacts of recent droughts have intensified the need for improved drought monitoring in South Africa (SA). This study has shown that not all indices can be universally applicable at all regions worldwide, and there is no single index that can represent all aspects of droughts. The aim of this study was to review the performance and applicability of the Palmer drought severity index (PDSI), surface water supply index (SWSI), vegetation condition index (VCI), standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), standardised streamflow index (SSI), standardised groundwater index (SGI), and GRACE (Gravity Recovery and Climate Experiment)-based drought indices in SA and provide guidelines for selecting feasible candidates for integrated drought monitoring. The review is based on the '2016 World Meteorological Organisation (WMO) Handbook of Drought Indicators and Indices' guidelines. The PDSI and SWSI are not feasible in SA, mainly because they are relatively complex to compute and interpret and cannot use readily available and accessible data. Combining the SPI, SPEI, VCI, SSI, and SGI using multi-index or hybrid methods is recommended. Hence, with best fitting probability distribution functions (PDFs) used, and with an informed choice between parametric and non-parametric approaches, this combination has the potential for integrated drought monitoring. Due to the scarcity of groundwater data, investigations on the use of GRACE-based groundwater drought indices must be carried out. These findings may contribute to improved drought early warning and monitoring in SA. [ABSTRACT FROM AUTHOR]

Deficiency in vitamin D (VitD), a lipid-soluble vitamin and steroid hormone, affects approximately 24% to 40% of the population of the Western world. In addition to its well-documented effects on the musculoskeletal system, VitD also contributes importantly to the promotion and preservation of cardiovascular health via modulating the immune and inflammatory functions and regulating cell proliferation and migration, endothelial function, renin expression, and extracellular matrix homeostasis. This brief overview focuses on the cardiovascular and cerebrovascular effects of VitD and the cellular, molecular, and functional changes that occur in the circulatory system in VitD deficiency (VDD). It explores the links among VDD and adverse vascular remodeling, endothelial dysfunction, vascular inflammation, and increased risk for cardiovascular and cerebrovascular diseases. Improved understanding of the complex role of VDD in the pathogenesis of atherosclerotic cardiovascular diseases, stroke, and vascular cognitive impairment is crucial for all cardiologists, dietitians, and geriatricians, as VDD presents an easy target for intervention. [ABSTRACT FROM AUTHOR]

Monitoring the agricultural drought of paddy rice fields is a crucial aspect of preparing for proper action in maintaining food security in Indonesia. The Aceh Province is one of Indonesia’s national rice production centers, especially Aceh Besar Regency; it includes three central districts; Indrapuri, Kuta Cot Glie, and Seulimeum. Satellite-Sentinel 2A data have been tested to monitor the drought levels of around 2,803 Ha in the three districts in this study. This study aimed to determine the drought level in Indrapuri, Kuta Cot Glie, and Seulimeum districts, Aceh Besar Regency’s paddy rice fields using Sentinel-2A data imagery. The vegetation conditions index (VCI) of Sentinel-2 data was utilized to identify a vegetative drought level in the area for the 2018, 2019, 2020, 2021, and 2022 growing seasons. The vegetation inertia index is derived from the Normalized Difference Vegetation Index (NDVI). The results show that the VCI looked volatile, but the trendline increased by four percent, from 92.56 in July 2019 to 96.08 in July 2021. Most areas on the dates investigated found that the no drought category was still dominant. The designated data analyzed found that the June 2022 data tend to be distributed to the drought in extreme, severe, moderate, and mild increases compared to the previous data investigated. This figure shows an increasing drought in the study area, and the average drought index is in the category of mild drought. In addition, there has been a trendline decline in the value of NDVI in recent years, causing agricultural land for paddy rice fields to be slightly vulnerable to drought. [ABSTRACT FROM AUTHOR]

Agriculture drought is a recurrent phenomenon that affects most parts of Sahel regions. With the advent of various satellite data, drought risk assessment has become more accessible, but the challenge lies in choosing a well-defined time scale. This study aims to contribute to agricultural drought monitoring, based on the calculation of composite indices using the MODIS sensor in the Senegalese Groundnut basin. The approach is based upon vegetation condition and health (VCI, VHI) and temperature (TCI) indices. Subsequently, those indices were evaluated based upon crop yield data, and a Holt-Winter forecasting was performed to determine the driest month in 2020-2021. Here we show that all investigated communes are sensitive to drought, especially Keur-Samba Gueye (in July, September, and October). Based on its significant correlation with VCI and TCI (R²>0.57), VHI was proven to be a reliable water stress indicator. Our approach demonstrates the Groundnut basin’s vulnerability to climate change. [ABSTRACT FROM AUTHOR]

Impairment of moment-to-moment adjustment of cerebral blood flow (CBF) to the increased oxygen and energy requirements of active brain regions via neurovascular coupling (NVC) contributes to the genesis of age-related cognitive impairment. Aging is associated with marked deficiency in the vasoprotective hormone insulin-like growth factor-1 (IGF-1). Preclinical studies on animal models of aging suggest that circulating IGF-1 deficiency is causally linked to impairment of NVC responses. The present study was designed to test the hypotheses that decreases in circulating IGF-1 levels in older adults also predict the magnitude of age-related decline of NVC responses. In a single-center cross-sectional study, we enrolled healthy young (n = 31, 11 female, 20 male, mean age: 28.4 + / − 4.2 years) and aged volunteers (n = 32, 18 female, 14 male, mean age: 67.9 + / − 4.1 years). Serum IGF-1 level, basal CBF (phase contrast magnetic resonance imaging (MRI)), and NVC responses during the trail making task (with transcranial Doppler sonography) were assessed. We found that circulating IGF-1 levels were significantly decreased with age and associated with decreased basal CBF. Age-related decline in IGF-1 levels predicted the magnitude of age-related decline in NVC responses. In conclusion, our study provides additional evidence in support of the concept that age-related circulating IGF-1 deficiency contributes to neurovascular aging, impairing CBF and functional hyperemia in older adults. [ABSTRACT FROM AUTHOR]

The present study was conducted in the Saurashtra region of Gujarat to demonstrate the development and validation of location and crop-specific composite drought index (CDI) using a linear combination of three parameters including meteorological drought index, vegetation drought index and inverse of maximum consecutive dry days % for major Kharif crops of the region i.e. cotton and groundnut. The performance of nine drought indices including six meteorological and three remote sensing-based vegetation indices was evaluated in terms of correlation with district scale crop yields. The district-wise expressions of CDI were developed by assigning principal component analysis (PCA) based weights to parameters. Standardized Precipitation Evapotranspiration Index (SPEI)/Reconnaissance Drought Index (RDI) among meteorological indices and NDVI Anomaly Index (NDVIA)/Vegetation Condition Index (VCI) among vegetation indices were found suitable for generating district specific CDI expressions. The developed CDI showed higher correlation with Kharif Cotton and Groundnut crop yields as compared to various meteorological as well as vegetation indices used in the study and effectively quantified major historic agricultural droughts. The average correlation coefficients of developed CDI with cotton and groundnut yields were 0.71 and 0.77 respectively. The correlations of CDI and crop yields for all CDI expression were highly significant with p<0.01. The method developed in the study will be useful to generate crop and region-specific multi-scalar drought indices by the amalgamation of multiple drought indices for assessing crop production losses. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the spatiotemporal dynamics of vegetation and its association with climate variables in the Borana rangelands of southern Ethiopia. Moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, namely, MODIS/TERRA, MOD09Q1 16-day L3 Global 250 m Version 6 from National Aeronautics and Space Administration (NASA) United States Geological Survey (USGS), and climate data from 2000 to 2018 obtained from the National Meteorological Agency (NMA) of Ethiopia are used to determine the vegetation dynamics both at annual and seasonal timescales. The results indicate that the NDVI varies between .31 and .41, and the study area is dominated by sparse vegetation, especially shrubs and grasslands. The Borana rangelands were previously dominated by open grasslands, perennial herbaceous and woody vegetations, and some encroaching woody vegetations. The NDVI revealed poorer vegetation growth than the vegetation condition index (VCI) between 2000 and 2018, although variations were observed spatiotemporally. Rainfall was more highly variable both at annual and seasonal scales than the temperature during the studied years. The climate variables significantly determined the condition of vegetation, particularly during the growing seasons compared to other periods. A significant strong positive correlation was observed between the NDVI, VCI, and climate variables during the main (Belg) and short (Meher) rainy seasons in the Borana rangelands. The results of this study are beneficial for the development of proper intervention policies and provide information for sustainable rangeland management.

The present study was conducted in the Saurashtra region of Gujarat to demonstrate the development and validation of location and crop-specific composite drought index (CDI) using a linear combination of three parameters including meteorological drought index, vegetation drought index and inverse of maximum consecutive dry days%for major Kharif crops of the region i.e. cotton and groundnut. The performance of nine drought indices including six meteorological and three remote sensing-based vegetation indices wasevaluated in terms of correlation with district scale crop yields.The district-wise expressions of CDI were developed by assigning principal component analysis (PCA) based weights to parameters.Standardized Precipitation Evapotranspiration Index (SPEI)/ Reconnaissance Drought Index (RDI) among meteorological indices and NDVI Anomaly Index (NDVIA)/ Vegetation Condition Index (VCI) among vegetation indices were found suitable for generating district specific CDI expressions. The developed CDI showed higher correlation with Kharif cotton and Groundnut crop yields as compared to various meteorological as well as vegetation indices used in the study and effectively quantified major historic agricultural droughts.The average correlation coefficients of developed CDI with cotton and groundnut yields were 0.71 and 0.77 respectively. The correlations of CDI and crop yields for all CDI expression were highly significant with p

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting models. However, it is still challenging to identify the most critical features from a dataset. Although either feature selection (FS) or feature extraction (FX) techniques have been employed, no research compares their performances and, more importantly, the benefits of combining both methods. Therefore, this paper proposes a framework that uses non-feature reduction (All-F) as a baseline to investigate the performance of FS, FX, and a combination of both (FSX). The case study employs the vegetation condition index (VCI)/temperature condition index (TCI) to develop 21 rice yield forecasting models for eight sub-regions in Vietnam based on ML methods, namely linear, support vector machine (SVM), decision tree (Tree), artificial neural network (ANN), and Ensemble. The results reveal that FSX takes full advantage of the FS and FX, leading FSX-based models to perform the best in 18 out of 21 models, while 2 (1) for FS-based (FX-based) models. These FXS-, FS-, and FX-based models improve All-F-based models at an average level of 21% and up to 60% in terms of RMSE. Furthermore, 21 of the best models are developed based on Ensemble (13 models), Tree (6 models), linear (1 model), and ANN (1 model). These findings highlight the significant role of FS, FX, and specially FSX coupled with a wide range of ML algorithms (especially Ensemble) for enhancing the accuracy of predicting crop yield. [ABSTRACT FROM AUTHOR]