Assessment Of Drought Severity Research Articles

A combined drought index for monitoring and assessment of drought severity over India by integrating CHIRPS, MODIS and GRACE data

A combined drought index for monitoring and assessment of drought severity over India by integrating CHIRPS, MODIS and GRACE data

Harnessing SMAP satellite soil moisture product to optimize soil properties to improve water resource management for agriculture

Estimation of accurate soil physical and hydraulic properties are of prime importance for the management of water resources in agriculture-dominant regions. This study introduces a simplified framework for estimating soil physical and hydraulic properties crucial for managing agricultural water resources. The developed framework optimizes soil properties for the Regional Hydrological Extremes Assessment System (RHEAS) to enhance the performance of its core hydrological model, Variable Infiltration Capacity (VIC). These soil properties were optimized using six years (2015–2021) of satellite soil moisture observations from NASA’s Soil Moisture Active Passive (SMAP) mission with a modified Shuffled Complex Evolution (SCE-UA) optimization algorithm. A total of three most sensitive soil properties that control model soil moisture simulations, such as Ksat (Saturated hydraulic conductivity), expt (exponent parameter in Campbell’s equation for hydraulic conductivity), and bd (Bulk density) were optimized for the Lower Mekong River (LMR) basin. To better assess the impact of optimized soil properties, streamflow simulation as well as agricultural drought severity assessment, were estimated using the RHEAS framework’s VIC Routing module and Soil Moisture Deficit Index (SMDI) module, respectively. The streamflow simulation involved four approaches: an initial open-loop setup, one optimized with SMAP soil moisture data (SMAP), another optimized with actual streamflow data (Runoff), and a final one combining the previous two datasets (SMAP_Runoff). Switching from the initial setup to the SMAP-optimized model increased the Nash-Sutcliffe Efficiency (NSE) by 56.4 % and upgrading from the streamflow-optimized to the combined data model raised the NSE by 21.9 %. This showcases the benefits of optimizing soil properties for more accurate simulations. Furthermore, the optimized model accurately represented the severity and extent of historical agricultural droughts, aligning with regional drought reports of LMR basin. This framework offers a valuable tool for hydrological modeling and drought management, particularly in data-scarce and agriculture-intensive regions, informing agricultural water resource management, irrigation decision-making, and food security initiatives within the LMR basin and beyond.

Early Drought Detection in Maize Using UAV Images and YOLOv8+

The escalating global climate change significantly impacts the yield and quality of maize, a vital staple crop worldwide, especially during seedling stage droughts. Traditional detection methods are limited by their single-scenario approach, requiring substantial human labor and time, and lack accuracy in the real-time monitoring and precise assessment of drought severity. In this study, a novel early drought detection method for maize based on unmanned aerial vehicle (UAV) images and Yolov8+ is proposed. In the Backbone section, the C2F-Conv module is adopted to reduce model parameters and deployment costs, while incorporating the CA attention mechanism module to effectively capture tiny feature information in the images. The Neck section utilizes the BiFPN fusion architecture and spatial attention mechanism to enhance the model’s ability to recognize small and occluded targets. The Head section introduces an additional 10 × 10 output, integrates loss functions, and enhances accuracy by 1.46%, reduces training time by 30.2%, and improves robustness. The experimental results demonstrate that the improved Yolov8+ model achieves precision and recall rates of approximately 90.6% and 88.7%, respectively. The mAP@50 and mAP@50:95 reach 89.16% and 71.14%, respectively, representing respective increases of 3.9% and 3.3% compared to the original Yolov8. The UAV image detection speed of the model is up to 24.63 ms, with a model size of 13.76 MB, optimized by 31.6% and 28.8% compared to the original model, respectively. In comparison with the Yolov8, Yolov7, and Yolo5s models, the proposed method exhibits varying degrees of superiority in mAP@50, mAP@50:95, and other metrics, utilizing drone imagery and deep learning techniques to truly propel agricultural modernization.

Spatial Insights into Drought Severity: Multi-Index Assessment in Małopolska, Poland, via Satellite Observations

This study focuses on the assessment of drought severity, employing a comparative analysis between the normalized multi-band drought index (NMDI; calculated using Sentinel-2 imagery) and the combined drought indicator (CDI). The research aims to pinpoint the most accurate and reliable remote sensing techniques, which reflect ground-based measurements, thereby enhancing the precision of drought monitoring systems. By investigating the specific area of Małopolska, the study not only contributes to the global knowledge base on drought assessment methodologies but also addresses the regional needs for improved drought management practices. Through an exhaustive analysis correlating satellite-derived indices with in situ meteorological data, this research elucidates the potential of integrating NMDI and CDI for a comprehensive understanding of drought dynamics in Małopolska. In particular, the indices depict different dynamics of drought levels, as well as the location of regions more prone to its occurrence. The findings are poised to advance drought monitoring capabilities, offering significant insights for agricultural sustainability and water resource management in the region.

Drought projection using GCM & statistical downscaling technique: A case study of Sirohi District

The natural hazard that has the greatest impact on people and is considered to be the most complicated but least understood is drought. Thus, the projection of drought is extremely necessary to be prepared against possible damage. There are many indices available for the assessment of drought severity, but the result of SPEI is more reliable than any other indices. The objective of the study was to predict drought in the Sirohi district (India) using different meteorological variables like temperature, precipitation, Potential Evapotranspiration (PET), etc. For the projection of drought, the statistical downscaling technique was applied by using observed data and Global Climate Model (GCM) data. Drought years in the period 2020–2099 were identified and the probability of occurrence of the different drought classes has been evaluated. Finally, the relationship between El Niño and droughts was investigated using Sea Surface Temperature (SST) anomaly data. Sirohi district has a 75.0 % chance of drought during El Niño years. The model can predict droughts with a 60 % accuracy using the statistical downscaling technique. From 2019 to 2099, there are 13.75 % chances of moderate droughts and 12.5 % chances of mild droughts. According to this research, there will not be any severe or extreme droughts in the future. This study concludes that the projection of drought can be done accurately by statistical downscaling techniques using GCM data. Results showed that major droughts in the South-West region of Rajasthan occurred during El Niño years. This will help the policymakers to prepare policies, drought contingency plans, and rules according to the drought conditions of the particular region.

Scientometric analysis-based review of drought indices for assessment and monitoring of drought

The major cause of a drought is due to the variations in the climatic conditions and the anthropogenic effects. Due to climate change and inadequate rainfall, the moisture in soil gets affected which reduces the supply of water to the vegetation and also to the groundwater resources. The onset of drought is difficult to predict but it can be monitored with the help of various influential parameters. Suitable drought resilience techniques should be adopted to recover the loss and mitigate the effect of drought in a region. Proper monitoring and management of drought mitigation strategies should be followed to prevent the occurrence of such a kind of disaster. In this study, the authors provided a scientometric analysis and a wide-ranging review on drought indices. The scientometric analysis using VOSviewer showcases the current trend in the research using the most frequently used keywords, most cited articles and authors, and the countries that contributed to the field of drought. A total of 175 articles were identified from various databases and initial screening was done to select the full text articles. The eligible full text articles were selected after excluding the least prominent articles. Finally, 45 articles were included for the final exclusive review process. The review article provides an insight on drought categorization and drought indices derived to determine the severity of drought. The best suited index for drought severity assessment is very hard to identify since it requires more time. The drought indices should be selected in such a manner, that it effectively measures and monitors the severity of drought. A widespread, informative examination of drought indices would benefit the researchers worldwide to reduce their time spent on each article. The aim of this review article is to review the scientific articles regarding drought indices and provide the best solution to derive the drought severity conditions.

Block-level Drought Assessment using Rainfall and Remote sensing Data

<p>Drought is a disastrous natural phenomenon. It is generally viewed as a sustained and regionally extensive occurrence of below average natural water availability either in the form of rainfall, river runoff or groundwater. Drought is defined based on different factors and generally assessed in meteorological, hydrological and agricultural contexts. Efficient management of drought over an area or catchment depends mainly on its correct assessment. In this study, Meteorological drought severity assessment was carried out based on the frequency analysis of IMD method producing Meteorological Drought Severity Index (MDSI). Agricultural drought severity assessment was carried out by developing an Agricultural Drought Severity Index (ADSI), which involves crop land and fallow land estimation, delineated from remote sensing data using NDVI analysis. The methodology was applied for Samba season (major crop season) of Palar Sub-basin encompassing Vellore (a chronic drought prone district), located in the North-East part of Tamil Nadu State, India. Block-wise drought severity maps were generated using geospatial environment to understand the drought severity status of the area. It is observed that 70% of the blocks in the study area were affected by moderate and severe droughts, which needs drought proofing measures.</p>

A global drought monitoring system and dataset based on ERA5 reanalysis: A focus on crop‐growing regions

AbstractDrought monitoring systems are real‐time information systems focused on drought severity data. They are useful for determining the drought onset and development and defining the spatial extent of drought at any time. Effective drought monitoring requires databases with high spatial and temporal resolution and large spatial and temporal coverage. Recent reanalysis datasets meet these requirements and offer an excellent alternative to observational data. In addition, reanalysis data allow better quantification of some variables that affect drought severity and are more seldom observed. This study presents a global drought dataset and a monitoring system based on the Standardized Precipitation Evapotranspiration Index (SPEI) and ERA5 reanalysis data. Computation of the atmospheric evaporative demand for the SPEI follows the FAO‐56 Penman‐Monteith equation. The system is updated weekly, providing near real‐time information at a 0.5° spatial resolution and global coverage. It also contains a historical dataset with the values of the SPEI at different time scales since January 1979. The drought monitoring system includes the assessment of drought severity for dominant crop‐growing areas. A comparison between SPEI computed from the ERA5 and CRU datasets shows generally good spatial and temporal agreement, albeit with some important differences originating mainly from the different spatial patterns of SPEI anomalies, as well as from employing long‐term climate trends for different regions worldwide. The results show that the ERA5 dataset offers robust results and supports its use for drought monitoring. The new system and dataset are publicly available at the link https://global‐drought‐crops.csic.es/.

Assessment of Drought Severity and Their Spatio-Temporal Variations in the Hyper Arid Regions of Kingdom of Saudi Arabia: A Case Study from Al-Lith and Khafji Watersheds

The goal of this study is to calculate meteorological drought using the Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) for the Al-Lith and Khafji basins of the Kingdom of Saudi Arabia (KSA) from 2001 to 2020. The in situ (rain gauges, RGs) and Integrated Multi-satellite Retrievals for GPM (IMERG) data are used in the current study. The meteorological drought is monitored across the AL-Lith and Khafji watersheds. The climate of the Khafji watershed is like the climate of Al-Lith to some extent. Still, due to complex terrain, Al-Lith receives relatively high precipitation and has a higher average temperature than the Khafji watershed. Results show that the total drought periods observed are 166 and 139 months based on SPEI and SPI on a multiple time scale (1, 3, 6, and 12 months) in the Al-Lith watershed, respectively. While, based on SPEI and SPI, the Khafji watershed experienced a drought of 129 and 72 months, respectively. This finding indicates that the SPEI-calculated drought is more severe and persistent in both watersheds than the SPI-calculated drought. Additionally, the correlation coefficient (CC) between SPI and SPEI is investigated; a very low correlation is observed at a smaller scale. CC values of 0.86 and 0.93 for Al-Lith and 0.61 and 0.79 for the Khafji watershed are observed between SPEI-1/SPI-1 and SPEI-3/SPI-3. However, the correlation is significant at high temporal scales, i.e., 6 and 12 months, with CC values of 0.95 and 0.98 for Al-Lith and 0.86 to 0.94 for the Khafji watershed. Overall, the study compared the performance of IMERG with RGs to monitor meteorological drought, and IMERG performed well across both watersheds during the study period. Therefore, the current study recommends the application of IMERG for drought monitoring across data-scarce regions of KSA. Furthermore, SPEI estimates a more severe and long-lasting drought than SPI because of the temperature factor it considers.

Drought assessment has been outpaced by climate change: empirical arguments for a paradigm shift

Despite the acceleration of climate change, erroneous assumptions of climate stationarity are still inculcated in the management of water resources in the United States (US). The US system for drought detection, which triggers billions of dollars in emergency resources, adheres to this assumption with preference towards 60-year (or longer) record lengths for drought characterization. Using observed data from 1,934 Global Historical Climate Network (GHCN) sites across the US, we show that conclusions based on long climate records can substantially bias assessment of drought severity. Bias emerges by assuming that conditions from the early and mid 20th century are as likely to occur in today’s climate. Numerical simulations reveal that drought assessment error is relatively low with limited climatology lengths (~30 year) and that error increases with longer record lengths where climate is changing rapidly. We assert that non-stationarity in climate must be accounted for in contemporary assessments to more accurately portray present drought risk.

Standardized precipitation index (SPI) for drought severity assessment of Almora, Uttarakhand, India

Standardized precipitation index (SPI) for drought severity assessment of Almora, Uttarakhand, India

Drought severity assessment in south Bihar Agro-Climatic zone

The present study has been focused to recognize and quantify the drought condition in Bihar Agro-climatic Zone-III, which falls in the drought-prone region of Bihar. The zone covers the whole South Bihar region, comprising of seventeen districts. The drought severity assessment in the study area has been carried out by two methods i.e., the rainfall departure analysis and the probability analysis. The research work has been carried out at a grid level consisting of fifty-five grids. The analysis of work has been further evaluated district-wise and in addition the result has been interpreted for the two agro-climatic zones {zone-iii (a) and zone-iii (b)}. For decades, the area is under the influence of frequent droughts due to uneven distribution of rainfall. The grid forty-seven of Bhagalpur district having maximum drought frequency of once in 1.87 years and also faces a maximum number of sixty-two drought years in the period of 116 years, as resulted from rainfall departure method. The maximum annual rainfall departure of -82.99% was observed for the grid-32 (G-32) of Banka district, during the year 1978-79, with a return period of once in 2.7 years. The probability analysis concludes that the areas influenced by grids 37,42,45, and 47 are drought-prone (probability of 75% mean rainfall being less than 80%). The departure as well as the probability analysis indicates that the grid-47 as drought-prone. This shows the compatibility of the two methods in meteorological drought assessment in general and particularly the most drought severe condition of the grid-47.

Drought severity assessment using automated land surface temperature retrieval technique

Drought severity assessment using automated land surface temperature retrieval technique

Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand

The Lam Phaniang River Basin is one of the areas in Northeast Thailand that experiences persistent drought almost every year. Therefore, this study was focused on the assessment of drought severity and vulnerability in the Lam Phaniang River Basin. The evaluation of drought severity was based on the Drought Hazard Index (DHI), which was derived from the Standardized Precipitation-Evapotranspiration Index (SPEI) calculated for 3-month (short-term), 12-month (intermediate-term), and 24-month (long-term) periods. Drought vulnerability was assessed by the Drought Vulnerability Index (DVI), which relied on water shortage, water demand, and runoff calculated from the WEAP model, and the Gross Provincial Product (GPP) data. A drought risk map was generated by multiplying the DHI and DVI indices, and the drought risk level was then defined afterwards. The CNRM-CM5, EC-EARTH, and NorESM1-M global climate simulations, and the TerrSet software were used to evaluate the potential impacts of future climate under RCPs 4.5 and 8.5, and land use during 2021–2100, respectively. The main findings compared to baseline (2000–2017) revealed that the average results of future rainfall, and maximum and minimum temperatures were expected to increase by 1.41 mm, and 0.015 °C/year and 0.019 °C/year, respectively, under RCP 4.5 and by 2.72 mm, and 0.034 °C/year and 0.044 °C/year, respectively, under RCP 8.5. During 2061–2080 under RCP 8.5, the future annual water demand and water shortage were projected to decrease by a maximum of 31.81% and 51.61%, respectively. Obviously, in the Lam Phaniang River Basin, the upper and lower parts were mainly dominated by low and moderate drought risk levels at all time scales under RCPs 4.5 and 8.5. Focusing on the central part, from 2021–2040, a very high risk of intermediate- and long-term droughts under RCPs 4.5 and 8.5 dominated, and occurred under RCP 8.5 from 2041–2060. From 2061 to 2080, at all time scales, the highest risk was identified under RCP 4.5, while low and moderate levels were found under RCP 8.5. From 2081–2100, the central region was found to be at low and moderate risk at all time scales under RCPs 4.5 and 8.5. Eventually, the obtained findings will enable stakeholders to formulate better proactive drought monitoring, so that preparedness, adaptation, and resilience to droughts can be strengthened.

PCA driven watershed prioritization based on runoff modeling and drought severity assessment in parts of Koel river basin, Jharkhand (India)

Abstract Global warming influencing regional climate is playing a significant role in triggering recurrent drought. The current study demonstrates a PCA (Principal Component Analysis) driven watershed prioritization in a part of Koel river basin by runoff computation during monsoon season along with assessment of Vegetation Health Index (VHI) derived from MODIS satellite data during the period from 2000 to 2017. Koel river catchment area of 7,261 sq km was divided into 82 sub-watersheds based on drainage networks derived from a Survey of India (SOI) topographical map at scale 1: 50,000. High resolution satellite image of Sentinel-2 was used to prepare a land use land cover map. Soil conservation service curve number method (SCS CN) was used to estimate runoff. The result obtained from runoff estimation of 82 sub watersheds shows high runoff (50 to 60% of rainfall) with 290,000 m3 total runoff volume in the upper and middle parts of the catchment dominated by agricultural/fallow and barren lands, whereas low runoff was estimated (20 to 30%) with 29,467 m3 in the lower catchments where a large area is covered with forests. The value of satellite based VHI ranges between 23 to 53 with major parts of the area exhibiting values less than 30, reflecting poor vegetation health. Most of the sub-watersheds in parts of Ranchi, Lohardaga, Gumla and Khunti districts experienced high total runoff, with poor vegetation health index reflecting more proneness to drought. Watershed prioritization was done based on correlation among four parameters viz., rainfall, drought zones, direct runoff and total runoff through PCA. Strong correlation between total runoff volume and drought areas was used for watershed prioritization, which indicated 42 sub-watersheds (4,703 sq km) in the upper catchment required high prioritization. The outcomes of the study would help proper planning of water resources and soil moisture management to overcome the recurrent drought conditions at watershed level.

Assessment of Drought Severity in Khar Yamaat Nature Reserve Using Satellite Derived Drought Indices

Assessment of Drought Severity in Khar Yamaat Nature Reserve Using Satellite Derived Drought Indices

Monitoring the extreme drought in the middle and lower reaches of the Yangtze River in 2019 from GRACE-FO satellites

In the summer and autumn of 2019, an extreme drought lasting 0.5 year in the middle and lower reaches of the Yangtze River had a major impact on local agricultural production and the ecological environment. Traditionally, drought severity assessment relies on drought-related parameters observed by in situ measurements, such as precipitation, evapotranspiration, runoff, and soil moisture. In the past two decades, GRACE/GRACE-FO gravity satellite data have been widely used to estimate global and regional terrestrial water storage anomalies (TWSA), but there are still limited studies on the quantitative assessment of the severity of drought using GRACE/GRACE-FO data. The objective of this study was to analyse the drought severity in the middle and lower reaches of the Yangtze River in the summer and autumn of 2019 using GRACE/GRACE-FO data and to compare our estimates with the traditional drought index. The GRACE-based drought severity index (GRACE-DSI) is a monthly global drought severity index based on the TWSA from the GRACE/GRACE-FO (GRACE-TWSA). In this study, we used spherical harmonic (SH) products from the GRACE/GRACE-FO to obtain the TWSA. GRACE data processing includes replacing the degree-1 and C 20 , removing the 2004–2009 mean SH, filtering to reduce correlated errors, Gaussian smoothing, calculating the equivalent water layer thickness on 1° ´ 1° grids, and correcting leakage errors with scale factors. We then calculated the GRACE-DSI based on the GRACE-TWSA with the trend and seasonal cycles removed. To ensure consistency, we processed the standardised precipitation evapotranspiration index (SPEI) similar to the GRACE data and standardised the new SPEI as the SPEI-Z. Our results indicated that the GRACE-DSI can successfully monitor the occurrence and development of drought in the middle and lower reaches of the Yangtze River in spatiotemporal domains. The GRACE/GRACE-FO successfully detected the extreme drought event that occurred in the east of Hubei, north of Jiangxi, and south of Anhui during the summer and autumn of 2019. In July, the arid region was located in the north of the Yangtze River basin, including Henan and Shandong provinces. In August, the drought intensified and moved south to the middle and lower reaches of the Yangtze River, reaching a maximum in September, especially in Hubei, Jiangxi, and Anhui provinces. The drought was weakened in October, but intensified again in November and December and expanded through the middle and lower reaches of the Yangtze River to the coastal regions, such as Fujian and Guangzhou provinces. The mean time series of the GRACE-DSI over Hubei, Jiangxi, Anhui, and Hunan provinces showed good agreement with the SPEI-Z in both amplitude and phase on the six month time scale (SPEI06-Z) with a correlation coefficient of 0.84. The time series of the GRACE-DSI and SPEI06-Z both clearly showed the extreme drought events that occurred in Hubei, Jiangxi, Anhui, and Hunan provinces during the spring and summer of 2011 and the summer and autumn of 2019. Our results also indicate that the GRACE-FO has the same drought monitoring capability as the GRACE.

A drought monitoring framework for data-scarce regions

Abstract Drought monitoring is a critical activity for drought risk management; however, the lack of ground-based observations of climatological and hydrological variables in many regions of the world hinders an adequate follow-up and investigation of this phenomenon. This paper introduces a transparent framework for monitoring the spatio-temporal distribution of drought hazard based on uni- and multivariate standardized drought indices that use reanalysis datasets of hydrological variables available freely and globally. In the case study of the 2015–2017 East-Southwest drought in Mexico, the introduced framework successfully detected the spatial and temporal patterns of drought conditions, even in regions where a benchmark drought monitoring system failed to detect deficits. In addition, the ability of the introduced framework to detect drought impacts on the annual agricultural maize production in Mexico was evaluated using data of 1980–2018, yielding scores of the false alarm ratio =0.32, the probability of detection = 0.71, and the proportion correct = 0.68 for the analysis at the national scale. Currently, the framework provides a significant extension to the capabilities for national drought monitoring, and it is being used by the Mexican water authority in the decision-making process related to drought severity assessment.

Multicomponent Satellite Assessment of Drought Severity in the Contiguous United States From 2002 to 2017 Using AMSR‐E and AMSR2

AbstractThe Advanced Microwave Scanning Radiometer for the Earth Observing System and Advanced Microwave Scanning Radiometer 2 sensors (AMSR) have provided multifrequency microwave measurements of the global terrestrial water cycle since 2002. A new AMSR surface wetness index (ASWI) was developed by analyzing the near‐surface atmospheric vapor pressure deficit (VPD), surface volumetric soil moisture (VSM), and land surface fractional open water (FW) time series from an established AMSR Land Parameter Data Record (LPDR). The ASWI allows for multicomponent and independent satellite assessments of near‐surface drought conditions by exploiting the weighted anomalies of VPD, VSM, and FW. Comparisons between ASWI and more traditional drought metrics, including the Palmer moisture anomaly index (PDSI‐Z) and the U.S. Drought Monitor, showed generally consistent classifications of drought severity for three major droughts over the Contiguous United States since 2002. The AWSI showed moderate (0.3 ≤ R ≤ 0.7 for 56% of area) to strong (R > 0.7 for 29% of area) correlations with the PDSI‐Z during the summer months (June–August) from 2002 to 2017. ASWI and PDSI‐Z differences were attributed to AMSR retrieval uncertainties and the different aspects of drought represented by the indices. Comparisons between ASWI and the Gravity Recovery and Climate Experiment drought severity index (GRACE‐DSI) showed strong correspondence (R = 0.61) in regions where possible long‐term total water storage changes occurred. The sole reliance of the ASWI on satellite microwave remote sensing and continuing AMSR2 operations enables effective global monitoring of drought conditions while providing new information on the atmosphere, soil, and surface water components of drought.

Spatial assessment of drought severity in Cape Town area, South Africa

In recent decades, drought has been identified as part of the several regular climate-related hazards happening in many African countries including South Africa, often with devastating implications on food security. Studies have shown that the earth temperature has increased over the recent years which can trigger drought occurrences and other climate-related hazards. Drought occurrence is principally a climate-related event that cannot be totally effaced though it can be managed. This study is aimed at appraising drought severity in Cape Town area, South Africa using Geographic Information System (GIS) and remotely sensed data obtained from United States Geological Survey (USGS) database between the years 2014 and 2018. The study revealed that the land use dynamics witnessed drastic changes where vegetation, water body and bare surface decreased from 2095 to 141 km2, 616 to 167 km2 and 2337 to 1381km2 respectively while built up and sparse vegetation increased from 5301 to 8191 km2 and 7382–7854 km2 during the period. Vegetation health and drought severity of the study area was assessed using vegetation indices and Normalized Drought Dryness Index (NDDI). The result reveals that Normalized Difference Water Index (NDWI) and other vegetation indices decreased considerably more in recent years (2017 and 2018) which might have triggered drought events during the period compared to the other years (2014–2016). Furthermore, the spatial trend of land surface temperature (LST) and NDDI increased in recent years with NDDI values ranging between moderate drought and severe drought threshold. Consequently, if the increment persists, it can lead to adverse impacts such as food insecurity, land degradation and environmental health deterioration. Evidently, this study reveals the current state of vegetation health regarding drought severity in the area using remotely sensed data.