Abstract
The contribution of this paper is to assess the drought patterns using the Land Surface Temperature (LST) estimation index and examine the correlation between the Normalized Difference Vegetation Index (NDVI). The main objective was to evaluate the spatiotemporal variation in agricultural drought patterns and severity in order to estimate and measure climate variability. According to the study's findings, the region experienced mild to severe meteorological drought periods 15–18 times over the study period. The majority of these periods (62.72%) were classified as mild drought (unusual dry circumstances), which usually only showed a slight departure from the distribution of rainfall that is close to normal. According to the findings, cropping seasons from 2013 to 2022 saw an increase in agricultural dryness and a decrease in grain yield, with varying degrees of severity in different geographic locations. Based on the result, the major difficulties and challenges identified were the shortage of drinking water, and impacts on air quality, and food and nutrition. Accordingly, agricultural drought risk mapping can be utilized to mitigate the risk associated with drought on agricultural productivity and guide decision-making processes in drought monitoring.
HIGHLIGHTS
Droughts, which are typically associated with high temperatures and low moisture levels, have become increasingly common and critical concerns for all parties involved.
The unique characterizations for the vegetative area which has an effect on many socioeconomics provided by the remote-sensing-based indices for evaluating the state of vegetation include biomass and growth status.
ABBREVIATIONS AND ACRONYMS
INTRODUCTION
Drought is a complex natural hazard brought on by climate variability and change that alters the water cycle as a result of precipitation levels lowering sharply over time (Du et al. 2013). It has a substantial impact on global food and water security, but how much of an impact relies on our capacity to lower social, economic, and environmental costs (Krishnamurthy et al. 2022). The most detrimental natural disaster is drought, which has an effect on many socioeconomic sectors, including agriculture, ecological services, human health, leisure, and water resources (Smith & Katz 2013). Because it causes water scarcity, agricultural drought, and starvation, drought has the most severe negative consequences on a variety of industries when compared to other natural disasters (Sheffield et al. 2018).
Remote sensing is crucial for locating, mapping, assessing, and keeping track of the earth's resources and natural hazards at spatiotemporal scales (Dubovyk 2017). To address and manage the drought situation, numerous techniques and indexes have been developed (Schucknecht et al. 2013). Droughts have been more frequent in recent years, and are frequently correlated with high temperatures and low moisture levels. The ecology and human life systems are currently highly vulnerable due to the drought calamity (Yang et al. 2021). Therefore, drought needs to be identified using appropriate approaches in order to create accurate temporal continuous mapping (Wijayanti et al. 2021). Thus, drought must be specified using formal methods for precise temporal continuous mapping. Geospatial Information System (GIS) can use remote sensing data to estimate the likelihood of drought by providing data on the earth's surface over time (Sur et al. 2015). Compared to traditional approaches, remote sensing has improved in mapping the earth's surface, enabling observations and monitoring connected to drought on a temporal and spatial scale (Putri & Nurjani 2018). GIS can make it easier to undertake geographic analyses, such as predicting the likelihood of drought in a region. While some remote sensing methods for tracking droughts have been developed, others have taken the effects of dryness on plants into consideration. One of the oldest vegetation indicators used to track dryness is the Normalized Difference Vegetation Index (NDVI), which has been used since the 1980s (Harun et al. 2015).
Numerous studies using the Landsat time-series dataset have been conducted to investigate spatiotemporal patterns of drought, but the majority of those studies focused on drought detection methods and associations between agricultural drought and each rainfall average and crop yield (Van Loon et al. 2016). Since they are some of the best at identifying the start of drought and calculating its intensity, length, and global impact, the other studies have published thorough reports on indices that are used to track the impacts of droughts using remote sensing (Liu et al. 2018). As a basis for the assessment of vegetation condition, the remote sensing-based indices for assessing the state of vegetation provide distinctive characterizations for the vegetative area, including biomass, growth status, and leaf area coverage (Wan et al. 2004). Surface temperature may be a basis for estimating vegetation conditions and evapotranspiration (Liu et al. 2018). The performance of drought indices generated based on Moderate Resolution Imaging Spectroradiometer (MRIS) reflectance and Land Surface Temperature (LST), in association with the Standardized Precipitation Index (SPI), was extensively investigated to assess drought conditions on a global to regional scale in the southern Great Plains, USA (Wan et al. 2004), China (Du et al. 2013), in eastern Africa, and in southern and southeastern Africa (Mutowo & Chikodzi 2014).
The specific objectives of this research are: (i) to assess the drought patterns using the NDVI estimation index; (ii) to assess the drought patterns using the LST estimation index, and (iii) to see the correlation between NDVI and LSTV estimation index at the end of 2022.
METHODOLOGY
Description of the study areas
Data sources
The NDVI and LST were calculated using remote sensing data from Earth Explorer using Landsat 8 images for the years 2013, 2017, and 2022 for the month of December (LST). The spatially labeled picture file format was used to collect all of the Landsat data. Table 1 details the Landsat acquisition time and related information. The present study's metrological data (December rainfall in 2013, 2017, and 2022) were derived from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. This dataset includes gridded rainfall time-series with 0.05° geographic resolution (about 5.3 km) for trend analysis and seasonal drought monitoring.
Specifications of Landsat data used in the study
Activity . | Path row . | December . | Year . | Resolution . | Cloud cover (%) . |
---|---|---|---|---|---|
Landsat 8 | 168_57 | 01 | 2013 | 30 m | 6 |
Landsat 8 | 168_57 | 12 | 2017 | 30 m | 6 |
Landsat 8 | 168_57 | 18 | 2022 | 30 m | 6 |
Activity . | Path row . | December . | Year . | Resolution . | Cloud cover (%) . |
---|---|---|---|---|---|
Landsat 8 | 168_57 | 01 | 2013 | 30 m | 6 |
Landsat 8 | 168_57 | 12 | 2017 | 30 m | 6 |
Landsat 8 | 168_57 | 18 | 2022 | 30 m | 6 |
Data analysis
NDVI estimation
LST estimation



The LST is the radiative temperature calculated using the top of atmosphere brightness temperature, wavelength of emitted radiance, and LSE.
RESULTS AND DISCUSSION
Correlation between LST and NDVI by using PS
The result of Figure 5 showed a negative association between LST and NDVI when energy is the limiting factor for vegetation development, which is the case at higher latitudes and elevations in the research area. In contrast, during the middle of the growing season, solar radiation less impacts the LST–NDVI correlation's nature since the radioactive flux throughout most of the study region is high enough not to restrict vegetation development. According to the result, the LST–NDVI correlations are typically negative throughout this sub-period except for the northernmost parts. Based on the result, as anticipated, the slope progressively changes from negative to positive from the south to the north. The correlations at the four main sites along this transect were determined to be inconsequential, and significant negative correlations were only observed at the two southernmost points.
Discussion
The frequency of droughts in the study area was prepared using a GIS map of historical drought intensity. The results confirmed that there was intricate regional heterogeneity in the frequency of drought episodes in the region. This shows that at least 15 rainfall deficiencies occurred in every district in the area. Over the course of the study period, the area experienced mild to severe meteorological drought periods 15–18 times. The majority of it (62.72%) occurred during a mild drought (abnormally dry conditions), which typically only showed a little departure from the distribution of rainfall that is close to normal. However, because of how this lack of rain affects vegetation growth, it frequently results in agricultural droughts that are brought on by high LST intensities which were supported by the findings of Sahana et al. (2021).
For the sake of the environment and the quality of life for those who live in rural regions, it is crucial to investigate the rate of LST in response to LULC change. It is important to look into how much rural life has changed for both urban residents and biodiversity. It is crucial to look at the rate of LST in response to LULC change for the sake of the environment and the standard of living for those who reside in rural areas. Examining how rural life has changed is essential to safeguarding biodiversity and urban people as highly related with the findings of Band et al. (2022). It is claimed that the increase in NDBI and LST during the research period was brought on by the loss in plant cover that was identified by the NDVI. Critical scholars highlighted the LST and NDVI's inverse linear relationship (Sahana et al. 2021). LST, on the other hand, enjoys a positive connection. The other researcher found that NDBI and LST have a positive relationship (Band et al. 2022).
Drought is a complicated natural event that can lead to reduced water supply, which can have a substantial impact on agriculture and economic activities and cause social dissatisfaction and political instability. Based on the result, drought indicators are used to pinpoint insufficiencies that was supported by the study findings of Shamshirband et al. (2020). This study looked into the use of the NDVI and LST data from Terra-MODIS to see if a method for monitoring drought in close to real-time would be useful (Marengo et al. 2009). This technique is known as the Vegetation Supply Water Index (VSWI), which takes into account thermal properties and land surface reflectance which was related to the findings of Shamshirband et al. (2020).
According to the data, a severe drought afflicted approximately 85% of the Borana semi-arid region between 2013 and 2022. The number of days of soil moisture deficit, computed with a simple water balance model and daily interpolated precipitation, was used to confirm the results (Sahana et al. 2021). According to a correlation analysis of Vermont Significant Wetland Inventory, precipitation, and soil moisture deficit, VSWI is closely related to rainfall and soil water content, especially in dry conditions, which proposes adopting VSWI as a strategy for drought monitoring in the near real-time (Band et al. 2022). In the analysis of the 2013–2022 droughts using the VSWI index, two crucial features of vegetation's response to drought conditions – vegetation's recovery and memory impacts – were brought to light.
CONCLUSION
Regarding its usefulness for drought monitoring, this study investigates the geographical and temporal relationship between LST and NDVI and its correlation in Ethiopia. This study aimed to use the remote sensing-based VSWI to explore the spatial and temporal aspects of vegetative drought in the study areas. According to the findings, it is possible to use the empirical LST–NDVI relationship as a reliable predictor of the geographical and temporal aspects of water stress situations in the studied areas. The 2013 and 2022 droughts impacted vegetated regions (crop/pasture), which the VSWI index successfully recognized. According to the study, locations outside the semi-arid boundary also experienced drought during the 2012–2013 droughts, even though the semi-arid zone is more susceptible to drought stress conditions. Monitoring results from indicators frequently differ due to the various information sources and principles used for drought indices. It can be seen that no one index is adequate for accurately capturing drought features. As a result, using multiple indicators simultaneously or using indices combining data from multiple sources may produce results closer to reality. This finding suggests that drought behavior is highly erratic. This has significant implications for planners and policymakers who are actively involved in drought mitigation and preparedness.
ACKNOWLEDGMENT
The authors acknowledge the research fund sponsorship of “the Projects of institute local cooperation of Chinese Academy of Engineering, grant number JS2020ZT12” and “Priority Academic Program Development of Jiangsu Higher Education Institutions Project (PAPD)'.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.