Assessing drought related to global climate phenomena Using Sentinel-3 SLSTR Data around East Java, Indonesia

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In East Java, Indonesia, where agriculture heavily depends on stable climatic conditions, understanding drought patterns is essential for effective environmental management. This study utilizes Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) data to analyze land surface temperature (LST) variations and their relationship with global climate phenomena, particularly El Niño-Southern Oscillation (ENSO). The study spans from 2021 to 2023, monitoring LST changes and validating them against in situ temperature data from meteorological stations. Results indicate a strong correlation between LST anomalies and ENSO phases, with El Niño events worsening drought conditions through increased temperatures and reduced precipitation. The findings also reveal that drought severity varies spatially, with the northern regions experiencing more prolonged dry periods compared to the southern, topographically diverse areas. This study highlights the importance of remote sensing technologies in tracking climatic variations and emphasizes the necessity for adaptive strategies to mitigate the adverse effects of climate change on water resources and food security. These insights contribute to a better understanding of regional drought dynamics and provide a foundation for developing more effective early warning systems and policy interventions to enhance resilience against future droughts. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Drought is a complicated environmental hazard that has serious consequences for human life, ecosystems, and the economy. It is distinguished by below-average rainfall throughout the year (Mursidi et al., 2017 ), which results in a scarcity of water resources, affecting agriculture, water supply, and human livelihoods. Although droughts have existed throughout history, their frequency, intensity, and regional distribution have become increasingly connected to global climatic phenomena, particularly changes in surface temperature. Climate change, caused by rising average temperatures, increases the likelihood of drought. Warmer temperatures increase evaporation rates, resulting in soil driering (Means, 2023 ). El Niño-Southern Oscillation (ENSO) is a global climate phenomenon that affects the weather and surface temperatures of Indonesia. These events affect the distribution of heat and moisture in the atmosphere, causing major fluctuations in rainfall patterns and contributing to the start and severity of droughts. Zahra’s ( 2023 ) study of Kalimantan Island, Indonesia, found that temperatures increased during the dry season under El Niño conditions and decreased during La Niña conditions between 2014 and 2020. In Kuching City, Malaysia, El Niño occurrences lead to higher land surface temperatures in urban areas than in vegetation, marshes, and water bodies. This trend continued during La Niña (Eboy & Kemarau, 2023 ). Drought intensity in Java, Indonesia, has increased annually since 1985 (Parkhurst et al., 2019 ). D'Arrigo et al. (2006) found a relationship between the meteorological drought in East Java during the early rainy season (October to November) and the Pacific Ocean Sea surface temperature anomalies. Extreme El Niño episodes, which are associated with severe droughts, have a stronger impact on meteorological droughts in Java than global warming (Mulyanti et al., 2023 ). According to statistics, the entire drought-prone area in East Java between 2022 and 2026 is 4,779,912 hectares, which is designated as high risk (BNPB, 2021 ). East Java’s geography varies dramatically, with alluvial plains in the north, appropriate for rice production, volcanic mountains in the center, and steep hills in the Monitoring Land Surface Temperature (LST) with satellite remote sensing is a viable tool for monitoring drought across wide areas and time periods. Several satellite-based products have been developed for LST retrieval, including the Moderate Resolution Imaging Spectroradiometer (MODIS) LST (Wan & Dozier, 1996 ), the Landsat Collection 2 surface temperature product (Malakar et al., 2018 ), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Kinetic Temperature (AST_08) product (Gillespie et al., 1998 ), and the Copernicus Global Land Operations LST product (Koetz et al., 2018 ). This study utilized LST data from the Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer), which is part of the Copernicus program managed by the European Space Agency (ESA). Sentinel-3 is designed to provide comprehensive environmental data for both land and sea applications. The SLSTR uses a dual-view scanning technique to measure the surface temperature with high accuracy, wide coverage, and sufficient resolution (Polehampton et al., 2023 ). As an upgrade from the AATSR instrument on Envisat, the SLSTR offers significant improvements in coverage and resolution, enabling the monitoring of land surface temperature, sea surface temperature, and other relevant parameters crucial for climate studies and disaster mitigation. The wide swath coverage (740 km in dual view and 1,400 km in single view) facilitates continuous, real-time monitoring, which is essential for environmental research (Polehampton et al., 2023 ). The purpose of this study was to determine the spatial and temporal distribution of LST for drought detection in East Java. The data used span 2021–2023 and are analysed monthly. In monitoring LST variations, other parameters were also used for validation and data sampling. We used other parameters, namely Total Column Water Vapor (TCWV) and air temperature data at 9 Meteorology, Climatology and Geophysics Agency (BMKG) stations in East Java. This research also shows how ENSO affects the LST as a drought factor. The results of this research are expected to help determine better environmental management strategies in the future. This paper is arranged by a description of the area studied, and the data and methods used are described in the next section. The results are presented in Section 3. Section 3 also contains a discussion section. Finally, the last section summarizes the conclusions of this study. Data and Methods Study area This study is being undertaken in East Java Province, which is located between longitudes 111°E and 115°E and latitudes 6°2′S and 8°2′S (as indicated in Fig. 1 ). East Java is one of the biggest provinces on the island of Java, with an estimated 42 million population in 2023 and an area of 48.033 km² (Pravitasari et al., 2024). The region’s terrain is diversified, with both lowland and mountainous areas. Data The LST data in this study were taken from the SLSTR sensor carried by the Sentinel 3 satellite. The Sentinel-3 satellite is an integral part of the Copernicus programme managed by the ESA. The satellite records from an altitude of 814.5 km and allows recording every 27 days. The average revisit time for the global coverage of SLSTR observations in dual view is 1.9 days at the equator (with one spacecraft operating) or 0.9 days (in a constellation with a 180° interplanetary distance between two spacecraft). The SLSTR is a dual-view multichannel satellite radiometer. It has nine spectral bands that detect the top of atmosphere (TOA) in the VNIR–SWIR–TIR region. The SLSTR instrument exploits two independent scanning sets to provide a wider instrument than its predecessor ATSR but maintains the same angle of incidence. The SLSTR data have a spatial resolution of 500 m for the solar reflectance channels S1-S6 and a resolution of 1 km for the thermal infrared channels S7-S9 and F1-F2. This study also detected drought using TCWV data obtained from SLSTR Sentinel 3 sensor recordings. TCWV is the amount of water vapor in the entire air column, from the surface to the top. Water vapor is a greenhouse gas that significantly contributes to atmospheric radiation. Water vapor absorbs and emits infrared radiation, which can influence the ground surface temperature. When the TCWV is high, more infrared radiation is transmitted back to the surface, potentially increasing the LST (Li et al., 2009 ). Using the TCWV measurements as a correlation with the LST allows researchers to acquire a more complete understanding of the interaction between the atmosphere and the land surface temperature. This is important for developing more effective adaptation and mitigation strategies to climate change and its impacts on the environment. In addition, this study also uses Oceanic Niño Index (ONI) data to represent the ENSO phenomenon. The data are available in monthly format and can be accessed through the National Oceanic and Atmospheric Administration (NOAA) website. In this study, in situ air temperature data were obtained from nine BMKG stations in East Java (see Fig. 1 ): Banyuwangi Meteorological Station, Tanjung Perak Maritime Meteorological Station, Perak I Surabaya Meteorological Station, Tuban Meteorological Station, Nganjuk Geophysical Station, Pasuruan Geophysical Station, Malang Geophysical Station, Juanda Meteorological Station, and East Java Climatological Station. Data on the land surface temperature and near-surface air temperature are inextricably linked. During the day, the heated ground surface heats the air above it, whereas at night, cooler air cools the ground surface. Therefore, the association between the land surface temperature and the air temperature is significant (Coll et al., 2009 ). Methods Prepossessing data The data were downloaded and extracted from the Sentinel-3 SLSTR Level 2 satellite, which was radiometrically and geometrically corrected. The image was then reprojected into WGS84 because the Sentinel-3 data is still oriented toward landscape acquisition. Then, the clipping and image subset processing is performed to adjust the data to be used. The image results are then converted to point values to obtain the average monthly LST value. In addition, the LST value was converted from kelvin to Celsius in accordance with the amount often used in temperature observations. The validation test was conducted using the average LST data provided by the BMKG using the Pearson correlation method. In addition, the relationship between LST and ONI was calculated using the Pearson correlation coefficient. Based on the Pearson correlation method, it is expected that the correlation results will show a strong relationship between the data used. The following formula is used for Pearson’s correlation. r = \(\:\frac{\text{n}\:\sum\:\text{x}\text{i}\text{y}\text{i}-(\sum\:\:\text{x}\text{i}\:)(\sum\:\:\text{y}\text{i}\:)}{\sqrt{(\text{n}\:\sum\:\:\text{x}\text{i}\:2-(\sum\:\:\text{x}\text{i}\:\left)2\right)(\text{n}\:\sum\:\:\text{y}\text{i}\:2-(\sum\:\:\text{y}\text{i}\:\left)2\right)}}\) 1 Results and Discussion Figure 2 shows the monthly LST distribution in East Java in 2021. Overall, significant temperature variations were observed in 2021. At the beginning of the year (January to March), temperatures are relatively lower in the northern parts of the region, ranging from 15°C to 25°C, while the southern and western parts are warmer (25–35°C). From April to June, temperatures increased overall, with many areas reaching 30–35°C and even up to 40°C in May. From July to September, temperatures remain high, although there is a slight decrease in some areas, with a temperature range of 25–35°C. October saw a rise in temperatures again in some areas, while November and December showed greater temperature variation, with some areas experiencing a drop in temperature (15–25°C) and others remaining warm (25–35°C). This trend suggests that the region tends to experience higher temperatures in the middle of the year and lower temperatures at the beginning and end of the year. Figure 3 shows the monthly LST distribution in East Java in 2022. Areas with high temperatures (35–40°C) appear dominant almost throughout the year, especially in the southern and central parts of the region. The monthly temperature changes did not show significant variations, with areas showing similar patterns monthly. At the beginning of the year (January-March), high temperatures are observed throughout the region, with a slight decrease in March. The transition to the dry season (April-June) showed continued high temperatures with a slight decrease in heat intensity. The peak of the dry season (July-September) saw high temperatures predominantly in July and August, while September began to show a small decrease. The transition to the rainy season (October-December) maintains high temperatures in October but starts to drop in November and December. Spatially, the northern region tends to show lower temperatures than the southern region, which is consistent with higher temperatures throughout the year. The central region also exhibits temperature variations, but they tend to be high. Overall, the region had high land surface temperatures throughout 2022, with a slight decrease at the end of the year. Figure 4 shows the monthly LST distribution in East Java in 2023. Areas with high temperatures (35–40°C) are still dominant for most of the year, especially in the southern and central parts of the region. However, there are some significant changes compared to 2022. At the beginning of the year (January-March), high temperatures are still visible throughout the region, but the intensity decreases slightly in March. The transition to the dry season (April-June) showed continued high temperatures, with a slight decrease in heat intensity, especially in June. The peak dry season (July-September) saw predominantly high temperatures in July and August, with August having a larger area of high temperatures. In September, temperatures began to slightly decrease, especially in the northern part of the region. This study uses the total column water vapor (TCWV), which shows the total amount of water vapor in the atmospheric column, where water vapor plays an important role in forming clouds and affecting weather conditions. Table 2 shows the results of the Pearson correlation calculation between the LST and TCWV at 10 points from 2021 to 2023. Data collection sample points are based on several factors, namely, environmental conditions, natural conditions and socio-cultural conditions of an area. It was found that eight of the ten samples had a moderate positive correlation. This shows that when the LST is low, the TCWV tends to be low; otherwise, when the LST is high, the TCWV tends to be high. Therefore, high temperatures trigger evaporation and rain cloud formation. Table 1 Pearson correlation calculation between the LST and TCWV at 10 points from 2021 to 2023 No Station Location Elevation (m) Correlation Value (r) LST Average % Correlation Lon Lat 1 Banyuwangi Meteorological station 113.2549 -8.0792 38 0.50 31.62 61% Moderate 2 Tanjung Maritime Meteorological station 114.356 -8.215 8 0.53 32.39 58% Moderate 3 Perak I Surabaya Meteorological station 106.8805 -6.1078 18 0.50 32.99 44% Moderate 4 Tuban Meteorological station 112.7239 -7.2236 191 0.43 31.73 64% Moderate 5 Nganjuk Geophysical station 111.9918 -6.8229 55 0.42 28.48 61% Moderate 6 Pasuruan Geophysical station 111.7668 -7.7349 85 0.49 30.66 58% Moderate 7 Malang Geophysical station 112.6353 -7.7046 176 0.67 27.65 67% Strong 8 Juanda Meteorological station 112.4500 -8.1500 33 0.40 32.89 69% Moderate 9 East Java Climatological station 112.7833 -7.3846 88 0.58 30.62 61% Moderate 10 Kalianget Meteorological Station 112.5979 -7.9008 126 0.33 28.18 61% Weak Figure 5 shows a graph of the relationship between the LST and ONI data, which represents the ENSO phenomenon. The ONI tracks the three-month average sea surface temperature in the east-central tropical Pacific between 120°N and 170°N, near the International Dateline, to determine whether it is higher or lower than the average. An ONI value of more than equal to + 0.5℃ indicates an El Niño. An ONI value of less than equal to -0.5℃ indicates the occurrence of La Niña phenomenon. In 2021, the ENSO phenomenon was observed to be neutral. Almost throughout 2022, a La Niña phenomenon was observed, but the value was not significant. The ONI then increased to its peak at the end of 2023, indicating the El Niño phenomenon. The increase and decrease in LST from 2021 to 2023 tended to be normal. Meanwhile, at the end of 2023, the LST reached its peak. The relationship between LST and ONI from 2021 to 2023 was calculated using Pearson’s correlation, resulting in a correlation value of r = 0.54. This value indicates a fairly strong relationship between the LST and the ENSO phenomenon in East Java. Based on the nine temperature monitoring station points, it can be concluded that the average correlation from 2021 to 2023 between the LST and temperature variables from BMKG has a correlation coefficient of 0.740. Thus, the average correlation coefficient value can be classified as strong. Thus, the correlation between the LST and temperature from the BMKG is that when the LST experiences an increase in temperature, the temperature monitoring station from the BMKG also experiences an increase, and the relationship between these two variables is quite strong. Table 2 Correlation between the LST data from Sentinel-3 and the air temperature data from BMKG. No Station Correlation Value (r) Corr 1 Banyuwangi Meteorological station 0.60 Strong 2 Tanjung Maritime Meteorological station 0.79 Strong 3 Perak I Surabaya Meteorological station 0.74 Strong 4 Tuban Meteorological station 0.87 Strong 5 Nganjuk Geophysical station 0.77 Strong 6 Pasuruan Geophysical station 0.87 Strong 7 Malang Geophysical station 0.64 Strong 8 Juanda Meteorological station 0.56 Moderate 9 East Java Climatological station 0.81 Strong Based on the results of this study, the monthly average LST in East Java was found to vary between 2021 and 2023. In 2021, the LST in East Java tended to be high in the middle of the year (southeast monsoon) and low at the beginning and end of the year (northwest monsoon). In 2022, LST variations in East Java tended to be stable, and there was a slight decrease in LST at the end of the year (northwest monsoon). However, at the end of 2023, there was a significant increase in temperature to reach 37.57℃. Temperature has a negative relationship with rainfall levels (Gede Nyoman Mindra Jaya et al., 2020 ). Thus, an increase in temperature is accompanied by a decrease in rainfall. Conversely, a decrease in temperature is accompanied by an increase in rainfall. Rainfall in Indonesia is influenced by the monsoon phenomenon, which occurs in one peak rainy season each year. In June-August (southeast monsoon), there is a dry season in Indonesia. Meanwhile, December-February (northwest monsoon) is the rainy season in Indonesia (Hermawan, 2015 ). This is also seen in the results of this study, where the LST in 2021 and 2022 is higher during the southeast monsoon and tends to be low during the northwest monsoon. The monsoon phenomenon can strengthen and weaken when influenced by the ENSO phenomenon. ENSO is strongly correlated with rainfall in eastern Java (Iskandar et al., 2020 ). This can be seen in Fig. 4 , where at the end of 2023, there was a high increase in LST accompanied by an increase in ONI values, starting from June until the peak in November reached 1.32℃, which indicates the El Niño phenomenon. Thus, it can be concluded that the El Niño phenomenon affected the temperature increase in East Java. This is in accordance with the research of Wisetya Dewi et al. ( 2020 ), where during the El Niño phase, there was an increase in temperature during the northwest monsoon in Java. Based on Fig. 1 – 3 , the LST in East Java varies. The northern part of East Java tends to be stable from 2021 to 2023. Meanwhile, the southern part of East Java experiences variations in increase and decrease and tends to be lower than the northern part. Increased temperatures cause more water to evaporate from the soil, plants, and water bodies such as rivers and lakes. This leads to faster soil drying and depletion of available water. Therefore, the northern area of East Java has a higher level of drought than the southern area. This is supported by the topography of the southern part of East Java, which is dominated by mountains such as Mount Bromo, Ijen, Semeru, and Argopuro. Thus, the drought level is lower and has a lower LST as well. It is also mentioned in Himayah et al. ( 2019 ) research that there is a correlation between LST and vegetation levels. The LST is higher in areas with low vegetation and lower in areas with high vegetation. Conclusion This study provides valuable insights into the relationship between land surface temperature, drought conditions, and global climate phenomena in East Java, Indonesia. Using the Sentinel-3 SLSTR satellite data, the research confirms a strong correlation between LST variations and ENSO phases, with temperature increases being particularly pronounced during El Niño events. The findings indicate that El Niño significantly amplifies drought severity by reducing precipitation levels, increasing evaporation rates, and worsening water shortages in the region. Additionally, spatial analysis suggests that the northern areas of East Java are more susceptible to prolonged droughts due to their relatively lower elevation and limited vegetation cover, whereas the southern regions, with their mountainous terrain, exhibit greater resilience to temperature fluctuations. This study underscores the importance of satellite-based remote sensing for continuous and large-scale environmental monitoring, enabling more accurate predictions of drought occurrences and their potential impacts. By integrating satellite data with meteorological station observations, policymakers and stakeholders can develop more effective drought mitigation strategies, including improved water management, adaptive agricultural practices, and early warning systems. These measures are crucial for minimizing the socioeconomic consequences of drought and ensuring the sustainability of the natural resources in East Java. Moving forward, further research incorporating additional climate variables, such as soil moisture and precipitation anomalies, could enhance the accuracy of drought assessments and improve long-term climate adaptation strategies in the region. Declarations Acknowledgment The authors thank the Directorate of Research and Community Service of the Institut Teknologi Surabaya under the scheme of Penelitian Kerja Sama Perguruan Tinggi ITS–LLDIKTI Wilayah VII Batch 1 2024 (contract number 1262/PKS/ITS/2024 Author Contributions: Eko Yuli Handoko and Muhammad Aldila Syariz conceived the experiments. Eko Yuli Handoko, Pamela Kuryawati, and Megivareza designed and performed the experiments; Eko Yuli Handoko, Regita FaridatunisaWijayanti, and Loryena Ayu Karondia wrote the manuscript. 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IOP Conference Series: Earth and Environmental Science , 1233 (1), doi:10.1088/1755-1315/1233/1/012057 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6299301","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436182433,"identity":"941db4ea-cf2a-4e02-9cec-c69f0b7803d1","order_by":0,"name":"Eko Yuli Handoko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYDACHgYGZoYKCSjPgJlYLWckJEjUwtjGALOGCC38PIePSRfOs6iTb2B++IGhwJqwFsnetjTpmdskJAwOsBlLMBikE9ZicJ7HTJoXpIWBwQzIPUxYiz1YyxwJCfkG9m/EaTHg7QFqaQCG2AEeIm2ROHMs2ZrnmITkhsM8xRIJxPiFvyf54G2emjp++fb2jR8+/CEixICABRInoBhJIEoDUO0HIhWOglEwCkbBSAUADsorEY9yuIYAAAAASUVORK5CYII=","orcid":"","institution":"Institut Teknologi Sepuluh Nopember","correspondingAuthor":true,"prefix":"","firstName":"Eko","middleName":"Yuli","lastName":"Handoko","suffix":""},{"id":436182434,"identity":"be7c2275-1e77-4a20-9c0e-443026b4335b","order_by":1,"name":"Muhammad Aldila Syariz","email":"","orcid":"","institution":"Institut Teknologi Sepuluh Nopember","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Aldila","lastName":"Syariz","suffix":""},{"id":436182435,"identity":"2f543c89-701b-4143-820e-9f20ebef5127","order_by":2,"name":"Megivareza Putri Hanansyah","email":"","orcid":"","institution":"Institut Teknologi Sepuluh Nopember","correspondingAuthor":false,"prefix":"","firstName":"Megivareza","middleName":"Putri","lastName":"Hanansyah","suffix":""},{"id":436182436,"identity":"7aad92ed-0269-4f7a-aee0-025f17931cc9","order_by":3,"name":"Pamela Kuryawati","email":"","orcid":"","institution":"Institut Teknologi Sepuluh Nopember","correspondingAuthor":false,"prefix":"","firstName":"Pamela","middleName":"","lastName":"Kuryawati","suffix":""},{"id":436182437,"identity":"b16d692e-025f-4128-b9a9-3cbda4a0b5cf","order_by":4,"name":"Regita Faridatunisa Wijayanti","email":"","orcid":"","institution":"Universitas Hasanuddin","correspondingAuthor":false,"prefix":"","firstName":"Regita","middleName":"Faridatunisa","lastName":"Wijayanti","suffix":""},{"id":436182438,"identity":"5c425c1c-20d5-4c5b-b58a-d8d59a405054","order_by":5,"name":"Loryena Ayu Karondia","email":"","orcid":"","institution":"sinar mas Polytechnique","correspondingAuthor":false,"prefix":"","firstName":"Loryena","middleName":"Ayu","lastName":"Karondia","suffix":""}],"badges":[],"createdAt":"2025-03-25 02:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6299301/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6299301/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85814165,"identity":"31ee3cea-1051-4c37-b2a0-0d3aaf96ae52","added_by":"auto","created_at":"2025-07-02 04:50:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":85932,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and East Java Province and Distribution of the Indonesian Meteorology, Climatology and Geophysics Agency (BMKG) in the red box.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6299301/v1/5288dae554f2963fa231631c.jpg"},{"id":85814164,"identity":"1ddd8528-9761-41e2-be42-af846473deb1","added_by":"auto","created_at":"2025-07-02 04:50:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78728,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly LST distribution in East Java, 2021.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6299301/v1/1cd9b8df43d0753d3333bad1.jpg"},{"id":85814759,"identity":"e3a8f1a4-3406-42a2-a3fb-3912724ac9ce","added_by":"auto","created_at":"2025-07-02 04:58:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80675,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly LST distribution in East Java, 2022.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6299301/v1/48fb780e7fbb2d044409a158.jpg"},{"id":85814171,"identity":"fab18ad1-98ce-4617-a352-8f74ebc998c8","added_by":"auto","created_at":"2025-07-02 04:50:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79556,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly LST distribution in East Java, 2023.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6299301/v1/6494925ded490a1ed7dfccd5.jpg"},{"id":85814760,"identity":"cc5b3857-26b2-48ba-92c2-cc08a61a9a13","added_by":"auto","created_at":"2025-07-02 04:58:11","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":40651,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5 Relationship between LST and ONI.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6299301/v1/838390b532a5af9c44f22a2d.jpg"},{"id":85814174,"identity":"a30997c4-14ca-4fee-9110-fb361d7ad270","added_by":"auto","created_at":"2025-07-02 04:50:11","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":78921,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5. Relationship between the LST data from Sentinel-3 and the air temperature data from BMKG.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6299301/v1/fb4c6e49196c608c7b279e7c.jpg"},{"id":104791987,"identity":"bc5be23d-e44c-4928-a3f2-4eb716a69c5d","added_by":"auto","created_at":"2026-03-17 08:43:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":996917,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6299301/v1/3179cf16-31ed-4aa5-9182-b762da422872.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing drought related to global climate phenomena Using Sentinel-3 SLSTR Data around East Java, Indonesia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDrought is a complicated environmental hazard that has serious consequences for human life, ecosystems, and the economy. It is distinguished by below-average rainfall throughout the year (Mursidi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which results in a scarcity of water resources, affecting agriculture, water supply, and human livelihoods. Although droughts have existed throughout history, their frequency, intensity, and regional distribution have become increasingly connected to global climatic phenomena, particularly changes in surface temperature. Climate change, caused by rising average temperatures, increases the likelihood of drought. Warmer temperatures increase evaporation rates, resulting in soil driering (Means, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEl Ni\u0026ntilde;o-Southern Oscillation (ENSO) is a global climate phenomenon that affects the weather and surface temperatures of Indonesia. These events affect the distribution of heat and moisture in the atmosphere, causing major fluctuations in rainfall patterns and contributing to the start and severity of droughts. Zahra\u0026rsquo;s (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) study of Kalimantan Island, Indonesia, found that temperatures increased during the dry season under El Ni\u0026ntilde;o conditions and decreased during La Ni\u0026ntilde;a conditions between 2014 and 2020. In Kuching City, Malaysia, El Ni\u0026ntilde;o occurrences lead to higher land surface temperatures in urban areas than in vegetation, marshes, and water bodies. This trend continued during La Ni\u0026ntilde;a (Eboy \u0026amp; Kemarau, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDrought intensity in Java, Indonesia, has increased annually since 1985 (Parkhurst et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). D'Arrigo et al. (2006) found a relationship between the meteorological drought in East Java during the early rainy season (October to November) and the Pacific Ocean Sea surface temperature anomalies. Extreme El Ni\u0026ntilde;o episodes, which are associated with severe droughts, have a stronger impact on meteorological droughts in Java than global warming (Mulyanti et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to statistics, the entire drought-prone area in East Java between 2022 and 2026 is 4,779,912 hectares, which is designated as high risk (BNPB, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). East Java\u0026rsquo;s geography varies dramatically, with alluvial plains in the north, appropriate for rice production, volcanic mountains in the center, and steep hills in the\u003c/p\u003e \u003cp\u003eMonitoring Land Surface Temperature (LST) with satellite remote sensing is a viable tool for monitoring drought across wide areas and time periods. Several satellite-based products have been developed for LST retrieval, including the Moderate Resolution Imaging Spectroradiometer (MODIS) LST (Wan \u0026amp; Dozier, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), the Landsat Collection 2 surface temperature product (Malakar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Kinetic Temperature (AST_08) product (Gillespie et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and the Copernicus Global Land Operations LST product (Koetz et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study utilized LST data from the Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer), which is part of the Copernicus program managed by the European Space Agency (ESA). Sentinel-3 is designed to provide comprehensive environmental data for both land and sea applications. The SLSTR uses a dual-view scanning technique to measure the surface temperature with high accuracy, wide coverage, and sufficient resolution (Polehampton et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As an upgrade from the AATSR instrument on Envisat, the SLSTR offers significant improvements in coverage and resolution, enabling the monitoring of land surface temperature, sea surface temperature, and other relevant parameters crucial for climate studies and disaster mitigation. The wide swath coverage (740 km in dual view and 1,400 km in single view) facilitates continuous, real-time monitoring, which is essential for environmental research (Polehampton et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe purpose of this study was to determine the spatial and temporal distribution of LST for drought detection in East Java. The data used span 2021\u0026ndash;2023 and are analysed monthly. In monitoring LST variations, other parameters were also used for validation and data sampling. We used other parameters, namely Total Column Water Vapor (TCWV) and air temperature data at 9 Meteorology, Climatology and Geophysics Agency (BMKG) stations in East Java. This research also shows how ENSO affects the LST as a drought factor. The results of this research are expected to help determine better environmental management strategies in the future. This paper is arranged by a description of the area studied, and the data and methods used are described in the next section. The results are presented in Section 3. Section 3 also contains a discussion section. Finally, the last section summarizes the conclusions of this study.\u003c/p\u003e"},{"header":"Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThis study is being undertaken in East Java Province, which is located between longitudes 111\u0026deg;E and 115\u0026deg;E and latitudes 6\u0026deg;2\u0026prime;S and 8\u0026deg;2\u0026prime;S (as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). East Java is one of the biggest provinces on the island of Java, with an estimated 42\u0026nbsp;million population in 2023 and an area of 48.033 km\u0026sup2; (Pravitasari et al., 2024). The region\u0026rsquo;s terrain is diversified, with both lowland and mountainous areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData\u003c/h3\u003e\n\u003cp\u003eThe LST data in this study were taken from the SLSTR sensor carried by the Sentinel 3 satellite. The Sentinel-3 satellite is an integral part of the Copernicus programme managed by the ESA. The satellite records from an altitude of 814.5 km and allows recording every 27 days. The average revisit time for the global coverage of SLSTR observations in dual view is 1.9 days at the equator (with one spacecraft operating) or 0.9 days (in a constellation with a 180\u0026deg; interplanetary distance between two spacecraft). The SLSTR is a dual-view multichannel satellite radiometer. It has nine spectral bands that detect the top of atmosphere (TOA) in the VNIR\u0026ndash;SWIR\u0026ndash;TIR region. The SLSTR instrument exploits two independent scanning sets to provide a wider instrument than its predecessor ATSR but maintains the same angle of incidence. The SLSTR data have a spatial resolution of 500 m for the solar reflectance channels S1-S6 and a resolution of 1 km for the thermal infrared channels S7-S9 and F1-F2.\u003c/p\u003e \u003cp\u003eThis study also detected drought using TCWV data obtained from SLSTR Sentinel 3 sensor recordings. TCWV is the amount of water vapor in the entire air column, from the surface to the top. Water vapor is a greenhouse gas that significantly contributes to atmospheric radiation. Water vapor absorbs and emits infrared radiation, which can influence the ground surface temperature. When the TCWV is high, more infrared radiation is transmitted back to the surface, potentially increasing the LST (Li et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Using the TCWV measurements as a correlation with the LST allows researchers to acquire a more complete understanding of the interaction between the atmosphere and the land surface temperature. This is important for developing more effective adaptation and mitigation strategies to climate change and its impacts on the environment. In addition, this study also uses Oceanic Ni\u0026ntilde;o Index (ONI) data to represent the ENSO phenomenon. The data are available in monthly format and can be accessed through the National Oceanic and Atmospheric Administration (NOAA) website.\u003c/p\u003e \u003cp\u003eIn this study, in situ air temperature data were obtained from nine BMKG stations in East Java (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): Banyuwangi Meteorological Station, Tanjung Perak Maritime Meteorological Station, Perak I Surabaya Meteorological Station, Tuban Meteorological Station, Nganjuk Geophysical Station, Pasuruan Geophysical Station, Malang Geophysical Station, Juanda Meteorological Station, and East Java Climatological Station. Data on the land surface temperature and near-surface air temperature are inextricably linked. During the day, the heated ground surface heats the air above it, whereas at night, cooler air cools the ground surface. Therefore, the association between the land surface temperature and the air temperature is significant (Coll et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePrepossessing data\u003c/h2\u003e \u003cp\u003eThe data were downloaded and extracted from the Sentinel-3 SLSTR Level 2 satellite, which was radiometrically and geometrically corrected. The image was then reprojected into WGS84 because the Sentinel-3 data is still oriented toward landscape acquisition. Then, the clipping and image subset processing is performed to adjust the data to be used. The image results are then converted to point values to obtain the average monthly LST value. In addition, the LST value was converted from kelvin to Celsius in accordance with the amount often used in temperature observations.\u003c/p\u003e \u003cp\u003eThe validation test was conducted using the average LST data provided by the BMKG using the Pearson correlation method. In addition, the relationship between LST and ONI was calculated using the Pearson correlation coefficient. Based on the Pearson correlation method, it is expected that the correlation results will show a strong relationship between the data used. The following formula is used for Pearson\u0026rsquo;s correlation.\u003c/p\u003e \u003cp\u003er = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{n}\\:\\sum\\:\\text{x}\\text{i}\\text{y}\\text{i}-(\\sum\\:\\:\\text{x}\\text{i}\\:)(\\sum\\:\\:\\text{y}\\text{i}\\:)}{\\sqrt{(\\text{n}\\:\\sum\\:\\:\\text{x}\\text{i}\\:2-(\\sum\\:\\:\\text{x}\\text{i}\\:\\left)2\\right)(\\text{n}\\:\\sum\\:\\:\\text{y}\\text{i}\\:2-(\\sum\\:\\:\\text{y}\\text{i}\\:\\left)2\\right)}}\\)\u003c/span\u003e\u003c/span\u003e 1\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the monthly LST distribution in East Java in 2021. Overall, significant temperature variations were observed in 2021. At the beginning of the year (January to March), temperatures are relatively lower in the northern parts of the region, ranging from 15\u0026deg;C to 25\u0026deg;C, while the southern and western parts are warmer (25\u0026ndash;35\u0026deg;C). From April to June, temperatures increased overall, with many areas reaching 30\u0026ndash;35\u0026deg;C and even up to 40\u0026deg;C in May. From July to September, temperatures remain high, although there is a slight decrease in some areas, with a temperature range of 25\u0026ndash;35\u0026deg;C. October saw a rise in temperatures again in some areas, while November and December showed greater temperature variation, with some areas experiencing a drop in temperature (15\u0026ndash;25\u0026deg;C) and others remaining warm (25\u0026ndash;35\u0026deg;C). This trend suggests that the region tends to experience higher temperatures in the middle of the year and lower temperatures at the beginning and end of the year.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the monthly LST distribution in East Java in 2022. Areas with high temperatures (35\u0026ndash;40\u0026deg;C) appear dominant almost throughout the year, especially in the southern and central parts of the region. The monthly temperature changes did not show significant variations, with areas showing similar patterns monthly. At the beginning of the year (January-March), high temperatures are observed throughout the region, with a slight decrease in March. The transition to the dry season (April-June) showed continued high temperatures with a slight decrease in heat intensity. The peak of the dry season (July-September) saw high temperatures predominantly in July and August, while September began to show a small decrease. The transition to the rainy season (October-December) maintains high temperatures in October but starts to drop in November and December. Spatially, the northern region tends to show lower temperatures than the southern region, which is consistent with higher temperatures throughout the year. The central region also exhibits temperature variations, but they tend to be high. Overall, the region had high land surface temperatures throughout 2022, with a slight decrease at the end of the year.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the monthly LST distribution in East Java in 2023. Areas with high temperatures (35\u0026ndash;40\u0026deg;C) are still dominant for most of the year, especially in the southern and central parts of the region. However, there are some significant changes compared to 2022. At the beginning of the year (January-March), high temperatures are still visible throughout the region, but the intensity decreases slightly in March. The transition to the dry season (April-June) showed continued high temperatures, with a slight decrease in heat intensity, especially in June. The peak dry season (July-September) saw predominantly high temperatures in July and August, with August having a larger area of high temperatures. In September, temperatures began to slightly decrease, especially in the northern part of the region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study uses the total column water vapor (TCWV), which shows the total amount of water vapor in the atmospheric column, where water vapor plays an important role in forming clouds and affecting weather conditions. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the Pearson correlation calculation between the LST and TCWV at 10 points from 2021 to 2023. Data collection sample points are based on several factors, namely, environmental conditions, natural conditions and socio-cultural conditions of an area. It was found that eight of the ten samples had a moderate positive correlation. This shows that when the LST is low, the TCWV tends to be low; otherwise, when the LST is high, the TCWV tends to be high. Therefore, high temperatures trigger evaporation and rain cloud formation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation calculation between the LST and TCWV at 10 points from 2021 to 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCorrelation Value (r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLST Average\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLat\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanyuwangi Meteorological station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113.2549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.0792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTanjung Maritime Meteorological station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerak I Surabaya Meteorological station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106.8805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.1078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTuban Meteorological station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112.7239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.2236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNganjuk Geophysical station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111.9918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.8229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePasuruan Geophysical station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111.7668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.7349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalang Geophysical station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112.6353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.7046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJuanda Meteorological station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112.4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEast Java Climatological station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112.7833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.3846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKalianget Meteorological Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112.5979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.9008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWeak\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows a graph of the relationship between the LST and ONI data, which represents the ENSO phenomenon. The ONI tracks the three-month average sea surface temperature in the east-central tropical Pacific between 120\u0026deg;N and 170\u0026deg;N, near the International Dateline, to determine whether it is higher or lower than the average. An ONI value of more than equal to +\u0026thinsp;0.5℃ indicates an El Ni\u0026ntilde;o. An ONI value of less than equal to -0.5℃ indicates the occurrence of La Ni\u0026ntilde;a phenomenon. In 2021, the ENSO phenomenon was observed to be neutral. Almost throughout 2022, a La Ni\u0026ntilde;a phenomenon was observed, but the value was not significant. The ONI then increased to its peak at the end of 2023, indicating the El Ni\u0026ntilde;o phenomenon. The increase and decrease in LST from 2021 to 2023 tended to be normal. Meanwhile, at the end of 2023, the LST reached its peak. The relationship between LST and ONI from 2021 to 2023 was calculated using Pearson\u0026rsquo;s correlation, resulting in a correlation value of r\u0026thinsp;=\u0026thinsp;0.54. This value indicates a fairly strong relationship between the LST and the ENSO phenomenon in East Java.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the nine temperature monitoring station points, it can be concluded that the average correlation from 2021 to 2023 between the LST and temperature variables from BMKG has a correlation coefficient of 0.740. Thus, the average correlation coefficient value can be classified as strong. Thus, the correlation between the LST and temperature from the BMKG is that when the LST experiences an increase in temperature, the temperature monitoring station from the BMKG also experiences an increase, and the relationship between these two variables is quite strong.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between the LST data from Sentinel-3 and the air temperature data from BMKG.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrelation Value (r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanyuwangi Meteorological station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTanjung Maritime Meteorological station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerak I Surabaya Meteorological station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTuban Meteorological station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNganjuk Geophysical station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePasuruan Geophysical station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalang Geophysical station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJuanda Meteorological station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEast Java Climatological station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the results of this study, the monthly average LST in East Java was found to vary between 2021 and 2023. In 2021, the LST in East Java tended to be high in the middle of the year (southeast monsoon) and low at the beginning and end of the year (northwest monsoon). In 2022, LST variations in East Java tended to be stable, and there was a slight decrease in LST at the end of the year (northwest monsoon). However, at the end of 2023, there was a significant increase in temperature to reach 37.57℃.\u003c/p\u003e \u003cp\u003eTemperature has a negative relationship with rainfall levels (Gede Nyoman Mindra Jaya et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, an increase in temperature is accompanied by a decrease in rainfall. Conversely, a decrease in temperature is accompanied by an increase in rainfall. Rainfall in Indonesia is influenced by the monsoon phenomenon, which occurs in one peak rainy season each year. In June-August (southeast monsoon), there is a dry season in Indonesia. Meanwhile, December-February (northwest monsoon) is the rainy season in Indonesia (Hermawan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This is also seen in the results of this study, where the LST in 2021 and 2022 is higher during the southeast monsoon and tends to be low during the northwest monsoon.\u003c/p\u003e \u003cp\u003eThe monsoon phenomenon can strengthen and weaken when influenced by the ENSO phenomenon. ENSO is strongly correlated with rainfall in eastern Java (Iskandar et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, where at the end of 2023, there was a high increase in LST accompanied by an increase in ONI values, starting from June until the peak in November reached 1.32℃, which indicates the El Ni\u0026ntilde;o phenomenon. Thus, it can be concluded that the El Ni\u0026ntilde;o phenomenon affected the temperature increase in East Java. This is in accordance with the research of Wisetya Dewi et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), where during the El Ni\u0026ntilde;o phase, there was an increase in temperature during the northwest monsoon in Java.\u003c/p\u003e \u003cp\u003eBased on Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the LST in East Java varies. The northern part of East Java tends to be stable from 2021 to 2023. Meanwhile, the southern part of East Java experiences variations in increase and decrease and tends to be lower than the northern part. Increased temperatures cause more water to evaporate from the soil, plants, and water bodies such as rivers and lakes. This leads to faster soil drying and depletion of available water. Therefore, the northern area of East Java has a higher level of drought than the southern area. This is supported by the topography of the southern part of East Java, which is dominated by mountains such as Mount Bromo, Ijen, Semeru, and Argopuro. Thus, the drought level is lower and has a lower LST as well. It is also mentioned in Himayah et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) research that there is a correlation between LST and vegetation levels. The LST is higher in areas with low vegetation and lower in areas with high vegetation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides valuable insights into the relationship between land surface temperature, drought conditions, and global climate phenomena in East Java, Indonesia. Using the Sentinel-3 SLSTR satellite data, the research confirms a strong correlation between LST variations and ENSO phases, with temperature increases being particularly pronounced during El Ni\u0026ntilde;o events. The findings indicate that El Ni\u0026ntilde;o significantly amplifies drought severity by reducing precipitation levels, increasing evaporation rates, and worsening water shortages in the region. Additionally, spatial analysis suggests that the northern areas of East Java are more susceptible to prolonged droughts due to their relatively lower elevation and limited vegetation cover, whereas the southern regions, with their mountainous terrain, exhibit greater resilience to temperature fluctuations. This study underscores the importance of satellite-based remote sensing for continuous and large-scale environmental monitoring, enabling more accurate predictions of drought occurrences and their potential impacts. By integrating satellite data with meteorological station observations, policymakers and stakeholders can develop more effective drought mitigation strategies, including improved water management, adaptive agricultural practices, and early warning systems. These measures are crucial for minimizing the socioeconomic consequences of drought and ensuring the sustainability of the natural resources in East Java. Moving forward, further research incorporating additional climate variables, such as soil moisture and precipitation anomalies, could enhance the accuracy of drought assessments and improve long-term climate adaptation strategies in the region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Directorate of Research and Community Service of the Institut Teknologi Surabaya under the scheme of Penelitian Kerja Sama Perguruan Tinggi ITS\u0026ndash;LLDIKTI Wilayah VII Batch 1 2024 (contract number 1262/PKS/ITS/2024\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEko Yuli Handoko and Muhammad Aldila Syariz conceived the experiments. Eko Yuli Handoko, Pamela Kuryawati, and Megivareza designed and performed the experiments; Eko Yuli Handoko, Regita FaridatunisaWijayanti, and Loryena Ayu Karondia wrote the manuscript. All authors analysed the data, reviewed the study, and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBNPB. (2021). \u003cem\u003eJakarani RISIKO BENCANA NASIONAL PROVINSI JAWA TIMUR 2022-2026\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eColl, C., Wan, Z., \u0026amp; Galve, J. M. (2009). 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(2023). \u003cem\u003eCopernicus Sentinel-3 SLSTR Land User Handbook: Vol. 1.3\u003c/em\u003e. ESA.\u003c/li\u003e\n\u003cli\u003eWan, Z. and Dozier, J. (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. \u003cem\u003eIEEE Transactions on Geoscience and Remote Sensing\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(4), 892\u0026ndash;905. doi: 10.1109/36.508406\u003c/li\u003e\n\u003cli\u003eWisetya Dewi, Y., Wirasatriya, A., Nugroho Sugianto, D., Helmi, M., Marwoto, J., \u0026amp; Maslukah, L. (2020). Effect of ENSO and IOD on the Variability of Sea Surface Temperature (SST) in Java Sea. \u003cem\u003eIOP Conference Series: Earth and Environmental Science\u003c/em\u003e, \u003cem\u003e530\u003c/em\u003e(1). doi:10.1088/1755-1315/530/1/012007\u003c/li\u003e\n\u003cli\u003eZahra, R. A. (2023). Application of MODIS land surface temperature data to ENSO-based analysis in Kalimantan. \u003cem\u003eIOP Conference Series: Earth and Environmental Science\u003c/em\u003e, \u003cem\u003e1233\u003c/em\u003e(1), doi:10.1088/1755-1315/1233/1/012057\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6299301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6299301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrought is a critical environmental issue with far-reaching impacts on ecosystems, human livelihoods, and agricultural productivity. In East Java, Indonesia, where agriculture heavily depends on stable climatic conditions, understanding drought patterns is essential for effective environmental management. This study utilizes Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) data to analyze land surface temperature (LST) variations and their relationship with global climate phenomena, particularly El Ni\u0026ntilde;o-Southern Oscillation (ENSO). The study spans from 2021 to 2023, monitoring LST changes and validating them against in situ temperature data from meteorological stations. Results indicate a strong correlation between LST anomalies and ENSO phases, with El Ni\u0026ntilde;o events worsening drought conditions through increased temperatures and reduced precipitation. The findings also reveal that drought severity varies spatially, with the northern regions experiencing more prolonged dry periods compared to the southern, topographically diverse areas. This study highlights the importance of remote sensing technologies in tracking climatic variations and emphasizes the necessity for adaptive strategies to mitigate the adverse effects of climate change on water resources and food security. These insights contribute to a better understanding of regional drought dynamics and provide a foundation for developing more effective early warning systems and policy interventions to enhance resilience against future droughts.\u003c/p\u003e","manuscriptTitle":"Assessing drought related to global climate phenomena Using Sentinel-3 SLSTR Data around East Java, Indonesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 04:50:07","doi":"10.21203/rs.3.rs-6299301/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"faa34602-5e5c-48f1-b9b9-07b80b849925","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-17T08:42:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 04:50:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6299301","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6299301","identity":"rs-6299301","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}