Climate patterns in Europe: A focus on ten countries through remote sensing

Year 2025, Volume: 10 Issue: 3, 398 - 418, 17.09.2025

Volkan Yılmaz

https://doi.org/10.26833/ijeg.1583206

Cited By: 2

Abstract

Leveraging high-temporal resolution remote sensing data enables the investigation of the impacts of climate change with unprecedented detail and accuracy. This approach provides consistent observations, allowing for tracking of short-term fluctuations and long-term trends in climate patterns. The majority of existing studies focus on local impacts, overlooking broader national-scale implications. This research addresses this gap, examining the effects of climate change on European countries, i.e., Türkiye, Germany, Belgium, the United Kingdom (UK), France, Spain, Switzerland, Italy, Ukraine and Poland from 2001 to 2023, emphasizing the interconnected nature of climate change and the need for comprehensive strategies on a national scale. This research involved a comprehensive examination of essential environmental variables, such as precipitation (PCP), land surface temperature (LST), evapotranspiration (ET), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI) and forest area loss (FAL) through an extensive time-series analysis. The primary aim was to reveal temporal patterns within these datasets. Subsequently, pair-wise correlations among the datasets were computed, offering valuable insights into the complex interconnections among the factors used. The experiments revealed that the UK experienced a significant decline in PCP, while Ukraine and Poland exhibited higher rates of LST increase. Switzerland, France and Italy showed higher ET rates; and Belgium, France and Italy exhibited the highest rate of PET increase. Türkiye, Poland and Italy had a more pronounced rise in vegetation health. The study found strong positive correlations (average 0.72) between LST and PET. Additionally, LST showed a notable correlation with NDVI (average 0.55) and VCI (average 0.42). PCP generally exhibited negative correlations with other factors and ET was generally correlated with both NDVI (average 0.55) and VCI (average 0.56). This study is expected to contribute to the understanding of the impacts of climate change on national scale.

Keywords

Climate Change , Remote Sensing , Time-series Analysis , Land Surface Temperature , Precipitation

References

• The Sustainable Development Goals Report 2019. https://sdgs.un.org/goals. (accessed 2 September 2024).
• Zhao, S., Liu, M., Tao, M., Zhou, W., Lu, X., Xiong, Y., Li, F. & Wang, Q. (2023). The role of satellite remote sensing in mitigating and adapting to global climate change. Science of the Total Environment, 166820.
• Reiners, P., Sobrino, J., & Kuenzer, C. (2023). Satellite-derived land surface temperature dynamics in the context of global change—A review. Remote Sensing, 15(7), 1857.
• Kotan, B., Tatmaz, A., Kılıç, S., & Erener, A. (2021). LST change for 16-year period for different land use classes. Advanced Remote Sensing, 1(1), 38-45.
• Sarp, G., Baydoğan, E., Güzel, F., & Otlukaya, T. (2021). Evaluation of the relationship between urban area and land surface temperature determined from optical satellite data: A case of Istanbul. Advanced Remote Sensing, 1(1), 31-37.
• Bünyan Ünel, F., Kuşak, L., Yakar, M., Doğan, H. (2023). Coğrafi bilgi sistemleri ve analitik hiyerarşi prosesi kullanarak Mersin ilinde otomatik meteoroloji gözlem istasyonu yer seçimi. Geomatik, 8(2), 107-123. https://doi.org/10.29128/geomatik.1136951.
• Mogaraju, J. K. (2024). Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India. International Journal of Engineering and Geosciences, 9(2), 233-246.
• Morsy, S., & Hadı, M. (2022). Impact of land use/land cover on land surface temperature and its relationship with spectral indices in Dakahlia Governorate, Egypt. International Journal of Engineering and Geosciences, 7(3), 272-282.
• Guha, S., & Govil, H. (2022). Estimating the seasonal relationship between land surface temperature and normalized difference bareness index using Landsat data series. International Journal of Engineering and Geosciences, 7(1), 9-16.
• Zheng, C., Jia, L., & Hu, G. (2022). Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations. Journal of Hydrology, 613, 128444.
• Tahsin, A., Abdullahi, J., Karabulut, A. İ., & Yesilnacar, M. İ. (2023). Spatiotemporal prediction of reference evapotranspiration in Araban Region, Türkiye: A machine learning based approach. Advanced Remote Sensing, 3(1), 27-37.
• Yağcı, A. L. (2023). Bolu Yeniçağa’da evapotranspirasyonun Landsat uydu görüntüleri ve trapezoid model ile izlenmesi. Geomatik, 8(1), 18-26.
• Kamran, K. V., Sourghali, M., & Bagheri, S. (2024). A comparative spectral assessment approach of SEBAL and SEBS for actual evaporation estimation in Ardabil Province. International Journal of Engineering and Geosciences, 9(2), 131-146.
• Tabari, H. (2020). Climate change impact on flood and extreme precipitation increases with water availability. Scientific Reports, 10(1), 13768.
• Cassia, R., Nocioni, M., Correa-Aragunde, N., & Lamattina, L. (2018). Climate change and the impact of greenhouse gasses: CO2 and NO, friends and foes of plant oxidative stress. Frontiers in Plant Science, 9, 273.
• Zheng, X., Streimikiene, D., Balezentis, T., Mardani, A., Cavallaro, F., & Liao, H. (2019). A review of greenhouse gas emission profiles, dynamics, and climate change mitigation efforts across the key climate change players. Journal of Cleaner Production, 234, 1113-1133.
• Choudhury, D., Das, K., & Das, A. (2019). Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. The Egyptian Journal of Remote Sensing and Space Science, 22(2), 203-218.
• Chao, Z., Wang, L., Che, M., & Hou, S. (2020). Effects of different urbanization levels on land surface temperature change: Taking Tokyo and Shanghai for example. Remote Sensing, 12(12), 2022.
• Rosas-Chavoya, M., López-Serrano, P. M., Vega-Nieva, D. J., Wehenkel, C. A., & Hernández-Díaz, J. C. (2022). Application of Land Surface temperature from Landsat series to monitor and analyze forest ecosystems: A bibliometric analysis. Forest Systems, 31(3), e021-e021.
• Kumar, A., Agarwal, V., Pal, L., Chandniha, S. K., & Mishra, V. (2021). Effect of land surface temperature on urban heat island in Varanasi City, India. J-Multidisciplinary Scientific Journal, 4(3), 420-429.
• Ghiat, I., Mackey, H. R., & Al-Ansari, T. (2021). A review of evapotranspiration measurement models, techniques and methods for open and closed agricultural field applications. Water, 13(18), 2523.
• Ma, S., Eichelmann, E., Wolf, S., Rey-Sanchez, C., & Baldocchi, D. D. (2020). Transpiration and evaporation in a Californian oak-grass savanna: Field measurements and partitioning model results. Agricultural and Forest Meteorology, 295, 108204.
• Tadese, M., Kumar, L., & Koech, R. (2020). Long-term variability in potential evapotranspiration, water availability and drought under climate change scenarios in the Awash River Basin, Ethiopia. Atmosphere, 11(9), 883.
• Ajjur, S. B., & Al-Ghamdi, S. G. (2021). Evapotranspiration and water availability response to climate change in the Middle East and North Africa. Climatic Change, 166(3), 28.
• Zhang, Q., Yang, Z., Hao, X., & Yue, P. (2019). Conversion features of evapotranspiration responding to climate warming in transitional climate regions in northern China. Climate Dynamics, 52, 3891-3903.
• Um, M. J., Kim, Y., Park, D., Jung, K., Wang, Z., Kim, M. M., & Shin, H. (2020). Impacts of potential evapotranspiration on drought phenomena in different regions and climate zones. Science of the Total Environment, 703, 135590.
• Felton, A. J., Slette, I. J., Smith, M. D., & Knapp, A. K. (2020). Precipitation amount and event size interact to reduce ecosystem functioning during dry years in a mesic grassland. Global Change Biology, 26(2), 658-668.
• Agovino, M., Casaccia, M., Ciommi, M., Ferrara, M., & Marchesano, K. (2019). Agriculture, climate change and sustainability: The case of EU-28. Ecological Indicators, 105, 525-543.
• Rahmani Fazli, A., & Salehian, S. (2022). The effects of Temperature and Precipitation changes on the occurrence of water resources instability in Zayandeh-Rud Basin. Journal of Arid Regions Geographic Studies, 8(29), 52-68.
• Manabe, S. (2019). Role of greenhouse gas in climate change. Tellus A: Dynamic Meteorology and Oceanography, 71(1), 1620078.
• Jeffry, L., Ong, M. Y., Nomanbhay, S., Mofijur, M., Mubashir, M., & Show, P. L. (2021). Greenhouse gases utilization: A review. Fuel, 301, 121017.
• Driga, A. M., & Drigas, A. S. (2019). Climate Change 101: How Everyday Activities Contribute to the Ever-Growing Issue. International Journal of Recent Contributions from Engineering, Science & IT, 7(1), 22-31.
• Dupuy, J. L., Fargeon, H., Martin-StPaul, N., Pimont, F., Ruffault, J., Guijarro, M., Hernando, C., Madrigal, J., & Fernandes, P. (2020). Climate change impact on future wildfire danger and activity in southern Europe: a review. Annals of Forest Science, 77, 1-24.
• Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., Xu, B., Shi, J., & Dickinson, R. (2013). The role of satellite remote sensing in climate change studies. Nature Climate Change, 3(10), 875-883.
• Kotchi, S. O., Bouchard, C., Ludwig, A., Rees, E. E., & Brazeau, S. (2019). Using Earth observation images to inform risk assessment and mapping of climate change-related infectious diseases. Canada Communicable Disease Report= Releve des Maladies Transmissibles au Canada, 45(5), 133-142.
• Şenol, H. İ., Kaya, Y., Yiğit, A. Y., & Yakar, M. (2024). Extraction and geospatial analysis of the Hersek Lagoon shoreline with Sentinel-2 satellite data. Survey Review, 56(397), 367-382.
• Mokhtar, K., Chuah, L. F., Abdullah, M. A., Oloruntobi, O., Ruslan, S. M. M., Albasher, G., Ali, A., & Akhtar, M. S. (2023). Assessing coastal bathymetry and climate change impacts on coastal ecosystems using Landsat 8 and Sentinel-2 satellite imagery. Environmental Research, 239, 117314.
• Hashim, B. M., Sultan, M. A., Attyia, M. N., Al Maliki, A. A., & Al-Ansari, N. (2019). Change detection and impact of climate changes to Iraqi southern marshes using Landsat 2 MSS, Landsat 8 OLI and Sentinel 2 MSI data and GIS applications. Applied Sciences, 9(10), 2016.
• Bannari, A., & Al-Ali, Z. M. (2020). Assessing climate change impact on soil salinity dynamics between 1987–2017 in arid landscape using Landsat TM, ETM+ and OLI data. Remote Sensing, 12(17), 2794.
• Eleftheriou, D., Kiachidis, K., Kalmintzis, G., Kalea, A., Bantasis, C., Koumadoraki, P., Spathara, M. E., Tsolaki, A., Tzampazidou, M. I., & Gemitzi, A. (2018). Determination of annual and seasonal daytime and nighttime trends of MODIS LST over Greece-climate change implications. Science of the Total Environment, 616, 937-947.
• Jabal, Z. K., Khayyun, T. S., & Alwan, I. A. (2022). Impact of climate change on crops productivity using MODIS-NDVI time series. Civil Engineering Journal, 8(6), 1136-1156.
• Tian, L., Tao, Y., Fu, W., Li, T., Ren, F., & Li, M. (2022). Dynamic simulation of land use/cover change and assessment of forest ecosystem carbon storage under climate change scenarios in Guangdong Province, China. Remote Sensing, 14(10), 2330.
• Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., & Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology. Earth System Science Data, 11(2), 717-739.
• Qu, C., Hao, X., & Qu, J. J. (2019). Monitoring extreme agricultural drought over the Horn of Africa (HOA) using remote sensing measurements. Remote Sensing, 11(8), 902.
• Imran, M., & Mehmood, A. (2020). Analysis and mapping of present and future drivers of local urban climate using remote sensing: a case of Lahore, Pakistan. Arabian Journal of Geosciences, 13(6), 278.
• Wu, L., Ma, X., Dou, X., Zhu, J., & Zhao, C. (2021). Impacts of climate change on vegetation phenology and net primary productivity in arid Central Asia. Science of the Total Environment, 796, 149055.
• Yi, L., Jing, W., Ke, C., Zhaonan, C. A. I., Dongxu, Y. A. N. G., & Lin, W. U. (2021). Satellite remote sensing of greenhouse gases: Progress and trends. National Remote Sensing Bulletin, 25(1), 53-64.
• Jiao, W., Wang, L., & McCabe, M. F. (2021). Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future. Remote Sensing of Environment, 256, 112313.
• Shah, P. B., & Patel, C. R. (2023). Integration of Remote Sensing and Big Data to Study Spatial Distribution of Urban Heat Island for Cities with Different Terrain. International Journal of Engineering, 36(1), 71-77.
• Mpandeli, S., Nhamo, L., Moeletsi, M., Masupha, T., Magidi, J., Tshikolomo, K., Liphadzi, S., Naidoo, D., & Mabhaudhi, T. (2019). Assessing climate change and adaptive capacity at local scale using observed and remotely sensed data. Weather and Climate Extremes, 26, 100240.
• Javadinejad, S., Eslamian, S., & Ostad-Ali-Askari, K. (2019). Investigation of monthly and seasonal changes of methane gas with respect to climate change using satellite data. Applied Water Science, 9, 1-8.
• Pádua, L., Marques, P., Adão, T., Guimarães, N., Sousa, A., Peres, E., & Sousa, J. J. (2019). Vineyard variability analysis through UAV-based vigour maps to assess climate change impacts. Agronomy, 9(10), 581.
• Park, H., Kim, K., & Lee, D. K. (2019). Prediction of severe drought area based on random forest: Using satellite image and topography data. Water, 11(4), 705.
• Orusa, T., & Borgogno Mondino, E. (2021). Exploring short-term climate change effects on rangelands and broad-leaved forests by free satellite data in Aosta Valley (Northwest Italy). Climate, 9(3), 47.
• Pareeth, S., & Karimi, P. (2023). Evapotranspiration estimation using Surface Energy Balance Model and medium resolution satellite data: An operational approach for continuous monitoring. Scientific Reports, 13(1), 12026.
• Climate Change Knowledge Portal, https://climateknowledgeportal.worldbank.org/ (accessed 2 September 2024).
• Climate of Germany, https://www.britannica.com/place/Germany/Climate (accessed 2 September 2024).
• Climate of Belgium, https://www.weatheronline.co.uk/reports/climate/Belgium (accessed 2 September 2024).
• Climate of the UK, https://climateknowledgeportal.worldbank.org/country/united-kingdom (accessed 2 September 2024).
• Climate of France, https://www.britannica.com/place/France/Climate (accessed 2 September 2024).
• Climate of Spain, https://www.weatheronline.co.uk/reports/climate/Spain (accessed 2 September 2024).
• Climate of Switzerland, https://www.britannica.com/place/Switzerland/Relief-and-drainage (accessed 2 September 2024).
• Climate of Italy, https://www.weatheronline.co.uk/reports/climate/Italy (accessed 2 September 2024).
• Climate of Ukraine https://www.worlddata.info/europe/ukraine/climate.php (accessed 2 September 2024).
• Climate of Poland https://www.britannica.com/place/Poland/Climate (accessed 2 September 2024).
• Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2(1), 1-21.
• Belay, A. S., Fenta, A. A., Yenehun, A., Nigate, F., Tilahun, S. A., Moges, M. M., Dessie, M., Adgo, E., Nyssen, J., Chen, M., Griensven, A. V., & Walraevens, K. (2019). Evaluation and application of multi-source satellite rainfall product CHIRPS to assess spatio-temporal rainfall variability on data-sparse western margins of Ethiopian highlands. Remote Sensing, 11(22), 2688.
• CHIRPS Daily: Climate Hazards Center InfraRed Precipitation With Station Data (Version 2.0 Final), https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY (accessed 2 September 2024).
• MOD11A1.061 Terra Land Surface Temperature and Emissivity Daily Global 1km, https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1 (accessed 2 September 2024).
• MOD16A2.061: Terra Net Evapotranspiration 8-Day Global 500m, https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2 (accessed 2 September 2024).
• MOD13Q1.061 Terra Vegetation Indices 16-Day Global 250m, https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13Q1 (accessed 2 September 2024).
• Jiao, W., Zhang, L., Chang, Q., Fu, D., Cen, Y., & Tong, Q. (2016). Evaluating an enhanced vegetation condition index (VCI) based on VIUPD for drought monitoring in the continental United States. Remote Sensing, 8(3), 224.
• Kogan, F. N. (1990). Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11(8), 1405-1419.
• Liang, L., Sun, Q., Luo, X., Wang, J., Zhang, L., Deng, M., Di, L., & Liu, Z. (2017). Long‐term spatial and temporal variations of vegetative drought based on vegetation condition index in China. Ecosphere, 8(8), e01919.
• Zhou, X., Wang, P., Tansey, K., Zhang, S., Li, H., & Wang, L. (2020). Developing a fused vegetation temperature condition index for drought monitoring at field scales using Sentinel-2 and MODIS imagery. Computers and Electronics in Agriculture, 168, 105144.
• Patel, N. R., Mukund, A., & Parida, B. R. (2022). Satellite-derived vegetation temperature condition index to infer root zone soil moisture in semi-arid province of Rajasthan, India. Geocarto International, 37(1), 179-195.
• Behifar, M., Kakroodi, A. A., Kiavarz, M., & Azizi, G. (2023). Satellite-based drought monitoring using optimal indices for diverse climates and land types. Ecological Informatics, 76, 102143.
• Zhang, Y., Wang, P., Tansey, K., Li, M., Guo, F., Liu, J., & Zhang, S. (2023). Spatiotemporal Data Fusion of Index-Based VTCI Using Sentinel-2 and-3 Satellite Data for Field-Scale Drought Monitoring. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-15.
• Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11), 91-100.
• Bento, V. A., Gouveia, C. M., DaCamara, C. C., Libonati, R., & Trigo, I. F. (2020). The roles of NDVI and Land Surface Temperature when using the Vegetation Health Index over dry regions. Global and Planetary Change, 190, 103198.
• Kogan, F. N. (2000). Satellite-observed sensitivity of world land ecosystems to El Nino/La Nina. Remote Sensing of Environment, 74(3), 445-462.
• Karnieli, A., Agam, N., Pinker, R. T., Anderson, M., Imhoff, M. L., Gutman, G. G., Panov, N., & Goldberg, A. (2010). Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of Climate, 23(3), 618-633.
• Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., & Townshend, J. R. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850-853.
• Hansen Global Forest Change v1.11 (2000-2023), https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2023_v1_11 (accessed 2 September 2024).
• Shimizu, K., Ota, T., & Mizoue, N. (2020). Accuracy assessments of local and global forest change data to estimate annual disturbances in temperate forests. Remote Sensing, 12(15), 2438.
• Macrotrends - The Premier Research Platform for Long Term Investors, https://www.macrotrends.net/ (accessed 2 September 2024).
• Turkish Statistical Institute, https://www.tuik.gov.tr/ (accessed 2 September 2024).
• Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 245-259.
• Kendall, M. G. (1975). Rank Correlation Methods. Griffin, London, UK.
• Danneberg, J. (2012). Changes in runoff time series in Thuringia, Germany–Mann-Kendall trend test and extreme value analysis. Advances in Geosciences, 31, 49-56.
• Ullah, W., Ahmad, K., Ullah, S., Tahir, A. A., Javed, M. F., Nazir, A., Abbasi, A. M., Aziz, M., & Mohamed, A. (2023). Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan region. Heliyon, 9(2).
• Fayshal, M. A. (2024). Simulating Land Cover Changes and It’s Impacts on Land Surface Temperature: A Case Study in Rajshahi, Bangladesh. Bs.C Thesis, Khulna University of Engineering & Technology.
• Salvati, L., Sateriano, A., & Zitti, M. (2013). Long-term land cover changes and climate variations–A country-scale approach for a new policy target. Land Use Policy, 30(1), 401-407.
• Szewczak, K., Łoś, H., Pudełko, R., Doroszewski, A., Gluba, Ł., Łukowski, M., Rafalska-Przysucha, A., Słomiński, J., & Usowicz, B. (2020). Agricultural drought monitoring by MODIS potential evapotranspiration remote sensing data application. Remote Sensing, 12(20), 3411.
• Prăvălie, R., Sîrodoev, I., Nita, I. A., Patriche, C., Dumitraşcu, M., Roşca, B., Tişcovschi, A., Bandoc, G., Săvulescu, I., Mănoiu, V., & Birsan, M. V. (2022). NDVI-based ecological dynamics of forest vegetation and its relationship to climate change in Romania during 1987–2018. Ecological Indicators, 136, 108629.
• Cheval, S., Dumitrescu, A., Irașoc, A., Paraschiv, M. G., Perry, M., & Ghent, D. (2022). MODIS-based climatology of the Surface Urban Heat Island at country scale (Romania). Urban Climate, 41, 101056.
• Cheval, S., Dumitrescu, A., Amihăesei, V., Irașoc, A., Paraschiv, M. G., & Ghent, D. (2023). A country scale assessment of the heat hazard-risk in urban areas. Building and Environment, 229, 109892.
• Xu, G., Zhang, H., Chen, B., Zhang, H., Innes, J. L., Wang, G., Yan, J., Zheng, Y., Zhu, Z., & Myneni, R. B. (2014). Changes in vegetation growth dynamics and relations with climate over China’s landmass from 1982 to 2011. Remote Sensing, 6(4), 3263-3283.
• Piao, S., Yin, G., Tan, J., Cheng, L., Huang, M., Li, Y., Liu, R., Mao, J., Myneni, R. B., Peng, S., Poulter, B., Shi, X., Xiao, Z., Zeng, N., Zheng, Z., & Wang, Y. (2015). Detection and attribution of vegetation greening trend in China over the last 30 years. Global Change Biology, 21(4), 1601-1609.
• Bhattarai, N., Mallick, K., Stuart, J., Vishwakarma, B. D., Niraula, R., Sen, S., & Jain, M. (2019). An automated multi-model evapotranspiration mapping framework using remotely sensed and reanalysis data. Remote Sensing of Environment, 229, 69-92.
• Rasul, A. O., Hameed, H. M., & Ibrahim, G. R. F. (2021). Dramatically increase of built-up area in Iraq during the last four decades. Advanced Remote Sensing, 1(1), 1-9.
• Ünel, F. B., Kuşak, L., Yakar, M., & Doğan, H. (2023). Coğrafi bilgi sistemleri ve analitik hiyerarşi prosesi kullanarak Mersin ilinde otomatik meteoroloji gözlem istasyonu yer seçimi. Geomatik, 8(2), 107-123.
• Mu, B., Zhao, X., Wu, D., Wang, X., Zhao, J., Wang, H., Zhou, Q., Du, X., & Liu, N. (2021). Vegetation cover change and its attribution in China from 2001 to 2018. Remote Sensing, 13(3), 496.
• Prodhan, F. A., Zhang, J., Yao, F., Shi, L., Pangali Sharma, T. P., Zhang, D., Cao, D., Zheng, M., Ahmed, N., & Mohana, H. P. (2021). Deep learning for monitoring agricultural drought in South Asia using remote sensing data. Remote Sensing, 13(9), 1715.
• Huang, W., Duan, W., & Chen, Y. (2021). Rapidly declining surface and terrestrial water resources in Central Asia driven by socio-economic and climatic changes. Science of the Total Environment, 784, 147193.
• Pradhan, B., Yoon, S., & Lee, S. (2024). Examining the dynamics of vegetation in South Korea: an integrated analysis using remote sensing and in situ data. Remote Sensing, 16(2), 300.
• Kim GangSun, K. G., Lim ChulHee, L. C., Kim SeaJin, K. S., Lee JongYeol, L. J., Son YowHan, S. Y., & Lee WooKyun, L. W. (2017). Effect of national-scale afforestation on forest water supply and soil loss in South Korea, 1971-2010. Sustainability, 9(6), 1-18.
• Choi, S. W., Kong, W. S., Hwang, G. Y., & Koo, K. A. (2021). Trends in the effects of climate change on terrestrial ecosystems in the Republic of Korea. Journal of Ecology and Environment, 45(1), 13.
• Tantawi, A. M. M. E. (2005). Climate change in Libya and desertification of Jifara Plain: using geographical information system and remote sensing techniques. Doctoral Thesis, Johannes Gutenberg-Universität Mainz.
• Abera, W., Tamene, L., Abegaz, A., & Solomon, D. (2019). Understanding climate and land surface changes impact on water resources using Budyko framework and remote sensing data in Ethiopia. Journal of Arid Environments, 167, 56-64.
• Mu, Q., Zhao, M., Kimball, J. S., McDowell, N. G., & Running, S. W. (2013). A remotely sensed global terrestrial drought severity index. Bulletin of the American Meteorological Society, 94(1), 83-98.
• McCabe, G. J., & Wolock, D. M. (2015). Variability and trends in global drought. Earth and Space Science, 2(6), 223-228.