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Monitoring of agricultural drought in Turkey with remote sensing data by use of Google Earth Engine

Year 2024, Volume: 30 Issue: 1, 71 - 80, 29.02.2024

Abstract

Studies on drought, which can have quite complex and devastating effects due to a number of interrelated factors, are developing continuously in order to eliminate the consequences of such a natural disaster and even further to facilitate actions for taking timely measures. This study, which is carried out in this holistic framework, is based upon the temporal and spatial assessment of drought phenomenon on the Google Earth Engine (GEE) platform through the use of remotely-sensed data. For this purpose, Temperature Condition Index (TCI), Vegetation Condition Index (VCI) and Vegetation Health Index (VHI) were calculated both for the recent period (2018-2021) and the previous periods (the years 2002, 2005, 2010 and 2015) examined through July and January months and the agricultural drought situation was revealed in this way.

References

  • [1] Earth Org. “Severe Drought in Turkey Visualized by NASA Satellite”. https://earth.org/data_visualization/severe-drought-in-turkey-visualized-by-nasa-satellite/ (10.06.2021).
  • [2] Luo L, Apps D, Arcand S, Xu H, Pan M, Hoerling M. “Contribution of temperature and precipitation anomalies to the California drought during 2012–2015”. Geophysical Research. Letters, 44, 3184–3192, 2017.
  • [3] Svoboda MD, Fuchs BA. Handbook of Drought Indicators and Indices. Editors: Wilhite D, Pulwarty RS. Drought and Water Crises: Integrating Science, Management, and Policy 2nd, Boca Raton, CRC Press, 2017.
  • [4] AghaKouchak A, Farahmand A, Melton FS, Teixeira J, Anderson M C, Wardlow BD, Hain CR. “Remote sensing of drought: Progress, challenges and opportunities, Reviews of Geophysics, 53, 452-480, 2015.
  • [5] Sun S, Chen H, Ju W, Wang G, Sun G, Huang J, Ma H, Gao C, Hua W, Yan G. “On the coupling between precipitation and potential evapotranspiration: contributions to decadal drought anomalies in the Southwest China”. Climate Dynamics, 48, 3779-3797, 2017.
  • [6] Sohn SJ, Tam CY, Ashok K, Ahn JB. “Quantifying the reliability of precipitation datasets for monitoring large-scale East Asian precipitation variations”. International Journal of Climatology, 32(10), 1520-1526, 2012.
  • [7] West H, Quinn N, Horswell M. “Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities”. Remote Sensing of Environment, 232, 1-14, 2019.
  • [8] Jiao W, Wang L, McCabe MF. “Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future”. Remote Sensing of Environment, 256, 1-23, 2021.
  • [9] Vicente-Serrano, SM., “Evaluating the impact of drought using remote sensing in a Mediterranean, Semi-arid Region”. Natural Hazards, 40(1), 173-208, 2007.
  • [10] Nicolai-Shaw N, Zscheischler J, Hirschi M, Gudmundsson L, Seneviratne S.I.. “A drought event composite analysis using satellite remote-sensing based soil moisture”. Remote Sensing of Environment, 203, 216-225, 2017.
  • [11] Vicente-Serrano SM, Cabello D, Tomás-Burguera M, Martín-Hernández N, Beguería S, Azorin-Molina C, Kenawy A El.. “Drought variability and land degradation in semiarid regions: Assessment using remote sensing data and drought indices (1982-2011)”. Remote Sensing, 7, 4391- 4423, 2015.
  • [12] Smith WK, Dannenberg MP, Yan D, Herrmann S, Barnes M L, Barron-Gafford GA, Biederman J A, Ferrenberg S, Fox A M, Hudson A, Knowles J F, MacBean N, Moore DJP, Nagler PL, Reed SC, Rutherford WA, Scott RL, Wang X, Yang J. “Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities”. Remote Sening of Enviroment, 233, 1-23, 2019.
  • [13] Hu T, Renzullo LJ, van Dijk AIJM, He J, Tian S, Xu Z, Zhou J, Liu T, Liu Q. “Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals”. Remote Sensing of Environment, 236, 1-13, 2020.
  • [14] Sakamoto T. “Incorporating environmental variables into a MODIS-based crop yield estimation method for United States corn and soybeans through the use of a random forest regression algorithm”. ISPRS Journal of Photogrammetry and Remote Sensing, 160, 208-228, 2020
  • [15] Xu L, Abbaszadeh P, Moradkhani H, Chen N, Zhang X. “Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index”. Remote Sensing of Environment, 250, 1-17, 2020.
  • [16] Özelkan E. “Uzaktan algılama ile belirlenen baraj gölü alanının zamansal değişiminin meteorolojik kuraklık ile değerlendirilmesi: Atikhisar Barajı (Çanakkale) örneği”, Türk Tarım ve Doğa Bilimleri Dergisi, 6(4), 904-916, 2019.
  • [17] Çelik MA, Karabulut M.”Uydu tabanlı kuraklık indisi (svı) kullanılarak yarı kurak akdeniz ikliminde (Kilis) buğday bitkisinin kurak koşullara verdiği tepkinin incelenmesi”. Manisa Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 15(1), 111-130, 2017.
  • [18] Çakaroz D, Özelkan E, Karaman M. “Sulak alanlarda uzaktan algılama ile belirlenen zamansal değişime kuraklığın etkisinin incelenmesi: Umurbey Deltası (Çanakkale) örneği”. Avrupa Bilim ve Teknoloji Dergisi, 20, 898-916, 2020.
  • [19] Kaya ÖA, Kaplan G. “Uzaktan algılama yöntemleri ile burdur gölü’ndeki alansal değişiminin belirlenmesi”. Doğal Afetler ve Çevre Dergisi, 7(1), 1-12, 2021.
  • [20] Dikici M, Aksel M. ”Evaluation of two vegetation ındices (NDVI and VCI) over Asi Basin in Turkey”. Teknik Dergi, 32(4), 10995-11011, 2021.
  • [21] Topçu E, Seçkin N, Açanal Haktanır N.”Drought analyses of Eastern Mediterranean, Seyhan, Ceyhan, and Asi Basins by using aggregate drought index (ADI)”. Theory of Applied Climatology, 147, 909–924 (2022).
  • [22] Bento VA, Gouveia CM, DaCamara CC, Trigo IF. “A climatological assessment of drought impact on vegetation health index”. Agricultural and Forest Meteorology, 259, 286-295, 2018.
  • [23] Li Y, Strapasson A, Rojas O. “Assessment of El Niño and La Niña impacts on China: enhancing the early warning system on food and agriculture”. Weather and Climate Extremes, 27, 1-13, 2020,
  • [24] Kogan FN. “Droughts of the late 1980s in the United States as derived from NOAA polar orbiting satellite data”. Bulletin of American Meteorological Society, 76, 655-668, 1995.
  • [25] Kogan FN. “Global drought watch from space”. Bulletin of the American Meteorological Society,78, 621-636, 1997.
  • [26] Ghaleb F, Mario M, Sandra AN. “Regional Landsat-based drought monitoring from 1982 to 2014. Climate, 3, 563-577, 2015.
  • [27] Hu T, van Dijk AIJM, Renzullo LJ, Xu Z, He J, Tian S, Zhou J, Li H. “On agricultural drought monitoring in Australia using Himawari-8 geostationary thermal infrared observations”. International Journal of Applied. Earth Observation Geoinformation, 91, 1-13, 2020.
  • [28] Zeng J, Zhang R, Qu Y, Bento V A, Zhou T, Lin Y, Wu X, Qi J, Shui W, Wei S, Wang Q. “Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018”. Weather and Climate Extremes, 35, 1-14, 2022.
  • [29] Thenkabail PS. Remote Sensing of Water Resources, Disasters, and Urban Studies. 1th ed. Oca Racon, CRC Press, Taylor & Francis Group, 2016.
  • [30] United Nations. “In Detail: Agriculture Drought Monitoring and Hazard Assessment using Google Earth Engine”. https://www.un-spider.org/advisory-support/recommended-practices/recommended-practice-agriculture-drought-monitoring/in-detail (05.04.2022).
  • [31] United States Geological Survey. “MOD11A2 v006 MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1 km SIN Grid”. (https://lpdaac.usgs.gov/products/mod11a2v006/) (01.03.2022).
  • [32] United States Geological Survey. “MOD13Q1 v006 MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid”. (https://lpdaac.usgs.gov/products/mod13q1v006/) (01.03.2022).
  • [33] Drisya J, Kumar DS, Roshni T. Global Case Studies in Mitigation and Recovery. Spatiotemporal Variability of Soil Moisture and Drought Estimation Using a Distributed Hydrological Model. Editors: Samui P, Kim D, Ghosh C. Integrating Disaster Science and Management, 451-460, Elsevier, 2018.
  • [34] Choubin B, Soleimani F, Pirnia A, Sajedi-Hosseini F, Alilou H, Rahmati O, Melesse A M, Singh V P, Shahabi H. Effects of drought on vegetative cover changes: Investigating spatiotemporal patterns, Editors: Melesse AM, Wossenu A., Senay G. Extreme Hydrology and Climate Variability Monitoring, Modelling, Adaptation, Mitigatiton, 213-222, Elsevier, 2019.
  • [35] Xie F, Fan H. “Deriving drought indices from MODIS vegetation indices (NDVI/EVI) and Land Surface Temperature (LST): Is data reconstruction necessary?”. International Journal of Applied Earth Observation and Geoinformation, 101, 1-16, 2021.
  • [36] Nanzad L, Zhang J, Tuvdendorj B, Nabil M, Zhang S, Bai Y. “NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016”. Journal of Arid Environments, 164, 69-77, 2019.
  • [37] Sruthi S., Aslam MAM. “Agricultural drought analysis using the NDVI and land surface temperature data; a case study of Raichur District”. Aquatic Procedia, 4, 1258-1264, 2015.
  • [38] Lakshmi V. Remote Sensing of Hydrological Extremes. 1th ed. Switzerland, Springer International Publishing, 2017.
  • [39] Spatial Thoughts. “Working with QA Bands and Bitmasks in Google Earth Engine”. https://spatialthoughts.com/2021/08/19/qa-bands-bitmasks-gee/ (10.12.2022).
  • [40] Google Earth Engine for Water Resources Management (Full Course Material). “Application-focused Introduction to Google Earth Engine”. https://courses.spatialthoughts.com/gee-water-resources-management.html (10.12.2022).

Türkiye’de tarımsal kuraklığın uzaktan algılama verileri ile Google Earth Engine üzerinden izlenmesi

Year 2024, Volume: 30 Issue: 1, 71 - 80, 29.02.2024

Abstract

Birbiriyle ilişkili birçok etkene bağlı olduğundan oldukça karmaşık ve yıkıcı etkileri olabilen kuraklıkla ilgili çalışmalar, bu doğal afetin sonuçlarının bertaraf edilebilmesi ve önlemlerin zamanında alınarak harekete geçilebilmesini kolaylaştırmak amacıyla bir süreklilik gösterecek şekilde gelişmektedir. Bütünsel çerçevede gerçekleştirilen bu çalışmada, kuraklık olgusunun zamansal ve mekânsal olarak, uzaktan algılama verileri kullanılarak Google Earth Engine (GEE) platformunda hesaplanmasına dayanmaktadır. Bu amaçla, Sıcaklık Durum İndisi (TCI), Bitki Örtüsü Durumu İndisi (VCI) ve Bitki Örtüsü Sağlığı İndisi (VHI), yakın geçmiş dönem (2018-2021) ile geçmiş dönemler (2002, 2005, 2010 ve 2015 yılları) için temmuz ve ocak ayları bazında incelenmiş ve böylece tarımsal kuraklık durumu ortaya konulmuştur.

References

  • [1] Earth Org. “Severe Drought in Turkey Visualized by NASA Satellite”. https://earth.org/data_visualization/severe-drought-in-turkey-visualized-by-nasa-satellite/ (10.06.2021).
  • [2] Luo L, Apps D, Arcand S, Xu H, Pan M, Hoerling M. “Contribution of temperature and precipitation anomalies to the California drought during 2012–2015”. Geophysical Research. Letters, 44, 3184–3192, 2017.
  • [3] Svoboda MD, Fuchs BA. Handbook of Drought Indicators and Indices. Editors: Wilhite D, Pulwarty RS. Drought and Water Crises: Integrating Science, Management, and Policy 2nd, Boca Raton, CRC Press, 2017.
  • [4] AghaKouchak A, Farahmand A, Melton FS, Teixeira J, Anderson M C, Wardlow BD, Hain CR. “Remote sensing of drought: Progress, challenges and opportunities, Reviews of Geophysics, 53, 452-480, 2015.
  • [5] Sun S, Chen H, Ju W, Wang G, Sun G, Huang J, Ma H, Gao C, Hua W, Yan G. “On the coupling between precipitation and potential evapotranspiration: contributions to decadal drought anomalies in the Southwest China”. Climate Dynamics, 48, 3779-3797, 2017.
  • [6] Sohn SJ, Tam CY, Ashok K, Ahn JB. “Quantifying the reliability of precipitation datasets for monitoring large-scale East Asian precipitation variations”. International Journal of Climatology, 32(10), 1520-1526, 2012.
  • [7] West H, Quinn N, Horswell M. “Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities”. Remote Sensing of Environment, 232, 1-14, 2019.
  • [8] Jiao W, Wang L, McCabe MF. “Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future”. Remote Sensing of Environment, 256, 1-23, 2021.
  • [9] Vicente-Serrano, SM., “Evaluating the impact of drought using remote sensing in a Mediterranean, Semi-arid Region”. Natural Hazards, 40(1), 173-208, 2007.
  • [10] Nicolai-Shaw N, Zscheischler J, Hirschi M, Gudmundsson L, Seneviratne S.I.. “A drought event composite analysis using satellite remote-sensing based soil moisture”. Remote Sensing of Environment, 203, 216-225, 2017.
  • [11] Vicente-Serrano SM, Cabello D, Tomás-Burguera M, Martín-Hernández N, Beguería S, Azorin-Molina C, Kenawy A El.. “Drought variability and land degradation in semiarid regions: Assessment using remote sensing data and drought indices (1982-2011)”. Remote Sensing, 7, 4391- 4423, 2015.
  • [12] Smith WK, Dannenberg MP, Yan D, Herrmann S, Barnes M L, Barron-Gafford GA, Biederman J A, Ferrenberg S, Fox A M, Hudson A, Knowles J F, MacBean N, Moore DJP, Nagler PL, Reed SC, Rutherford WA, Scott RL, Wang X, Yang J. “Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities”. Remote Sening of Enviroment, 233, 1-23, 2019.
  • [13] Hu T, Renzullo LJ, van Dijk AIJM, He J, Tian S, Xu Z, Zhou J, Liu T, Liu Q. “Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals”. Remote Sensing of Environment, 236, 1-13, 2020.
  • [14] Sakamoto T. “Incorporating environmental variables into a MODIS-based crop yield estimation method for United States corn and soybeans through the use of a random forest regression algorithm”. ISPRS Journal of Photogrammetry and Remote Sensing, 160, 208-228, 2020
  • [15] Xu L, Abbaszadeh P, Moradkhani H, Chen N, Zhang X. “Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index”. Remote Sensing of Environment, 250, 1-17, 2020.
  • [16] Özelkan E. “Uzaktan algılama ile belirlenen baraj gölü alanının zamansal değişiminin meteorolojik kuraklık ile değerlendirilmesi: Atikhisar Barajı (Çanakkale) örneği”, Türk Tarım ve Doğa Bilimleri Dergisi, 6(4), 904-916, 2019.
  • [17] Çelik MA, Karabulut M.”Uydu tabanlı kuraklık indisi (svı) kullanılarak yarı kurak akdeniz ikliminde (Kilis) buğday bitkisinin kurak koşullara verdiği tepkinin incelenmesi”. Manisa Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 15(1), 111-130, 2017.
  • [18] Çakaroz D, Özelkan E, Karaman M. “Sulak alanlarda uzaktan algılama ile belirlenen zamansal değişime kuraklığın etkisinin incelenmesi: Umurbey Deltası (Çanakkale) örneği”. Avrupa Bilim ve Teknoloji Dergisi, 20, 898-916, 2020.
  • [19] Kaya ÖA, Kaplan G. “Uzaktan algılama yöntemleri ile burdur gölü’ndeki alansal değişiminin belirlenmesi”. Doğal Afetler ve Çevre Dergisi, 7(1), 1-12, 2021.
  • [20] Dikici M, Aksel M. ”Evaluation of two vegetation ındices (NDVI and VCI) over Asi Basin in Turkey”. Teknik Dergi, 32(4), 10995-11011, 2021.
  • [21] Topçu E, Seçkin N, Açanal Haktanır N.”Drought analyses of Eastern Mediterranean, Seyhan, Ceyhan, and Asi Basins by using aggregate drought index (ADI)”. Theory of Applied Climatology, 147, 909–924 (2022).
  • [22] Bento VA, Gouveia CM, DaCamara CC, Trigo IF. “A climatological assessment of drought impact on vegetation health index”. Agricultural and Forest Meteorology, 259, 286-295, 2018.
  • [23] Li Y, Strapasson A, Rojas O. “Assessment of El Niño and La Niña impacts on China: enhancing the early warning system on food and agriculture”. Weather and Climate Extremes, 27, 1-13, 2020,
  • [24] Kogan FN. “Droughts of the late 1980s in the United States as derived from NOAA polar orbiting satellite data”. Bulletin of American Meteorological Society, 76, 655-668, 1995.
  • [25] Kogan FN. “Global drought watch from space”. Bulletin of the American Meteorological Society,78, 621-636, 1997.
  • [26] Ghaleb F, Mario M, Sandra AN. “Regional Landsat-based drought monitoring from 1982 to 2014. Climate, 3, 563-577, 2015.
  • [27] Hu T, van Dijk AIJM, Renzullo LJ, Xu Z, He J, Tian S, Zhou J, Li H. “On agricultural drought monitoring in Australia using Himawari-8 geostationary thermal infrared observations”. International Journal of Applied. Earth Observation Geoinformation, 91, 1-13, 2020.
  • [28] Zeng J, Zhang R, Qu Y, Bento V A, Zhou T, Lin Y, Wu X, Qi J, Shui W, Wei S, Wang Q. “Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018”. Weather and Climate Extremes, 35, 1-14, 2022.
  • [29] Thenkabail PS. Remote Sensing of Water Resources, Disasters, and Urban Studies. 1th ed. Oca Racon, CRC Press, Taylor & Francis Group, 2016.
  • [30] United Nations. “In Detail: Agriculture Drought Monitoring and Hazard Assessment using Google Earth Engine”. https://www.un-spider.org/advisory-support/recommended-practices/recommended-practice-agriculture-drought-monitoring/in-detail (05.04.2022).
  • [31] United States Geological Survey. “MOD11A2 v006 MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1 km SIN Grid”. (https://lpdaac.usgs.gov/products/mod11a2v006/) (01.03.2022).
  • [32] United States Geological Survey. “MOD13Q1 v006 MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid”. (https://lpdaac.usgs.gov/products/mod13q1v006/) (01.03.2022).
  • [33] Drisya J, Kumar DS, Roshni T. Global Case Studies in Mitigation and Recovery. Spatiotemporal Variability of Soil Moisture and Drought Estimation Using a Distributed Hydrological Model. Editors: Samui P, Kim D, Ghosh C. Integrating Disaster Science and Management, 451-460, Elsevier, 2018.
  • [34] Choubin B, Soleimani F, Pirnia A, Sajedi-Hosseini F, Alilou H, Rahmati O, Melesse A M, Singh V P, Shahabi H. Effects of drought on vegetative cover changes: Investigating spatiotemporal patterns, Editors: Melesse AM, Wossenu A., Senay G. Extreme Hydrology and Climate Variability Monitoring, Modelling, Adaptation, Mitigatiton, 213-222, Elsevier, 2019.
  • [35] Xie F, Fan H. “Deriving drought indices from MODIS vegetation indices (NDVI/EVI) and Land Surface Temperature (LST): Is data reconstruction necessary?”. International Journal of Applied Earth Observation and Geoinformation, 101, 1-16, 2021.
  • [36] Nanzad L, Zhang J, Tuvdendorj B, Nabil M, Zhang S, Bai Y. “NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016”. Journal of Arid Environments, 164, 69-77, 2019.
  • [37] Sruthi S., Aslam MAM. “Agricultural drought analysis using the NDVI and land surface temperature data; a case study of Raichur District”. Aquatic Procedia, 4, 1258-1264, 2015.
  • [38] Lakshmi V. Remote Sensing of Hydrological Extremes. 1th ed. Switzerland, Springer International Publishing, 2017.
  • [39] Spatial Thoughts. “Working with QA Bands and Bitmasks in Google Earth Engine”. https://spatialthoughts.com/2021/08/19/qa-bands-bitmasks-gee/ (10.12.2022).
  • [40] Google Earth Engine for Water Resources Management (Full Course Material). “Application-focused Introduction to Google Earth Engine”. https://courses.spatialthoughts.com/gee-water-resources-management.html (10.12.2022).
There are 40 citations in total.

Details

Primary Language Turkish
Subjects Civil Engineering (Other)
Journal Section Research Article
Authors

Gülay Onuşluel Gül

Publication Date February 29, 2024
Published in Issue Year 2024 Volume: 30 Issue: 1

Cite

APA Onuşluel Gül, G. (2024). Türkiye’de tarımsal kuraklığın uzaktan algılama verileri ile Google Earth Engine üzerinden izlenmesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 30(1), 71-80.
AMA Onuşluel Gül G. Türkiye’de tarımsal kuraklığın uzaktan algılama verileri ile Google Earth Engine üzerinden izlenmesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. February 2024;30(1):71-80.
Chicago Onuşluel Gül, Gülay. “Türkiye’de tarımsal kuraklığın Uzaktan algılama Verileri Ile Google Earth Engine üzerinden Izlenmesi”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 30, no. 1 (February 2024): 71-80.
EndNote Onuşluel Gül G (February 1, 2024) Türkiye’de tarımsal kuraklığın uzaktan algılama verileri ile Google Earth Engine üzerinden izlenmesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 30 1 71–80.
IEEE G. Onuşluel Gül, “Türkiye’de tarımsal kuraklığın uzaktan algılama verileri ile Google Earth Engine üzerinden izlenmesi”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 30, no. 1, pp. 71–80, 2024.
ISNAD Onuşluel Gül, Gülay. “Türkiye’de tarımsal kuraklığın Uzaktan algılama Verileri Ile Google Earth Engine üzerinden Izlenmesi”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 30/1 (February 2024), 71-80.
JAMA Onuşluel Gül G. Türkiye’de tarımsal kuraklığın uzaktan algılama verileri ile Google Earth Engine üzerinden izlenmesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2024;30:71–80.
MLA Onuşluel Gül, Gülay. “Türkiye’de tarımsal kuraklığın Uzaktan algılama Verileri Ile Google Earth Engine üzerinden Izlenmesi”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 30, no. 1, 2024, pp. 71-80.
Vancouver Onuşluel Gül G. Türkiye’de tarımsal kuraklığın uzaktan algılama verileri ile Google Earth Engine üzerinden izlenmesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2024;30(1):71-80.

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