نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری، گروه علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه بین المللی امام خمینی(ره)، قزوین، ایران
2 دانشیار گروه علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه بین المللی امام خمینی(ره)، قزوین، ایران.
چکیده
این مطالعه به شناسایی و توصیف خشکسالی با استفاده از شاخص‌های چندگانه مبتنی بر MODIS پرداخته بنابراین ویژگی‌های زمانی- مکانی شدت و فراوانی خشکسالی در کل کشور در بازه زمانی سال‌های 2001 تا 2021 با استفاده از شاخص بارش استاندارد شده به صورت یک ماهه SPI-1، سه ماهه SPI-3 و یکساله SPI-12، براساس مجموعه داده بارش CHIRPS با قدرت تفکیک مکانی 5 کیلومتر، شاخص وضعیت پوشش گیاهی (VCI)، شاخص وضعیت دما (TCI) و شاخص سلامت پوشش گیاهی (VHI) مورد بررسی قرار گرفت. پراکندگی بارندگی در نواحی جنوب شرقی و نواحی مرکزی کشور کمتر از 200 میلی‌متر در سال است. بررسی نسبت کلاس‌های خشکسالی بر اساس شاخص TCI نشان می‌دهد نسبت مساحت مناطقی که در سال 2020 و 2021 در کلاس خشکی شدید قرار دارند به ترتیب 7/36% و 2/43% بوده که حدود 7 درصد افزایش داشته است. مقایسه مساحت کلاس خشکسالی دو شاخص TCI و VCI نیز نشان می‌دهد که شاخص VCI در سال 2020 و 2021 به میزان 7/3% و 1/5% سطح مناطقی که در کلاس خشکسالی شدید قرار دارند را بیشتر برآورد کرده است. همچنین شاخص VHI نشان می‌دهد 6 استان ناحیه جنوبی کشور، بین بازه زمانی سال‌های 2009 تا 2021 خشکسالی طولانی مدت را تجربه کرده‌اند.
کلیدواژه‌ها
موضوعات
Alahacoon, N., Edirisinghe, M. & Ranagalage, M. (2021). Satellite-based meteorological and agricultural drought monitoring for agricultural sustainability in Sri Lanka, Sustainability, 13(6), 3427. https://doi.org/10.3390/su13063427
Bayissa, Y. A., Tadesse, T., Svoboda, M., Wardlow, B., Poulsen, C., Swigart, J. & Van Andel, S. J. (2019). Developing a satellite-based combined drought indicator to monitor agricultural drought: A case study for Ethiopia, GIScience & Remote Sensing, 56(5), 718–748. https://doi.org/10.1080/15481603.2018.1552508
Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R. & Ilyushchenko, S. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping, Scientific Data, 9(1), 251. https://doi.org/10.1038/s41597-022-01307-4
Carrão, H., Naumann, G. & Barbosa, P. (2016). Mapping global patterns of drought risk: An empirical framework based on sub-national estimates of hazard, exposure and vulnerability, Global Environmental Change, 39, 108–124. https://doi.org/10.1016/j.gloenvcha.2016.04.012
Dasari, H. P., V, B. R., Ssvs, R. & Gunta, P. (2017). On the movement of tropical cyclone LEHAR, Earth Systems and Environment, 1, 1–14. https://doi.org/10.1007/s41748-017-0025-7
Doughty, R., Xiao, X., Wu, X., Zhang, Y., Bajgain, R., Zhou, Y., Qin, Y., Zou, Z., McCarthy, H. & Friedman, J. (2018). Responses of gross primary production of grasslands and croplands under drought, pluvial, and irrigation conditions during 2010–2016, Oklahoma, USA, Agricultural Water Management, 204, 47–59. https://doi.org/10.1016/j.agwat.2018.04.001
Dutta, D., Kundu, A., Patel, N. R., Saha, S. K. & Siddiqui, A. R. (2015). Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI), The Egyptian Journal of Remote Sensing and Space Science, 18(1), 53–63. https://doi.org/10.1016/j.ejrs.2015.03.006
Ejaz, N., Bahrawi, J., Alghamdi, K. M., Rahman, K. U. & Shang, S. (2023). Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case study from Al-Lith Watershed, Kingdom of Saudi Arabia, Remote Sensing, 15(4), 984. https://doi.org/10.3390/rs15040984
Fang, W., Huang, S., Huang, Q., Huang, G., Wang, H., Leng, G., Wang, L. & Guo, Y. (2019). Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China, Remote Sensing of Environment, 232, 111290. https://doi.org/10.1016/j.rse.2019.111290
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L. & Hoell, A. (2015). The climate hazards infrared precipitation with stations—a newenvironmental record for monitoring extremes, Scientific Data, 2(1), 1–21. Gidey, E., Dikinya, O., Sebego, R., Segosebe, E. & Zenebe, A. (2018). Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using Vegetation Health Index (VHI) in Raya and its environs, Northern Ethiopia, Environmental Systems Research, 7, 1–18. https://doi.org/10.1186/s40068-018-0115-z
Golian, S., Mazdiyasni, O. & AghaKouchak, A. (2015). Trends in meteorological and agricultural droughts in Iran, Theoretical and Applied Climatology,119, 679–688. https://doi.org/10.1007/s00704-014-1139-6
Iranian Ministry of Energy (2018). Annual water resources report, IME publication. [In Persian]
Jafari, S. M., Nikoo, M. R., Dehghani, M. & Alijanian, M. (2020). Evaluation of two satellite-based products against ground-based observation for drought analysis in the southern part of Iran, Natural Hazards, 102, 1249–1267. https://doi.org/10.1007/s11069-020-03965-2
Jalayer, S., Sharifi, A., Abbasi-Moghadam, D., Tariq, A. & Qin, S. (2022). Modeling and predicting land use land cover spatiotemporal changes: A case study in chalus watershed, Iran, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5496–5513. DOI:10.1109/JSTARS.2022.3189528
Ji, T., Li, G., Yang, H., Liu, R. & He, T. (2018). Comprehensive drought index as an indicator for use in drought monitoring integrating multi-source remote sensing data: a case study covering the Sichuan-Chongqing region, International Journal of Remote Sensing, 39(3), 786–809. https://doi.org/10.1080/01431161.2017.1392635
Kaboli, S., Hekmatzadeh, A. A., Darabi, H. & Haghighi, A. T. (2021). Variation in physical characteristics of rainfall in Iran, determined using daily rainfall concentration index and monthly rainfall percentage index, Theoretical and Applied Climatology, 144, 507–520. https://doi.org/10.1007/s00704-021-03553-9
Khalili, N., Mosaedi, A. & Latifi, N. (2007). Investigation of drought and its occurrence trend in Urmia, Journal of Agricultural Sciences and Natural Resources, 14(5), 156-146. [In Persian]
Kogan, F. (2002). World droughts in the new millennium from AVHRR‐based vegetation health indices, Eos, Transactions American Geophysical Union, 83(48), 557–563. https://doi.org/10.1029/2002EO000382
Kogan, F., Guo, W., strashnaia, A., Kleshenko, A., Chub, O. & Virchenko, O. (2016). Modelling and prediction of crop losses from NOAA polarorbiting operational satellites, Geomatics, Natural Hazards and Risk, 7(3), 886–900. https://doi.org/10.1080/19475705.2015.1009178
Kogan, F. N. (1990). Remote sensing of weather impacts on vegetation in non-homogeneous areas, International Journal of Remote Sensing, 11(8), 1405–1419. https://doi.org/10.1080/01431169008955102
Kogan, F. N. (2001). Operational space technology for global vegetation assessment, Bulletin of the American Meteorological Society, 82(9), 1949–1964. https://doi.org/10.1175/1520-0477
Kogan, F. N. (1995). Application of vegetation index and brightnesstemperature for drought detection, Advances in space research, 15(11), 91-100.
Liu, Q., Zhang, J., Zhang, H., Yao, F., Bai, Y., Zhang, S., Meng, X. & Liu, Q. (2021). Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China, Science of the Total Environment, 789, 147803. https://doi.org/10.1016/j.scitotenv.2021.147803
Lloyd‐Hughes, B. & Saunders, M. A. (2002). A drought climatology for Europe, International Journal of Climatology: A Journal of the Royal Meteorological Society, 22(13), 1571–1592. DOI: 10.1002/joc.846
McKee, T. B., Doesken, N. J. & Kleist, J. (1993). The relationship of drought frequency and duration to time scales, Proceedings of the 8th Conference on Applied Climatology, 17(22), 179–183.
Paredes-Trejo, F. J., Barbosa, H. A. & Kumar, T. V. L. (2017). Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil, Journal of Arid Environments, 139, 26–40. https://doi.org/10.1016/j.jaridenv.2016.12.009
Palmer, W. C. (1965). Meteorological drought. Vol.30, US Department of Commerce, Weather Bureau.
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. https://doi.org/10.3390/rs13091715
Qin, Q., Wu, Z., Zhang, T., Sagan, V., Zhang, Z., Zhang, Y., Zhang, C., Ren, H., Sun, Y. & Xu, W. (2021). Optical and thermal remote sensing for monitoring agricultural drought, Remote Sensing, 13(24), 5092. https://doi.org/10.3390/rs13245092
Rahaman, S. A. & Venkatesh, R. (2020). Application of remote sensing and google earth engine for monitoring environmental degradation in the Nilgiri biosphere reserve and its ecosystem of Western Ghats, India, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 933–940. DOI:10.5194/isprs-archives-XLIII-B3-2020-933-2020
Rahimi, J., Ebrahimpour, M. & Khalili, A. (2013). Spatial changes of extended De Martonne climatic zones affected by climate change in Iran, Theoretical and Applied Climatology, 112, 409–418. https://doi.org/10.1007/s00704-012-0741-8
Shahabfar, A. & Eitzinger, J. (2013). Spatio-temporal analysis of droughts in semi-arid regions by using meteorological drought indices, Atmosphere, 4(2), 94–12.https://doi.org/10.3390/atmos4020094
Sharifi, A., Mahdipour, H., Moradi, E. & Tariq, A. (2022). Agricultural field extraction with deep learning algorithm and satellite imagery, Journal of the Indian Society of Remote Sensing, 1–7. https://doi.org/10.1007/s12524-021-01475-7
Silleos, N. G., Alexandridis, T. K., Gitas, I. Z. & Perakis, K. (2006). Vegetation indices: advances made in biomass estimation and vegetation monitoring in the last 30 years, Geocarto International, 21(4), 21–28. https://doi.org/10.1080/10106040608542399
Sivakumar, M. (2021). Climate change, agriculture adaptation, and sustainability, Climate Resilience and Environmental Sustainability Approaches:Global Lessons and Local Challenges, 87–109. https://doi.org/10.1007/978-981-16-0902-2_6
Souza, A. G. S. S., Neto, A. R. & de Souza, L. L. (2021). Soil moisture-based index for agricultural drought assessment: SMADI application in Pernambuco State-Brazil, Remote Sensing of Environment, 252, 112124. https://doi.org/10.1016/j.rse.2020.112124
Tucker, C. J. & Choudhury, B. J. (1987). Satellite remote sensing of drought conditions, Remote Sensing of Environment, 23(2), 243–251. https://doi.org/10.1016/0034-4257(87)90040-X
Wang, L., Chen, W., Fu, Q., Huang, G., Wang, Q., Chotamonsak, C. & Limsakul, A. (2022). Super droughts over East Asia since 1960 under the impacts of global warming and decadal variability, International Journal of Climatology, 42(9), 4508–4521. DOI: 10.1002/joc.7483
Wassie, S. B., Mengistu, D. A. & Birlie, A. B. (2022). Agricultural drought assessment and monitoring using MODIS-based multiple indices: the case of North Wollo, Ethiopia, Environmental Monitoring and Assessment, 194(11), 787.https://doi.org/10.1007/s10661-022-10455-4
Won, J., Choi, J., Lee, O. & Kim, S. (2020). Copula-based Joint Drought Index using SPI and EDDI and its application to climate change, Science of the Total Environment, 744, 140701. https://doi.org/10.1016/j.scitotenv.2020.140701
Xu, H., Wang, X. & Zhao, C. (2021). Drought sensitivity of vegetation photosynthesis along the aridity gradient in northern China, International Journal of Applied Earth Observation and Geoinformation, 102, 102418. https://doi.org/10.1016/j.jag.2021.102418
Zhao, X., Xia, H., Liu, B. & Jiao, W. (2022). Spatiotemporal comparison of drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 using various drought indices in google earth engine, Remote Sensing, 14(7), 1570. https://doi.org/10.3390/rs14071570
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. https://doi.org/10.1016/j.compag.2019.105144
Bayissa, Y. A., Tadesse, T., Svoboda, M., Wardlow, B., Poulsen, C., Swigart, J. & Van Andel, S. J. (2019). Developing a satellite-based combined drought indicator to monitor agricultural drought: A case study for Ethiopia, GIScience & Remote Sensing, 56(5), 718–748. https://doi.org/10.1080/15481603.2018.1552508
Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R. & Ilyushchenko, S. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping, Scientific Data, 9(1), 251. https://doi.org/10.1038/s41597-022-01307-4
Carrão, H., Naumann, G. & Barbosa, P. (2016). Mapping global patterns of drought risk: An empirical framework based on sub-national estimates of hazard, exposure and vulnerability, Global Environmental Change, 39, 108–124. https://doi.org/10.1016/j.gloenvcha.2016.04.012
Dasari, H. P., V, B. R., Ssvs, R. & Gunta, P. (2017). On the movement of tropical cyclone LEHAR, Earth Systems and Environment, 1, 1–14. https://doi.org/10.1007/s41748-017-0025-7
Doughty, R., Xiao, X., Wu, X., Zhang, Y., Bajgain, R., Zhou, Y., Qin, Y., Zou, Z., McCarthy, H. & Friedman, J. (2018). Responses of gross primary production of grasslands and croplands under drought, pluvial, and irrigation conditions during 2010–2016, Oklahoma, USA, Agricultural Water Management, 204, 47–59. https://doi.org/10.1016/j.agwat.2018.04.001
Dutta, D., Kundu, A., Patel, N. R., Saha, S. K. & Siddiqui, A. R. (2015). Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI), The Egyptian Journal of Remote Sensing and Space Science, 18(1), 53–63. https://doi.org/10.1016/j.ejrs.2015.03.006
Ejaz, N., Bahrawi, J., Alghamdi, K. M., Rahman, K. U. & Shang, S. (2023). Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case study from Al-Lith Watershed, Kingdom of Saudi Arabia, Remote Sensing, 15(4), 984. https://doi.org/10.3390/rs15040984
Fang, W., Huang, S., Huang, Q., Huang, G., Wang, H., Leng, G., Wang, L. & Guo, Y. (2019). Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China, Remote Sensing of Environment, 232, 111290. https://doi.org/10.1016/j.rse.2019.111290
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L. & Hoell, A. (2015). The climate hazards infrared precipitation with stations—a newenvironmental record for monitoring extremes, Scientific Data, 2(1), 1–21. Gidey, E., Dikinya, O., Sebego, R., Segosebe, E. & Zenebe, A. (2018). Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using Vegetation Health Index (VHI) in Raya and its environs, Northern Ethiopia, Environmental Systems Research, 7, 1–18. https://doi.org/10.1186/s40068-018-0115-z
Golian, S., Mazdiyasni, O. & AghaKouchak, A. (2015). Trends in meteorological and agricultural droughts in Iran, Theoretical and Applied Climatology,119, 679–688. https://doi.org/10.1007/s00704-014-1139-6
Iranian Ministry of Energy (2018). Annual water resources report, IME publication. [In Persian]
Jafari, S. M., Nikoo, M. R., Dehghani, M. & Alijanian, M. (2020). Evaluation of two satellite-based products against ground-based observation for drought analysis in the southern part of Iran, Natural Hazards, 102, 1249–1267. https://doi.org/10.1007/s11069-020-03965-2
Jalayer, S., Sharifi, A., Abbasi-Moghadam, D., Tariq, A. & Qin, S. (2022). Modeling and predicting land use land cover spatiotemporal changes: A case study in chalus watershed, Iran, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5496–5513. DOI:10.1109/JSTARS.2022.3189528
Ji, T., Li, G., Yang, H., Liu, R. & He, T. (2018). Comprehensive drought index as an indicator for use in drought monitoring integrating multi-source remote sensing data: a case study covering the Sichuan-Chongqing region, International Journal of Remote Sensing, 39(3), 786–809. https://doi.org/10.1080/01431161.2017.1392635
Kaboli, S., Hekmatzadeh, A. A., Darabi, H. & Haghighi, A. T. (2021). Variation in physical characteristics of rainfall in Iran, determined using daily rainfall concentration index and monthly rainfall percentage index, Theoretical and Applied Climatology, 144, 507–520. https://doi.org/10.1007/s00704-021-03553-9
Khalili, N., Mosaedi, A. & Latifi, N. (2007). Investigation of drought and its occurrence trend in Urmia, Journal of Agricultural Sciences and Natural Resources, 14(5), 156-146. [In Persian]
Kogan, F. (2002). World droughts in the new millennium from AVHRR‐based vegetation health indices, Eos, Transactions American Geophysical Union, 83(48), 557–563. https://doi.org/10.1029/2002EO000382
Kogan, F., Guo, W., strashnaia, A., Kleshenko, A., Chub, O. & Virchenko, O. (2016). Modelling and prediction of crop losses from NOAA polarorbiting operational satellites, Geomatics, Natural Hazards and Risk, 7(3), 886–900. https://doi.org/10.1080/19475705.2015.1009178
Kogan, F. N. (1990). Remote sensing of weather impacts on vegetation in non-homogeneous areas, International Journal of Remote Sensing, 11(8), 1405–1419. https://doi.org/10.1080/01431169008955102
Kogan, F. N. (2001). Operational space technology for global vegetation assessment, Bulletin of the American Meteorological Society, 82(9), 1949–1964. https://doi.org/10.1175/1520-0477
Kogan, F. N. (1995). Application of vegetation index and brightnesstemperature for drought detection, Advances in space research, 15(11), 91-100.
Liu, Q., Zhang, J., Zhang, H., Yao, F., Bai, Y., Zhang, S., Meng, X. & Liu, Q. (2021). Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China, Science of the Total Environment, 789, 147803. https://doi.org/10.1016/j.scitotenv.2021.147803
Lloyd‐Hughes, B. & Saunders, M. A. (2002). A drought climatology for Europe, International Journal of Climatology: A Journal of the Royal Meteorological Society, 22(13), 1571–1592. DOI: 10.1002/joc.846
McKee, T. B., Doesken, N. J. & Kleist, J. (1993). The relationship of drought frequency and duration to time scales, Proceedings of the 8th Conference on Applied Climatology, 17(22), 179–183.
Paredes-Trejo, F. J., Barbosa, H. A. & Kumar, T. V. L. (2017). Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil, Journal of Arid Environments, 139, 26–40. https://doi.org/10.1016/j.jaridenv.2016.12.009
Palmer, W. C. (1965). Meteorological drought. Vol.30, US Department of Commerce, Weather Bureau.
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. https://doi.org/10.3390/rs13091715
Qin, Q., Wu, Z., Zhang, T., Sagan, V., Zhang, Z., Zhang, Y., Zhang, C., Ren, H., Sun, Y. & Xu, W. (2021). Optical and thermal remote sensing for monitoring agricultural drought, Remote Sensing, 13(24), 5092. https://doi.org/10.3390/rs13245092
Rahaman, S. A. & Venkatesh, R. (2020). Application of remote sensing and google earth engine for monitoring environmental degradation in the Nilgiri biosphere reserve and its ecosystem of Western Ghats, India, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 933–940. DOI:10.5194/isprs-archives-XLIII-B3-2020-933-2020
Rahimi, J., Ebrahimpour, M. & Khalili, A. (2013). Spatial changes of extended De Martonne climatic zones affected by climate change in Iran, Theoretical and Applied Climatology, 112, 409–418. https://doi.org/10.1007/s00704-012-0741-8
Shahabfar, A. & Eitzinger, J. (2013). Spatio-temporal analysis of droughts in semi-arid regions by using meteorological drought indices, Atmosphere, 4(2), 94–12.https://doi.org/10.3390/atmos4020094
Sharifi, A., Mahdipour, H., Moradi, E. & Tariq, A. (2022). Agricultural field extraction with deep learning algorithm and satellite imagery, Journal of the Indian Society of Remote Sensing, 1–7. https://doi.org/10.1007/s12524-021-01475-7
Silleos, N. G., Alexandridis, T. K., Gitas, I. Z. & Perakis, K. (2006). Vegetation indices: advances made in biomass estimation and vegetation monitoring in the last 30 years, Geocarto International, 21(4), 21–28. https://doi.org/10.1080/10106040608542399
Sivakumar, M. (2021). Climate change, agriculture adaptation, and sustainability, Climate Resilience and Environmental Sustainability Approaches:Global Lessons and Local Challenges, 87–109. https://doi.org/10.1007/978-981-16-0902-2_6
Souza, A. G. S. S., Neto, A. R. & de Souza, L. L. (2021). Soil moisture-based index for agricultural drought assessment: SMADI application in Pernambuco State-Brazil, Remote Sensing of Environment, 252, 112124. https://doi.org/10.1016/j.rse.2020.112124
Tucker, C. J. & Choudhury, B. J. (1987). Satellite remote sensing of drought conditions, Remote Sensing of Environment, 23(2), 243–251. https://doi.org/10.1016/0034-4257(87)90040-X
Wang, L., Chen, W., Fu, Q., Huang, G., Wang, Q., Chotamonsak, C. & Limsakul, A. (2022). Super droughts over East Asia since 1960 under the impacts of global warming and decadal variability, International Journal of Climatology, 42(9), 4508–4521. DOI: 10.1002/joc.7483
Wassie, S. B., Mengistu, D. A. & Birlie, A. B. (2022). Agricultural drought assessment and monitoring using MODIS-based multiple indices: the case of North Wollo, Ethiopia, Environmental Monitoring and Assessment, 194(11), 787.https://doi.org/10.1007/s10661-022-10455-4
Won, J., Choi, J., Lee, O. & Kim, S. (2020). Copula-based Joint Drought Index using SPI and EDDI and its application to climate change, Science of the Total Environment, 744, 140701. https://doi.org/10.1016/j.scitotenv.2020.140701
Xu, H., Wang, X. & Zhao, C. (2021). Drought sensitivity of vegetation photosynthesis along the aridity gradient in northern China, International Journal of Applied Earth Observation and Geoinformation, 102, 102418. https://doi.org/10.1016/j.jag.2021.102418
Zhao, X., Xia, H., Liu, B. & Jiao, W. (2022). Spatiotemporal comparison of drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 using various drought indices in google earth engine, Remote Sensing, 14(7), 1570. https://doi.org/10.3390/rs14071570
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. https://doi.org/10.1016/j.compag.2019.105144
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