پایش خشکسالی کشاورزی در حوضۀ آبریز رودخانۀ ارس با استفاده از شاخص‌های ماهواره‌ای و هواشناسی

 [۱۸] سیاسر، هادی؛ محمدرضا‌پور، ام‌البنی؛ و خدامرادپور، مهرانه (۱۴۰۳). پایش خشکسالی با استفاده از داده‌های سنجندۀ MODIS و مقایسه با شاخص هواشناسی SPI در دوره‌های کوتاه‌مدت مطالعۀ موردی: استان گلستان. جغرافیا و توسعه، ۲۲(۷۴)، ۱۶۶-۱۸۶. doi: 10.22111/gdij.2024.8175

[31] Ali, S., Zha, H., Ma, Q., Sun, L., Jiang, N., Qianmin, J., & Fujian, J. (2021). Monitoring drought events and vegetation dynamics in relation to climate change over mainland China from 1983 to 2016. Environmental Science and Pollution Research. 28(17), 21910–21925. DOI:10.1007/s11356-020-12146-4.

[32] Alzurqani, S. A., Zurqani, H. A., Don White Jr, D., Bridges, K., & Shawn Jackson, S. (2024). Google Earth Engine application for mapping and monitoring drought patterns and trends: A case study in Arkansas, USA. Ecological Indicators. 168, 112759. https://doi.org/10.1016/j.ecolind.2024.112759.

[33] Araghinejad, S. (2013). Data-driven modeling: using MATLAB® in water resources and environmental engineering. Springer Science & Business Media. 67.

[34] Bhuiyan, C., Singh, R. P., & Kogan, F. N. (2006). Monitoring drought dynamics in the Aravalli region (India) sing different indices based on ground and remote sensing data. International Journal of Applied Earth Observationand Geoinformation. 8, 289–302. doi:10.1016/j.jag.2006.03.002.

[35] Burka, A., Biazin, B., & Bewket, W. (2024). Spatial drought occurrences and distribution using VCI, TCI, VHI, and Google Earth Engine in Bilate River Watershed, Rift Valley of Ethiopia, Geomatics. Natural Hazards and Risk. 15(1). https://doi.org/10.1080/19475705.2024.2377672.

 [36] Du, L., Qingjiu, T., Tao, Y., Meng, Q., Jancso, T., Udvardy, P., & Huang, Y. (2013). A comprehensive drought monitoring method integrating MODIS and TRMM data. International Journal of Applied Earth Observation and Geoinformation. 23(1), 245–25. doi:10.1016/j.jag.2012.09.010.

[37] Du, T.L.T., Du Bui, D., Nguyen, M.D., & Lee, H. (2018). Satellite-based، multi-indices for evaluation of agricultural droughts in a highly dynamic tropical catchment، Central Vietnam. Water (Switzerland). 10(5), 1-24.  doi:10.3390/w10050659.

[38] 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 S tudy from Al-Lith Watershed. Kingdom of Saudi Arabia، Remote Sensing. 15(4), 984. doi:10.3390/rs15040984.

[39] 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), 1–18. doi:10.1186/s40068-018-0115-z.

[40] Gouveia, C.M., Trigo, R.M., Beguería, S., & Vicente-Serrano, S.M. (2016). Drought impacts on vegetation activity in the Mediterranean region: an assessment using remote sensing data and multi-scale drought indicators. Global and Planetary Change. doi: 10.1016/j.gloplacha.2016.06.011.

 [41] Jahangir, k., Wang, P., Xie, Y., Wang, L., & Li, L. (2018). Mapping MODIS LST NDVI Imagery for Drought Monitoring in Punjab Pakistan. IEEE. 6, 19898-19991. doi:10.1109/ACCESS.2018.2821717

[42] Javed, T., Li, Y., Feng, K., Ayantobo, O. O., Ahmad, S., Chen, X., Rashid, S., & Suon, S. (2021). Monitoring responses of vegetation phenology and productivity to extreme climatic conditions using remote sensing across different sub-regions of China. Environmental Science and Pollution Research. 28, 3644-3659. doi:10.1007/s11356-020-10769-1.

[43] 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 s tudy covering the Sichuan-Chongqing region. International Journal of Remote Sensing. 39(3), 786–809. doi: 10.1080/01431161.2017.1392635.

[44] 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. doi:10.3390/rs8030224.

[45] Li, K., Tong, Z., Liu, X., Zhang, J., & Tong, S. (2020). Quantitative assessment and driving force analysis of vegetation drought risk to climate change: Methodology and application in Northeast China. Agricultural and Forest Meteorology. 282, 107865. http://dx.doi.org/10.1016/j.agrformet.2019.107865.

[46] 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 mois ture dynamics in various vegetation regions over China. Science of the Total Environment. 789, 147803. doi: 10.1016/j.scitotenv.2021.147803.

[47] McKee, T., & Doesken Kleist, N. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Preprints 8th Conference on Applied Climatology. 17-22 January. Anaheim, CA. 170-184.

[48] Moisa, M. B., Merga, B. B., & Gemeda, D.O. (2022). Multiple indices-based assessment of agricultural drought: A case study in Gilgel Gibe Sub-basin, Southern Ethiopia. Theoretical and Applied Climatology. 148(1), 455–464. doi:10.1007/s00704-022-03962-4.

[49] Nejadrekabi, M., Eslamian, S., & Zareian, M. J. (2022). Spatial statistics techniques for SPEI and NDVI drought indices: A case study of Khuzestan Province. International Journal of Environmental Science and Technology. 19(7), 6573-6594. doi:10.1007/s13762-021-03852-8.

[50] Palchaudhuri, M., & Biswas, S. (2020). Application of LISS III and MODIS-derived vegetation indices for assessment of micro-level agricultural drought. The Egyptian Journal of Remote Sensing and Space Science. 23(2), 221–229.  doi:10.1016/j.ejrs.2019.12.004.

[51] Peng, J., Loew, A., Merlin, O., & Verhoest, N.E. (2017). A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics. 55(2), 341-366. http://dx.doi.org/10.1002/2016RG000543.

[52] Pradhan, P., & Patra, S. (2024). Chapter 11 - Agricultural drought monitoring using Google Earth Engine: a study of Paschim Medinipur district, West Bengal. Modern Cartography Series. 12, 261-297. https://doi.org/10.1016/B978-0-443-23890-1.00011-6.

[53] Singh, R.P., Roy, S., Kogan, F. (2003). Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. International Journal of Remote Sensing. 24(22), 4393-4402. https://doi.org/10.1080/0143116031000084323.

[54] Sun, B., Qian, J., Chen, X., & Zhou, Q. (2020). Comparison and Evaluation of Remote Sensing Indices for Agricultural Drought Monitoring over Kazakhstan. The International Archives of Photogrammetry. Remote Sensing and Spatial Information Sciences. 3, 899–903. doi:10.5194/isprs-archives-XLIII-B3-2020-899-2020.

[55] Tabassum, B., & Ahmed Khan, I. (2024). Comparative Analysis of Drought Indices on Google Earth Engine. Gu Journal of phytosciences, 4(1), 318-330, https://doi.org/10.5281/zenodo.10755127.

[56] Thilagaraj, P.,Masilamani, P., Venkatesh, R., & Killivalavan, J. (2021). Google Earth Engine based agricultural drought monitoring in Kodavanar Watershed, part of amaravathi basin, tamil nadu, india. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XLIII-B5-2021 XXIV ISPRS Congress. 43-49.

[57] Wang, K., Li, T., & Wei, J. (2019). Exploring drought conditions in the Three River Headwaters Region from 2002 to 2011 using multiple drought indices. Water. 11(2), 190-210.  doi:10.3390/w11020190.

[58] Wassie, S. B., Mengis tu, 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.  doi:10.1007/s10661-022-10455-4.

[59] Zhang, L., Jiao, W., Zhang, H., Huang, Ch., & Tong, Q. (2017). Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote Sensing of Environment. 190, 96-106. doi: 10.1016/j.rse.2016.12.010.

[60] 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 usingg Sentinel-2 and MODIS imagery. Computers and electronics in agriculture. 168, 105144. http://dx.doi.org/10.1016/j.compag.2019.105144.