Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data

References

• Aghabozorgi, S., A. S. Shirkhorshidi, and T. Y. Wah. 2015. “Time-Series Clustering–A Decade Review.” Information Systems 53: 16–23. doi:10.1016/j.is.2015.04.007.
   Web of Science ®Google Scholar
• Amarasinghe, U. A., C. T. Hoanh, D. D’Haeze, and T. Q. Hung. 2015. “Toward Sustainders, A., Tian, Y., Varadi, F.Able Coffee Production in Vietnam: More Coffee with Less Water.” Agricultural systems 136: 96–105. doi:10.1016/j.agsy.2015.02.008.
   Web of Science ®Google Scholar
• Anderson, M. C., C. Hain, B. Wardlow, A. Pimstein, J. R. Mecikalski, and W. P. Kustas. 2011. “Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration Over the Continental United States.” Journal of Climate 24 (8): 2025–2044. doi:10.1175/2010JCLI3812.1.
   Web of Science ®Google Scholar
• Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. A. Otkin, and W. P. Kustas. 2007a. “A Climatological Study of Evapotranspiration and Moisture Stress Across the Continental United States Based on Thermal Remote Sensing: 1. Model Formulation.” Journal of Geophysical Research Atmospheres 112 (D10). doi:10.1029/2006JD007506.
   Web of Science ®Google Scholar
• Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. A. Otkin, and W. P. Kustas. 2007b. “A Climatological Study of Evapotranspiration and Moisture Stress Across the Continental United States Based on Thermal Remote Sensing: 1. Model Formulation.” Journal of Geophysical Research Atmospheres 112 (D10). doi:10.1029/2006JD007506.
   Web of Science ®Google Scholar
• Aniruddha, B. 2020. Feature Scaling | Standardization Vs Normalization. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2020/04/feature-scaling-machine-learning-normalization-standardization/.
   Google Scholar
• Bento, V. A., C. M. Gouveia, C. C. DaCamara, R. Libonati, and I. F. Trigo. 2020. “The Roles of NDVI and Land Surface Temperature When Using the Vegetation Health Index Over Dry Regions.” Global and Planetary Change 190: 103198. doi:10.1016/j.gloplacha.2020.103198.
   Web of Science ®Google Scholar
• Bradford, J. B., D. R. Schlaepfer, W. K. Lauenroth, and K. A. Palmquist. 2020. “Robust Ecological Drought Projections for Drylands in the 21st Century.” Global Change Biology 26 (7): 3906–3919. doi:10.1111/gcb.15075.
   PubMed Web of Science ®Google Scholar
• Brust, C., J. S. Kimball, M. P. Maneta, K. Jencso, M. He, and R. H. Reichle. 2021. “Using SMAP Level-4 Soil Moisture to Constrain MOD16 Evapotranspiration Over the Contiguous USA.” Remote Sensing of Environment 255: 112277. doi:10.1016/j.rse.2020.112277.
   Web of Science ®Google Scholar
• Bushra, N., R. V. Rohli, N. S. N. Lam, L. Zou, R. B. Mostafiz, and V. Mihunov. 2019. “The Relationship Between the Normalized Difference Vegetation Index and Drought Indices in the South Central United States.” Natural Hazards 96 (2): 791–808. doi:10.1007/s11069-019-03569-5.
   Web of Science ®Google Scholar
• Cao, Y., S. Chen, L. Wang, B. Zhu, T. Lu, and Y. Yu. 2019. “An Agricultural Drought Index for Assessing Droughts Using a Water Balance Method: A Case Study in Jilin Province, Northeast China.” Remote Sensing 11 (9): 1066. doi:10.3390/rs11091066.
   Web of Science ®Google Scholar
• Christian, J. I., J. B. Basara, J. A. Otkin, E. D. Hunt, R. A. Wakefield, P. X. Flanagan, and X. Xiao. 2019. “A Methodology for Flash Drought Identification: Application of Flash Drought Frequency Across the United States.” Journal of Hydrometeorology 20 (5): 833–846. doi:10.1175/JHM-D-18-0198.1.
   Web of Science ®Google Scholar
• Chuyen, T., 2020. Giảm diện tích rừng ở Tây nguyên [ online]. Thanh niên. Available from: https://thanhnien.vn/thoi-su/giam-dien-tich-rung-o-tay-nguyen-1242144.html [Accessed 13 Mar 2021].
   Google Scholar
• Crausbay, S. D., A. R. Ramirez, S. L. Carter, M. S. Cross, K. R. Hall, D. J. Bathke, J. L. Betancourt, et al. 2017. “Defining Ecological Drought for the Twenty-First Century.” Bulletin of the American Meteorological Society 98 (12): 2543–2550. doi:10.1175/BAMS-D-16-0292.1.
   Web of Science ®Google Scholar
• de Carvalho Júnior, O. A., R. F. Guimarães, C. R. Silva, and R. A. T. Gomes. 2015. “Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset.” Remote Sensing 7 (6): 6950–6985. doi:10.3390/rs70606950.
   Web of Science ®Google Scholar
• Dinh, N. T., N. T. T. Ha, N. T. P. Thao, and N. T. Linh, 2017. 30 Years Monitoring Spatial - Temporal Dynamics of Agricultural Drought in the Central Highlands Using Landsat Data. In: Geo-spatial Technologies and Earth Resources. Presented at the The International Conference on Geo-Spatial Technologies and Earth Resources, Hanoi City, Vietnam: Publishing House for Science and Technology, 181–188.
   Google Scholar
• Dorjsuren, M., Y. -A. Liou, and C. -H. Cheng. 2016. “Time Series MODIS and in situ Data Analysis for Mongolia Drought.” Remote Sensing 8 (6): 509. doi:10.3390/rs8060509.
   Web of Science ®Google Scholar
• Dzierżak, R. 2019. “Comparison of the Influence of Standardization and Normalization of Data on the Effectiveness of Spongy Tissue Texture Classification.” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 9 (3): 66–69. doi:10.35784/iapgos.62.
   Google Scholar
• Elhag, K., and W. Zhang. 2018. “Monitoring and Assessment of Drought Focused on Its Impact on Sorghum Yield Over Sudan by Using Meteorological Drought Indices for the Period 2001–2011.” Remote Sensing 10 (8): 1231. doi:10.3390/rs10081231.
   Web of Science ®Google Scholar
• Erickson, T., 2014. Multi-Source Geospatial Data Analysis with Google Earth Engine. In: AGU Fall Meeting Abstracts 2014: IN53E-05, San Francisco, US.
   Google Scholar
• FAO. 2021. The Impact of Disasters and Crises on Agriculture and Food Security. Rome, Italy: Food and Agriculture Organization of the United Nations.
   Google Scholar
• Frazier, R. J., N. C. Coops, M. A. Wulder, T. Hermosilla, and J. C. White. 2018. “Analyzing Spatial and Temporal Variability in Short-Term Rates of Post-Fire Vegetation Return from Landsat Time Series.” Remote Sensing of Environment 205: 32–45. doi:10.1016/j.rse.2017.11.007.
   Web of Science ®Google Scholar
• General Assembly of the UN. 2015. “Sustainable Development Goals.” SDGs, Transforming Our World: The 2030, 338–350. New York: UN.
   Google Scholar
• Ghil, M., M. R. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, M. E. Mann, A. W. Robertson, et al. 2002. “Advanced Spectral MetMapping Droughts Over the Central Highland of Vietnam in El Niño Years Using Landsat Imagerieshods for Climatic Time Series.” Reviews of Geophysics 40 (1): 3-3–41. doi:10.1029/2000RG000092
   Google Scholar
• Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. doi:10.1016/j.rse.2017.06.031.
   Web of Science ®Google Scholar
• Grabusts, P., and A. Borisov. 2009. “Clustering Methodology for Time Series Mining.” Computer Science 40 (1): 40. doi:10.2478/v10143-010-0011-0.
   Google Scholar
• GSO. 2021. Viet Nam Statistical Yearbook of 2020. General Statistics Office. Hanoi City, Vietnam: Statistical Publishing House.
   Google Scholar
• Guerschman, J. P., T. R. McVicar, J. Vleeshower, T. G. Van Niel, J. L. Peña-Arancibia, and Y. Chen. 2022. “Estimating Actual Evapotranspiration at Field-to-Continent Scales by Calibrating the CMRSET Algorithm with MODIS, VIIRS, Landsat and Sentinel-2 Data.” Journal of Hydrology 605: 127318. doi:10.1016/j.jhydrol.2021.127318.
   Web of Science ®Google Scholar
• Guijo-Rubio, D., A. M. Durán-Rosal, P. A. Gutiérrez, A. Troncoso, and C. Hervás-Martínez. 2020. “Time-Series Clustering Based on the Characterization of Segment Typologies.” IEEE Transactions on Cybernetics 51 (11): 5409–5422. doi:10.1109/TCYB.2019.2962584.
   Web of Science ®Google Scholar
• Guttman, N. B. 1998. “Comparing the Palmer Drought Index and the Standardized Precipitation Index 1.” JAWRA Journal of the American Water Resources Association 34 (1): 113–121. doi:10.1111/j.1752-1688.1998.tb05964.x.
   Web of Science ®Google Scholar
• Guyet, T., and H. Nicolas. 2016. “Long Term Analysis of Time Series of Satellite Images.” Pattern recognition letters 70: 17–23. doi:10.1016/j.patrec.2015.11.005.
   Web of Science ®Google Scholar
• Ha, T. V., J. Huth, F. Bachofer, and C. Kuenzer. 2022. “A Review of Earth Observation-Based Drought Studies in Southeast Asia.” Remote Sensing 14 (15): 3763. doi:10.3390/rs14153763.
   Web of Science ®Google Scholar
• Hao, Z., and A. AghaKouchak. 2013. “Multivariate Standardized Drought Index: A Parametric Multi-Index Model.” Advances in Water Resources 57: 12–18. doi:10.1016/j.advwatres.2013.03.009.
   Web of Science ®Google Scholar
• Hao, C., J. Zhang, and F. Yao. 2015. “Combination of Multi-Sensor Remote Sensing Data for Drought Monitoring Over Southwest China.” International Journal of Applied Earth Observation and Geoinformation 35: 270–283. doi:10.1016/j.jag.2014.09.011.
   Web of Science ®Google Scholar
• Heo, J., and T. W. FitzHugh. 2000. “A Standardized Radiometric Normalization Method for Change Detection Using Remotely Sensed Imagery.” Photogrammetric Engineering and Remote Sensing 66 (2): 173–181.
   Web of Science ®Google Scholar
• Hieu, B., and L. Hao. 2000. Irrigation techniques for crops. Hanoi City, Vietnam: Agriculture Publishing House.
   Google Scholar
• Honeck, E., R. Castello, B. Chatenoux, J. -P. Richard, A. Lehmann, and G. Giuliani. 2018. “From a Vegetation Index to a Sustainable Development Goal Indicator: Forest Trend Monitoring Using Three Decades of Earth Observations Across Switzerland.” ISPRS International Journal of Geo-Information 7 (12): 455. doi:10.3390/ijgi7120455.
   Web of Science ®Google Scholar
• Hosking, J. R. M., and J. R. Wallis. 1997. Regional Frequency Analysis: An Approach Based on L-Moments. Cambridge, UK: Cambridge University Press.
   Google Scholar
• Hu, C., J. Xia, D. She, L. Li, Z. Song, and S. Hong. 2021. “A New Framework for the Identification of Flash Drought: Multivariable and Probabilistic Statistic Perspectives: Identification of Flash Drought.” International Journal of Climatology 41 (13): 5862–5878. doi:10.1002/joc.7157.
   Web of Science ®Google Scholar
• Jiao, W., L. Wang, and M. F. McCabe. 2021. “Multi-Sensor Remote Sensing for Drought Characterization: Current Status, Opportunities and a Roadmap for the Future.” Remote sensing of environment 256: 112313. doi:10.1016/j.rse.2021.112313.
   Web of Science ®Google Scholar
• Jönsson, P., Z. Cai, E. Melaas, M. A. Friedl, and L. Eklundh. 2018. “A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data.” Remote Sensing 10 (4): 635. doi:10.3390/rs10040635.
   Web of Science ®Google Scholar
• Jung, M., M. Reichstein, P. Ciais, S. I. Seneviratne, J. Sheffield, M. L. Goulden, G. Bonan, et al. 2010. “Recent Decline in the Global Land Evapotranspiration Trend Due to Limited Moisture Supply.” Nature 467 (7318): 951–954. doi:10.1038/nature09396.
   PubMed Web of Science ®Google Scholar
• Karnieli, A., M. Bayasgalan, Y. Bayarjargal, N. Agam, S. Khudulmur, and C. J. Tucker. 2006. “Comments on the Use of the Vegetation Health Index Over Mongolia.” International Journal of Remote Sensing 27 (10): 2017–2024. doi:10.1080/01431160500121727.
   Web of Science ®Google Scholar
• Kerr, Y. H., J. -P. Wigneron, A. Al Bitar, A. Mialon, and P. K. Srivastava. 2016. “Soil moisture from space: Techniques and limitations.” In Satellite Soil Moisture Retrieval, 3–27. Amsterdam, Netherlands: Elsevier.
   Google Scholar
• Kogan, F. N. 1995. “Application of Vegetation Index and Brightness Temperature for Drought Detection.” Advances in Space Research 15 (11): 91–100. doi:10.1016/0273-1177(95)00079-T.
   PubMed Web of Science ®Google Scholar
• Kogan, F., and J. Sullivan. 1993. “Development of Global Drought-Watch System Using NOAA/AVHRR Data.” Advances in Space Research 13 (5): 219–222. doi:10.1016/0273-1177(93)90548-P.
   Web of Science ®Google Scholar
• Le, T., C. Sun, S. Choy, and Y. Kuleshov. 2021. “Regional Drought Risk Assessment in the Central Highlands and the South of Vietnam.” Geomatics, Natural Hazards and Risk 12 (1): 3140–3159. doi:10.1080/19475705.2021.1998232.
   Web of Science ®Google Scholar
• Lokman, S. -F., A. T. Othman, M. H. A. Bakar, and S. Musa, 2019. “The Impact of Different Feature Scaling Methods on Intrusion Detection for In-Vehicle Controller Area Network (CAN)”. In: International Conference on Advances in Cyber Security, pp. 195–205. Singapore: Springer.
   Google Scholar
• MARD, M. of A. and R.D., 2021. Quyết định số 1558/QĐ-BNN-TCLN về công bố hiện trạng rừng năm 2020.
   Google Scholar
• McKee, T. B., N. J. Doesken, and J. Kleist, 1993. The Relationship of Drought Frequency and Duration to Time Scales. In: Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society Boston, MA, 179–183.
   Google Scholar
• McVicar, T., J. Vleeshouwer, T. Van Niel, and J. Guerschman. 2022. “Actual Evapotranspiration for Australia Using CMRSET Algorithm.” Terrestrial Ecosystem Research Network. doi:10.25901/gg27-ck96.
   Google Scholar
• Mu, Q., F. A. Heinsch, M. Zhao, and S. W. Running. 2007. “Development of a Global Evapotranspiration Algorithm Based on MODIS and Global Meteorology Data.” Remote Sensing of Environment 111 (4): 519–536. doi:10.1016/j.rse.2007.04.015.
   Web of Science ®Google Scholar
• Mu, Q., M. Zhao, J. S. Kimball, N. G. McDowell, and S. W. Running. 2013. “A Remotely Sensed Global Terrestrial Drought Severity Index.” Bulletin of the American Meteorological Society 94 (1): 83–98. doi:10.1175/BAMS-D-11-00213.1.
   Web of Science ®Google Scholar
• Mu, Q., M. Zhao, and S. W. Running. 2011. “Improvements to a MODIS Global Terrestrial Evapotranspiration Algorithm.” Remote sensing of environment 115 (8): 1781–1800. doi:10.1016/j.rse.2011.02.019.
   Web of Science ®Google Scholar
• Myronidis, D., K. Ioannou, D. Fotakis, and G. Dörflinger. 2018. “Streamflow and Hydrological Drought Trend Analysis and Forecasting in Cyprus.” Water Resources Management 32 (5): 1759–1776. doi:10.1007/s11269-018-1902-z.
   Web of Science ®Google Scholar
• NDMC. 2022. How Does Drought Affect Our Lives? [Online]. National Drought Mitigation Center. Available from: https://drought.unl.edu/Education/DroughtforKids/DroughtEffects.aspx.
   Google Scholar
• Nguyen, H. T. T., N. T. Mai, C. D. Bui, and T. T. P. Nguyen. 2016. ”Mapping Droughts Over the Central Highland of Vietnam in El Niño Years Using Landsat Imageries.” VNU Journal of Science: Social Sciences and Humanities 32 (1S): 255–263.
   Google Scholar
• Nguyen, Q. T. N., L. D. Nguyen, N. D. Nguyen, T. Nguyen, L. T. Bui, and L. K. Nguyen. 2017. “Phân vùng hạn hán dựa trên chỉ số hạn và mô phỏng chế độ thủy văn trên lưu vực Srepok vùng Tây Nguyên.” VNU Journal of Science: Earth and Environmental Sciences 33 (1): 65–81.
   Google Scholar
• Ontel, I., A. Irimescu, G. Boldeanu, D. Mihailescu, C. -V. Angearu, A. Nertan, V. Craciunescu, and S. Negreanu. 2021. “Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania.” Sensors 21 (24): 8371. doi:10.3390/s21248371.
   PubMed Web of Science ®Google Scholar
• Orvos, P. I., V. Homonnai, A. Várai, Z. Bozóki, and I. M. Jánosi. 2015. “Global Trend Analysis of the MODIS Drought Severity Index.” Geoscientific Instrumentation, Methods and Data Systems 4 (2): 189–196. doi:10.5194/gi-4-189-2015.
   Web of Science ®Google Scholar
• Paulo, A. A., and L. S. Pereira. 2006. “Drought Concepts and Characterization.” Water International 31 (1): 37–49. doi:10.1080/02508060608691913.
   Web of Science ®Google Scholar
• Peng, J., A. Loew, O. Merlin, and N. E. C. Verhoest. 2017. “A Review of Spatial Downscaling of Satellite Remotely Sensed Soil Moisture.” Reviews of Geophysics 55 (2): 341–366. doi:10.1002/2016RG000543.
   Web of Science ®Google Scholar
• Rey, D., I. P. Holman, and J. W. Knox. 2017. “Developing Drought Resilience in Irrigated Agriculture in the Face of Increasing Water Scarcity.” Regional Environmental Change 17 (5): 1527–1540. doi:10.1007/s10113-017-1116-6.
   PubMed Web of Science ®Google Scholar
• Rhee, J., J. Im, and G. J. Carbone. 2010. “Monitoring Agricultural Drought for Arid and Humid Regions Using Multi-Sensor Remote Sensing Data.” Remote sensing of environment 114 (12): 2875–2887. doi:10.1016/j.rse.2010.07.005.
   Web of Science ®Google Scholar
• Running, S. W., Q. Mu, and M. Zhao. 2019. MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V006 [Data set]. Distributed Active Archive Center: NASA DAAC.
   Google Scholar
• Seiler, R. A., F. Kogan, and J. Sullivan. 1998. “AVHRR-Based Vegetation and Temperature Condition Indices for Drought Detection in Argentina.” Advances in Space Research 21 (3): 481–484. doi:10.1016/S0273-1177(97)00884-3.
   Web of Science ®Google Scholar
• Shah, D., and V. Mishra. 2020. “Integrated Drought Index (IDI) for Drought Monitoring and Assessment in India.” Water Resources Research 56 (2): e2019WR026284. doi:10.1029/2019WR026284.
   Web of Science ®Google Scholar
• Sherval, M., L. E. Askew, and P. M. McGuirk. 2014. ”Manifestations of Drought.” In Encyclopedia of Quality of Life and Well-Being Research, A. C. Michalos ed., 3756–3761. Dordrecht: Springer Netherlands
   Google Scholar
• Shumway, R. H., and D. S. Stoffer. 2017. “Characteristics of Time Series.” In Time Series Analysis and Its Applications: With R Examples, edited by R. H. Shumway and D. S. Stoffer, 1–44. Cham: Springer International Publishing.
   Google Scholar
• Singh, R. P., S. Roy, and F. Kogan. 2003. “Vegetation and Temperature Condition Indices from NOAA AVHRR Data for Drought Monitoring Over India.” International Journal of Remote Sensing 24 (22): 4393–4402. doi:10.1080/0143116031000084323.
   Web of Science ®Google Scholar
• Son, B., S. Park, J. Im, S. Park, Y. Ke, and L. J. Quackenbush. 2021. “A New Drought Monitoring Approach: Vector Projection Analysis (VPA).” Remote Sensing of Environment 252: 112145. doi:10.1016/j.rse.2020.112145.
   Web of Science ®Google Scholar
• Spinoni, J., P. Barbosa, A. De Jager, N. McCormick, G. Naumann, J. V. Vogt, D. Magni, D. Masante, and M. Mazzeschi. 2019. “A New Global Database of Meteorological Drought Events from 1951 to 2016.” Regional Studies 22: 100593. doi:10.1016/j.ejrh.2019.100593.
   Web of Science ®Google Scholar
• Sulla-Menashe, D., and M. A. Friedl. 2018. User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product, 1–18. Reston, VA, USA: USGS.
   Google Scholar
• Sun, X., M. Wang, G. Li, and Y. Wang. 2020. “Regional-Scale Drought Monitor Using Synthesized Index Based on Remote Sensing in Northeast China.” Open Geosciences 12 (1): 163–173. doi:10.1515/geo-2020-0037.
   Web of Science ®Google Scholar
• Tan, N. V., N. V. Chien, D. Hong, and N. Le. 2018. Modular Textbook: Caring for Coffee Plants - Coffee Farming. Hanoi City, Vietnam: Agriculture Publishing House.
   Google Scholar
• Thornthwaite, C. W. 1947. “Climate and Moisture Conservation.” Annals of the Association of American Geographers 37 (2): 87–100. doi:10.1080/00045604709351946.
   Web of Science ®Google Scholar
• Thuong, T., C. Xuan, N. Hieu, P. Tuan, and D. Mon. 2022. “Examininging Drought Intensity Inentral Highlands During the 2001–2020 Period Using Daily MODIS Time–Series Imagery.” Vietnam Journal of Hydrometeorology 733: 1–14.
   Google Scholar
• Tigkas, D., H. Vangelis, and G. Tsakiris. 2013. “The RDI as a Composite Climatic Index.” Europe Water 41: 17–22.
   Google Scholar
• Tigkas, D., H. Vangelis, and G. Tsakiris. 2015. “DrinC: A Software for Drought Analysis Based on Drought Indices.” Earth Science Informatics 8 (3): 697–709. doi:10.1007/s12145-014-0178-y.
   Web of Science ®Google Scholar
• Toan, P. N., and P. T. Dac. 1975. “Climatic Conditions of Vietnam.” Science and Technics Publishing House. Hanoi, Vietnam.
   Google Scholar
• Tran, H. T., J. B. Campbell, T. D. Tran, and H. T. Tran. 2017. “Monitoring Drought Vulnerability Using Multispectral Indices Observed from Sequential Remote Sensing (Case Study: Tuy Phong, Binh Thuan, Vietnam).” GIScience & Remote Sensing 54 (2): 167–184. doi:10.1080/15481603.2017.1287838.
   Web of Science ®Google Scholar
• Tran, T. V., D. X. Tran, S. W. Myint, P. Latorre-Carmona, D. D. Ho, P. H. Tran, and H. N. Dao. 2019. “Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta.” Remote Sensing 11 (23): 2742. doi:10.3390/rs11232742.
   Web of Science ®Google Scholar
• Trieu, V., T. Pham, and T. Dao. 2021. Kết quả thực hiện Chiến lược phát triển Lâm nghiệp Việt Nam giai đoạn 2006–2020 và đề xuất nội dung Chiến lược phát triển Lâm nghiệp Việt Nam giai đoạn 2021–2030, tầm nhìn đến năm 2050. Bogor, Indonesia: Tổ chức Nghiên cứu Lâm nghiệp Quốc tế (CIFOR).
   Google Scholar
• Tsakiris, G., D. Pangalou, and H. Vangelis. 2007. “Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI).” Water Resources Management 21 (5): 821–833. doi:10.1007/s11269-006-9105-4.
   Web of Science ®Google Scholar
• Tsakiris, G., and H. Vangelis. 2005. “Establishing a Drought Index Incorporating Evapotranspiration.” European Water 9 (10): 3–11.
   Google Scholar
• Um, M. -J., Y. Kim, and D. Park. 2018. “Evaluation and Modification of the Drought Severity Index (DSI) in East Asia.” Remote sensing of environment 209: 66–76. doi:10.1016/j.rse.2018.02.044.
   Web of Science ®Google Scholar
• UNDRR, 2021. GAR Special Report on Drought 2021. United Nations Office for Disaster Risk Reduction. Geneva, Switzerland.
   Google Scholar
• Vu, A. T., T. H. Vu, and H. D. Trinh. 2019. “The Characteristics and Trends of Meteorological Drought in Central Highlands.” Vietnam Journal of Hydrometeorology 3: 50–58.
   Google Scholar
• Wan, X. 2019. “Influence of Feature Scaling on Convergence of Gradient Iterative Algorithm.” Journal of Physics Conference Series 1213 (3): 032021. doi:10.1088/1742-6596/1213/3/032021.
   Google Scholar
• West, H., N. Quinn, and M. Horswell. 2019. “Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities.” Remote sensing of environment 232: 111291. doi:10.1016/j.rse.2019.111291.
   Web of Science ®Google Scholar
• Wilhite, D. A., and M. H. Glantz. 1985. “Understanding: The Drought Phenomenon: The Role of Definitions.” Water International 10 (3): 111–120. doi:10.1080/02508068508686328.
   Google Scholar
• WMO, 2016. Drought. World Meteorological Organization. URL: https://public.wmo.int/en/our-mandate/water/drought [accessed on 20/10/2022].
   Google Scholar
• WMO, 2022. Droughts Threaten Sustainable Development. World Meteorological Organization. URL: https://public.wmo.int/en/media/news/droughts-threaten-sustainable-development [accessed on 20/10/2022].
   Google Scholar
• WMO and GWP. 2016. Handbook of Drought Indicators and Indices. (M. Svoboda and B.A. Fuchs). Integrated Drought Management Programme (IDMP), Integrated Drought Management Tools and Guidelines Series 2. Geneva: World Meteorological Organization and Global Water Partnership.
   Google Scholar
• Wu, B., Z. Ma, and N. Yan. 2020. “Agricultural Drought Mitigating Indices Derived from the Changes in Drought Characteristics.” Remote sensing of environment 244: 111813. doi:10.1016/j.rse.2020.111813.
   Web of Science ®Google Scholar
• Yao, Y., S. Liang, Q. Qin, and K. Wang. 2010. “Monitoring Drought Over the Conterminous United States Using MODIS and NCEP Reanalysis-2 Data.” Journal of Applied Meteorology and Climatology 49 (8): 1665–1680. doi:10.1175/2010JAMC2328.1.
   Web of Science ®Google Scholar
• Yoo, J., H. -H. Kwon, T. -W. Kim, and J. -H. Ahn. 2012. “Drought Frequency Analysis Using Cluster Analysis and Bivariate Probability Distribution.” Journal of Hydrology 420-421: 102–111. doi:10.1016/j.jhydrol.2011.11.046.
   Web of Science ®Google Scholar
• Yu, H., Q. Zhang, C. -Y. Xu, J. Du, P. Sun, and P. Hu. 2019. “Modified Palmer Drought Severity Index: Model Improvement and Application.” Environment international 130: 104951. doi:10.1016/j.envint.2019.104951.
   PubMed Web of Science ®Google Scholar
• Zakaria, J., 2019. When and Why to Standardize Your Data? [ online]. Built In. Available from: https://builtin.com/data-science/when-and-why-standardize-your-data [Accessed 2 Sep 2021].
   Google Scholar
• Zeng, J., R. Zhang, Y. Qu, V. A. Bento, T. Zhou, Y. Lin, X. Wu, J. Qi, W. Shui, and Q. Wang. 2022. “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: 100412. doi:10.1016/j.wace.2022.100412.
   Web of Science ®Google Scholar
• Zhang, Z., P. Tang, L. Huo, and Z. Zhou. 2014. “MODIS NDVI Time Series Clustering Under Dynamic Time Warping.” International Journal of Wavelets, Multiresolution and Information Processing 12 (05): 1461011. doi:10.1142/S0219691314610116.
   Google Scholar