Abdel-Rahman, E. M., Mutanga, O., Adam, E., & Ismail, R. (2013). Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers. ISPRS Journal of Photogrammetry and Remotev Sensing, 88, 48-59. https://doi.org/10.1016/j.isprsjprs.2013.11.013

Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data. USGS Professional Paper. https://doi.org/10.3133/pp964

Benhammou, Y., Alcaraz-Segura, D., Guirado, E., Khaldi, R., Achchab, B., Herrera, F., & Tabik, S. (2022). Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning. Sci. Data 2022, 9, 20. https://doi.org/10.1038/s41597-022-01775-8

Bui, Q. D., Luu, C., Ha, H., & Nguyen, V. (2024). A holistic approach to salinity intrusion vulnerability assessment using geospatial technologies: an application for Mekong Delta of Vietnam. International Journal of Disaster Risk Reduction, 113, 104854. https://doi.org/10.1016/j.ijdrr.2024.104854

Cochran, W. G. (1977). Sampling Techniques. 3rd Edition, John Wiley & Sons, New York.

Congalton, R. G., & Green, K. (1999). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publishers, Boca Raton. https://doi.org/10.1201/9781420048568

Dahhani, S., Raji, M., Hakdaoui, M., & Lhissou, R. (2022). Land cover mapping using Sentinel-1 Time-Series data and Machine-Learning classifiers in agricultural Sub-Saharan landscape. Remote Sensing, 15(1), 65. https://doi.org/10.3390/rs15010065

Datcu, M., Schwarz, G., & Dumitru, C. O. (2020). Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures. In Recent Trends in Artificial Neural Networks. From Training to Prediction, Sadollah, A., Ed., InTech Open: London, UK. https://doi.org/10.5772/intechopen.90910

Department of Water Resources Management. (2010). Mekong Delta provinces: Struggling with over extraction of groundwater (in Vietnamese). http://dwrm.gov.vn/index.php?language=vi&nv=news&op=Hoat-dong-cua-dia-phuong/Cac-tinh-DBSCL-Hut-hoi-hut-nuoc-ngam-1155

Duan, Y. (2016). Saltwater Intrusion and Agriculture: A Comparative Study Between the Netherlands and China. TRITALWR Degree Project 2016:20. https://www.diva-portal.org/smash/get/diva2:1060822/FULLTEXT01.pdf

Gallopin, G. C. (2006). Linkages between Vulnerability, Resilience, and Adaptive Capacity. Global Environmental Change, 16(3), 293-303. https://doi.org/10.1016/j.gloenvcha.2006.02.004

Gatdula, N., & Blanco, A. (2024). Assessment of the vulnerability of coastal agriculture to seawater intrusion using remote sensing, GIS, and Multi-Criteria Decision Analysis. International Journal of Digital Earth, 17(1). https://doi.org/10.1080/17538947.2024.2367733

General Statistics Office of Vietnam (GSO). (2016). Statistical Yearbook of Viet Nam (in Vietnamese). https://www.gso.gov.vn/en/data-and-statistics/2019/10/statistical-yearbook-of-vietnam-2016/

General Statistics Office of Vietnam (GSO). (2021). Statistical Yearbook of Viet Nam (in Vietnamese). https://www.gso.gov.vn/en/data-and-statistics/2022/08/statistical-yearbook-of-vietnam-2021/

Google Earth Engine. (2024). Sentinel-1 algorithms. https://developers.google.com/earth-engine/guides/sentinel1.

Hahn, M. B., Riederer, A. M., & Foster, S. O. (2009). The Livelihood Vulnerability Index: A pragmatic approach to assessing risks from climate variability and change—A case study in Mozambique. Global Environmental Change, 19(1), 74-88. https://doi.org/10.1016/j.gloenvcha.2008.11.002

Hoeser, T., & Kuenzer, C. (2020). Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends. Remote Sensing, 12(10), 1667. https://doi.org/10.3390/rs12101667

IPCC. (2001). Climate Change 2001: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Third Assessment Report. Cambridge University Press, Cambridge, UK.

IPCC. (2007). Climate change 2007: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.

IPCC. (2014). Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects, Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge, UK: Cambridge University Press.

Islami, F. A., Tarigan, S. D., Wahjunie, E. D., & Dasanto, B. D. (2022). Accuracy assessment of land use change analysis using Google Earth in Sadar Watershed Mojokerto Regency. IOP Conference Series Earth and Environmental Science, 950(1), 012091. https://doi.org/10.1088/1755-1315/950/1/012091

Ivkovic, K., S. Marshall, L. Morgan, A. Werner, H. Carey, S. Cook, B. Sundaram, et al. (2012). National-scale Vulnerability Assessment of Seawater Intrusion: Summary Report. https://core.ac.uk/download/pdf/30677605.pdf

Jin, Y., Liu, X., Chen, Y., & Liang, X. (2018). Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: a case study of central Shandong. International Journal of Remote Sensing, 39(23), 8703-8723. https://doi.org/10.1080/01431161.2018.1490976

Koh, J. (2011). Local Vulnerability Assessment of Climate Change and its Implications: The Case of Gyeonggi-Do, Korea. Resilient Cities 1: 411-427. https://doi.org/10.1007/978-94-007-0785-6_42.

Le, K. V., & Nguyen, C. H. (2011). Physical characteristics of soil in rainfed rice cultivation areas in Long Phu district, Soc Trang province. Can Tho University Journal of Science, 2011(18b), 284-294 (in Vietnamese).

Le, T. N. (2017). A review of research on climate change vulnerability assessment. Journal of Science and Technology Development, 20(1), 5-20 (in Vietnamese).https://vjol.info.vn/index.php/JSTD/article/view/33037/28117

Lee, C. E., Downey, K., Colby, R. S., Freire, C. A., Nichols, S., Burgess, M. N., & Judy, K. J. (2022). Recognizing Salinity Threats in the Climate Crisis. Integrative and Comparative Biology, 62(2), 441-460. https://doi.org/10.1093/icb/icac069

Luers, A. L. (2005). The Surface of Vulnerability: An Analytical Framework for Examining Environmental Change. Global Environmental Change, 15(3), 214-223. https://doi.org/10.1016/j.gloenvcha.2005.04.003

MARD. (2020). Mekong Delta takes measures to reduce saltwater intrusion (in Vietnamese). https ://www.mard.gov.vn/en/Pages/mekong-delta-takes-measures-to-reduce-saltwaterintrusion.aspx?item=16.

McMichael, A. J. (2013). Globalization, climate change, and human health. New England Journal of Medicine, 368(14), 1335-1343. https://doi.org/10.1056/nejmra1109341

Ministry of Agriculture and Rural Development. (2010). The harms outweigh the benefits as farmers rush to cultivate a third rice crop in Soc Trang (in Vietnamese). https://www.mard.gov.vn/Pages/loi-bat-cap-hai-vi-dua-nhau-lam-lua-vu-3-o-soc-trang-2131.aspx

Ministry of Natural Resources and Environment of Viet Nam (MONRE). (2020). Climate Change Scenarios (in Vietnamese). http://vnmha.gov.vn/upload/files/kich-ban-bien-doi-khi-hau-phien-ban-cap-nhat-nam-2020.pdf

Nguyen, B. V., Van, T. P. D., Tran, H. T. T., & Nguyen, A. T. (2017a). Impact of saltwater intrusion on water resource management in agricultural production in Long Phu district, Soc Trang province. Can Tho University Journal of Science, 52(2017), 104-112 (in Vietnamese). https://doi.org/10.22144/ctu.jvn.2017.116

Nguyen, D. T. H., & Nguyen, N. T. (2023). Monitoring the rice cropping pattern in Soc Trang province from 2016 to 2022 using SENTINEL-1 imagery. Land Management Conference 2023 (in Vietnamese).

Nguyen, D. T. H., Huynh, N. T., & Nguyen, N. T. (2024a). Assessment of agricultural risks under the impact of salinity intrusion in Tra Vinh Province. Journal of Soil Science, 75, 133 - 137 (in Vietnamese).

Nguyen, D. T. H., Nguyen, C. T., Phan, D. K., & Tran, M. L. (2019). Spatial analysis of land use patterns under the impact of saltwater intrusion in Soc Trang province. Can Tho University Journal of Science, 55(Environment), 1 (in Vietnamese). https://doi.org/10.22144/ctu.jsi.2019.125

Nguyen, D. T. H., Nguyen, N. M., Cao, P. N., Duong, H. C., Nguyen, N. T., & Phan, D. K. (2023). Analysis of agricultural land vulnerability to saltwater intrusion in Tien Giang province in 2020 using remote sensing. Can Tho University Journal of Science, 59(Environment and Climate Change), 185-192 (in Vietnamese). https://doi.org/10.22144/ctu.jvn.2023.120

Nguyen, S. H., Dao, C. D., Pham, V. K., & Phan, H. A. (2024b). Assessing vulnerability to climate change in agricultural production in Huong Hoa district, Quang Tri province. Journal of Hydro-meteorology, 5(761), 1-12 (in Vietnamese). https://doi.org/10.36335/vnjhm.2024(761).1-12

Nguyen, T. N. N., Truong, T. V., Nguyen, T. V., & Nguyen, L. T. (2017b). Status and vulnerability to salinization in the context of climate change in Da Nang City. VNU Journal of Science: Earth and Environmental Sciences, 33(2), 90-107 (in Vietnamese). https://doi.org/10.25073/2588-1094/vnuees.4105

Nguyen, T. T. X., & Woodroffe, C. D. (2015). Assessing relative vulnerability to sea-level rise in the western part of the Mekong River Delta in Vietnam. Sustainability Science, 11(4), 645-659. https://doi.org/10.1007/s11625-015-0336-2

Nguyen, T. V. (2022). Salinity intrusion in the Vietnamese Mekong Delta, a threat: possible causes, effects on people’s life and production, and temporary solutions and adaptable strategies. In Environmental science and engineering (pp. 1-10). https://doi.org/10.1007/978-3-031-07500-1_1

Nguyen, Y. T. B., & Duong, H. T. (2018). Assessing climate change vulnerability using the Livelihood Vulnerability Index: A case study of the Thai and H’mong ethnic groups in Chieng Dong commune, Yen Chau district, Son La province. Journal of Agriculture and Rural Development, 12(2018), 129-137 (in Vietnamese).

Oswald, S. M., Hollosi, B., Žuvela-Aloise, M., See, L., Guggenberger, S., Hafner, W., Prokop, G., Storch, A., & Schieder, W. (2020). Using urban climate modelling and improved land use classifications to support climate change adaptation in urban environments: A case study for the city of Klagenfurt, Austria. Urban Climate, 31(2020), 100582. https://doi.org/10.1016/j.uclim.2020.100582

People’s Committee of Soc Trang Province. (2021). Soc Trang Province Master Plan for the Period 2021 - 2030, with a Vision to 2050 (in Vietnamese).

People's Council of Soc Trang Province. (2018). Natural conditions of Soc Trang Province (in Vietnamese).https://dbnd.soctrang.gov.vn/Default.aspx?sname=hdnd&sid=1290&pageid=32103&catid=54684&id=266991&catname=%u0110i%u1ec1u+ki%u1ec7n+t%u1ef1+nhi%u00ean+S%u00f3c+Tr%u0103ng

Pham, H. V., Nguyen, G. V., Nguyen, B. A., Le, H. V. H., Pham, T. D., Hasanlou, M., & Bui, D. T. (2019). Soil salinity mapping using SAR Sentinel-1 data and advanced machine learning algorithms: a case study at Ben Tre province of the Mekong River Delta (Vietnam). Remote Sensing, 11(2), 128. https://doi.org/10.3390/rs11020128

Praticò, S., Solano, F., Di, F. S., & Modica, G. (2021). Machine Learning classification of Mediterranean forest habitats in Google Earth Engine based on Seasonal Sentinel-2 Time-Series and input image Composition optimisation. Remote Sensing, 13(4), 586. https://doi.org/10.3390/rs13040586

Quan, N. M., Ha, D. V., & Nguyen, N. Q. (2013). Analysis of cost efficiency and scale efficiency of purple onion farming households in Vinh Chau district, Soc Trang province: A non-parametric approach. Can Tho University Journal of Science, 28(2013), 33-37 (in Vietnamese). https://ctujsvn.ctu.edu.vn/index.php/ctujsvn/article/view/1681

Salonen, A. O., & Reiser, D. (2023). Climate change. In Encyclopedia of Sustainable Management (565-569). Springer Nature. https://doi.org/10.1007/978-3-031-25984-5_75

Sebastian, L., Sander, B., Simelton, E., Zheng, S., Chu, H. T., Tran, N., Buu, C., Cao, Q., & Ngo, M. D. (2016). The drought and salinity intrusion in the Mekong River Delta of Vietnam: Assessment report. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). https://hdl.handle.net/10568/75633

Soc Trang Provincial Statistics Office. (2023). Soc Trang Statistical Yearbook 2023 (in Vietnamese). https://storage-vnportal.vnpt.vn/sme-g1/5808/PhongTonghop/NG%20Soc%20Trang%209.2023.signed.pdf

Sun, J., & Ongsomwang, S. (2023). Optimal parameters of random forest for land cover classification with suitable data type and dataset on Google Earth Engine. Frontiers in Earth Science, 11. https://doi.org/10.3389/feart.2023.1188093

Swami, D., & Parthasarathy, D. (2020). Dynamics of exposure, sensitivity, adaptive capacity and agricultural vulnerability at district scale for Maharashtra, India. Ecological Indicators, 121(2021), 107206. https://doi.org/10.1016/j.ecolind.2020.107206

Tarolli, P., Luo, J., Straffelini, E., Liou, Y., Nguyen, K., Laurenti, R., Masin, R., & D’Agostino, V. (2023). Saltwater intrusion and climate change impact on coastal agriculture. PLOS Water, 2(4), e0000121. https://doi.org/10.1371/journal.pwat.0000121

Thach, K. S. R., Lee, J. Y., Ha, M. T., Cao, M. T., Nayga, R. M., & Yang, J. (2023). Effect of saline intrusion on rice production in the Mekong River Delta. Heliyon, 9(10), e20367. https://doi.org/10.1016/j.heliyon.2023.e20367

Tran, T. N. T., & Nguyen, P. T. (2014). The impact of climate change on salinity intrusion and Pangasius (Pangasianodon Hypophthalmus) farming in the Mekong Delta, Vietnam. Aquaculture International, 23(2), 523-534. https://doi.org/10.1007/s10499-014-9833-z

Vo, T. Q., Nguyen, T. V., & Pham, V. Q. (2020). Integration of radar and optical imagery for land use mapping in Can Tho City. Can Tho University Journal of Science, 56(5), 20-29 (in Vietnamese). https://doi.org/10.22144/ctu.jvn.2020.108

Wassmann, R., Nguyen, H. X., Chu, H. T., & To, T. P. (2004). Sea level rise affecting the Vietnamese Mekong Delta: water elevation in the flood season and implications for rice production. Climatic Change, 66(1/2), 89-107. https://doi.org/10.1023/b:clim.0000043144.69736.b7

Yommy, A. S., Liu, R., & Wu, A. S. (2015). SAR Image Despeckling Using Refined Lee Filter. 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, IEEE (2015). https://doi.org/10.1109/ihmsc.2015.236