{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T22:15:30Z","timestamp":1774044930544,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,12]],"date-time":"2020-09-12T00:00:00Z","timestamp":1599868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010712","name":"Viet Nam National University Ho Chi Minh City","doi-asserted-by":"publisher","award":["C2017-20-21"],"award-info":[{"award-number":["C2017-20-21"]}],"id":[{"id":"10.13039\/501100010712","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, short droughts in the dry season have occurred more frequently and caused serious damages to agriculture and human living in the Mekong River Delta of Vietnam (MRD). The paper attempts to quantify the trends of drought changes in the dry seasons from 2001 to 2015 in the region, using daily MODIS MOD09GQ and MOD11A1 data products. Here, we exploit the Temperature Vegetation Dryness Index (TVDI) to assess levels of droughts. For each image-acquisition time, the TVDI image is computed, based on the Normalized Difference Vegetation Index (NDVI), derived from red and near infrared reflectance data, and the Land Surface Temperature (LST), derived from thermal infrared data. Subsequently, a spatiotemporal pattern of drought changes is estimated, based on mean TVDI values of the dry seasons during the observed period, by a linear regression. As a result, the state of drought in the dry seasons in the MRD has mostly been at light and moderate levels, occupying approximately 62% and 34% of the total area. Several sub-areas in the center have an increased trend of drought change, occupying approximately 12.5% of the total area, because impervious surface areas increase, e.g., the obvious land use change, from forest land and land for cultivation for perennial trees being strongly converted to built-up land for residence and public transportation. Meanwhile, several sub-areas in the coastal regions have a negative trend of drought change because water and absorbent surface areas increase, e.g., most of land for cultivation for perennial trees has been converted to aquaculture land. These cases usually occur in and surrounding forest and wet land, also occupying approximately 12.5% of the total area.<\/jats:p>","DOI":"10.3390\/rs12182974","type":"journal-article","created":{"date-parts":[[2020,9,13]],"date-time":"2020-09-13T21:11:32Z","timestamp":1600031492000},"page":"2974","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Trends in Long-Term Drought Changes in the Mekong River Delta of Vietnam"],"prefix":"10.3390","volume":"12","author":[{"given":"Vu Hien","family":"Phan","sequence":"first","affiliation":[{"name":"Department of Physics, International University, VNU-HCM, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 71308, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vi Tung","family":"Dinh","sequence":"additional","affiliation":[{"name":"Department of Geomatics Engineering, University of Technology, VNU-HCM, Ward 14, District 10, Ho Chi Minh City 72506, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongbo","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Water Resource, ITC, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100593","DOI":"10.1016\/j.ejrh.2019.100593","article-title":"A new global database of meteorological drought events from 1951 to 2016","volume":"22","author":"Spinoni","year":"2019","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_2","first-page":"S36","article-title":"State of the Climate in 2017","volume":"99","author":"Blunden","year":"2018","journal-title":"Bull. Amer. Meteor. Soc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.wace.2014.06.003","article-title":"Droughts in Asian Least Developed Countries: Vulnerability and sustainability","volume":"7","year":"2015","journal-title":"Weather. Clim. Extrem."},{"key":"ref_4","first-page":"231","article-title":"Droughts, floods, and wildfires","volume":"Volume 1","author":"Wehner","year":"2017","journal-title":"Climate Science Special Report: Fourth National Climate Assessment"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/nclimate2067","article-title":"Global warming and changes in drought","volume":"4","author":"Trenberth","year":"2014","journal-title":"Nat. Clim. Change."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1002\/joc.5958","article-title":"Global drought trends under 1.5 and 2 \u00b0C warming","volume":"39","author":"Xu","year":"2018","journal-title":"Int. J. Climatol."},{"key":"ref_7","unstructured":"National Drought Mitigation Center, USA (2019, April 22). Types of Drought. Available online: https:\/\/drought.unl.edu\/Education\/DroughtIn-depth\/TypesofDrought.aspx."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1175\/1520-0477-83.8.1149","article-title":"A review of twentieth-century drought indices used in the United States","volume":"83","author":"Heim","year":"2002","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jhydrol.2010.07.012","article-title":"A review of drought concepts","volume":"391","author":"Mishra","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_10","first-page":"267","article-title":"New drought indices","volume":"80","author":"Niemeyer","year":"2008","journal-title":"Options M\u00e9diterr. Seri A"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1139\/a11-013","article-title":"A review of drought indices","volume":"19","author":"Zargar","year":"2011","journal-title":"Environ. Rev."},{"key":"ref_12","unstructured":"Shafer, B.A., and Dezman, L.E. (1982, January 19\u201323). Development of a Surface Water Supply Index (SWSI) to Assess the Severity of Drought Conditions in Snowpack Runoff Areas. Proceedings of the Western Snow Conference, Colorado State University, Fort Collins, CO, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s11269-008-9305-1","article-title":"Assessment of hydrological drought revisited","volume":"23","author":"Nalbantis","year":"2009","journal-title":"Water Resour. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Su, Z., He, Y., Dong, X., and Wang, L. (2017). Drought monitoring and assessment using remote sensing. Remote Sensing of Hydrological Extremes, Springer. Venkat Lakshmi.","DOI":"10.1007\/978-3-319-43744-6_8"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/02757259409532220","article-title":"A Method to Make Use of Thermal Infrared Temperature and NDVI measurements to Infer Surface Soil Water","volume":"9","author":"Carlson","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0034-4257(95)00139-R","article-title":"A New Look at the Simplified Method for Remote-Sensing of Daily Evapotranspiration","volume":"54","author":"Carlson","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/014311697219286","article-title":"Multi-Sensor Analysis of NDVI, Surface Temperature and Biophysical Variables at a Mixed Grassland Site","volume":"18","author":"Goetz","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3145","DOI":"10.1080\/014311697217026","article-title":"A Verification of the \u2018Triangle\u2019 Method for Obtaining Surface Soil Water Content and Energy Fluxes from Remote Measurements of the Normalized Difference Vegetation Index (NDVI) and Surface Radiant Temperature","volume":"18","author":"Gillies","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(01)00274-7","article-title":"A simple interpretation of the surface temperature\/vegetation index space for assessment of the surface moisture status","volume":"79","author":"Sandholt","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2006.06.003","article-title":"A comparative study of NOAA-AVHRR derived drought indices using change vector analysis","volume":"105","author":"Bayarjargal","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1109\/JSTARS.2015.2500605","article-title":"Two-Stage Trapezoid: A New Interpretation of the Land Surface Temperature and Fractional Vegetation Coverage Space","volume":"9","author":"Sun","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/S2095-3119(15)61302-8","article-title":"Comparison between TVDI and CWSI for drought monitoring in the Guanzhong Plain, China","volume":"16","author":"Bai","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_23","first-page":"495","article-title":"Integrating temperature vegetation dryness index (TVDI) and regional water stress index (RWSI) for drought assessment with the aid of LANDSAT TM\/ETM+ images","volume":"13","author":"Gao","year":"2011","journal-title":"J. Appl. Gerontol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107707","DOI":"10.1016\/j.agrformet.2019.107707","article-title":"Agricultural drought monitoring using European Space Agency Sentinel 3A land surface temperature and normalized difference vegetation index imageries","volume":"279","author":"Hu","year":"2019","journal-title":"Agr. For. Meteorol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, C., and Li, H. (2006). Study on winter wheat drought monitoring by TVDI in Hebei Province, Proc. SPIE 6298. Remote Sensing and Modeling of Ecosystems for Sustainability III, International Society for Optics and Photonics.","DOI":"10.1117\/12.678450"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Yue, H. (2018). The Temperature Vegetation Dryness Index (TVDI) Based on Bi-Parabolic NDVI-Ts Space and Gradient-Based Structural Similarity (GSSIM) for Long-Term Drought Assessment across Shaanxi Province, China (2000\u20132016). Remote Sens., 10.","DOI":"10.3390\/rs10060959"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zou, J., Ding, J., Welp, M., Huang, S., and Liu, B. (2020). Assessing the Response of Ecosystem Water Use Efficiency to Drought During and after Drought Events across Central Asia. Sensors, 20.","DOI":"10.3390\/s20030581"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"671","DOI":"10.5589\/m04-029","article-title":"Evaluating soil moisture status in China using the temperature\u2013vegetation dryness index (TVDI)","volume":"30","author":"Wang","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/07038992.2015.1065705","article-title":"Biparabolic NDVI-Ts Space and Soil Moisture Remote Sensing in an Arid and Semi-arid Area","volume":"41","author":"Liu","year":"2015","journal-title":"Can. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/01431160802108497","article-title":"Assessing potential of MODIS derived temperature\/vegetation condition index (TVDI) to infer soil moisture status","volume":"30","author":"Patel","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/S2095-3119(18)61988-4","article-title":"Remotely sensed estimation and mapping of soil moisture by eliminating the effect of vegetation cover","volume":"18","author":"Wu","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3114","DOI":"10.3390\/rs70303114","article-title":"Evaluation of the Airborne CASI\/TASI Ts-VI Space Method for Estimating Near-Surface Soil Moisture","volume":"7","author":"Fan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"11401","DOI":"10.3390\/su70911401","article-title":"Temperature Vegetation Dryness Index Estimation of Soil Moisture under Different Tree Species","volume":"7","author":"Chen","year":"2015","journal-title":"Sustainability"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, B., Ti, C., Zhao, Y., and Yan, X. (2016). Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies. Remote Sens., 8.","DOI":"10.3390\/rs8010038"},{"key":"ref_35","first-page":"82","article-title":"Understanding TVDI as an index that expresses soil moisture","volume":"7","author":"Schirmbeck","year":"2017","journal-title":"J. Human Reprod. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.jaridenv.2006.12.021","article-title":"Estimation of moisture availability over the Liudaogou river basin of the Loess Plateau using new indices with surface temperature","volume":"70","author":"Kimura","year":"2007","journal-title":"J. Arid. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"17473","DOI":"10.1038\/s41598-017-17810-3","article-title":"Spatial-Temporal Variation of Drought in China from 1982 to 2010 Based on a modified Temperature Vegetation Drought Index (mTVDI)","volume":"7","author":"Zhao","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Du, L., Song, N., Liu, K., Hou, J., Hu, Y., Zhu, Y., Wang, X., Wang, L., and Guo, Y. (2017). Comparison of Two Simulation Methods of the Temperature Vegetation Dryness Index (TVDI) for Drought Monitoring in Semi-Arid Regions of China. Remote Sens., 9.","DOI":"10.3390\/rs9020177"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5533","DOI":"10.1002\/2017JD026607","article-title":"Development and evaluation of the MTVDI for soil moisture monitoring","volume":"122","author":"Zhu","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_40","first-page":"417","article-title":"Monitoring agricultural drought in the lower Mekong basin using MODIS NDVI and land surface temperature data","volume":"18","author":"Son","year":"2012","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1016\/S2095-3119(14)60813-3","article-title":"Drought Change Trend Using MODIS TVDI and Its Relationship with Climate Factors in China from 2001 to 2010","volume":"13","author":"Liang","year":"2014","journal-title":"J. Integr. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Han, Y., Li, Z., Huang, C., Zhou, Y., Zong, S., Hao, T., Niu, H., and Yao, H. (2020). Monitoring Droughts in the Greater Changbai Mountains Using Multiple Remote Sensing-Based Drought Indices. Remote Sens., 12.","DOI":"10.3390\/rs12030530"},{"key":"ref_43","unstructured":"(2019, April 22). General Statistics Office of Vietnam, Available online: http:\/\/www.gso.gov.vn\/Default_en.aspx?tabid=491."},{"key":"ref_44","unstructured":"UNDRMT: UN Disaster Risk Management Team Secretariat (2019, April 22). Viet Nam Consolidated Report on Drought and Saltwater Intrusion Reporting Period: October 2015\u20149 March 2016. United Nations in Vietnam., Available online: https:\/\/reliefweb.int\/sites\/reliefweb.int\/files\/resources\/Vietnam%20Consolidated%20Report%20on%20Drought%202015-2016-Final_11%20Mar%202016.pdf."},{"key":"ref_45","unstructured":"CGIAR Research Program on Climate Change, Agriculture and Food Security\u2014Southeast Asia (CCAFS SEA) (2016). Assessment report: The drought and salinity intrusion in the mekong river delta of Vietnam., CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)."},{"key":"ref_46","unstructured":"Grosjean, G., Monteils, F., Hamilton, S.D., Blaustein-Rejto, D., Gatto, M., Talsma, T., Bourgoin, C., Sebastian, L.S., Catacutan, D., and Mulia, R. (2016). Increasing Resilience to Droughts in Vietnam; The Role of Forests, Agroforests and Climate Smart Agriculture. International Center for Tropical Agriculture (CIAT); CCAFS, UN, REDD. CCAFS-CIAT-UN-REDD Position Paper n. 1."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.wace.2017.07.004","article-title":"Characterization of future drought conditions in the Lower Mekong River Basin","volume":"17","author":"Thilakarathne","year":"2017","journal-title":"Weather Clim. Extrem."},{"key":"ref_48","unstructured":"Mekong River Commission (2019, April 22). Mekong Climate Change Adaptation Strategy and Action Plan. Vientiane: Mekong River Commission., Available online: http:\/\/www.mrcmekong.org\/assets\/Publications\/MASAP-book-28-Aug18.pdf."},{"key":"ref_49","unstructured":"Mekong River Commission (2019, April 22). Drought Management Strategy for the Lower Mekong Basin 2020\u20132025. Vientiane: Mekong River Commission., Available online: http:\/\/www.mrcmekong.org\/assets\/Publications\/MRC-DMS-2020\u20132025-Fourth-draft-V3-v2.0-_formatted.pdf."},{"key":"ref_50","unstructured":"(2019, April 22). MODIS Surface Reflectance User\u2019s Guide. Available online: http:\/\/modis-sr.ltdri.org\/guide\/MOD09_UserGuide_v1.4.pdf."},{"key":"ref_51","unstructured":"(2019, April 22). MOD09GQ\u2014MODIS\/Terra Surface Reflectance Daily L2G Global 250 m SIN Grid, Available online: https:\/\/ladsweb.modaps.eosdis.nasa.gov\/missions-and-measurements\/products\/land-surface-reflectance\/MOD09GQ\/."},{"key":"ref_52","unstructured":"(2019, April 22). MODIS Level 1B Product User\u2019s Guide, Available online: https:\/\/mcst.gsfc.nasa.gov\/sites\/default\/files\/file_attachments\/M1054E_PUG_2017_0901_V6.2.2_Terra_V6.2.1_Aqua.pdf."},{"key":"ref_53","unstructured":"(2019, April 22). MOD21 MODIS\/Terra Land Surface Temperature\/3-Band Emissivity 5-Min L2 1 km V006, Available online: https:\/\/lpdaac.usgs.gov\/products\/mod21v006\/."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"7231","DOI":"10.1029\/JD089iD05p07231","article-title":"Land surface temperature measurements from the split window channel of the NOAA 7 Advanced Very High Resolution Radiometer","volume":"89","author":"Price","year":"1984","journal-title":"J. Geophys. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/S0034-4257(97)00091-6","article-title":"A comparative study of algorithms for estimating land surface temperature from AVHRR data","volume":"62","author":"Vazquez","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0034-4257(96)00137-X","article-title":"Multi-temporal, Multi-channel AVHRR data sets for land biosphere studies\u2014Artifacts and corrections","volume":"60","author":"Cihlar","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_57","unstructured":"Hong, S., Hendrickx, J.M.H., and Borchers, B. (April, January 28). Effect of Scaling Transfer between Evapotranspiration Maps Derived from LandSat 7 and MODIS Images. Proceedings of Targets and Backgrounds XI: Characterization and Representation Vol. 5811, Defense and Security, Orlando, Florida, USA."},{"key":"ref_58","unstructured":"Teunissen, P.J.G. (2003). Adjustment Theory: An Introduction, VSSD."},{"key":"ref_59","unstructured":"MRC Mekong-HYCOS Project (2019, April 22). The Mekong Hydrological Cycle Observing System Mekong-HYCOS, A Hydrological Information System in the Mekong River Basin: Project Document. WMO. Available online: https:\/\/library.wmo.int\/doc_num.php?explnum_id=4698."},{"key":"ref_60","unstructured":"National Drought Mitigation Center (2019, April 22). Standardized Precipitation Index. Available online: https:\/\/drought.unl.edu\/droughtmonitoring\/SPI.aspx."},{"key":"ref_61","unstructured":"Tran, T.B. (2016). The Integrated System of Remote Sensing, GIS and Mathematical Model for Assessing Climate Change in Southern Vietnam, Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology. (In Vietnamese)."},{"key":"ref_62","unstructured":"Pham, T.M.T. (2016). Using VNREDSat-1 Satellite Data and Similar Imagery to Monitor Agricultural Land in the Mekong River Delta of Vietnam for Socio-Economic Development and Climate Change Adaptation. Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology. (In Vietnamese)."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2974\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:09:28Z","timestamp":1760177368000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2974"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,12]]},"references-count":62,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12182974"],"URL":"https:\/\/doi.org\/10.3390\/rs12182974","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,12]]}}}