{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T19:16:13Z","timestamp":1773256573641,"version":"3.50.1"},"reference-count":99,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T00:00:00Z","timestamp":1618185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004895","name":"European Social Fund","doi-asserted-by":"publisher","award":["POCU\/380\/6\/13\/123623"],"award-info":[{"award-number":["POCU\/380\/6\/13\/123623"]}],"id":[{"id":"10.13039\/501100004895","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CNCS-UEFISCDI","award":["PN-III-P1-1.1-TE-2019-0286"],"award-info":[{"award-number":["PN-III-P1-1.1-TE-2019-0286"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The aim of this study was to evaluate the frequency and severity of drought over the arable lands of Romania using the Normalized Difference Drought Index (NDDI). This index was obtained from the Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensor of the Terra satellite. The interval between March and September was investigated to study the drought occurrence from the early stage of crop growth to its harvest time. The study covered a long period (2001\u20132020), hence it is able to provide a sound climatological image of crop vegetation conditions. Corine Land Cover 2018 (CLC) was used to extract the arable land surfaces. According to this index, the driest year was 2003 with 25.6% of arable land affected by drought. On the contrary, the wettest year was 2016, with only 10.8% of arable land affected by drought. Regarding the multiannual average of the period 2001\u20132020, it can be seen that drought is not a phenomenon that occurs consistently each year, therefore only 11.7% of arable land was affected constantly by severe and extreme drought. The correlation between NDDI and precipitation amount was also investigated. Although the correlations at weekly or monthly levels are more complicated, the annual regional mean NDDI is overall negatively correlated with annual rainfall. Thus, from a climatic perspective, we consider that NDDI is a reliable and valuable tool for the assessment of droughts over the arable lands in Romania.<\/jats:p>","DOI":"10.3390\/rs13081478","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T11:05:06Z","timestamp":1618225506000},"page":"1478","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Drought Extent and Severity on Arable Lands in Romania Derived from Normalized Difference Drought Index (2001\u20132020)"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3119-6953","authenticated-orcid":false,"given":"Radu-Vlad","family":"Dobri","sequence":"first","affiliation":[{"name":"Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Ia\u0219i, 20A Carol I Blvd., 700 505 Iasi, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7725-682X","authenticated-orcid":false,"given":"Lucian","family":"Sf\u00eec\u0103","sequence":"additional","affiliation":[{"name":"Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Ia\u0219i, 20A Carol I Blvd., 700 505 Iasi, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6681-1330","authenticated-orcid":false,"given":"Vlad-Alexandru","family":"Amih\u0103esei","sequence":"additional","affiliation":[{"name":"Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Ia\u0219i, 20A Carol I Blvd., 700 505 Iasi, Romania"},{"name":"Meteo Romania, National Meteorological Administration, 013686 Bucharest, Romania"}]},{"given":"Liviu","family":"Apostol","sequence":"additional","affiliation":[{"name":"Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Ia\u0219i, 20A Carol I Blvd., 700 505 Iasi, Romania"}]},{"given":"Simona","family":"\u021a\u00eempu","sequence":"additional","affiliation":[{"name":"Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Ia\u0219i, 20A Carol I Blvd., 700 505 Iasi, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,12]]},"reference":[{"key":"ref_1","unstructured":"IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_2","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. Chang."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.jhydrol.2015.05.031","article-title":"Drought characterization from a multivariate perspective: A review","volume":"527","author":"Hao","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_5","first-page":"297","article-title":"Effects and Consequences of Global Climate Change in the Carpathian Basin","volume":"12","author":"Rakonczai","year":"2011","journal-title":"Clim. Chang. Geophys. Found. Ecol. Eff."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1515\/jengeo-2015-0008","article-title":"Drought Monitoring with Spectral Indices Calculated from Modis Satellite Images in Hungary","volume":"8","year":"2015","journal-title":"J. Environ Geogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1080\/02508068508686328","article-title":"Understanding: The Drought Phenomenon: The Role of Definitions","volume":"10","author":"Wilhite","year":"1985","journal-title":"Water Int."},{"key":"ref_8","unstructured":"Palmer, W.C. (1965). Meteorological Drought, Research Paper No. 45."},{"key":"ref_9","unstructured":"McKee, T.B., Doesken, N.J., and Kleist, J. (1993, January 17\u201322). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, Anaheim, CA, USA."},{"key":"ref_10","unstructured":"Hayes, M.J., Svoboda, M.D., Wardlow, B.D., Anderson, M.C., and Kogan, F. (2012). Drought monitoring: Historicaland current perspectives. Remote Sensing of Drought: Innovative Monitoring Approaches, CRC Press."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"P\u00e1scoa, P., Gouveia, C., Russo, A., Bojariu, R., Vicente-Serrano, S., and Trigo, R. (2020). Drought Impacts on Vegetation in Southeastern Europe. Remote Sens., 12.","DOI":"10.3390\/rs12132156"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/0273-1177(95)00079-T","article-title":"Application of vegetation index and brightness temperature for drought detection","volume":"15","author":"Kogan","year":"1995","journal-title":"Adv. Space Res."},{"key":"ref_13","first-page":"141","article-title":"Vegetation temperature condition index and its application for drought monitoring","volume":"1","author":"Wang","year":"2001","journal-title":"Int. Geosci. Remote Sens."},{"key":"ref_14","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 surface moisture status","volume":"79","author":"Sandholt","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2011.10.009","article-title":"Comparative evaluation of the Vegetation Dryness Index (VDI), the Temperature Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran","volume":"68","author":"Omasa","year":"2012","journal-title":"ISPRS J. Photogramm."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/s00704-013-1025-7","article-title":"The impracticality of a universal drought definition","volume":"117","year":"2014","journal-title":"Appl. Clim."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1080\/01431160500168686","article-title":"An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data","volume":"26","author":"Tucker","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.1016\/j.rse.2009.04.004","article-title":"Evaluation of earth observation based long term vegetation trends\u2014Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data","volume":"113","author":"Fensholt","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.rse.2005.10.021","article-title":"Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI","volume":"100","author":"Beck","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gu, Y., Brown, J.F., Verdin, J.P., and Wardlow, B. (2007). A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophys. Res. Lett., 34.","DOI":"10.1029\/2006GL029127"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"241","DOI":"10.5589\/m02-092","article-title":"Landsat-5 TM and Landsat-7 ETM+ based accuracy assessment of leaf area index products for Canada derived from SPOT-4 VEGETATION data","volume":"29","author":"Fernandes","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.5194\/nhess-12-3123-2012","article-title":"Drought impacts on vegetation in the pre- and post-fire events over Iberian Peninsula","volume":"12","author":"Gouveia","year":"2012","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/0002-1571(81)90032-7","article-title":"Normalizing the stress-degree-day parameter for environmental variability","volume":"24","author":"Idso","year":"1981","journal-title":"Agric. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/S0034-4257(02)00037-8","article-title":"Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1","volume":"82","author":"Ceccato","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wagner, W., Scipal, K., Pathe, C., Gerten, D., Lucht, W., and Rudolf, B. (2003). Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data. J. Geophys. Res. Space Phys., 108.","DOI":"10.1029\/2003JD003663"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, L., Xiang, D., Dong, X., and Zhou, Z. (2008, January 23\u201324). Improvement of the Drought Monitoring Model Based on the Cloud Parameters Methodand Remote Sensing Data. Proceedings of the First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), Adelaide, Australia.","DOI":"10.1109\/WKDD.2008.27"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s12517-012-0707-2","article-title":"Drought risk assessment using remote sensing and GIS techniques","volume":"7","author":"Belal","year":"2014","journal-title":"Arab. J. Geosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s11069-006-0009-7","article-title":"Evaluating the Impact of Drought Using Remote Sensing in a Mediterranean, Semi-arid Region","volume":"40","year":"2007","journal-title":"Nat Hazards"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1080\/01431160500296032","article-title":"Early prediction of crop production using drought indices at different time-scales and remote sensing data: Application in the Ebro Valley (north-east Spain)","volume":"27","author":"Romo","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.gloplacha.2016.06.011","article-title":"Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators","volume":"151","author":"Gouveia","year":"2017","journal-title":"Glob. Planet. Chang."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.5194\/nhess-12-3519-2012","article-title":"Development of a Combined Drought Indicator to detect agricultural drought in Europe","volume":"12","author":"Horion","year":"2012","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3139","DOI":"10.5194\/nhess-12-3139-2012","article-title":"Assessment of remotely sensed drought features in vulnerable agriculture","volume":"12","author":"Dalezios","year":"2012","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1080\/01431160412331330293","article-title":"The state of vegetation in Europe following the 2003 drought","volume":"26","author":"Gobron","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.5194\/bg-17-1655-2020","article-title":"Quantifying impacts of the 2018 drought on European ecosystems in comparison to 2003","volume":"17","author":"Buras","year":"2020","journal-title":"Biogeosciences"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.gloplacha.2015.01.012","article-title":"European drought climatologies and trends based on a multi-indicator approach","volume":"127","author":"Spinoni","year":"2015","journal-title":"Glob. Planet. Chang."},{"key":"ref_36","first-page":"1091","article-title":"Remote sensing, GIS and HEC-RAS techniques, applied for flood extentvalidation, based on Landsat imagery, LiDAR and hydrological data. Case study: Baseu River, Romania","volume":"19","author":"Enea","year":"2018","journal-title":"J. Environ. Prot. Ecol"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rusu, A., Ursu, A., Stoleriu, C.C., Groza, O., Niac\u0219u, L., Sf\u00eec\u0103, L., Minea, I., and Stoleriu, O.M. (2020). Structural Changes in the Romanian Economy Reflected through Corine Land Cover Datasets. Remote Sens., 12.","DOI":"10.3390\/rs12081323"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10113-008-0050-z","article-title":"Land use change in Southern Romania after the collapse of socialism","volume":"9","author":"Kuemmerle","year":"2009","journal-title":"Reg. Environ. Chang."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"M\u0103rg\u0103rint, M.C., and Niculi\u0163\u0103, M. (2017). Landslide type and pattern in Moldavian Plateau, NE Romania. Landform Dynamics and Evolution in Romania, Springer.","DOI":"10.1007\/978-3-319-32589-7_12"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"M\u0103rmureanu, L., Marin, C.A., Andrei, S., Antonescu, B., Ene, D., Boldeanu, M., Vasilescu, J., Vi\u0163elaru, C., Cadar, O., and Levei, E. (2019). Orange Snow\u2014A Saharan Dust Intrusion over Romania During Winter Conditions. Remote Sens., 11.","DOI":"10.3390\/rs11212466"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"\u021a\u00eempu, S., Sf\u00eec\u0103, L., Dobri, R.-V., Cazacu, M.-M., Nita, A.-I., and Birsan, M.-V. (2020). Tropospheric Dust and Associated Atmospheric Circulations over the Mediterranean Region with Focus on Romania\u2019s Territory. Atmosphere, 11.","DOI":"10.3390\/atmos11040349"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/2052-336X-12-2","article-title":"Changes in the forest ecosystems in areas impacted by aridization in south-western Romania","volume":"12","author":"Peptenatu","year":"2014","journal-title":"J. Env. Health Sci. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s00704-014-1250-8","article-title":"The summer surface urban heat island of Bucharest (Romania) retrieved from MODIS images","volume":"121","author":"Cheval","year":"2015","journal-title":"Appl. Clim."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s00704-017-2196-4","article-title":"The impact of heat waves on surface urban heat island and local economy in Cluj-Napoca city, Romania","volume":"133","author":"Herbel","year":"2018","journal-title":"Appl. Clim."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.15551\/pesd2020142001","article-title":"Summer urban heat island of Gala\u021bi city (Romania) detected using satellite products","volume":"14","author":"Ichim","year":"2020","journal-title":"Present Environ. Sustain. Dev."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1007\/s10661-015-4428-3","article-title":"Assessing and monitoring the risk of desertification in Dobrogea, Romania, using Landsat data and decision tree classifier","volume":"187","author":"Vorovencii","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Angearu, C.-V., Ontel, I., Boldeanu, G., Mihailescu, D., Nertan, A., Craciunescu, V., Catana, S., and Irimescu, A. (2020). Multi-Temporal Analysis and Trends of the Drought based on MODIS Data in Agricultural Areas, Romania. Remote Sens., 12.","DOI":"10.3390\/rs12233940"},{"key":"ref_48","unstructured":"Geografia Rom\u00e2niei, I. (1983). Geografia Fizic\u0103 (Geography of Romania, I. Physical Geography), Romanian Academy Publishing. (In Romanian)."},{"key":"ref_49","unstructured":"Sandu, I., Pescaru, V.I., Poian\u0103, I., Geicu, A., C\u00e2ndea, I., and \u0162\u00e2\u015ftea, D. (2008). Clima Rom\u00e2niei (Climate of Romania), Romanian Academy Publishing. (In Romanian)."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.catena.2018.08.028","article-title":"Spatio-temporal changes of the climatic water balance in Romania as a response to precipitation and reference evapotranspiration trends during 1961\u20132013","volume":"172","author":"Piticar","year":"2019","journal-title":"Catena"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1007\/s11442-014-1122-2","article-title":"Detecting climate change effects on forest ecosystems in Southwestern Romania using Landsat TM NDVI data","volume":"24","author":"Peptenatu","year":"2014","journal-title":"J. Geogr. Sci."},{"key":"ref_52","first-page":"62","article-title":"The Distribution of the Monthly 24-Hour Maximum Amount of Precipitation in Romania According to their Synoptic Causes","volume":"12","author":"Dobri","year":"2017","journal-title":"Geogr. Tech."},{"key":"ref_53","first-page":"706","article-title":"Changes in cyclone intensity over Romania according to 12 tracking methods","volume":"72","author":"Nita","year":"2020","journal-title":"Rom. Rep. Phys."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.gloplacha.2013.09.004","article-title":"Recent changes in reference evapotranspiration in Romania","volume":"111","author":"Croitoru","year":"2013","journal-title":"Glob. Planet. Chang."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.gloplacha.2014.06.005","article-title":"Reference evapotranspiration variability and trends in Spain, 1961\u20132011","volume":"121","author":"Revuelto","year":"2014","journal-title":"Glob. Planet. Chang."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.ecolind.2014.09.031","article-title":"Assessing trends in climate aridity and vulnerability to soil degradation in Italy","volume":"48","author":"Colantoni","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.atmosres.2011.06.017","article-title":"Spatial and temporal variability of the Aridity Index in Greece","volume":"119","author":"Nastos","year":"2013","journal-title":"Atmos. Res."},{"key":"ref_58","first-page":"91","article-title":"Farm structure adjustments under the irrigation systems rehabilitation in the Southern plain of Romania: A first step towards sustainabile developments","volume":"10","author":"Rusu","year":"2015","journal-title":"Carpathian J. Earth Environ. Sci."},{"key":"ref_59","unstructured":"Didan, K. (2020, September 22). MOD13Q1 MODIS\/Terra Vegetation Indices 16-Day L3 Global 250 m SIN grid V006. NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MOD13Q1.006."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Du, T.L.T., Du Bui, D., Nguyen, M.D., and Lee, H. (2018). Satellite-Based, Multi-Indices for Evaluation of Agricultural Droughts in a Highly Dynamic Tropical Catchment, Central Vietnam. Water, 10.","DOI":"10.3390\/w10050659"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"285","DOI":"10.5721\/EuJRS20144718","article-title":"Correcting MODIS 16-day composite NDVI time-series with actual acquisition dates","volume":"47","author":"Testa","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_62","unstructured":"Team, A. (2020, September 22). Application for Extracting and Exploring Analysis Ready Samples (AppEEARS). NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS\/Earth Resources Observation and Science (EROS) Center: Sioux Falls, SD, USA, Available online: https:\/\/lpdaacsvc.cr.usgs.gov\/appeears\/."},{"key":"ref_63","unstructured":"(2020, January 07). Corine Land Cover, Copernicus Programme. Available online: https:\/\/land.copernicus.eu\/pan-european\/corine-land-cover\/clc2018."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Haylock, M.R., Hofstra, N., Tank, A.M.G.K., Klok, E.J., Jones, P.D., and New, M. (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950\u20132006. J. Geophys. Res. Space Phys., 113.","DOI":"10.1029\/2008JD010201"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1002\/joc.1779","article-title":"Updated and extended European dataset of daily climate observations","volume":"29","author":"Klok","year":"2009","journal-title":"Int. J. Clim."},{"key":"ref_66","unstructured":"(2020, October 07). Copernicus Climate Change Service (C3S). Climate Data Store (CDS). Available online: https:\/\/cds.climate.copernicus.eu\/#!\/home."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.1175\/1520-0426(2003)020<1839:ECADFF>2.0.CO;2","article-title":"EOF calculations and data filling from incomplete oceanographic data sets","volume":"20","author":"Beckers","year":"2003","journal-title":"J. Atmos. Oceanic Technol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.ocemod.2004.08.001","article-title":"Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: Application to the Adriatic Sea surface temperature","volume":"9","author":"Barth","year":"2005","journal-title":"Ocean Model."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Alvera-Azc\u00e1rate, A., Barth, A., Beckers, J.-M., and Weisberg, R.H. (2007). Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields. J. Geophys. Res. Space Phys., 112.","DOI":"10.1029\/2006JC003660"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.seares.2010.08.002","article-title":"Cloud filling of ocean and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology","volume":"65","author":"Sirjacobs","year":"2011","journal-title":"J. Sea Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.rse.2016.02.044","article-title":"Analysis of SMOS sea surface salinity data using DINEOF","volume":"180","author":"Barth","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"183","DOI":"10.5194\/os-2-183-2006","article-title":"DINEOF reconstruction of clouded images including error maps\u2013application to the Sea-Surface Temperature around Corsican Island","volume":"2","author":"Beckers","year":"2006","journal-title":"Ocean Sci."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Hilborn, A., and Costa, M. (2018). Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region. Remote Sens., 10.","DOI":"10.3390\/rs10091449"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Cheval, S., Dumitrescu, A., and Amihaesei, V.-A. (2020). Exploratory Analysis of Urban Climate Using a Gap-Filled Landsat 8 Land Surface Temperature Data Set. Sensors, 20.","DOI":"10.3390\/s20185336"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Filipponi, F., Valentini, E., Xuan, A.N., Guerra, C.A., Wolf, F., Andrzejak, M., and Taramelli, A. (2018). Global MODIS Fraction of Green Vegetation Cover for Monitoring Abrupt and Gradual Vegetation Changes. Remote Sens., 10.","DOI":"10.3390\/rs10040653"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1109\/JSTARS.2012.2224849","article-title":"A Simple Atmospheric Correction Algorithm for MODIS in Shallow Turbid Waters: A Case Study in Taihu Lake","volume":"6","author":"Chen","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2004.10.006","article-title":"On the relationship of NDVI with leaf area index in a deciduous forest site","volume":"94","author":"Wang","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"28","DOI":"10.2307\/1942049","article-title":"Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types","volume":"5","author":"Gamon","year":"1995","journal-title":"Ecol. Appl."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/0034-4257(94)90016-7","article-title":"On the relationship between FAPAR and NDVI","volume":"49","author":"Myneni","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_83","first-page":"336","article-title":"Evaluation of Droughts and Fires in the Dobrogea Region, Using Modis Satellite Data","volume":"1","author":"Angearu","year":"2018","journal-title":"Agric. Life. Life Agric. Conf. Proc."},{"key":"ref_84","first-page":"61","article-title":"Analiza secetei asupra terenurilor arabile din Rom\u00e2nia pe baza imaginilor satelitare","volume":"1","author":"Angearu","year":"2018","journal-title":"Rev. Stiintifica A Adm. Natl. Meteorol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2013.02.023","article-title":"Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data","volume":"134","author":"Zhang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_86","unstructured":"Park, S., Im, J., and Park, S. (2020, October 10). Probabilistic Drought Intensification Forecasts Using Temporal Patterns of Satellite-Derived Drought Indicators. EGU General Assembly Conference Abstracts. 2016; EPSC2016-11264. Available online: https:\/\/meetingorganizer.copernicus.org\/EGU2016\/EGU2016-11264-1.pdf."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2205\/2018ES000647","article-title":"Application of remote sensing technique for drought assessment based on normalized difference drought index, a case study of Bac Binh district, Binh Thuan province (Vietnam)","volume":"19","author":"Trinh","year":"2019","journal-title":"Russ. J. Earth Sci."},{"key":"ref_88","first-page":"29","article-title":"Drought monitoring of forest vegetation using MODIS-based normalized difference drought index in Hungary","volume":"67","year":"2018","journal-title":"Hung. Geogr. Bull."},{"key":"ref_89","unstructured":"Erdenetuya, M., Bulgan, D., and Erdenetsetseg, B. (2011, January 3\u20137). Drought monitoring and assessment using multi satellite data in Mongolia. Proceedings of the 32nd Asian Conference on Remote Sensing, Tapei, Taiwan."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Cheng-lin, L., and Jian-jun, W. (2008, January 7\u201311). Crop drought monitoring using MODIS NDDI over mid-territory of China. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779491"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1016\/j.rse.2010.07.005","article-title":"Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data","volume":"114","author":"Rhee","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_92","first-page":"100","article-title":"Agricultural Drought Monitoring Using Satellite\u2014Based Products in Romania","volume":"Volume 1","author":"Stancalie","year":"2014","journal-title":"Proceedings of the Third International Conference on Telecommunications and Remote Sensing"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"235","DOI":"10.3354\/cr01245","article-title":"Spatiotemporal variability of meteorological drought in Romania using the standardized precipitation index (SPI)","volume":"60","author":"Cheval","year":"2014","journal-title":"Clim. Res."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1007\/s11069-015-2141-8","article-title":"Assessment of droughts in Romania using the Standardized Precipitation Index","volume":"81","author":"Ionita","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s00704-008-0061-1","article-title":"Combining the standardized precipitation index and climatic water deficit in characterizing droughts: A case study in Romania","volume":"97","author":"Paltineanu","year":"2008","journal-title":"Theor. Appl. Clim."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"104407","DOI":"10.1016\/j.catena.2019.104407","article-title":"Spatial assessment of land sensitivity to degradation across Romania. A quantitative approach based on the modified MEDALUS methodology","volume":"187","author":"Patriche","year":"2020","journal-title":"Catena"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Minea, I., Iosub, M., and Boicu, D. (2020). Groundwater Resources from Eastern Romania under Human and Climatic Pressure. Sustainability, 12.","DOI":"10.3390\/su122410341"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1002\/joc.4481","article-title":"Impact of agricultural drought on main crop yields in the Republic of Moldova","volume":"36","author":"Boroneant","year":"2016","journal-title":"Int. J. Climatol."},{"key":"ref_99","unstructured":"(2020, December 10). National Institute of Statistics in Romania (NISR). Available online: https:\/\/insse.ro\/cms\/en."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1478\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:26:53Z","timestamp":1760362013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1478"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,12]]},"references-count":99,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081478"],"URL":"https:\/\/doi.org\/10.3390\/rs13081478","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,12]]}}}