{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T10:35:35Z","timestamp":1773916535647,"version":"3.50.1"},"reference-count":111,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T00:00:00Z","timestamp":1651190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014013","name":"UK Research and Innovation GCRF","doi-asserted-by":"publisher","award":["323036\/ARCP011217"],"award-info":[{"award-number":["323036\/ARCP011217"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Global change impacts including climate change, increased CO2 and nitrogen deposition can be determined through a more precise characterisation of Land Surface Phenology (LSP) parameters. In addition, accurate estimation of LSP dates is being increasingly used in applications such as mapping vegetation types, yield forecasting, and irrigation management. However, there has not been any attempt to characterise Middle East vegetation phenology at the fine spatial resolution appropriate for such applications. Remote-sensing based approaches have proved to be a useful tool in such regions since access is restricted in some areas due to security issues and their inter-annual vegetation phenology parameters vary considerably because of high uncertainty in rainfall. This study aims to establish for the first time a comprehensive characterisation of the vegetation phenological characteristics of the major vegetation types in the Middle East at a fine spatial resolution of 30 m using Landsat Normalized Difference Vegetation Index (NDVI) time series data over a temporal range of 20 years (2000\u20132020). Overall, a progressive pattern in phenophases was observed from low to high latitude. The earliest start of the season was concentrated in the central and east of the region associated mainly with grassland and cultivated land, while the significantly delayed end of the season was mainly distributed in northern Turkey and Iran corresponding to the forest, resulting in the prolonged length of the season in the study area. There was a significant positive correlation between LSP parameters and latitude, which indicates a delay in the start of the season of 4.83 days (R2 = 0.86, p &lt; 0.001) and a delay in the end of the season of 6.54 days (R2 = 0.83, p &lt; 0.001) per degree of latitude increase. In addition, we have discussed the advantages of fine resolution LSP parameters over the available coarse datasets and showed how such outputs can improve many applications in the region. This study shows the potential of Landsat data to quantify the LSP of major land cover types in heterogeneous landscapes of the Middle East which enhances our understanding of the spatial-temporal dynamics of vegetation dynamics in arid and semi-arid settings in the world.<\/jats:p>","DOI":"10.3390\/rs14092136","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T07:08:58Z","timestamp":1651475338000},"page":"2136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Characterising the Land Surface Phenology of Middle Eastern Countries Using Moderate Resolution Landsat Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Sarchil Hama","family":"Qader","sequence":"first","affiliation":[{"name":"School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK"},{"name":"Natural Resources Department, College of Agricultural Engineering Sciences, University of Sulaimani, Sulaimani 334, Kurdistan Region, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1203-2651","authenticated-orcid":false,"given":"Rhorom","family":"Priyatikanto","sequence":"additional","affiliation":[{"name":"School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK"},{"name":"Research Center for Space, National Research and Innovation Agency, Bandung 40173, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0183-8388","authenticated-orcid":false,"given":"Nabaz R.","family":"Khwarahm","sequence":"additional","affiliation":[{"name":"Department of Biology, College of Education, University of Sulaimani, Sulaimani 334, Kurdistan Region, Iraq"}]},{"given":"Andrew J.","family":"Tatem","sequence":"additional","affiliation":[{"name":"School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK"}]},{"given":"Jadunandan","family":"Dash","sequence":"additional","affiliation":[{"name":"School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lieth, H. (1973). Phenology in productivity studies. Analysis of Temperate Forest Ecosystems, Springer.","DOI":"10.1007\/978-3-642-85587-0_4"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.isprsjprs.2020.11.019","article-title":"Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review","volume":"171","author":"Dash","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1111\/j.1365-2486.2006.01193.x","article-title":"European phenological response to climate change matches the warming pattern","volume":"12","author":"Menzel","year":"2006","journal-title":"Glob. Chang. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1038\/nature01286","article-title":"A globally coherent fingerprint of climate change impacts across natural systems","volume":"421","author":"Parmesan","year":"2003","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111477","DOI":"10.1016\/j.rse.2019.111477","article-title":"Urbanization and climate change jointly shift land surface phenology in the northern mid-latitude large cities","volume":"236","author":"Qiu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1038\/nature11014","article-title":"Warming experiments underpredict plant phenological responses to climate change","volume":"485","author":"Wolkovich","year":"2012","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1111\/j.1469-8137.2004.01059.x","article-title":"Responses of spring phenology to climate change","volume":"162","author":"Badeck","year":"2004","journal-title":"New Phytol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Junttila, O., and Nilsen, J. (1993). Growth and development of northern forest trees as affected by temperature and light. Forest Development in Cold Climates, Springer.","DOI":"10.1007\/978-1-4899-1600-6_3"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5247","DOI":"10.1080\/01431161.2010.496470","article-title":"Changes in vegetation spring dates in the second half of the twentieth century","volume":"32","author":"Sobrino","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s13717-020-00259-0","article-title":"Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq","volume":"9","author":"Khwarahm","year":"2020","journal-title":"Ecol. Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"054006","DOI":"10.1088\/1748-9326\/9\/5\/054006","article-title":"A tale of two springs: Using recent climate anomalies to characterize the sensitivity of temperate forest phenology to climate change","volume":"9","author":"Friedl","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108237","DOI":"10.1016\/j.agrformet.2020.108237","article-title":"Longer greenup periods associated with greater wood volume growth in managed pine stands","volume":"297","author":"Gao","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1111\/gcb.14619","article-title":"Plant phenology and global climate change: Current progresses and challenges","volume":"25","author":"Piao","year":"2019","journal-title":"Glob. Change Biol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.agrformet.2012.09.012","article-title":"Climate change, phenology, and phenological control of vegetation feedbacks to the climate system","volume":"169","author":"Richardson","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e12007","DOI":"10.1002\/2688-8319.12007","article-title":"Using phenology data to improve control of invasive plant species: A case study on Midway Atoll NWR","volume":"1","author":"Taylor","year":"2020","journal-title":"Ecol. Solut. Evid."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1890\/110281","article-title":"From Caprio\u2019s lilacs to the USA National Phenology Network","volume":"10","author":"Schwartz","year":"2012","journal-title":"Front. Ecol. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fenvs.2019.00014","article-title":"Multi-scale phenology of temperate grasslands: Improving monitoring and management with near-surface phenocams","volume":"7","author":"Watson","year":"2019","journal-title":"Front. Environ. Sci."},{"key":"ref_18","first-page":"82","article-title":"Introducing digital cameras to monitor plant phenology in the tropics: Applications for conservation","volume":"15","author":"Alberton","year":"2017","journal-title":"Perspect. Ecol. Conserv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Berra, E.F., Gaulton, R., and Barr, S. (2016, January 10\u201315). Use of a digital camera onboard a UAV to monitor spring phenology at individual tree level. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729904"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"125002","DOI":"10.1088\/1748-9326\/abbf7d","article-title":"Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites","volume":"15","author":"Assmann","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Qader, S.H., Dash, J., Alegana, V.A., Khwarahm, N.R., Tatem, A.J., and Atkinson, P.M. (2021). The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World. Remote Sens., 13.","DOI":"10.3390\/rs13173382"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3451","DOI":"10.1080\/014311699211499","article-title":"Surface phenology and satellite sensor-derived onset of greenness: An initial comparison","volume":"20","author":"Schwartz","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/j.scitotenv.2017.07.237","article-title":"Land surface phenology: What do we really \u2018see\u2019from space?","volume":"618","author":"Helman","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111511","DOI":"10.1016\/j.rse.2019.111511","article-title":"A review of vegetation phenological metrics extraction using time-series, multispectral satellite data","volume":"237","author":"Zeng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.sajb.2017.03.007","article-title":"Long-term trends in vegetation phenology and productivity over Namaqualand using the GIMMS AVHRR NDVI3g data from 1982 to 2011","volume":"111","author":"Davis","year":"2017","journal-title":"South Afr. J. Bot."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111307","DOI":"10.1016\/j.rse.2019.111307","article-title":"Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992\u20132012","volume":"232","author":"Tong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111017","DOI":"10.1016\/j.rse.2018.12.016","article-title":"Characterizing land cover\/land use from multiple years of Landsat and MODIS time series: A novel approach using land surface phenology modeling and random forest classifier","volume":"238","author":"Nguyen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111675","DOI":"10.1016\/j.rse.2020.111675","article-title":"Land surface phenology in the highland pastures of montane Central Asia: Interactions with snow cover seasonality and terrain characteristics","volume":"240","author":"Tomaszewska","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2833","DOI":"10.3390\/s8042833","article-title":"Alpine grassland phenology as seen in AVHRR, VEGETATION, and MODIS NDVI time series-a comparison with in situ measurements","volume":"8","author":"Fontana","year":"2008","journal-title":"Sensors"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1080\/014311601300074540","article-title":"Application of NOAA-AVHRR NDVI time-series data to assess changes in Saudi Arabia\u2019s rangelands","volume":"22","author":"Weiss","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","first-page":"107","article-title":"Spatiotemporal variation in the terrestrial vegetation phenology of Iraq and its relation with elevation","volume":"41","author":"Qader","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.5194\/se-6-1185-2015","article-title":"MODIS normalized difference vegetation index (NDVI) and vegetation phenology dynamics in the Inner Mongolia grassland","volume":"6","author":"Gong","year":"2015","journal-title":"Solid Earth"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.rse.2018.03.014","article-title":"Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island","volume":"215","author":"Vrieling","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"112456","DOI":"10.1016\/j.rse.2021.112456","article-title":"Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe","volume":"260","author":"Tian","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1016\/j.rse.2010.01.021","article-title":"The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India","volume":"114","author":"Dash","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1007\/s10980-010-9490-1","article-title":"Characterising the spatial pattern of phenology for the tropical vegetation of India using multi-temporal MERIS chlorophyll data","volume":"25","author":"Jeganathan","year":"2010","journal-title":"Landsc. Ecol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"9390","DOI":"10.3390\/rs70709390","article-title":"Characterising the land surface phenology of Europe using decadal MERIS data","volume":"7","author":"Dash","year":"2015","journal-title":"Remote Sens."},{"key":"ref_38","first-page":"101974","article-title":"Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests","volume":"84","author":"Bornez","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1111\/j.1365-2486.2007.01505.x","article-title":"Spring phenology in boreal Eurasia over a nearly century time scale","volume":"14","author":"Delbart","year":"2008","journal-title":"Glob. Change Biol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"073485","DOI":"10.1117\/1.JRS.7.073485","article-title":"Remote sensing-based quantification of spatial variation in canopy phenology of four dominant tree species in Europe","volume":"7","author":"Han","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1093\/nsr\/nwv058","article-title":"Plant phenological responses to climate change on the Tibetan Plateau: Research status and challenges","volume":"2","author":"Shen","year":"2015","journal-title":"Natl. Sci. Rev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"703","DOI":"10.2307\/3235884","article-title":"Measuring phenological variability from satellite imagery","volume":"5","author":"Reed","year":"1994","journal-title":"J. Veg. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1016\/j.rse.2011.08.010","article-title":"Comparison of different vegetation indices for the remote assessment of green leaf area index of crops","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.agrformet.2016.11.193","article-title":"Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites","volume":"233","author":"Wu","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.rse.2007.05.011","article-title":"Interannual vegetation phenology estimates from global AVHRR measurements: Comparison with in situ data and applications","volume":"112","author":"Maignan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.scitotenv.2016.11.004","article-title":"Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series","volume":"578","author":"Khwarahm","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.04.031","article-title":"A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems","volume":"196","author":"Wang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Abbas, N., Wasimi, S.A., Al-Ansari, N., and Nasrin Baby, S. (2018). Recent trends and long-range forecasts of water resources of northeast Iraq and climate change adaptation measures. Water, 10.","DOI":"10.3390\/w10111562"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.envint.2018.05.014","article-title":"Escalating heat-stress mortality risk due to global warming in the Middle East and North Africa (MENA)","volume":"117","author":"Ahmadalipour","year":"2018","journal-title":"Environ. Int."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hameed, M., Ahmadalipour, A., and Moradkhani, H. (2018). Apprehensive drought characteristics over Iraq: Results of a multidecadal spatiotemporal assessment. Geosciences, 8.","DOI":"10.3390\/geosciences8020058"},{"key":"ref_52","unstructured":"Tolba, M.K.S., and Najib, W. (2009). Arab Environment: Climate Change: Impact of Climate Change on Arab Countries, Arab Forum for Environment and Development (AFED)."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"107816","DOI":"10.1016\/j.agrformet.2019.107816","article-title":"Drought and food security in the middle east: An analytical framework","volume":"281","author":"Hameed","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1007\/s10584-012-0418-4","article-title":"Climate change and impacts in the Eastern Mediterranean and the Middle East","volume":"114","author":"Lelieveld","year":"2012","journal-title":"Clim. Change"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/S0140-1963(03)00121-6","article-title":"Discrimination between climate and human-induced dryland degradation","volume":"57","author":"Evans","year":"2004","journal-title":"J. Arid Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.scitotenv.2017.09.057","article-title":"Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq","volume":"613","author":"Qader","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"835","DOI":"10.2166\/wcc.2018.142","article-title":"Predicting vegetation phenology in response to climate change using bioclimatic indices in Iraq","volume":"10","author":"Daham","year":"2019","journal-title":"J. Water Clim. Chang."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/JSTARS.2015.2508639","article-title":"Classification of vegetation type in Iraq using satellite-based phenological parameters","volume":"9","author":"Qader","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_59","unstructured":"Eklund, L., Persson, A., Tang, J., Selander, M., and Borg, M. (2022, February 25). Using Crop Phenology to Assess Changes in Cultivated Land after the Anfal Genocide in Iraqi Kurdistan. Available online: https:\/\/agile-online.org\/conference_paper\/cds\/agile_2014\/agile2014_113.pdf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"107682","DOI":"10.1016\/j.agrformet.2019.107682","article-title":"Associations between large-scale climate oscillations and land surface phenology in Iran","volume":"278","author":"Araghi","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4961","DOI":"10.1109\/JSTARS.2017.2736938","article-title":"Trends in Phenological Parameters and Relationship Between Land Surface Phenology and Climate Data in the Hyrcanian Forests of Iran","volume":"10","author":"Kiapasha","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1080\/15481603.2019.1646977","article-title":"Spatio-temporal reconstruction of MODIS NDVI by regional land surface phenology and harmonic analysis of time-series","volume":"56","author":"Padhee","year":"2019","journal-title":"GIScience Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"5270","DOI":"10.3390\/s8095270","article-title":"Deriving vegetation dynamics of natural terrestrial ecosystems from MODIS NDVI\/EVI data over Turkey","volume":"8","author":"Evrendilek","year":"2008","journal-title":"Sensors"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Mermer, A., Y\u0131ld\u0131z, H., \u00dcnal, E., Aydo\u011fdu, M., \u00d6zayd\u0131n, A., Dedeo\u011flu, F., Urla, O., Aydo\u011fmu\u015f, O., Torunlar, H., and Tu\u011fa\u00e7, M. (2015, January 20\u201324). Monitoring rangeland vegetation through time series satellite images (NDVI) in Central Anatolia Region. Proceedings of the 2015 Fourth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Istanbul, Turkey.","DOI":"10.1109\/Agro-Geoinformatics.2015.7248137"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2012.03.012","article-title":"Ground-based Network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes","volume":"123","author":"Soudani","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_66","first-page":"510","article-title":"Index-based assessment of agricultural drought using remote sensing in the semi-arid region of Western Turkey","volume":"24","year":"2018","journal-title":"J. Agric. Sci."},{"key":"ref_67","first-page":"263","article-title":"Classification of some strategic crops in Egypt using multi remotely sensing sensors and time series analysis","volume":"22","author":"Farg","year":"2019","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4459","DOI":"10.1080\/01431161.2017.1323285","article-title":"Monitoring cropland changes along the Nile River in Egypt over past three decades (1984\u20132015) using remote sensing","volume":"38","author":"Xu","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1007\/s12665-018-7534-z","article-title":"Derivation of vegetation density and land-use type pattern in mountain regions of Jordan using multi-seasonal SPOT images","volume":"77","author":"Makhamreh","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_70","unstructured":"Saba, M., Al-Naber, G., and Mohawesh, Y. (2011). Analysis of Jordan vegetation cover dynamics using MODIS\/NDVI from 2000 to 2009. Food Security and Climate Change in Dry Areas, Proceedings of the an International Conference, Amman, Jordan, 1\u20134 February 2010, International Center for Agricultural Research in the Dry Areas."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"141146","DOI":"10.1016\/j.scitotenv.2020.141146","article-title":"Long-term effects of climatic and hydrological variation on natural vegetation production and characteristics in a semiarid watershed: The northern Negev, Israel","volume":"747","author":"Argaman","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1080\/014311600210399","article-title":"Temporal and spatial vegetation cover changes in Israeli transition zone: AVHRR-based assessment of rainfall impact","volume":"21","author":"Schmidt","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1006\/jare.1996.0193","article-title":"Phenology of ten common plant species in western Saudi Arabia","volume":"35","year":"1997","journal-title":"J. Arid. Environ."},{"key":"ref_74","first-page":"302","article-title":"Object-based dimensionality reduction in land surface phenology classification AIMS","volume":"2","author":"Bunker","year":"2016","journal-title":"Geosciences"},{"key":"ref_75","unstructured":"World Atlas (2022, February 07). How Many Countries Are There In the Middle East?. Available online: https:\/\/www.worldatlas.com\/articles\/which-are-the-middle-eastern-countries.html#:~:text=Middle%20East%20includes%2018%20countries,United%20Arab%20Emirates%20and%20Yemen."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3924","DOI":"10.1175\/JCLI4223.1","article-title":"Climate and vegetation in the Middle East: Interannual variability and drought feedbacks","volume":"20","author":"Zaitchik","year":"2007","journal-title":"J. Clim."},{"key":"ref_77","unstructured":"GlobeLand30 (2022, January 11). Global Land Cover Mapping at 30 m Resolution (2020). Available online: http:\/\/www.globallandcover.com\/."},{"key":"ref_78","unstructured":"DIVA-GIS (2021, December 12). Free Spatial Data by Country. Available online: https:\/\/www.diva-gis.org\/gdata."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1038\/514434c","article-title":"Open access to Earth land-cover map","volume":"514","author":"Jun","year":"2014","journal-title":"Nature"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.rse.2012.12.003","article-title":"The global availability of Landsat 5 TM and Landsat 7 ETM + land surface observations and implications for global 30 m Landsat data product generation","volume":"130","author":"Kovalsky","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1109\/TGRS.2004.840720","article-title":"Landsat sensor performance: History and current status","volume":"42","author":"Markham","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/S0034-4257(01)00248-6","article-title":"Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets","volume":"78","author":"Teillet","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.14358\/PERS.72.10.1137","article-title":"Landsat-7 long-term acquisition plan: Development and validation","volume":"72","author":"Ardvison","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2010.12.010","article-title":"A simple and effective method for filling gaps in Landsat ETM + SLC-off images","volume":"115","author":"Chen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_86","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, W.D. (1973, January 10\u201314). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third ERTS Symposium, NASA SP\u2013351, Washington, DC, USA."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Schmidt, G., Jenkerson, C.B., Masek, J., Vermote, E., and Gao, F. (2013). Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) Algorithm Description, U.S. Geological Survey. Technical Report.","DOI":"10.3133\/ofr20131057"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_89","first-page":"461","article-title":"Harmonic analysis of time-series AVHRR NDVI data","volume":"67","author":"Jakubauskas","year":"2001","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.1080\/01431160600639743","article-title":"Assessing spatio\u2013temporal variations in plant phenology using Fourier analysis on NDVI time series: Results from a dry savannah environment in Namibia","volume":"27","author":"Wagenseil","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"506","DOI":"10.3389\/fpls.2020.00506","article-title":"CO2 Elevation and Photoperiods North of Seed Origin Change Autumn and Spring Phenology as Well as Cold Hardiness in Boreal White Birch","volume":"11","author":"Tedla","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"094055","DOI":"10.1088\/1748-9326\/aba57f","article-title":"Satellite-observed decrease in the sensitivity of spring phenology to climate change under high nitrogen deposition","volume":"15","author":"Wang","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Mancino, G., Ferrara, A., Padula, A., and Nol\u00e8, A. (2020). Cross-Comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) Derived Vegetation Indices in a Mediterranean Environment. Remote Sens., 12.","DOI":"10.3390\/rs12020291"},{"key":"ref_95","unstructured":"Friedl, M., Gray, J., and Sulla-Menashe, D. (2022, February 25). MCD12Q2 MODIS\/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500 m SIN Grid V006 [Data Set]; NASA EOSDIS Land Processes DAAC, Available online: https:\/\/doi.org\/10.5067\/MODIS\/MCD12Q2.006."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1016\/j.scitotenv.2016.05.142","article-title":"Soil moisture controls on phenology and productivity in a semi-arid critical zone","volume":"568","author":"Cleverly","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"5294","DOI":"10.1029\/2019GL082716","article-title":"Phenology Dynamics of Dryland Ecosystems along North Australian Tropical Transect Revealed by Satellite Solar-Induced Chlorophyll Fluorescence","volume":"46","author":"Wang","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"107055","DOI":"10.1016\/j.ecolind.2020.107055","article-title":"Evaluation and comparison of growing season metrics in arid and semi-arid areas of northern China under climate change","volume":"121","author":"Cui","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.isprsjprs.2022.01.017","article-title":"Land surface phenology retrievals for arid and semi-arid ecosystems","volume":"185","author":"Xie","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1093\/jpe\/rtm005","article-title":"Remote sensing imagery in vegetation mapping: A review","volume":"1","author":"Xie","year":"2008","journal-title":"J. Plant Ecol."},{"key":"ref_101","first-page":"e61052","article-title":"Ground-level Unmanned Aerial System Imagery Coupled with Spatially Balanced Sampling and Route Optimization to Monitor Rangeland Vegetation","volume":"160","author":"Curran","year":"2020","journal-title":"J. Vis. Exp."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Schwartz, M.D. (2003). Remote Sensing Phenology. Phenology: An Integrative Environmental Science. Tasks for Vegetation Science, Springer.","DOI":"10.1007\/978-94-007-0632-3"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Matongera, T.N., Mutanga, O., Sibanda, M., and Odindi, J. (2021). Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges. Remote Sens., 13.","DOI":"10.3390\/rs13112060"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.habitatint.2016.02.003","article-title":"GlobeLand30 as an alternative fine-scale global land cover map: Challenges, possibilities, and implications for developing countries","volume":"55","author":"Tayyebi","year":"2016","journal-title":"Habitat Int."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.5194\/isprs-archives-XLI-B8-1313-2016","article-title":"Uncertainty assessment of GlobeLand30 land cover data set over central Asia","volume":"41","author":"Sun","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_106","first-page":"182","article-title":"Analysis of forest change and deforestation in Turkey","volume":"21","year":"2019","journal-title":"Int. For. Rev."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Co\u015fgun, U., and Gonz\u00e1lez-Cab\u00e1n, A. (2019). Factors explaining forest fires in the Serik and Ta\u015fa\u011f\u0131l forest provinces (SW Anatolia-Turkey). Proceedings of the Fifth International Symposium on Fire Economics, Planning, and Policy: Ecosystem Services and Wildfires, USDA Department of Agriculture, Forest Service, Pacific Southwest Research Station. General Technical Report PNW-GTR-261.","DOI":"10.2737\/PSW-GTR-261"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"112133","DOI":"10.1016\/j.rse.2020.112133","article-title":"Investigation of land surface phenology detections in shrublands using multiple scale satellite data","volume":"252","author":"Peng","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_109","unstructured":"Schnepf, R. (2004). Iraq Agriculture and Food Supply: Background and Issues, Congressional Research Service, The Library of Congress."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"885","DOI":"10.14358\/PERS.78.8.895","article-title":"Three decades of war and food insecurity in Iraq","volume":"78","author":"Gibson","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.apgeog.2017.12.006","article-title":"Characterising the land surface phenology of Africa using 500 m MODIS EVI","volume":"90","author":"Adole","year":"2018","journal-title":"Appl. Geogr."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2136\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:04:03Z","timestamp":1760137443000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,29]]},"references-count":111,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092136"],"URL":"https:\/\/doi.org\/10.3390\/rs14092136","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,29]]}}}