{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:21:21Z","timestamp":1778718081754,"version":"3.51.4"},"reference-count":70,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,3,2]],"date-time":"2019-03-02T00:00:00Z","timestamp":1551484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["CGL2017-89804-R"],"award-info":[{"award-number":["CGL2017-89804-R"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land Surface Phenology (LSP) metrics are increasingly being used as indicators of climate change impacts in ecosystems. For this purpose, it is necessary to use methods that can be applied to large areas with different types of vegetation, including vulnerable semiarid ecosystems that exhibit high spatial variability and low signal-to-noise ratio in seasonality. In this work, we evaluated the use of hidden Markov models (HMM) to extract phenological parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI). We analyzed NDVI time-series data for the period 2000\u20132018 across a range of land cover types in Southeast Spain, including rice croplands, shrublands, mixed pine forests, and semiarid steppes. Start of Season (SOS) and End of Season (EOS) metrics derived from HMM were compared with those obtained using well-established smoothing methods. When a clear and consistent seasonal variation was present, as was the case in the rice croplands, and when adjusting average curves, the smoothing methods performed as well as expected, with HMM providing consistent results. When spatial variability was high and seasonality was less clearly defined, as in the semiarid shrublands and steppe, the performance of the smoothing methods degraded. In these cases, the results from HMM were also less consistent, yet they were able to provide pixel-wise estimations of the metrics even when comparison methods did not.<\/jats:p>","DOI":"10.3390\/rs11050507","type":"journal-article","created":{"date-parts":[[2019,3,4]],"date-time":"2019-03-04T05:45:36Z","timestamp":1551678336000},"page":"507","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Using Hidden Markov Models for Land Surface Phenology: An Evaluation Across a Range of Land Cover Types in Southeast Spain"],"prefix":"10.3390","volume":"11","author":[{"given":"Miguel A.","family":"Garc\u00eda","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, University of Alicante, Apdo. 99, 03080 Alicante, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9610-8718","authenticated-orcid":false,"given":"Hassane","family":"Moutahir","sequence":"additional","affiliation":[{"name":"Department of Ecology and Multidisciplinary Institute for Environmental Studies (IMEM), University of Alicante, Apdo. 99, 03080 Alicante, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Grant M.","family":"Casady","sequence":"additional","affiliation":[{"name":"Department of Biology, Whitworth University, 300 W. Hawthorne Road, Spokane, WA 99251, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7175-7076","authenticated-orcid":false,"given":"Susana","family":"Bautista","sequence":"additional","affiliation":[{"name":"Department of Ecology and Multidisciplinary Institute for Environmental Studies (IMEM), University of Alicante, Apdo. 99, 03080 Alicante, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0753-7826","authenticated-orcid":false,"given":"Francisco","family":"Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Multidisciplinary Institute for Environmental Studies (IMEM), University of Alicante, Apdo. 99, 03080 Alicante, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,2]]},"reference":[{"key":"ref_1","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_2","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\u2019 from space?","volume":"618","author":"Helman","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1890\/070217","article-title":"Tracking the rhythm of the seasons in the face of global change: Phenological research in the 21st century","volume":"7","author":"Morisette","year":"2009","journal-title":"Front. Ecol. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.gloplacha.2012.03.010","article-title":"Combining satellite derived phenology with climate data for climate change impact assessment","volume":"88\u201389","author":"Ivits","year":"2012","journal-title":"Glob. Planet. Chang."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1111\/gcb.13168","article-title":"Variability and evolution of global land surface phenology over the past three decades (1982\u20132012)","volume":"22","author":"Garonna","year":"2016","journal-title":"Glo. Chang. Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1175\/1520-0442(1997)010<1154:GSAOVP>2.0.CO;2","article-title":"Global-scale assessment of vegetation phenology using NOAA\/AVHRR satellite measurements","volume":"10","author":"Moulin","year":"1997","journal-title":"J. Clim."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(02)00135-9","article-title":"Monitoring vegetation phenology using MODIS","volume":"84","author":"Zhang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1111\/j.1529-8817.2003.00784.x","article-title":"Climate controls on vegetation phenological patterns in northern mid\u2014And high latitudes inferred from MODIS data","volume":"10","author":"Zhang","year":"2004","journal-title":"Glob. Chang. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.rse.2007.01.004","article-title":"Cross-scalar satellite phenology from ground, Landsat, and MODIS data","volume":"109","author":"Fisher","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1071\/WF08078","article-title":"Monitoring postwildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel","volume":"19","author":"Casady","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Wang, S., Yang, B., Yang, Q., Lu, L., Wang, X., and Peng, Y. (2016). Temporal Trends and Spatial Variability of Vegetation Phenology over the Northern Hemisphere during 1982\u20132012. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0157134"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liang, L., Chen, F., Shi, L., and Niu, S. (2018). NDVI-derived forest area change and its driving factors in China. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0205885"},{"key":"ref_14","first-page":"e00366","article-title":"Climate change and its effects on vegetation phenology across ecoregions of Ethiopia","volume":"13","author":"Workie","year":"2018","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1016\/j.rse.2010.04.005","article-title":"Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product","volume":"114","author":"Ganguly","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_16","unstructured":"Didan, K. (2015). MOD13Q1 MODIS\/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006. NASA EOSDIS Land Process. DAAC."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7597","DOI":"10.3390\/rs70607597","article-title":"Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring","volume":"7","author":"Lu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","first-page":"132","article-title":"MODIS-derived EVI, NDVI and WDRVI time series to estimate phonological metrics in French deciduous forests","volume":"64","author":"Testaa","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ecolind.2018.06.029","article-title":"Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland","volume":"94","author":"Ferna","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/S0034-4257(00)00175-9","article-title":"Land-surface phenologies from AVHRR using the discrete Fourier transform","volume":"75","author":"Moody","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality extraction and noise removal by function fitting to time-series of satellite sensor data","volume":"40","author":"Eklundh","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.1080\/01431160600639743","article-title":"Assessing spatio-temporal 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_24","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.rse.2006.08.002","article-title":"A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data","volume":"106","author":"Bradley","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_25","unstructured":"Hudson, I.L., and Keatley, M.R. (2010). Spatio-temporal statistical methods for modeling land surface phenology. Phenological Research: Methods for Environmental and Climate Change Analysis, Springer."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.rse.2012.04.001","article-title":"Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology","volume":"123","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cai, Z., J\u00f6nsson, P., Jin, H., and Eklundh, L. (2017). Performance of smoothing methods for reconstructing NDVI time-series and estimating vegetation phenology from MODIS data. Remote Sens., 9.","DOI":"10.3390\/rs9121271"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/5.18626","article-title":"A tutorial on hidden Markov models and selected applications in speech recognition","volume":"77","author":"Rabiner","year":"1989","journal-title":"Proc. IEEE"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1561\/2000000004","article-title":"The application of hidden Markov models in speech recognition","volume":"1","author":"Gales","year":"2008","journal-title":"Found. Trends Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3354\/cr015001","article-title":"A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts","volume":"15","author":"Bellone","year":"2000","journal-title":"Clim. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1002\/qj.788","article-title":"Downscaling projections of Indian monsoon rainfall using a non-homogeneous hidden Markov model","volume":"137","author":"Greene","year":"2011","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1016\/S0031-3203(98)00109-5","article-title":"Applications of hidden Markov chains in image analysis","volume":"32","author":"Aas","year":"1999","journal-title":"Pattern Recognit."},{"key":"ref_33","first-page":"185","article-title":"Fitting hidden Markov models to psychological data","volume":"10","author":"Visser","year":"2002","journal-title":"Sci. Program."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1080\/00273171.2016.1192983","article-title":"Hidden Markov item response theory models for responses and response times","volume":"51","author":"Molenaar","year":"2016","journal-title":"Multivar. Behav. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1002\/bimj.4710370606","article-title":"Hidden Markov models and animal behavior","volume":"37","author":"Macdonald","year":"1995","journal-title":"Biom. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1023\/A:1018582614125","article-title":"Using hidden Markov chains and empirical Bayes change-point estimation for transect data","volume":"4","author":"Cressie","year":"1997","journal-title":"Environ. Ecol. Stat."},{"key":"ref_37","unstructured":"Brebbia, C.A., Villacampa, Y., and Us\u00f3, J.L. (2001). Patch-gap analysis of presence-absence data in vegetation transects using hidden Markov models, with application to the characterisation of post-fire plant pattern disturbance in a semiarid pine forest. Ecosystems and Sustainable Development III, Advances in Ecological Sciences 10, WIT Press."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.ecolmodel.2003.06.004","article-title":"Analysis of movements and behavior of caribou (Rangifer tarandus) using hidden Markov models","volume":"173","author":"Franke","year":"2004","journal-title":"Ecol. Model."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.ecolmodel.2004.11.021","article-title":"On the use of stationary versus hidden Markov models to detect simple versus complex ecological dynamics","volume":"185","author":"Tucker","year":"2005","journal-title":"Ecol. Model."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.ecolmodel.2006.02.043","article-title":"Prediction of wolf (Canis lupus) kill-sites using hidden Markov models","volume":"197","author":"Franke","year":"2006","journal-title":"Ecol. Model."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1073\/pnas.91.3.1059","article-title":"Hidden Markov models of biological primary sequence information","volume":"91","author":"Baldi","year":"1994","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gollery, M. (2008). Handbook of Hidden Markov Models in Bioinformatics, Chapman and Hall\/CRC.","DOI":"10.1201\/9781420011807"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"402","DOI":"10.2174\/138920209789177575","article-title":"Hidden Markov models and their applications in biological sequence analysis","volume":"10","author":"Yoon","year":"2009","journal-title":"Curr. Genom."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Westhead, D.R., and Vijayabaskar, M.S. (2017). Hidden Markov Models. Methods and Protocols, Humana Press\/Springer.","DOI":"10.1007\/978-1-4939-6753-7"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/TGRS.2003.809940","article-title":"Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields","volume":"41","author":"Fjortoft","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.1080\/01431160512331337844","article-title":"Combining spectral and spatial information into hidden Markov models for unsupervised image classification","volume":"26","author":"Tso","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.patrec.2010.02.008","article-title":"Hidden Markov models for crop recognition in remote sensing image sequences","volume":"32","author":"Pakzad","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TGRS.2013.2263282","article-title":"Spectral\u2013spatial classification of hyperspectral images based on hidden Markov random fields","volume":"52","author":"Ghamisi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3633","DOI":"10.3390\/rs70403633","article-title":"A hidden Markov models approach for crop classification: Linking crop phenology to time series of multi-sensor remote sensing data","volume":"7","author":"Siachalou","year":"2015","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"15318","DOI":"10.3390\/rs71115318","article-title":"Continuous change detection and classification using hidden Markov model: A case study for monitoring urban encroachment onto farmland in Beijing","volume":"7","author":"Yuan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1109\/TGRS.2015.2463689","article-title":"Improving the consistency of multitemporal land cover maps using a hidden Markov model","volume":"54","author":"Abercrombie","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gong, W., Fang, S., Yang, G., and Ge, M. (2017). Using a hidden Markov model for improving the spatial-temporal consistency of time series land cover classification. ISPRS Int. J. Geo Inf., 6.","DOI":"10.3390\/ijgi6100292"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Lin, L., Chen, J., Sahli, H., Chen, Y., Wang, C., and Wu, B. (2019). A new framework for modelling and monitoring the conversion of cultivated land to built-up land based on a hierarchical hidden semi-Markov model using satellite image time series. Remote Sens., 11.","DOI":"10.3390\/rs11020210"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1109\/36.298019","article-title":"Hidden Markov models applied to vegetation dynamics analysis using satellite remote sensing","volume":"32","author":"Viovy","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.3390\/rs5041734","article-title":"Hidden Markov models for real-time estimation of corn progress stages using MODIS and meteorological data","volume":"5","author":"Shen","year":"2013","journal-title":"Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"92291K","DOI":"10.1117\/12.2066318","article-title":"Determination of phenological parameters from MODIS derived NDVI data using hidden Markov models","volume":"Volume 9229","author":"Hadjimitsis","year":"2014","journal-title":"Proceedings of the SPIE Second International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2014)"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Capp\u00e9, O., Moulines, E., and Ryd\u00e9n, T. (2005). Inference in Hidden Markov Models, Springer.","DOI":"10.1007\/0-387-28982-8"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1214\/aoms\/1177697196","article-title":"A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains","volume":"41","author":"Baum","year":"1970","journal-title":"Ann. Math. Stat."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TIT.1967.1054010","article-title":"Error bounds for convolutional codes and an asymptotically optimal decoding algorithm","volume":"13","author":"Viterbi","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_61","unstructured":"R Core Team (2018). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_62","unstructured":"Murphy, K. (2018, June 12). Hidden Markov Model (HMM) Toolbox for Matlab. Available online: https:\/\/www.cs.ubc.ca\/~murphyk\/Software\/HMM\/hmm.html."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.3390\/rs5052113","article-title":"Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology","volume":"5","author":"Forkel","year":"2013","journal-title":"Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3414","DOI":"10.1111\/gcb.12950","article-title":"Codominant water control on global interannual variability and trends in land surface phenology and greenness","volume":"21","author":"Forkel","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_65","unstructured":"Forkel, M., and Wutzler, T. (2018, May 10). Greenbrown\u2014Land Surface Phenology and Trend Analysis. A Package for the R Software. Version 2.2, 2015-04-15. Available online: http:\/\/greenbrown.r-forge.r-project.org\/."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.cageo.2004.05.006","article-title":"TIMESAT\u2014A program for analyzing time-series of satellite sensor data","volume":"30","author":"Eklundh","year":"2004","journal-title":"Comput. Geosci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1029\/97GB00330","article-title":"A continental phenology model for monitoring vegetation responses to interannual climatic variability","volume":"11","author":"White","year":"1997","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"388","DOI":"10.3390\/rs2020388","article-title":"Phenological characterization of desert sky island vegetation communities with remotely sensed and climate time series data","volume":"2","author":"Davison","year":"2010","journal-title":"Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3803","DOI":"10.3390\/rs5083803","article-title":"Disentangling the relationships between Net primary production and precipitation in Southern Africa savannas using satellite observations from 1982 to 2010","volume":"5","author":"Zhu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Yu, S.-Z. (2016). Hidden Semi-Markov Models: Theory, Algorithms and Applications, Elsevier.","DOI":"10.1016\/B978-0-12-802767-7.00002-4"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/5\/507\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:35:45Z","timestamp":1760186145000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/5\/507"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,2]]},"references-count":70,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["rs11050507"],"URL":"https:\/\/doi.org\/10.3390\/rs11050507","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,2]]}}}