{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T03:17:44Z","timestamp":1774235864674,"version":"3.50.1"},"reference-count":97,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T00:00:00Z","timestamp":1643846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The availability of high spatial resolution and dense revisit time satellite observations, such as Sentinel-2 satellites, allows high resolution phenological metrics to be estimated, able to provide key information from time series and to discriminate vegetation typologies. This paper presents an automated and transferable procedure that combines validated methodologies based on local curve fitting and local derivatives to exploit full satellite Earth observation time series to produce information about plant phenology. Multivariate statistical analysis is performed for the purpose of demonstrating the capacity of the generated smoothed vegetation curve, temporal statistics, and phenological metrics to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients. The results show smoothed vegetation curve and temporal statistics able to highlight seasonal gradient and leaf type characteristics to discriminate forest types, with additional information about forest and leaf productivity provided by temporal statistics analysis. Furthermore, temporal, altitudinal, and latitudinal gradients are obtained from phenological metrics analysis, which also allows to associate temporal gradient with specific phenophases that support forest types distinction. This study highlights the importance of integrated data and methodologies to support the processes of vegetation recognition and monitoring activities.<\/jats:p>","DOI":"10.3390\/rs14030721","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:38:40Z","timestamp":1644179920000},"page":"721","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1419-3734","authenticated-orcid":false,"given":"Federico","family":"Filipponi","sequence":"first","affiliation":[{"name":"Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0427-248X","authenticated-orcid":false,"given":"Daniela","family":"Smiraglia","sequence":"additional","affiliation":[{"name":"Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2346-8346","authenticated-orcid":false,"given":"Emiliano","family":"Agrillo","sequence":"additional","affiliation":[{"name":"Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1029\/2007EO190007","article-title":"Evolving plans for the USA National Phenology Network","volume":"88","author":"Betancourt","year":"2007","journal-title":"Eos Trans. AGU"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1007\/s00484-003-0174-2","article-title":"The European Phenology Network","volume":"47","author":"Bellens","year":"2003","journal-title":"Int. J. Biometeorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11284-014-1239-x","article-title":"Review: Development of an in situ observation network for terrestrial ecological remote sensing: The Phenological Eyes Network (PEN)","volume":"30","author":"Nasahara","year":"2015","journal-title":"Ecol. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1007\/s00484-018-1512-8","article-title":"Pan European Phenological database (PEP725): A single point of access for European data","volume":"62","author":"Templ","year":"2018","journal-title":"Int. J. Biometeorol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lieth, H. (1974). Phenology and Seasonality Modeling, Springer.","DOI":"10.1007\/978-3-642-51863-8"},{"key":"ref_6","unstructured":"Baddour, O., and Kontongomde, H. (2009). Guidelines for Plant Phenological Observations, World Meteorological Organization. WMO-TD No. 1484."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Schwartz, M.D. (2013). Phenology: An Integrative Environmental Science, Springer.","DOI":"10.1007\/978-94-007-6925-0"},{"key":"ref_8","unstructured":"Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K., and Johnson, C.A. (2001). Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1038\/386698a0","article-title":"Increased plant growth in the northern high latitudes from 1981 to 1991","volume":"386","author":"Myneni","year":"1997","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s004840050069","article-title":"Changes in phenology of the locust tree (Robinia pseudoacacia L) in Hungary","volume":"41","author":"Walkovszky","year":"1998","journal-title":"Int. J. Biometeorol."},{"key":"ref_11","first-page":"1","article-title":"Phenological trends in Europe in relation to climatic changes","volume":"7","author":"Chmielewski","year":"2000","journal-title":"Agrarmeteorol. Schr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1002\/1097-0088(20000630)20:8<929::AID-JOC557>3.0.CO;2-5","article-title":"Changes in North American spring","volume":"20","author":"Schwartz","year":"2000","journal-title":"Int. J. Climatol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1111\/j.1365-2486.2001.00430.x","article-title":"Spatial and temporal variability of the phenological seasons in Germany from 1951\u20131996","volume":"7","author":"Menzel","year":"2001","journal-title":"Global Change Biol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2286","DOI":"10.1016\/j.rse.2010.05.005","article-title":"The response of African land surface phenology to large scale climate oscillations","volume":"10","author":"Brown","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1007\/s00484-011-0466-x","article-title":"Effects of recent warm and cold spells on European plant phenology","volume":"55","author":"Menzel","year":"2011","journal-title":"Int. J. Biometeorol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Noormets, A. (2009). Phenology of Ecosystem Processes, Springer.","DOI":"10.1007\/978-1-4419-0026-5"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1098\/rstb.2010.0111","article-title":"Phenology, seasonal timing and circannual rhythms: Towards a unified framework","volume":"365","author":"Visser","year":"2010","journal-title":"Philosoph. Trans. R. Soc. B\u2014Biol. Sci."},{"key":"ref_18","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. Change Biol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1127\/0941-2948\/2005\/0040","article-title":"Analysis of long-term time-series of beginning of flowering by Bayesian function estimation","volume":"14","author":"Menzel","year":"2005","journal-title":"Meteorologische Zeitschrift"},{"key":"ref_20","first-page":"41","article-title":"The BBCH system to coding the phenological growth stages of plants\u2014History and publications","volume":"61","author":"Meier","year":"2009","journal-title":"J. f\u00fcr Kulturpflanzen"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.agrformet.2016.01.006","article-title":"Phenopix: AR package for image-based vegetation phenology","volume":"220","author":"Filippa","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bajocco, S., Raparelli, E., Teofili, T., Bascietto, M., and Ricotta, C. (2019). Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues. Remote Sens., 11.","DOI":"10.3390\/rs11232751"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1111\/j.1365-2486.2011.02397.x","article-title":"Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982\u20132008","volume":"17","author":"Jeong","year":"2011","journal-title":"Glob. Change Biol."},{"key":"ref_25","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_26","unstructured":"USA-NPN National Phenology Network (2021, November 21). Land Surface Phenology and Remote Sensing (LSP\/RS). Available online: https:\/\/usanpn.org\/node\/14."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1111\/j.1365-2486.2005.00949.x","article-title":"Land surface phenology and temperature variation in the International Geosphere\u2013Biosphere Program high-latitude transects","volume":"11","author":"Henebry","year":"2005","journal-title":"Glob. Change Biol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1109\/LGRS.2006.875433","article-title":"The global impact of cloud on the production of MODIS bi-directional reflectance model based composites for terrestrial monitoring","volume":"3","author":"Roy","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/S0034-4257(98)00067-4","article-title":"Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA\/AVHRR data","volume":"67","author":"Duchemin","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.rse.2006.11.025","article-title":"AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982\u20132005","volume":"108","author":"Heumann","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1109\/JSTARS.2010.2075916","article-title":"An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data","volume":"4","author":"Tan","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, J., and Roy, D.P. (2017). A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring. Remote Sens., 9.","DOI":"10.3390\/rs9090902"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/S0034-4257(03)00103-2","article-title":"A regional phenology model for detecting onset of greenness in temperate mixed forests, Korea: An application of MODIS leaf area index","volume":"86","author":"Kang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2440","DOI":"10.1080\/01431161.2014.883105","article-title":"A global NDVI and EVI reference data set for land-surface phenology using 13 years of daily SPOT-VEGETATION observations","volume":"35","author":"Verhegghen","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"L04705","DOI":"10.1029\/2004GL021961","article-title":"A global framework for monitoring phenological responses to climate change","volume":"32","author":"White","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_39","first-page":"1150","article-title":"Toward a national early warning system for forest disturbances using remotely sensed canopy phenology","volume":"75","author":"Hargrove","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6535","DOI":"10.3390\/rs70606535","article-title":"Rapid Assessment of Crop Status: An Application of MODIS and SAR Data to Rice Areas in Leyte, Philippines Affected by Typhoon Haiyan","volume":"7","author":"Boschetti","year":"2015","journal-title":"Remote Sens."},{"key":"ref_41","first-page":"314","article-title":"Remotely-sensed phenology of Italian forests: Going beyond the species","volume":"74","author":"Bajocco","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Pesaresi, S., Mancini, A., and Casavecchia, S. (2020). Recognition and Characterization of Forest Plant Communities through Remote-Sensing NDVI Time Series. Diversity, 12.","DOI":"10.3390\/d12080313"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Valero, S., Morin, D., Inglada, J., Sepulcre, G., Arias, M., Hagolle, O., Dedieu, G., Bontemps, S., Defourny, P., and Koetz, B. (2016). Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions. Remote Sens., 8.","DOI":"10.3390\/rs8010055"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.rse.2017.03.029","article-title":"PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series","volume":"194","author":"Boschetti","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"703","DOI":"10.2307\/3235884","article-title":"Measuring phenological variability from satellite data","volume":"5","author":"Reed","year":"1994","journal-title":"J. Veg. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"526","DOI":"10.3390\/rs2020526","article-title":"Phenological Classification of the United States: A Geographic Framework for Extending Multi-Sensor Time-Series Data","volume":"2","author":"Gu","year":"2010","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.3390\/rs4061781","article-title":"Exploring the Use of MODIS NDVI-Based Phenology Indicators for Classifying Forest General Habitat Categories","volume":"4","author":"Clerici","year":"2012","journal-title":"Remote Sens."},{"key":"ref_48","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_49","unstructured":"Curnel, Y., and Oger, R. (2021, December 22). Agrophenology Indicators from Remote Sensing: State of the Art. Available online: https:\/\/www.isprs.org\/proceedings\/XXXVI\/8-W48\/."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"4660","DOI":"10.3390\/rs6064660","article-title":"Evaluating Remotely Sensed Phenological Metrics in a Dynamic Ecosystem Model","volume":"6","author":"Xu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_52","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_53","unstructured":"European Environment Agency (EEA) (2021). High Resolution Vegetation Phenology and Productivity (HR-VPP), Seasonal Trajectories and VPP parameters, Copernicus Land Monitoring Service, European Environment Agency."},{"key":"ref_54","unstructured":"(2021, December 22). High Resolution Vegetation Phenology and Productivity. Available online: https:\/\/land.copernicus.eu\/pan-european\/biophysical-parameters\/high-resolution-vegetation-phenology-and-productivity."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1080\/17445647.2014.891472","article-title":"Bioclimate of Italy: Application of the worldwide bioclimatic classification system","volume":"10","author":"Pesaresi","year":"2014","journal-title":"J. Maps"},{"key":"ref_57","unstructured":"(2021, December 22). Copertura del Suolo, Available online: https:\/\/www.isprambiente.gov.it\/it\/attivita\/suolo-e-territorio\/copertura-del-suolo."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1111\/avsc.12191","article-title":"European Vegetation Archive (EVA): An integrated database of European vegetation plots","volume":"19","author":"Hennekens","year":"2016","journal-title":"Appl. Veg. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1127\/phyto\/2017\/0139","article-title":"Nationwide Vegetation Plot Database-Sapienza University of Rome: State of the art, basic figures and future perspectives","volume":"47","author":"Agrillo","year":"2017","journal-title":"Phytocoenologia"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1111\/avsc.12519","article-title":"EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats","volume":"23","author":"Hennekens","year":"2020","journal-title":"Appl. Veg. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Agrillo, E., Filipponi, F., Pezzarossa, A., Casella, L., Smiraglia, D., Orasi, A., Attorre, F., and Taramelli, A. (2021). Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping. Remote Sens., 13.","DOI":"10.3390\/rs13071231"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"L\u00f6w, M., and Koukal, T. (2020). Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 Time Series in Austria. Remote Sens., 12.","DOI":"10.21203\/rs.3.rs-26379\/v1"},{"key":"ref_63","unstructured":"Hagolle, O., Huc, M., Desjardins, C., Auer, S., and Richter, R. (2017). MAJA Algorithm Theoretical Basis Document, CNES."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Rouqui\u00e9, B., Hagolle, O., Br\u00e9on, F.M., Boucher, O., Desjardins, C., and R\u00e9my, S. (2017). Using Copernicus atmosphere monitoring service products to constrain the aerosol type in the atmospheric correction processor MAJA. Remote Sens., 9.","DOI":"10.3390\/rs9121230"},{"key":"ref_65","unstructured":"Weiss, M., and Baret, F. (2021, November 21). S2 ToolBox Level 2 Products: LAI, FAPAR, FCOVER. Available online: https:\/\/step.esa.int\/docs\/extra\/ATBD_S2ToolBox_L2B_V1.1.pdf."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.rse.2019.03.020","article-title":"Validation of the Sentinel Simplified Level 2 Product Prototype Processor (SL2P) for mapping cropland biophysical variables using Sentinel-2\/MSI and Landsat-8\/OLI data","volume":"225","author":"Djamai","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Scheffler, D., Hollstein, A., Diedrich, H., Segl, K., and Hostert, P. (2017). AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data. Remote Sens., 9.","DOI":"10.3390\/rs9070676"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Filipponi, F. (2019). Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires. Remote Sens., 11.","DOI":"10.3390\/rs11060622"},{"key":"ref_69","first-page":"85311E","article-title":"Testing Automatic Procedures to Map Rice Area and Detect Phenological Crop Information Exploiting Time Series Analysis of Remote Sensed MODIS Data","volume":"Volume 8531","author":"Manfron","year":"2012","journal-title":"Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, Proceedings of the SPIE 8531, Edinburgh, UK, 24\u201326 September 2012"},{"key":"ref_70","unstructured":"Johannesson, T., and Bjornsson, H. (2021, November 21). Stinepack: Stineman, a Consistently Well Behaved Method of Interpolation. R Package Version 1.3. Available online: https:\/\/CRAN.R-project.org\/package=stinepack."},{"key":"ref_71","first-page":"54","article-title":"A Consistently Well Behaved Method of Interpolation","volume":"6","author":"Stineman","year":"1980","journal-title":"Creat. Comput."},{"key":"ref_72","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-Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1017\/S0013091500077853","article-title":"On a new method of graduation","volume":"41","author":"Whittaker","year":"1923","journal-title":"Proc. Edinb. Math. Soc."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1021\/ac034173t","article-title":"A perfect smoother","volume":"75","author":"Eilers","year":"2003","journal-title":"Anal. Chem."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.chemolab.2010.04.008","article-title":"Improved parametric time warping for proteomics","volume":"104","author":"Bloemberg","year":"2010","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Noormets, A. (2009). Characterizing the Seasonal Dynamics of Plant Community Photosynthesis Across a Range of Vegetation Types. Phenology of Ecosystem Processes, Springer.","DOI":"10.1007\/978-1-4419-0026-5"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Huang, X., Liu, J., Zhu, W., Atzberger, C., and Liu, Q. (2019). The Optimal Threshold and Vegetation Index Time Series for Retrieving Crop Phenology Based on a Modified Dynamic Threshold Method. Remote Sens., 11.","DOI":"10.3390\/rs11232725"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"180028","DOI":"10.1038\/sdata.2018.28","article-title":"Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery","volume":"5","author":"Richardson","year":"2018","journal-title":"Sci. Data"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1038\/s41597-019-0229-9","article-title":"Tracking vegetation phenology across diverse biomes using version 2.0 of the phenocam dataset","volume":"6","author":"Seyednasrollah","year":"2019","journal-title":"Sci. Data"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Huberty, C.J., and Olejnik, S. (2006). Applied MANOVA and Discriminant Analysis, Wiley. [2nd ed.].","DOI":"10.1002\/047178947X"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Gittins, R. (1985). Canonical Analysis. A Review with Application in Ecology, Springer.","DOI":"10.1007\/978-3-642-69878-1"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The Use of Multiple Measurements in Taxonomic Problems","volume":"7","author":"Fisher","year":"1936","journal-title":"Ann. Eugen."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"e1602244","DOI":"10.1126\/sciadv.1602244","article-title":"Canopy near-infrared reflectance and terrestrial photosynthesis","volume":"3","author":"Badgley","year":"2017","journal-title":"Sci. Adv."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"eabc7447","DOI":"10.1126\/sciadv.abc7447","article-title":"A unified vegetation index for quantifying the terrestrial biosphere","volume":"7","author":"Walther","year":"2021","journal-title":"Sci. Adv."},{"key":"ref_85","unstructured":"Earth Resources Observation and Science (EROS) Center (2021, December 22). USGS EROS Archive-Vegetation Monitoring-eMODIS Remote Sensing Phenology, Available online: https:\/\/www.usgs.gov\/centers\/eros\/science\/usgs-eros-archive-vegetation-monitoring-emodis-remote-sensing-phenology."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.cageo.2004.05.006","article-title":"TIMESAT-a program for analyzing time-series of satellite sensor data","volume":"30","author":"Eklundh","year":"2004","journal-title":"Comput. Geosci."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Baetens, L., Desjardins, C., and Hagolle, O. (2019). Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. Remote Sens., 11.","DOI":"10.3390\/rs11040433"},{"key":"ref_88","first-page":"19","article-title":"Spatially detailed retrievals of spring phenology from single-season high-resolution image time series","volume":"59","author":"Vrieling","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.rse.2014.03.017","article-title":"Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty","volume":"148","author":"White","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Liu, J., and Huang, X. (2019, January 16\u201319). Evaluating Crop Phenology Retrieving Accuracies Based on Ground Observations. Proceedings of the 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Istanbul, Turkey.","DOI":"10.1109\/Agro-Geoinformatics.2019.8820703"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Frantz, D. (2019). FORCE\u2014Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sens., 11.","DOI":"10.3390\/rs11091124"},{"key":"ref_92","first-page":"102208","article-title":"Exploring the Potential of Land Surface Phenology and Seasonal Cloud Free Composites of One Year of Sentinel-2 Imagery for Tree Species Mapping in a Mountainous Region","volume":"94","author":"Kollert","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Kolecka, N., Ginzler, C., Pazur, R., Price, B., and Verburg, P.H. (2018). Regional Scale Mapping of Grassland Mowing Frequency with Sentinel-2 Time Series. Remote Sens., 10.","DOI":"10.3390\/rs10081221"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Filipponi, F., Smiraglia, D., Mandrone, S., and Tornato, A. (2021). Cropland mapping using Earth Observation derived phenological metrics. Biol. Life Sci. Forum, submitted for publication.","DOI":"10.3390\/IECAG2021-09732"},{"key":"ref_95","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_96","doi-asserted-by":"crossref","unstructured":"R\u00fcetschi, M., Schaepman, M.E., and Small, D. (2018). Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland. Remote Sens., 10.","DOI":"10.3390\/rs10010055"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Frison, P.-L., Fruneau, B., Kmiha, S., Soudani, K., Dufr\u00eane, E., Le Toan, T., Koleck, T., Villard, L., Mougin, E., and Rudant, J.-P. (2018). Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology. 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