{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T21:15:00Z","timestamp":1773868500568,"version":"3.50.1"},"reference-count":168,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of South Africa","award":["84157"],"award-info":[{"award-number":["84157"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.<\/jats:p>","DOI":"10.3390\/rs13112060","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:01:20Z","timestamp":1621814480000},"page":"2060","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6290-1427","authenticated-orcid":false,"given":"Trylee Nyasha","family":"Matongera","sequence":"first","affiliation":[{"name":"Discipline of Geography, University of KwaZulu-Natal, Pietermaritzburg 3201, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7358-8111","authenticated-orcid":false,"given":"Onisimo","family":"Mutanga","sequence":"additional","affiliation":[{"name":"Discipline of Geography, University of KwaZulu-Natal, Pietermaritzburg 3201, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4589-7099","authenticated-orcid":false,"given":"Mbulisi","family":"Sibanda","sequence":"additional","affiliation":[{"name":"Department of Geography, Environmental Studies and Tourism, University of Western Cape, Cape Town 7535, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Odindi","sequence":"additional","affiliation":[{"name":"Discipline of Geography, University of KwaZulu-Natal, Pietermaritzburg 3201, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112004","DOI":"10.1016\/j.rse.2020.112004","article-title":"Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2","volume":"248","author":"Cheng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Coppock, D.L., Fern\u00e1ndez-Gim\u00e9nez, M., Hiernaux, P., Huber-Sannwald, E., Schloeder, C., Valdivia, C., Arredondo, J.T., Jacobs, M., Turin, C., and Turner, M. (2017). Rangeland systems in developing nations: Conceptual advances and societal implications. Rangel. Syst., 569.","DOI":"10.1007\/978-3-319-46709-2_17"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1890\/120333","article-title":"Earth stewardship of rangelands: Coping with ecological, economic, and political marginality","volume":"11","author":"Sayre","year":"2013","journal-title":"Front. Ecol. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"569","DOI":"10.2111\/REM-D-11-00157.1","article-title":"Introduced and invasive species in novel rangeland ecosystems: Friends or foes?","volume":"65","author":"Belnap","year":"2012","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1111\/gfs.12089","article-title":"The choice of grass species to combat desertification in semi-arid Kenyan rangelands is greatly influenced by their forage value for livestock","volume":"70","author":"Mganga","year":"2015","journal-title":"Grass Forage Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107483","DOI":"10.1016\/j.ecolind.2021.107483","article-title":"Promote the advance of the start of the growing season from combined effects of climate change and wildfire","volume":"125","author":"Rihan","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_7","first-page":"459","article-title":"The impact of invasive alien plants on rangelands in South Africa","volume":"14","year":"2020","journal-title":"Biol. Invasions S. Afr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1080\/21513732.2018.1450291","article-title":"Alien tree invasion into a South African montane grassland ecosystem: Impact of Acacia species on rangeland condition and livestock carrying capacity","volume":"14","author":"Yapi","year":"2018","journal-title":"Int. J. Biodivers. Sci. Ecosyst. Serv. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11694","DOI":"10.1002\/ece3.4621","article-title":"Bush encroachment dynamics and rangeland management implications in southern Ethiopia","volume":"8","author":"Liao","year":"2018","journal-title":"Ecol. Evol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/10106049.2016.1240719","article-title":"Detection and mapping of bracken fern weeds using multispectral remotely sensed data: A review of progress and challenges","volume":"33","author":"Matongera","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rala.2019.02.001","article-title":"Plant phenology: Taking the pulse of rangelands","volume":"41","author":"Browning","year":"2019","journal-title":"Rangelands"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","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_13","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_14","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_15","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_16","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_17","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_18","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1080\/2150704X.2016.1168945","article-title":"Assessing the performance of smoothing functions to estimate land surface phenology on temperate grassland","volume":"37","author":"Lara","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.ecolind.2013.02.004","article-title":"Phenology-based, remote sensing of post-burn disturbance windows in rangelands","volume":"30","author":"Sankey","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_21","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_22","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_23","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":"S. Afr. J. Bot."},{"key":"ref_24","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_25","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_26","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_27","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_28","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_29","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.agrformet.2014.09.009","article-title":"An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data","volume":"200","author":"Cao","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1111\/j.1744-697X.2005.00006.x","article-title":"Comparing MODIS vegetation indices with AVHRR NDVI for monitoring the forage quantity and quality in Inner Mongolia grassland, China","volume":"51","author":"Kawamura","year":"2005","journal-title":"Grassl. Sci."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Ma, X., Huete, A., Tran, N.N., Bi, J., Gao, S., and Zeng, Y. (2020). Sun-angle effects on remote-sensing phenology observed and modelled using himawari-8. Remote Sens., 12.","DOI":"10.3390\/rs12081339"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Misra, G., Cawkwell, F., and Wingler, A. (2020). Status of phenological research using Sentinel-2 data: A review. Remote Sens., 12.","DOI":"10.3390\/rs12172760"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111685","DOI":"10.1016\/j.rse.2020.111685","article-title":"Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery","volume":"240","author":"Bolton","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.isprsjprs.2020.01.012","article-title":"Development and evaluation of a new algorithm for detecting 30 m land surface phenology from VIIRS and HLS time series","volume":"161","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pastick, N.J., Dahal, D., Wylie, B.K., Parajuli, S., Boyte, S.P., and Wu, Z. (2020). Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony. Remote Sens., 12.","DOI":"10.3390\/rs12040725"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.tree.2007.04.003","article-title":"Shifting plant phenology in response to global change","volume":"22","author":"Cleland","year":"2007","journal-title":"Trends Ecol. Evol."},{"key":"ref_40","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_41","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_42","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_43","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.rse.2005.03.011","article-title":"Determination of phenological dates in boreal regions using normalized difference water index","volume":"97","author":"Delbart","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1078\/0176-1617-01176","article-title":"Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation","volume":"161","author":"Gitelson","year":"2004","journal-title":"J. Plant Physiol."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-52076-x","article-title":"Improved characterisation of vegetation and land surface seasonal dynamics in central Japan with Himawari-8 hypertemporal data","volume":"9","author":"Miura","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Xue, J., and Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. J. Sens., 2017.","DOI":"10.1155\/2017\/1353691"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.compag.2018.03.007","article-title":"QPhenoMetrics: An open source software application to assess vegetation phenology metrics","volume":"148","author":"Duarte","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_50","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_51","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_52","doi-asserted-by":"crossref","first-page":"118663","DOI":"10.1016\/j.foreco.2020.118663","article-title":"Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics","volume":"480","author":"Berra","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.2307\/1936825","article-title":"Phenology and ecophysiology of tropical trees: Erythrina poeppigiana OF Cook","volume":"61","author":"Borchert","year":"1980","journal-title":"Ecology"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"881","DOI":"10.2307\/2258961","article-title":"Comparative phenological studies of trees in tropical wet and dry forests in the lowlands of Costa Rica","volume":"62","author":"Frankie","year":"1974","journal-title":"J. Ecol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2307\/2937357","article-title":"The phenology, growth and ecosystem dynamics of Erythronium americanum in the northern hardwood forest","volume":"48","author":"Muller","year":"1978","journal-title":"Ecol. Monogr."},{"key":"ref_56","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."},{"key":"ref_57","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_58","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_59","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_60","doi-asserted-by":"crossref","first-page":"4867","DOI":"10.1109\/TGRS.2016.2552462","article-title":"A comparison of tropical rainforest phenology retrieved from geostationary (SEVIRI) and polar-orbiting (MODIS) sensors across the Congo Basin","volume":"54","author":"Yan","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.rse.2005.10.022","article-title":"Green leaf phenology at Landsat resolution: Scaling from the field to the satellite","volume":"100","author":"Fisher","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/j.rse.2010.10.006","article-title":"Monitoring elevation variations in leaf phenology of deciduous broadleaf forests from SPOT\/VEGETATION time-series","volume":"115","author":"Guyon","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"8421","DOI":"10.1080\/01431161.2010.542194","article-title":"Phenology of vegetation in Southern England from Envisat MERIS terrestrial chlorophyll index (MTCI) data","volume":"32","author":"Boyd","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Myers, E., Kerekes, J., Daughtry, C., and Russ, A. (2019). Assessing the Impact of Satellite Revisit Rate on Estimation of Corn Phenological Transition Timing through Shape Model Fitting. Remote Sens., 11.","DOI":"10.3390\/rs11212558"},{"key":"ref_65","unstructured":"Zhang, X., Ye, Y., Wang, W., and Wang, Y. (2021, April 23). Detection of Land Surface Phenology from New Generation Geostationary Satellites and Its Compassion with Observations from Polar-Orbiting Satellites. Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/2019AGUFM.A34F..03Z\/abstract."},{"key":"ref_66","unstructured":"Rouse, J., Haas, R., Deering, D., Schell, J., and Harlan, J. (1974). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, Great Plains."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.rse.2013.01.011","article-title":"Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM\/ETM+ data","volume":"132","author":"Melaas","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2007.04.004","article-title":"Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series","volume":"112","author":"Busetto","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_69","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_70","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1080\/01431168508948281","article-title":"Analysis of the phenology of global vegetation using meteorological satellite data","volume":"6","author":"Justice","year":"1985","journal-title":"Int. J. Remote Sens."},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1002\/2017JG003811","article-title":"Comparisons of global land surface seasonality and phenology derived from AVHRR, MODIS, and VIIRS data","volume":"122","author":"Zhang","year":"2017","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3303","DOI":"10.1080\/01431160310001618149","article-title":"European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset","volume":"25","author":"Vidale","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/0034-4257(91)90017-Z","article-title":"Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer","volume":"35","author":"Goward","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.rse.2015.01.012","article-title":"Comparing land surface phenology with leafing and flowering observations from the PlantWatch citizen network","volume":"160","author":"Delbart","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_76","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_77","unstructured":"Dash, J., and Curran, P.J. (2004, January 20\u201324). Evaluation of the MERIS terrestrial chlorophyll index. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2004), Anchorage, AK, USA."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"413","DOI":"10.3319\/TAO.2012.03.12.01(A)","article-title":"Characterizing Spatial-Temporal Variations in Vegetation Phenology over the North-South Transect of Northeast Asia Based upon the MERIS Terrestrial Chlorophyll Index","volume":"23","author":"Jin","year":"2012","journal-title":"Terr. Atmos. Ocean. Sci."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1002\/2015GL063586","article-title":"Intercomparison of satellite sensor land surface phenology and ground phenology in Europe","volume":"42","author":"Dash","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Zhang, X., Friedl, M.A., and Schaaf, C.B. (2006). Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements. J. Geophys. Res. Biogeosci., 111.","DOI":"10.1029\/2006JG000217"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2005.03.008","article-title":"A crop phenology detection method using time-series MODIS data","volume":"96","author":"Sakamoto","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Yu, X., Zhuang, D., Chen, S., Hou, X., and Chen, H. (2004). Vegetation Phenology from Multi-Temporal EOS MODIS Data. Weather and Environmental Satellites, International Society for Optics and Photonics.","DOI":"10.1117\/12.560250"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rse.2019.03.034","article-title":"Long-term continuity in land surface phenology measurements: A comparative assessment of the MODIS land cover dynamics and VIIRS land surface phenology products","volume":"226","author":"Moon","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Ga\u0161parovi\u0107, M., Medak, D., Pila\u0161, I., Jurjevi\u0107, L., and Balenovi\u0107, I. (2018, January 10\u201312). Fusion of Sentinel-2 and PlanetScope Imagery for Vegetation Detection and Monitorin. Proceedings of the ISPRS TC I Mid-term Symposium Innovative Sensing-From Sensors to Methods and Applications, Karlsruhe, Germany.","DOI":"10.5194\/isprs-archives-XLII-1-155-2018"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1002\/2016JG003441","article-title":"Characterizing land surface phenology and responses to rainfall in the Sahara desert","volume":"121","author":"Yan","year":"2016","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_86","first-page":"188","article-title":"Mapping crop phenology using NDVI time-series derived from HJ-1 A\/B data","volume":"34","author":"Pan","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_87","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The MERIS terrestrial chlorophyll index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.3390\/rs2102369","article-title":"Applicability of green-red vegetation index for remote sensing of vegetation phenology","volume":"2","author":"Motohka","year":"2010","journal-title":"Remote Sens."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.rse.2003.07.002","article-title":"Derivation of a shortwave infrared water stress index from MODIS near-and shortwave infrared data in a semiarid environment","volume":"87","author":"Fensholt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.rse.2017.06.015","article-title":"Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index","volume":"198","author":"Jin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Gonsamo, A., Chen, J.M., Price, D.T., Kurz, W.A., and Wu, C. (2012). Land surface phenology from optical satellite measurement and CO2 eddy covariance technique. J. Geophys. Res. Biogeosci., 117.","DOI":"10.1029\/2012JG002070"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0034-4257(89)90046-1","article-title":"Detection of changes in leaf water content using near-and middle-infrared reflectances","volume":"30","author":"Hunt","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.rse.2015.04.008","article-title":"Using phase-spaces to characterize land surface phenology in a seasonally snow-covered landscape","volume":"166","author":"Thompson","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1016\/j.rse.2011.01.005","article-title":"Land surface phenology of North American mountain environments using moderate resolution imaging spectroradiometer data","volume":"115","author":"Dunn","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1007\/s11769-019-1070-y","article-title":"Effect of Mathematical Expression of Vegetation Indices on the Estimation of Phenology Trends from Satellite Data","volume":"29","author":"Zuo","year":"2019","journal-title":"Chin. Geogr. Sci."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1080\/01431161.2013.875636","article-title":"Can EVI-derived land-surface phenology be used as a surrogate for phenology of canopy photosynthesis?","volume":"35","author":"Shen","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_100","first-page":"25","article-title":"The match and mismatch between photosynthesis and land surface phenology of deciduous forests","volume":"214","author":"Gonsamo","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.rse.2013.05.004","article-title":"Characterisation of land surface phenology and land cover based on moderate resolution satellite data in cloud prone areas\u2014A novel product for the Mekong Basin","volume":"136","author":"Leinenkugel","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Wang, S., Lu, X., Cheng, X., Li, X., Peichl, M., and Mammarella, I. (2018). Limitations and challenges of MODIS-derived phenological metrics across different landscapes in pan-Arctic regions. Remote Sens., 10.","DOI":"10.3390\/rs10111784"},{"key":"ref_103","first-page":"132","article-title":"MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests","volume":"64","author":"Testa","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1080\/01431160902897858","article-title":"A comparison of MODIS 250-m EVI and NDVI data for crop mapping: A case study for southwest Kansas","volume":"31","author":"Wardlow","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Wu, B., Zhang, M., and Zeng, H. (2016). Crop phenology detection using high spatio-temporal resolution data fused from SPOT5 and MODIS products. Sensors, 16.","DOI":"10.3390\/s16122099"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"2146","DOI":"10.1016\/j.rse.2010.04.019","article-title":"A two-step filtering approach for detecting maize and soybean phenology with time-series MODIS data","volume":"114","author":"Sakamoto","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"6202","DOI":"10.1080\/01431161.2012.682660","article-title":"In situ examination of the relationship between various vegetation indices and canopy phenology in an evergreen coniferous forest, Japan","volume":"33","author":"Nagai","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_108","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_109","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.rse.2014.03.001","article-title":"Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS","volume":"147","author":"Wu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"4343","DOI":"10.1080\/01431160802549369","article-title":"Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset","volume":"30","author":"Motohka","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Jeganathan, C., Ganguly, S., Dash, J., Friedl, M., and Atkinson, P.M. (2010, January 25\u201330). Terrestrial vegetation phenology from MODIS and MERIS. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5650124"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Wang, C., Li, J., Liu, Q., Zhong, B., Wu, S., and Xia, C. (2017). Analysis of differences in phenology extracted from the enhanced vegetation index and the leaf area index. Sensors, 17.","DOI":"10.3390\/s17091982"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Ding, C., Liu, X., and Huang, F. (2017). Temporal interpolation of satellite-derived leaf area index time series by introducing spatial-temporal constraints for heterogeneous grasslands. Remote Sens., 9.","DOI":"10.3390\/rs9090968"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Cho, M.A., Ramoelo, A., and Dziba, L. (2017). Response of land surface phenology to variation in tree cover during green-up and senescence periods in the semi-arid savanna of Southern Africa. Remote Sens., 9.","DOI":"10.3390\/rs9070689"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"B\u00f3rnez, K., Verger, A., Filella, I., and Penuelas, J. (2017, January 27\u201329). Land surface phenology from Copernicus Global Land time series. Proceedings of the 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Brugge, Belgium.","DOI":"10.1109\/Multi-Temp.2017.8035262"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Yao, T., and Zhang, Q. (2016, January 10\u201315). Assessment of terrestrial vegetation dynamics from MODIS fAPAR chl product and land surface model. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729327"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Dannenberg, M., Wang, X., Yan, D., and Smith, W. (2020). Phenological characteristics of global ecosystems based on optical, fluorescence, and microwave remote sensing. Remote Sens., 12.","DOI":"10.3390\/rs12040671"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2015). Remote Sensing of Land Resources: Monitoring, Modeling, and Mapping Advances over the Last 50 Years and a Vision for the Future, CRC Press.","DOI":"10.1201\/b19322"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/S0034-4257(01)00300-5","article-title":"Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements","volume":"80","author":"Chen","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0168-1923(94)90107-4","article-title":"A comparison of optical and direct methods for estimating foliage surface area index in forests","volume":"71","author":"Fassnacht","year":"1994","journal-title":"Agric. For. Meteorol."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.rse.2008.09.003","article-title":"Noise reduction of NDVI time series: An empirical comparison of selected techniques","volume":"113","author":"Hird","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_122","first-page":"93","article-title":"Detection and mapping the spatial distribution of bracken fern weeds using the Landsat 8 OLI new generation sensor","volume":"57","author":"Matongera","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"De Beurs, K.M., and Henebry, G.M. (2010). Spatio-temporal statistical methods for modelling land surface phenology. Phenol. Res., 177\u2013208.","DOI":"10.1007\/978-90-481-3335-2_9"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Li, H., Jia, M., Zhang, R., Ren, Y., and Wen, X. (2019). Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform. Remote Sens., 11.","DOI":"10.3390\/rs11212479"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2015.03.018","article-title":"Reconstruction of global MODIS NDVI time series: Performance of Harmonic ANalysis of Time Series (HANTS)","volume":"163","author":"Zhou","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"20","DOI":"10.11108\/kagis.2014.17.3.020","article-title":"Analysis of the MODIS-based vegetation phenology using the HANTS algorithm","volume":"17","author":"Choi","year":"2014","journal-title":"J. Korean Assoc. Geogr. Inf. Stud."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality extraction by function fitting to time-series of satellite sensor data","volume":"40","author":"Jonsson","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Stanimirova, R., Cai, Z., Melaas, E.K., Gray, J.M., Eklundh, L., J\u00f6nsson, P., and Friedl, M.A. (2019). An Empirical Assessment of the MODIS Land Cover Dynamics and TIMESAT Land Surface Phenology Algorithms. Remote Sens., 11.","DOI":"10.3390\/rs11192201"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s11707-012-0321-3","article-title":"Assessing phenological change in China from 1982 to 2006 using AVHRR imagery","volume":"6","author":"Wei","year":"2012","journal-title":"Front. Earth Sci."},{"key":"ref_130","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 Obs. Remote Sens."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/LGRS.2007.907971","article-title":"An algorithm to produce temporally and spatially continuous MODIS-LAI time series","volume":"5","author":"Gao","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/JSTARS.2010.2051942","article-title":"TimeStats: A software tool for the retrieval of temporal patterns from global satellite archives","volume":"4","author":"Udelhoven","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Rodrigues, A., Marcal, A.R., and Cunha, M. (2011, January 12\u201314). PhenoSat\u2014A tool for vegetation temporal analysis from satellite image data. Proceedings of the 2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (Multi-Temp), Trento, Italy.","DOI":"10.1109\/Multi-Temp.2011.6005044"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1080\/01431161.2010.550330","article-title":"Linking ground-based to satellite-derived phenological metrics in support of habitat assessment","volume":"3","author":"Coops","year":"2012","journal-title":"Remote Sens. Lett."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s00484-006-0080-5","article-title":"A comparative study of satellite and ground-based phenology","volume":"51","author":"Studer","year":"2007","journal-title":"Int. J. Biometeorol."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1023\/A:1025597829895","article-title":"Plant phenology in western Canada: Trends and links to the view from space","volume":"88","author":"Beaubien","year":"2003","journal-title":"Environ. Monit. Assess."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"2239","DOI":"10.1038\/srep02239","article-title":"Citizen Science: Linking the recent rapid advances of plant flowering in Canada with climate variability","volume":"3","author":"Gonsamo","year":"2013","journal-title":"Sci. Rep."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.ecolind.2017.02.024","article-title":"Spring green-up phenology products derived from MODIS NDVI and EVI: Intercomparison, interpretation and validation using National Phenology Network and AmeriFlux observations","volume":"77","author":"Peng","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.3732\/ajb.1500237","article-title":"Herbarium records are reliable sources of phenological change driven by climate and provide novel insights into species\u2019 phenological cueing mechanisms","volume":"102","author":"Davis","year":"2015","journal-title":"Am. J. Bot."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"751","DOI":"10.3732\/ajb.1100198","article-title":"Herbarium specimens, photographs, and field observations show Philadelphia area plants are responding to climate change","volume":"99","author":"Panchen","year":"2012","journal-title":"Am. J. Bot."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1086\/430097","article-title":"Herbarium data reveal an association between fleshy fruit type and earlier flowering time","volume":"166","author":"Bolmgren","year":"2005","journal-title":"Int. J. Plant Sci."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"e01224","DOI":"10.1002\/aps3.1224","article-title":"A new method and insights for estimating phenological events from herbarium specimens","volume":"7","author":"Pearson","year":"2019","journal-title":"Appl. Plant Sci."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1093\/biosci\/biaa044","article-title":"Machine learning using digitized herbarium specimens to advance phenological research","volume":"70","author":"Pearson","year":"2020","journal-title":"BioScience"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1098\/rspb.2010.2259","article-title":"Continental-scale patterns of Cecropia reproductive phenology: Evidence from herbarium specimens","volume":"278","author":"Zalamea","year":"2011","journal-title":"Proc. R. Soc. B Biol. Sci."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Berman, E.E., Graves, T.A., Mikle, N.L., Merkle, J.A., Johnston, A.N., and Chong, G.W. (2020). Comparative Quality and Trend of Remotely Sensed Phenology and Productivity Metrics across the Western United States. Remote Sens., 12.","DOI":"10.3390\/rs12162538"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"e01436","DOI":"10.1002\/ecs2.1436","article-title":"Emerging opportunities and challenges in phenology: A review","volume":"7","author":"Tang","year":"2016","journal-title":"Ecosphere"},{"key":"ref_147","first-page":"71","article-title":"Evaluating land surface phenology from the Advanced Himawari Imager using observations from MODIS and the Phenological Eyes Network","volume":"79","author":"Yan","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-23804-6","article-title":"Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1. 0 and MODIS satellite remote sensing","volume":"8","author":"Richardson","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Zhu, W., Chen, G., Jiang, N., Liu, J., and Mou, M. (2013). Estimating carbon flux phenology with satellite-derived land surface phenology and climate drivers for different biomes: A synthesis of AmeriFlux observations. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0084990"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/2013JG002460","article-title":"Beyond leaf color: Comparing camera-based phenological metrics with leaf biochemical, biophysical, and spectral properties throughout the growing season of a temperate deciduous forest","volume":"119","author":"Yang","year":"2014","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_151","first-page":"1","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_152","unstructured":"Browning, D., Laliberte, A., Rango, A., and Herrick, J. Prospects for Phenological Monitoring in an Arid Southwestern US Rangeland Using Field Observations with Hyperspatial and Moderate Resolution Imagery, Available online: https:\/\/www.semanticscholar.org\/paper\/Prospects-for-phenological-monitoring-in-an-arid-Browning-Laliberte\/fc3bfadd2f6cb83261710f6b1866bbc363235078."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.agrformet.2012.06.006","article-title":"Predicting deciduous forest carbon uptake phenology by upscaling FLUXNET measurements using remote sensing data","volume":"165","author":"Gonsamo","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1111\/j.1365-2486.2005.001012.x","article-title":"Spatial analysis of growing season length control over net ecosystem exchange","volume":"11","author":"Churkina","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_155","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_156","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s00484-003-0171-5","article-title":"Physiology-based phenology models for forest tree species in Germany","volume":"47","author":"Schaber","year":"2003","journal-title":"Int. J. Biometeorol."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.1002\/joc.2008","article-title":"Intercomparing multiple measures of the onset of spring in eastern North America","volume":"30","author":"Schwartz","year":"2010","journal-title":"Int. J. Climatol."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1111\/j.1365-2486.2009.01910.x","article-title":"Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982\u20132006","volume":"15","author":"White","year":"2009","journal-title":"Glob. Chang. Biol."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2015.12.017","article-title":"Matching the phenology of Net Ecosystem Exchange and vegetation indices estimated with MODIS and FLUXNET in-situ observations","volume":"174","author":"Balzarolo","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.rse.2010.08.013","article-title":"Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest","volume":"115","author":"Liang","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"2849","DOI":"10.1002\/jgrd.50278","article-title":"A global survey of the effect of cloud contamination on the aerosol optical thickness and its long-term trend derived from operational AVHRR satellite observations","volume":"118","author":"Zhao","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_162","first-page":"101974","article-title":"Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests","volume":"84","author":"Descals","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"18865","DOI":"10.3390\/s150818865","article-title":"Optimal atmospheric correction for above-ground forest biomass estimation with the ETM+ remote sensor","volume":"15","author":"Nguyen","year":"2015","journal-title":"Sensors"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.rse.2018.04.030","article-title":"The mixed pixel effect in land surface phenology: A simulation study","volume":"211","author":"Chen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2013.01.010","article-title":"Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements","volume":"132","author":"Hmimina","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.agrformet.2012.11.018","article-title":"Using FLUXNET data to improve models of springtime vegetation activity onset in forest ecosystems","volume":"171","author":"Melaas","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_167","unstructured":"Eklundh, L., and J\u00f6nsson, P. (2012). TIMESAT 3.1 Software Manual, Lund University."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"111745","DOI":"10.1016\/j.rse.2020.111745","article-title":"Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion","volume":"242","author":"Tian","year":"2020","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2060\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:06:32Z","timestamp":1760162792000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2060"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,24]]},"references-count":168,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13112060"],"URL":"https:\/\/doi.org\/10.3390\/rs13112060","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,24]]}}}