{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T03:11:18Z","timestamp":1776309078882,"version":"3.50.1"},"reference-count":113,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T00:00:00Z","timestamp":1634256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971288"],"award-info":[{"award-number":["41971288"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41571326"],"award-info":[{"award-number":["41571326"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Vegetation indices are widely used to derive land surface phenology (LSP). However, due to inconsistent illumination geometries, reflectance varies with solar zenith angles (SZA), which in turn affects the vegetation indices, and thus the derived LSP. To examine the SZA effect on LSP, the MODIS bidirectional reflectance distribution function (BRDF) product and a BRDF model were employed to derive LSPs under several constant SZAs (i.e., 0\u00b0, 15\u00b0, 30\u00b0, 45\u00b0, and 60\u00b0) in the Harvard Forest, Massachusetts, USA. The LSPs derived under varying SZAs from the MODIS nadir BRDF-adjusted reflectance (NBAR) and MODIS vegetation index products were used as baselines. The results show that with increasing SZA, NDVI increases but EVI decreases. The magnitude of SZA-induced NDVI\/EVI changes suggests that EVI is more sensitive to varying SZAs than NDVI. NDVI and EVI are comparable in deriving the start of season (SOS), but EVI is more accurate when deriving the end of season (EOS). Specifically, NDVI\/EVI-derived SOSs are relatively close to those derived from ground measurements, with an absolute mean difference of 8.01 days for NDVI-derived SOSs and 9.07 days for EVI-derived SOSs over ten years. However, a considerable lag exists for EOSs derived from vegetation indices, especially from the NDVI time series, with an absolute mean difference of 14.67 days relative to that derived from ground measurements. The SOSs derived from NDVI time series are generally earlier, while those from EVI time series are delayed. In contrast, the EOSs derived from NDVI time series are delayed; those derived from the simulated EVI time series under a fixed illumination geometry are also delayed, but those derived from the products with varying illumination geometries (i.e., MODIS NBAR product and MODIS vegetation index product) are advanced. LSPs derived from varying illumination geometries could lead to a difference spanning from a few days to a month in this case study, which highlights the importance of normalizing the illumination geometry when deriving LSP from NDVI\/EVI time series.<\/jats:p>","DOI":"10.3390\/rs13204126","type":"journal-article","created":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T23:25:15Z","timestamp":1634513115000},"page":"4126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Influence of Varying Solar Zenith Angles on Land Surface Phenology Derived from Vegetation Indices: A Case Study in the Harvard Forest"],"prefix":"10.3390","volume":"13","author":[{"given":"Yang","family":"Li","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Environmental Science Graduate Program, The Ohio State University, Columbus, OH 43210, USA"},{"name":"School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3701-0830","authenticated-orcid":false,"given":"Ziti","family":"Jiao","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"The Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China"}]},{"given":"Kaiguang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Environmental Science Graduate Program, The Ohio State University, Columbus, OH 43210, USA"},{"name":"School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1257-9449","authenticated-orcid":false,"given":"Yadong","family":"Dong","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"The Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1765-6789","authenticated-orcid":false,"given":"Yuyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4267-1841","authenticated-orcid":false,"given":"Yelu","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA"}]},{"given":"Haiqing","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA"}]},{"given":"Xiaoning","family":"Zhang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4773-1022","authenticated-orcid":false,"given":"Tongxi","family":"Hu","sequence":"additional","affiliation":[{"name":"Environmental Science Graduate Program, The Ohio State University, Columbus, OH 43210, USA"},{"name":"School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9477-9155","authenticated-orcid":false,"given":"Lei","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Schwartz, M.D. (2003). Phenology: An Integrative Environmental Science, Kluwer Academic Publishers. [2nd ed.].","DOI":"10.1007\/978-94-007-0632-3"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.rse.2014.04.015","article-title":"Monitoring Multi-Layer Canopy Spring Phenology of Temperate Deciduous and Evergreen Forests Using Low-Cost Spectral Sensors","volume":"149","author":"Ryu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"20130016","DOI":"10.1098\/rspb.2013.0016","article-title":"Annual Rhythms That Underlie Phenology: Biological Time-Keeping Meets Environmental Change","volume":"280","author":"Helm","year":"2013","journal-title":"Proc. R. Soc. B Biol. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1111\/j.1461-9563.2008.00392.x","article-title":"Effects of Variable Phytochemistry and Budbreak Phenology on Defoliation of Aspen during a Forest Tent Caterpillar Outbreak","volume":"10","author":"Donaldson","year":"2008","journal-title":"Agric. For. Entomol."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1126\/science.1173004","article-title":"Phenology Feedbacks on Climate Change","volume":"324","author":"Rutishauser","year":"2009","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.rse.2018.12.036","article-title":"Characterizing the Relationship between Satellite Phenology and Pollen Season: A Case Study of Birch","volume":"222","author":"Li","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e207551","DOI":"10.1001\/jamanetworkopen.2020.7551","article-title":"Association Between Changes in Timing of Spring Onset and Asthma Hospitalization in Maryland","volume":"3","author":"Sapkota","year":"2020","journal-title":"JAMA Netw. Open"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.rse.2005.05.003","article-title":"Global Mapping of Foliage Clumping Index Using Multi-Angular Satellite Data","volume":"97","author":"Chen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1016\/j.rse.2018.02.041","article-title":"An Algorithm for the Retrieval of the Clumping Index (CI) from the MODIS BRDF Product Using an Adjusted Version of the Kernel-Driven BRDF Model","volume":"209","author":"Jiao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cui, L., Jiao, Z., Dong, Y., Sun, M., Zhang, X., Yin, S., Ding, A., Chang, Y., Guo, J., and Xie, R. (2019). Estimating Forest Canopy Height Using MODIS BRDF Data Emphasizing Typical-Angle Reflectances. Remote Sens., 11.","DOI":"10.3390\/rs11192239"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2013.10.017","article-title":"An Anisotropic Flat Index (AFX) to Derive BRDF Archetypes from MODIS","volume":"141","author":"Jiao","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1080\/15481603.2015.1134140","article-title":"Investigation of Terrain Illumination Effects on Vegetation Indices and VI-Derived Phenological Metrics in Subtropical Deciduous Forests","volume":"53","author":"Breunig","year":"2016","journal-title":"GISci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Karkauskaite, P., Tagesson, T., and Fensholt, R. (2017). Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone. Remote Sens., 9.","DOI":"10.3390\/rs9050485"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Tan, B., Morisette, J.T., Wolfe, R.E., Gao, F., Ederer, G.A., Nightingale, J., and Pedelty, J.A. (2008, January 6\u201311). Vegetation Phenology Metrics Derived from Temporally Smoothed and Gap-Filled MODIS Data. Proceedings of the IGARSS 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779417"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"881","DOI":"10.5194\/essd-11-881-2019","article-title":"A Dataset of 30 m Annual Vegetation Phenology Indicators (1985\u20132015) in Urban Areas of the Conterminous United States","volume":"11","author":"Li","year":"2019","journal-title":"Earth Syst. Sci. Data Online"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111752","DOI":"10.1016\/j.rse.2020.111752","article-title":"A Within-Season Approach for Detecting Early Growth Stages in Corn and Soybean Using High Temporal and Spatial Resolution Imagery","volume":"242","author":"Gao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111698","DOI":"10.1016\/j.rse.2020.111698","article-title":"Change Point Estimation of Deciduous Forest Land Surface Phenology","volume":"240","author":"Xie","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111181","DOI":"10.1016\/j.rse.2019.04.034","article-title":"Detecting Change-Point, Trend, and Seasonality in Satellite Time Series Data to Track Abrupt Changes and Nonlinear Dynamics: A Bayesian Ensemble Algorithm","volume":"232","author":"Zhao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/j.scitotenv.2017.06.245","article-title":"Characterizing Spatiotemporal Dynamics in Phenology of Urban Ecosystems Based on Landsat Data","volume":"605\u2013606","author":"Li","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.rse.2016.11.021","article-title":"Application of Satellite Solar-Induced Chlorophyll Fluorescence to Understanding Large-Scale Variations in Vegetation Phenology and Function over Northern High Latitude Forests","volume":"190","author":"Jeong","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","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_25","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1111\/geb.12210","article-title":"Recent Spring Phenology Shifts in Western Central Europe Based on Multiscale Observations","volume":"23","author":"Fu","year":"2014","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.rse.2017.01.001","article-title":"Exploration of Scaling Effects on Coarse Resolution Land Surface Phenology","volume":"190","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2061","DOI":"10.1080\/01431160802549237","article-title":"Sensitivity of Vegetation Phenology Detection to the Temporal Resolution of Satellite Data","volume":"30","author":"Zhang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.agrformet.2017.10.015","article-title":"Fine-Scale Perspectives on Landscape Phenology from Unmanned Aerial Vehicle (UAV) Photography","volume":"248","author":"Klosterman","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_30","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_31","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_32","doi-asserted-by":"crossref","first-page":"6993","DOI":"10.1080\/01431161.2019.1597307","article-title":"A Continuous Global Record of Near-Surface Soil Freeze\/Thaw Status from AMSR-E and AMSR2 Data","volume":"40","author":"Hu","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4480","DOI":"10.1109\/JSTARS.2014.2343592","article-title":"Angular Effects and Correction for Medium Resolution Sensors to Support Crop Monitoring","volume":"7","author":"Gao","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.rse.2005.11.013","article-title":"Analysing NDVI for the African Continent Using the Geostationary Meteosat Second Generation SEVIRI Sensor","volume":"101","author":"Fensholt","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6163","DOI":"10.1080\/01431160903401387","article-title":"Assessment of MODIS Sun-Sensor Geometry Variations Effect on Observed NDVI Using MSG SEVIRI Geostationary Data","volume":"31","author":"Fensholt","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1080\/01431160500380539","article-title":"Influence of Solar Zenith Angles on Observed Trends in the NOAA\/NASA 8-km Pathfinder Normalized Difference Vegetation Index over the African Sahel","volume":"27","author":"Eklundh","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111701","DOI":"10.1016\/j.rse.2020.111701","article-title":"A Conterminous United States Analysis of the Impact of Landsat 5 Orbit Drift on the Temporal Consistency of Landsat 5 Thematic Mapper Data","volume":"240","author":"Roy","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1080\/15481603.2019.1668595","article-title":"A Hyperspectral Experiment over Tropical Forests Based on the EO-1 Orbit Change and PROSAIL Simulation","volume":"57","author":"Breunig","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hao, D., Wen, J., Xiao, Q., Wu, S., Lin, X., Dou, B., You, D., and Tang, Y. (2018). Simulation and Analysis of the Topographic Effects on Snow-Free Albedo over Rugged Terrain. Remote Sens., 10.","DOI":"10.3390\/rs10020278"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.3390\/s7112636","article-title":"Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-Density Cypress Forest","volume":"7","author":"Matsushita","year":"2007","journal-title":"Sensors"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4501","DOI":"10.1080\/01431161.2015.1084437","article-title":"Relationships between MODIS Phenological Metrics, Topographic Shade, and Anomalous Temperature Patterns in Seasonal Deciduous Forests of South Brazil","volume":"36","author":"Teles","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Franch, B., Vermote, E., Skakun, S., Roger, J.-C., Masek, J., Ju, J., Villaescusa-Nadal, J.L., and Santamaria-Artigas, A. (2019). A Method for Landsat and Sentinel 2 (HLS) BRDF Normalization. Remote Sens., 11.","DOI":"10.3390\/rs11060632"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-Annual Reflectance Composites from Sentinel-2 and Landsat for National-Scale Crop and Land Cover Mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_45","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_46","first-page":"102172","article-title":"Characterizing Spring Phenology of Temperate Broadleaf Forests Using Landsat and Sentinel-2 Time Series","volume":"92","author":"Kowalski","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"7513","DOI":"10.1080\/01431161.2010.524675","article-title":"Assessing Viewing and Illumination Geometry Effects on the MODIS Vegetation Index (MOD13Q1) Time Series: Implications for Monitoring Phenology and Disturbances in Forest Communities in Queensland, Australia","volume":"32","author":"Bhandari","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","first-page":"215","article-title":"Effect of NOAA Satellite Orbital Drift on AVHRR-Derived Phenological Metrics","volume":"62","author":"Ji","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_49","first-page":"294","article-title":"Spectral Anisotropy of Subtropical Deciduous Forest Using MISR and MODIS Data Acquired under Large Seasonal Variation in Solar Zenith Angle","volume":"35","author":"Breunig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ma, X., Huete, A., and Tran, N.N. (2019). Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled Using MODIS. Remote Sens., 11.","DOI":"10.3390\/rs11121398"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2015.02.003","article-title":"Modeling Grassland Spring Onset across the Western United States Using Climate Variables and MODIS-Derived Phenology Metrics","volume":"161","author":"Xin","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_52","unstructured":"O\u2019Keefe, J. (2021, October 01). Phenology of Woody Species at Harvard Forest since 1990. Harvard Forest Data Archive: HF003. Available online: https:\/\/harvardforest1.fas.harvard.edu\/exist\/apps\/datasets\/showData.html?id=HF003."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"054006","DOI":"10.1088\/1748-9326\/9\/5\/054006","article-title":"A Tale of Two Springs: Using Recent Climate Anomalies to Characterize the Sensitivity of Temperate Forest Phenology to Climate Change","volume":"9","author":"Friedl","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1038\/nclimate2253","article-title":"Net Carbon Uptake Has Increased through Warming-Induced Changes in Temperate Forest Phenology","volume":"4","author":"Keenan","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1890\/12-0747.1","article-title":"Rate My Data: Quantifying the Value of Ecological Data for the Development of Models of the Terrestrial Carbon Cycle","volume":"23","author":"Keenan","year":"2013","journal-title":"Ecol. Appl."},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"2634","DOI":"10.1111\/gcb.12890","article-title":"The Timing of Autumn Senescence Is Affected by the Timing of Spring Phenology: Implications for Predictive Models","volume":"21","author":"Keenan","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Hadley, J.L., O\u2019Keefe, J., Munger, J.W., Hollinger, D.Y., and Richardson, A.D. (2009). Phenology of Forest-Atmosphere Carbon Exchange for Deciduous and Coniferous Forests in Southern and Northern New England. Phenology of Ecosystem Processes, Springer.","DOI":"10.1007\/978-1-4419-0026-5_5"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1093\/treephys\/tpn040","article-title":"Influence of Spring Phenology on Seasonal and Annual Carbon Balance in Two Contrasting New England Forests","volume":"29","author":"Richardson","year":"2009","journal-title":"Tree Physiol."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yang, X., Mustard, J.F., Tang, J., and Xu, H. (2012). Regional-Scale Phenology Modeling Based on Meteorological Records and Remote Sensing Observations. J. Geophys. Res. Biogeosci., 117.","DOI":"10.1029\/2012JG001977"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/S0034-4257(02)00091-3","article-title":"First Operational BRDF, Albedo Nadir Reflectance Products from MODIS","volume":"83","author":"Schaaf","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.rse.2013.08.025","article-title":"Evaluation of MODIS Albedo Product (MCD43A) over Grassland, Agriculture and Forest Surface Types during Dormant and Snow-Covered Periods","volume":"140","author":"Wang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Shuai, Y., Schaaf, C.B., Strahler, A.H., Liu, J., and Jiao, Z. (2008). Quality Assessment of BRDF\/Albedo Retrievals in MODIS Operational System. Geophys. Res. Lett., 35.","DOI":"10.1029\/2007GL032568"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.rse.2014.07.010","article-title":"A Physically Based Vegetation Index for Improved Monitoring of Plant Phenology","volume":"152","author":"Jin","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2015.01.011","article-title":"Evaluating the Potential of MODIS Satellite Data to Track Temporal Dynamics of Autumn Phenology in a Temperate Mixed Forest","volume":"160","author":"Liu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.rse.2006.05.003","article-title":"Monitoring Spring Canopy Phenology of a Deciduous Broadleaf Forest Using MODIS","volume":"104","author":"Ahl","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_67","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_68","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.ecolind.2016.08.022","article-title":"Assessing the Ability of MODIS EVI to Estimate Terrestrial Ecosystem Gross Primary Production of Multiple Land Cover Types","volume":"72","author":"Shi","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2016.08.007","article-title":"A Method for Improving Hotspot Directional Signatures in BRDF Models Used for MODIS","volume":"186","author":"Jiao","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Jin, Y., Schaaf, C.B., Gao, F., Li, X., Strahler, A.H., Lucht, W., and Liang, S. (2003). Consistency of MODIS Surface Bidirectional Reflectance Distribution Function and Albedo Retrievals: 1. Algorithm Performance. J. Geophys. Res. Atmos., 108.","DOI":"10.1029\/2002JD002803"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2004.09.001","article-title":"Accuracy Assessment of the MODIS 16-Day Albedo Product for Snow: Comparisons with Greenland in Situ Measurements","volume":"94","author":"Stroeve","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Dong, Y., Jiao, Z., Yin, S., Zhang, H., Zhang, X., Cui, L., He, D., Ding, A., Chang, Y., and Yang, S. (2018). Influence of Snow on the Magnitude and Seasonal Variation of the Clumping Index Retrieved from MODIS BRDF Products. Remote Sens., 10.","DOI":"10.3390\/rs10081194"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"29529","DOI":"10.1029\/97JD01215","article-title":"Estimating Spectral Albedo and Nadir Reflectance through Inversion of Simple BRDF Models with AVHRR\/MODIS-like Data","volume":"102","author":"Privette","year":"1997","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"20455","DOI":"10.1029\/92JD01411","article-title":"A Bidirectional Reflectance Model of the Earth\u2019s Surface for the Correction of Remote Sensing Data","volume":"97","author":"Roujean","year":"1992","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"21077","DOI":"10.1029\/95JD02371","article-title":"On the Derivation of Kernels for Kernel-Driven Models of Bidirectional Reflectance","volume":"100","author":"Wanner","year":"1995","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/S0034-4257(03)00100-7","article-title":"Detecting Vegetation Structure Using a Kernel-Based BRDF Model","volume":"86","author":"Gao","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Zhang, H., Jiao, Z., Chen, L., Dong, Y., Zhang, X., Lian, Y., Qian, D., and Cui, T. (2018). Quantifying the Reflectance Anisotropy Effect on Albedo Retrieval from Remotely Sensed Observations Using Archetypal BRDFs. Remote Sens., 10.","DOI":"10.3390\/rs10101628"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ross, J. (1981). The Radiation Regime and Architecture of Plant Stands, Wilhelm Junk.","DOI":"10.1007\/978-94-009-8647-3"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/36.134078","article-title":"Geometric-Optical Bidirectional Reflectance Modeling of the Discrete Crown Vegetation Canopy: Effect of Crown Shape and Mutual Shadowing","volume":"30","author":"Li","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1109\/36.841980","article-title":"An Algorithm for the Retrieval of Albedo from Space Using Semiempirical BRDF Models","volume":"38","author":"Lucht","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2003.12.006","article-title":"Bidirectional Reflectance of Earth Targets: Evaluation of Analytical Models Using a Large Set of Spaceborne Measurements with Emphasis on the Hot Spot","volume":"90","author":"Maignan","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"828626","DOI":"10.1117\/12.913039","article-title":"Salt-Marsh Geomorphological Patterns Analysis Based on Remote Sensing Images and Lidar-Derived Digital Elevation Model: A Case Study of Xiaoyangkou, Jiangsu","volume":"Volume 8286","author":"Xie","year":"2001","journal-title":"Proceedings of the International Symposium on Lidar and Radar Mapping 2011: Technologies and Applications"},{"key":"ref_83","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_84","first-page":"335","article-title":"Review on Methods of Remote Sensing Time-Series Data Reconstruction","volume":"13","author":"Li","year":"2009","journal-title":"J. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Song, C., Huang, B., and You, S. (2012, January 22\u201327). Comparison of Three Time-Series NDVI Reconstruction Methods Based on TIMESAT. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351057"},{"key":"ref_86","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_87","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_88","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2013.11.020","article-title":"Remotely Sensed Trends in the Phenology of Northern High Latitude Terrestrial Vegetation, Controlling for Land Cover Change and Vegetation Type","volume":"143","author":"Jeganathan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1111\/gcb.14619","article-title":"Plant Phenology and Global Climate Change: Current Progresses and Challenges","volume":"25","author":"Piao","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_90","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_91","doi-asserted-by":"crossref","first-page":"4228","DOI":"10.1073\/pnas.1911117117","article-title":"Urban Warming Advances Spring Phenology but Reduces the Response of Phenology to Temperature in the Conterminous United States","volume":"117","author":"Meng","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"2818","DOI":"10.1111\/gcb.13562","article-title":"Response of Vegetation Phenology to Urbanization in the Conterminous United States","volume":"23","author":"Li","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_93","unstructured":"Eklundh, L., and J\u00f6nsson, P. (2017). TIMESAT 3.3 with Seasonal Trend Decomposition and Parallel Processing Software Manual, Lund University."},{"key":"ref_94","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_95","doi-asserted-by":"crossref","unstructured":"Zheng, Z., and Zhu, W. (2017). Uncertainty of Remote Sensing Data in Monitoring Vegetation Phenology: A Comparison of MODIS C5 and C6 Vegetation Index Products on the Tibetan Plateau. Remote Sens., 9.","DOI":"10.3390\/rs9121288"},{"key":"ref_96","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_97","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2016.01.021","article-title":"Improved Modeling of Land Surface Phenology Using MODIS Land Surface Reflectance and Temperature at Evergreen Needleleaf Forests of Central North America","volume":"176","author":"Liu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_98","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_99","doi-asserted-by":"crossref","first-page":"3520","DOI":"10.1080\/01431161.2014.907937","article-title":"Relationship between Spatio-Temporal Characteristics of Leaf-Fall Phenology and Seasonal Variations in near Surface- and Satellite-Observed Vegetation Indices in a Cool-Temperate Deciduous Broad-Leaved Forest in Japan","volume":"35","author":"Nagai","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.rse.2017.11.009","article-title":"Remote Sensing of Mangrove Forest Phenology and Its Environmental Drivers","volume":"205","author":"Dash","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"2996","DOI":"10.1080\/01431161.2014.894660","article-title":"Comparison of Vegetation Phenology in the Western USA Determined from Reflected GPS Microwave Signals and NDVI","volume":"35","author":"Evans","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.agrformet.2019.06.002","article-title":"Assessing Consistency of Spring Phenology of Snow-Covered Forests as Estimated by Vegetation Indices, Gross Primary Production, and Solar-Induced Chlorophyll Fluorescence","volume":"275","author":"Chang","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1109\/TGRS.2002.800241","article-title":"Bidirectional NDVI and Atmospherically Resistant BRDF Inversion for Vegetation Canopy","volume":"40","author":"Gao","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/TGRS.2003.815674","article-title":"The Effect of Solar Illumination Angle and Sensor View Angle on Observed Patterns of Spatial Structure in Tallgrass Prairie","volume":"42","author":"Goodin","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2016.08.022","article-title":"Landsat 5 Thematic Mapper Reflectance and NDVI 27-Year Time Series Inconsistencies Due to Satellite Orbit Change","volume":"186","author":"Zhang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.isprsjprs.2017.07.006","article-title":"Spectral Analysis of Amazon Canopy Phenology during the Dry Season Using a Tower Hyperspectral Camera and Modis Observations","volume":"131","author":"Hilker","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"2350","DOI":"10.1016\/j.rse.2011.04.035","article-title":"On Intra-Annual EVI Variability in the Dry Season of Tropical Forest: A Case Study with MODIS and Hyperspectral Data","volume":"115","author":"Roberts","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_108","unstructured":"Aguado, E., and Burt, J.E. (2014). Energy Balance and Temperature. Understanding Weather and Climate, Pearson Education Inc."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Noormets, A. (2009). Remote Sensing Phenology. Phenology of Ecosystem Processes: Applications in Global Change Research, Springer.","DOI":"10.1007\/978-1-4419-0026-5"},{"key":"ref_110","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_111","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_112","doi-asserted-by":"crossref","first-page":"621","DOI":"10.5194\/bg-18-621-2021","article-title":"Retrieval and Validation of Forest Background Reflectivity from Daily Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) Data across European Forests","volume":"18","author":"Pisek","year":"2021","journal-title":"Biogeosciences"},{"key":"ref_113","unstructured":"Noormets, A. (2009). Phenological Differences Between Understory and Overstory. Phenology of Ecosystem Processes: Applications in Global Change Research, Springer."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4126\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:15:05Z","timestamp":1760166905000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4126"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,15]]},"references-count":113,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13204126"],"URL":"https:\/\/doi.org\/10.3390\/rs13204126","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,15]]}}}