{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T18:52:57Z","timestamp":1776365577352,"version":"3.51.2"},"reference-count":75,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000199","name":"United States Department of Agriculture","doi-asserted-by":"publisher","award":["F430-C-16-0017, F430-C-17-0020, F430-19-C-0029, 12445021P0060, 19-CS-11120101-412"],"award-info":[{"award-number":["F430-C-16-0017, F430-C-17-0020, F430-19-C-0029, 12445021P0060, 19-CS-11120101-412"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["project no. 42022060"],"award-info":[{"award-number":["project no. 42022060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004377","name":"Hong Kong Polytechnic University","doi-asserted-by":"publisher","award":["ZVN6 and ZVVF"],"award-info":[{"award-number":["ZVN6 and ZVVF"]}],"id":[{"id":"10.13039\/501100004377","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fine-resolution satellite imagery is needed for characterizing dry-season phenology in tropical forests since many tropical forests are very spatially heterogeneous due to their diverse species and environmental background. However, fine-resolution satellite imagery, such as Landsat, has a 16-day revisit cycle that makes it hard to obtain a high-quality vegetation index time series due to persistent clouds in tropical regions. To solve this challenge, this study explored the feasibility of employing a series of advanced technologies for reconstructing a high-quality Landsat time series from 2005 to 2009 for detecting dry-season phenology in tropical forests; Puerto Rico was selected as a testbed. We combined bidirectional reflectance distribution function (BRDF) correction, cloud and shadow screening, and contaminated pixel interpolation to process the raw Landsat time series and developed a thresholding method to extract 15 phenology metrics. The cloud-masked and gap-filled reconstructed images were tested with simulated clouds. In addition, the derived phenology metrics for grassland and forest in the tropical dry forest zone of Puerto Rico were evaluated with ground observations from PhenoCam data and field plots. Results show that clouds and cloud shadows are more accurately detected than the Landsat cloud quality assessment (QA) band, and that data gaps resulting from those clouds and shadows can be accurately reconstructed (R2 = 0.89). In the tropical dry forest zone, the detected phenology dates (such as greenup, browndown, and dry-season length) generally agree with the PhenoCam observations (R2 = 0.69), and Landsat-based phenology is better than MODIS-based phenology for modeling aboveground biomass and leaf area index collected in field plots (plot size is roughly equivalent to a 3 \u00d7 3 Landsat pixels). This study suggests that the Landsat time series can be used to characterize the dry-season phenology of tropical forests after careful processing, which will help to improve our understanding of vegetation\u2013climate interactions at fine scales in tropical forests.<\/jats:p>","DOI":"10.3390\/rs13234736","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4736","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Characterization of Dry-Season Phenology in Tropical Forests by Reconstructing Cloud-Free Landsat Time Series"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6967-786X","authenticated-orcid":false,"given":"Xiaolin","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3731-0056","authenticated-orcid":false,"given":"Eileen H.","family":"Helmer","sequence":"additional","affiliation":[{"name":"International Institute of Tropical Forestry, USDA Forest Service, R\u00edo Piedras, San Juan, PR 00926, USA"}]},{"given":"David","family":"Gwenzi","sequence":"additional","affiliation":[{"name":"Department of Environmental Science & Management, Humboldt State University, Arcata, CA 95521, USA"}]},{"given":"Melissa","family":"Collin","sequence":"additional","affiliation":[{"name":"Department of Environmental Science & Management, Humboldt State University, Arcata, CA 95521, USA"}]},{"given":"Sean","family":"Fleming","sequence":"additional","affiliation":[{"name":"Department of Environmental Science & Management, Humboldt State University, Arcata, CA 95521, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5463-1532","authenticated-orcid":false,"given":"Jiaqi","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China"}]},{"given":"Humfredo","family":"Marcano-Vega","sequence":"additional","affiliation":[{"name":"Southern Research Station, USDA Forest Service, Knoxville, TN 37919, USA"}]},{"given":"Elvia J.","family":"Mel\u00e9ndez-Ackerman","sequence":"additional","affiliation":[{"name":"Department of Environmental Sciences, University of Puerto Rico, R\u00edo Piedras, San Juan, PR 00926, USA"}]},{"given":"Jess K.","family":"Zimmerman","sequence":"additional","affiliation":[{"name":"Department of Environmental Sciences, University of Puerto Rico, R\u00edo Piedras, San Juan, PR 00926, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"ref_1","unstructured":"Bustamante, M., Helmer, E.H., Schill, S., Belnap, J., Brown, L.K., Brugnoli, E., Compton, J.E., Coupe, R.H., Hern\u00e1ndez-Blanco, M., and Isbell, F. (2018). Chapter 4: Direct and indirect drivers of change in biodiversity and nature\u2019s contributions to people. Regional Assessment Report on Biodiversity and Ecosystem Services for the Americas, IPBES Secretariat."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1038\/s41558-019-0512-y","article-title":"Widespread increase of boreal summer dry season length over the Congo rainforest","volume":"9","author":"Jiang","year":"2019","journal-title":"Nat. Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sarvia, F., De Petris, S., and Borgogno-Mondino, E. (2021). Exploring Climate Change Effects on Vegetation Phenology by MOD13Q1 Data: The Piemonte Region Case Study in the Period 2001\u20132019. Agronomy, 11.","DOI":"10.3390\/agronomy11030555"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1111\/j.1469-8137.2010.03310.x","article-title":"Drought impacts on the Amazon forest: The remote sensing perspective","volume":"187","author":"Asner","year":"2010","journal-title":"New Phytol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6347","DOI":"10.1073\/pnas.1305499111","article-title":"Abrupt increases in Amazonian tree mortality due to drought\u2014Fire interactions","volume":"111","author":"Brando","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1016\/j.biocon.2010.07.024","article-title":"Habitat fragmentation and the desiccation of forest canopies: A case study from eastern Amazonia","volume":"143","author":"Briant","year":"2010","journal-title":"Biol. Conserv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3389\/ffgc.2019.00047","article-title":"Climate Benefits of Intact Amazon Forests and the Biophysical Consequences of Disturbance","volume":"2","author":"Baker","year":"2019","journal-title":"Front. For. Glob. Chang."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.foreco.2009.09.001","article-title":"A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests","volume":"259","author":"Allen","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Shao, M., Jia, X., and Wei, X. (2017). Relationship of Climatic and Forest Factors to Drought- and Heat-Induced Tree Mortality. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169770"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1111\/nph.15027","article-title":"Drivers and mechanisms of tree mortality in moist tropical forests","volume":"219","author":"Mcdowell","year":"2018","journal-title":"New Phytol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3122","DOI":"10.1111\/gcb.15037","article-title":"A catastrophic tropical drought kills hydraulically vulnerable tree species","volume":"26","author":"Powers","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1038\/s41586-018-0300-2","article-title":"The tropical forest carbon cycle and climate change","volume":"559","author":"Mitchard","year":"2018","journal-title":"Nature"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1111\/j.1461-0248.2009.01294.x","article-title":"Prospects for tropical forest biodiversity in a human-modified world","volume":"12","author":"Gardner","year":"2009","journal-title":"Ecol. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1038\/s41586-018-0301-1","article-title":"The future of hyperdiverse tropical ecosystems","volume":"559","author":"Barlow","year":"2018","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"64014","DOI":"10.1088\/1748-9326\/10\/6\/064014","article-title":"Sunlight mediated seasonality in canopy structure and photosynthetic activity of Amazonian rainforests","volume":"10","author":"Bi","year":"2015","journal-title":"Environ. Res. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e01834","DOI":"10.1002\/eap.1834","article-title":"Assessing regional drought impacts on vegetation and evapotranspiration: A case study in Guanacaste, Costa Rica","volume":"29","author":"Cooley","year":"2019","journal-title":"Ecol. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111489","DOI":"10.1016\/j.rse.2019.111489","article-title":"Both near-surface and satellite remote sensing confirm drought legacy effect on tropical forest leaf phenology after 2015\/2016 ENSO drought","volume":"237","author":"Lopes","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111865","DOI":"10.1016\/j.rse.2020.111865","article-title":"Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest","volume":"246","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Helmer, E.H., Ruzycki, T.S., Wilson, B.T., Sherrill, K.R., Lefsky, M.A., Marcano-Vega, H., Brandeis, T.J., Erickson, H.E., and Ruefenacht, B. (2018). Tropical Deforestation and Recolonization by Exotic and Native Trees: Spatial Patterns of Tropical Forest Biomass, Functional Groups, and Species Counts and Links to Stand Age, Geoclimate, and Sustainability Goals. Remote Sens., 10.","DOI":"10.3390\/rs10111724"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1007\/s00442-015-3354-y","article-title":"Effects of precipitation regime and soil nitrogen on leaf traits in seasonally dry tropical forests of the Yucatan Peninsula, Mexico","volume":"179","author":"Templer","year":"2015","journal-title":"Oecologia"},{"key":"ref_21","first-page":"51","article-title":"Shifts in taxonomic and functional composition of trees along rainfall and phosphorus gradients in central Panama","volume":"109","author":"Wright","year":"2020","journal-title":"J. Ecol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1111\/ele.12974","article-title":"Beyond the fast\u2014Slow continuum: Demographic dimensions structuring a tropical tree community","volume":"21","author":"Ruger","year":"2018","journal-title":"Ecol. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e03052","DOI":"10.1002\/ecy.3052","article-title":"Competition influences tree growth, but not mortality, across environmental gradients in Amazonia and tropical Africa","volume":"101","author":"Rozendaal","year":"2020","journal-title":"Ecology"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wieczynski, D.J., Singla, P., Doan, A., Singleton, A., Han, Z., Votzke, S., Yammine, A., and Gibert, J.P. (2021). Simple traits predict complex temperature responses across ecological scales. Res. Sq.","DOI":"10.21203\/rs.3.rs-116110\/v1"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Newman, E.A., Breckheimer, I.K., and Park, D.S. (2021). Disentangling the effects of climate change, landscape heterogeneity, and scale on phenological metrics. bioRxiv, 1\u201320.","DOI":"10.1101\/2021.02.05.429398"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tian, J., Zhu, X., Wu, J., Shen, M., and Chen, J. (2020). Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology. Remote Sens., 12.","DOI":"10.3390\/rs12010117"},{"key":"ref_27","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_28","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_29","doi-asserted-by":"crossref","unstructured":"J\u00f6nsson, P., Cai, Z., Melaas, E., Friedl, M.A., and Eklundh, L. (2018). A method for robust estimation of vegetation seasonality from Landsat and Sentinel-2 time series data. Remote Sens., 10.","DOI":"10.3390\/rs10040635"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"205","DOI":"10.4081\/jae.2016.571","article-title":"Using Landsat 8 imagery in detecting cork oak (Quercus suber L.) woodlands: A case study in Calabria (Italy)","volume":"47","author":"Modica","year":"2016","journal-title":"J. Agric. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9541","DOI":"10.1080\/01431161.2019.1633702","article-title":"Mapping bamboo with regional phenological characteristics derived from dense Landsat time series using Google Earth Engine","volume":"40","author":"Zhang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pratic\u00f2, S., Solano, F., Di Fazio, S., and Modica, G. (2021). Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sens., 13.","DOI":"10.3390\/rs13040586"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2017.03.047","article-title":"A multi-resolution approach to national-scale cultivated area estimation of soybean","volume":"195","author":"King","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.rse.2017.01.008","article-title":"National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey","volume":"190","author":"Song","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"101872","article-title":"Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series","volume":"82","author":"Bendini","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.05.024","article-title":"An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions","volume":"214","author":"Zhu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Weng, Q. (2018). An Automatic System for Reconstructing High-Quality Seasonal Landsat Time Series. Remote Sensing Time Series Image Processing, CRC Press.","DOI":"10.1201\/9781315166636"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7086","DOI":"10.1109\/TGRS.2014.2307354","article-title":"Recovering Quantitative Remote Sensing Products Contaminated by Thick Clouds and Shadows Using Multitemporal Dictionary Learning","volume":"52","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2016.01.023","article-title":"A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance","volume":"176","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1038\/nature13006","article-title":"Amazon forests maintain consistent canopy structure and greenness during the dry season","volume":"506","author":"Morton","year":"2014","journal-title":"Nature"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Petri, C.A., and Galv\u00e3o, L.S. (2019). Sensitivity of Seven MODIS Vegetation Indices to BRDF Effects during the Amazonian Dry Season. Remote Sens., 11.","DOI":"10.3390\/rs11141650"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.isprsjprs.2014.09.006","article-title":"Bidirectional effects in Landsat reflectance estimates: Is there a problem to solve?","volume":"103","author":"Nagol","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ross, J. (1981). The Radiation Regime and Architecture of Plant Stands, Springer.","DOI":"10.1007\/978-94-009-8647-3"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.rse.2007.08.011","article-title":"The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally","volume":"112","author":"Ju","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2020.02.008","article-title":"Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning","volume":"162","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"6653","DOI":"10.1080\/01431161.2017.1363432","article-title":"A comparison of gap-filling approaches for Landsat-7 satellite data","volume":"38","author":"Yin","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2010.12.010","article-title":"A simple and effective method for filling gaps in Landsat ETM+ SLC-off images","volume":"115","author":"Chen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1109\/LGRS.2011.2173290","article-title":"A modified neighborhood similar pixel interpolator approach for removing thick clouds in landsat images","volume":"9","author":"Zhu","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.rse.2017.07.020","article-title":"The relationship between threshold-based and inflexion-based approaches for extraction of land surface phenology","volume":"199","author":"Shang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_55","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_56","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_57","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1038\/nclimate2533","article-title":"Three decades of multi-dimensional change in global leaf phenology","volume":"5","author":"Buitenwerf","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.agrformet.2012.06.009","article-title":"Spring vegetation green-up date in China inferred from SPOT NDVI data: A multiple model analysis","volume":"165","author":"Cong","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","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_61","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1111\/nph.14051","article-title":"Convergence in relationships between leaf traits, spectra and age across diverse canopy environments and two contrasting tropical forests","volume":"214","author":"Wu","year":"2017","journal-title":"New Phytol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2016.05.009","article-title":"Leaf flush drives dry season green-up of the Central Amazon","volume":"182","author":"Lopes","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1093\/jpe\/rtt014","article-title":"Spatial patterns of distribution and abundance of Harrisia portoricensis, an endangered Caribbean cactus","volume":"6","year":"2013","journal-title":"J. Plant Ecol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"225","DOI":"10.7809\/b-e.00079","article-title":"Forest Inventory and Analysis Database of the United States of America (FIA)","volume":"4","author":"Gray","year":"2012","journal-title":"Biodivers. Ecol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.rse.2014.10.014","article-title":"Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape","volume":"156","author":"Ru","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Al-Shammari, D., Fuentes, I., Whelan, B.M., Filippi, P., and Bishop, T.F.A. (2020). Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12183038"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13021-018-0097-1","article-title":"Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna","volume":"13","author":"Schwieder","year":"2018","journal-title":"Carbon Balance Manag."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Khare, S., and Rossi, S. (2019, January 24\u201326). Phenology analysis of moist decedous forest using time series Landsat-8 remote sensing data. Proceedings of the  2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Portici, Italy.","DOI":"10.1109\/MetroAgriFor.2019.8909249"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Venkatappa, M., Anantsuksomsri, S., Castillo, J.A., Smith, B., and Sasaki, N. (2020). Mapping the Natural Distribution of Bamboo and Related Carbon Stocks in the Tropics Using Google Earth Engine, Phenological Behavior, Landsat 8, and Sentinel-2. Remote Sens., 12.","DOI":"10.3390\/rs12183109"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Faber-langendoen, D., Keeler-wolf, T., Meidinger, D., Josse, C., Weakley, A., Tart, D., Navarro, G., Hoagland, B., Ponomarenko, S., and Fults, G. (2016). Classification and Description of World Formation Types, General Technical Report RMRS-GTR-346.","DOI":"10.2737\/RMRS-GTR-346"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1093\/jpe\/rty017","article-title":"ter Water availability drives gradients of tree diversity, structure and functional traits in the Atlantic\u2014Cerrado\u2014Caatinga transition, Brazil","volume":"11","author":"Terra","year":"2018","journal-title":"J. Plant Ecol."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Gash, J., Keller, M., Dias, P.S., and Bustamante, M. (2009). Evapotranspiration. Amazonia and Global Change, American Geophysical Union.","DOI":"10.1029\/GM186"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1016\/j.agrformet.2008.01.012","article-title":"Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in Monsoon Asia","volume":"148","author":"Huete","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1890\/ES12-00232.1","article-title":"Seasonal coupling of canopy structure and function in African tropical forests and its environmental controls","volume":"4","author":"Guan","year":"2013","journal-title":"Ecosphere"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Zhu, X., Cai, F., Tian, J., and Williams, T.K.A. (2018). Spatiotemporal fusion of multisource remote sensing data: Literature survey, taxonomy, principles, applications, and future directions. Remote Sens., 10.","DOI":"10.3390\/rs10040527"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4736\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:34:39Z","timestamp":1760168079000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,23]]},"references-count":75,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234736"],"URL":"https:\/\/doi.org\/10.3390\/rs13234736","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,23]]}}}