{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T23:53:48Z","timestamp":1789170828272,"version":"build-2803163510"},"reference-count":59,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,23]],"date-time":"2017-01-23T00:00:00Z","timestamp":1485129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["NNH13ZDA001N-TERAQ"],"award-info":[{"award-number":["NNH13ZDA001N-TERAQ"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1531511"],"award-info":[{"award-number":["1531511"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding the effects that the Urban Heat Island (UHI) has on plant phenology is important in predicting ecological impacts of expanding cities and the impacts of the projected global warming. However, the underlying methods to monitor phenological events often limit this understanding. Generally, one can either have a small sample of in situ measurements or use satellite data to observe large areas of land surface phenology (LSP). In the latter, a tradeoff exists among platforms with some allowing better temporal resolution to pick up discrete events and others possessing the spatial resolution appropriate for observing heterogeneous landscapes, such as urban areas. To overcome these limitations, we applied the Spatial and Temporal Adaptive Reflectance Model (STARFM) to fuse Landsat surface reflectance and MODIS nadir BRDF-adjusted reflectance (NBAR) data with three separate selection conditions for input data across two versions of the software. From the fused images, we derived a time-series of high temporal and high spatial resolution synthetic Normalized Difference Vegetation Index (NDVI) imagery to identify the dates of the start of the growing season (SOS), end of the season (EOS), and the length of the season (LOS). The results were compared between the urban and exurban developed areas within the vicinity of Ogden, UT and across all three data scenarios. The results generally show an earlier urban SOS, later urban EOS, and longer urban LOS, with variation across the results suggesting that phenological parameters are sensitive to input changes. Although there was strong evidence that STARFM has the potential to produce images capable of capturing the UHI effect on phenology, we recommend that future work refine the proposed methods and compare the results against ground events.<\/jats:p>","DOI":"10.3390\/rs9010099","type":"journal-article","created":{"date-parts":[[2017,1,23]],"date-time":"2017-01-23T10:40:33Z","timestamp":1485168033000},"page":"99","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data"],"prefix":"10.3390","volume":"9","author":[{"given":"Norman","family":"Gervais","sequence":"first","affiliation":[{"name":"Department of Information Science, University at Albany, State University of New York, New York, NY 12222, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1670-0741","authenticated-orcid":false,"given":"Alexander","family":"Buyantuev","sequence":"additional","affiliation":[{"name":"Department of Geography and Planning, University at Albany, State University of New York, New York, NY 12222, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1865-2846","authenticated-orcid":false,"given":"Feng","family":"Gao","sequence":"additional","affiliation":[{"name":"Hydrology and Remote Sensing Laboratory, Agricultural Research Services, United States Department of Agriculture, Beltsville, MD 20705, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,23]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2014). 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