{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:48:12Z","timestamp":1766137692809,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,22]],"date-time":"2021-05-22T00:00:00Z","timestamp":1621641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000203","name":"U.S. Geological Survey","doi-asserted-by":"publisher","award":["G18AP00077"],"award-info":[{"award-number":["G18AP00077"]}],"id":[{"id":"10.13039\/100000203","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability.<\/jats:p>","DOI":"10.3390\/rs13112045","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:01:20Z","timestamp":1621814480000},"page":"2045","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland"],"prefix":"10.3390","volume":"13","author":[{"given":"Ana\u00ed","family":"Capar\u00f3 Bellido","sequence":"first","affiliation":[{"name":"Department of Geography &amp; GISc, University of North Dakota, P.O. Box 9020, Grand Forks, ND 58202, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2572-9792","authenticated-orcid":false,"given":"Bradley C.","family":"Rundquist","sequence":"additional","affiliation":[{"name":"Department of Geography &amp; GISc, University of North Dakota, P.O. Box 9020, Grand Forks, ND 58202, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,22]]},"reference":[{"key":"ref_1","unstructured":"Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., and Hanson, C.E. (2007). Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Reed, B.C., Schwartz, M.D., and Xiao, X. (2009). Remote sensing phenology. Phenology of Ecosystem Processes, Springer.","DOI":"10.1007\/978-1-4419-0026-5_10"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1080\/01431160500474357","article-title":"Quantification of grassland properties: How it can benefit from geoinformatic technologies?","volume":"27","author":"Gao","year":"2006","journal-title":"Int. J. 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