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MODIS Monthly Fog and Low Cloud Cover Rasters 2000-2022

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dc.contributor.authorWerner, Zackary
dc.contributor.authorChoi, Christopher Tsz Hin
dc.contributor.authorWinter, Anna
dc.contributor.authorVorster, Anthony G.
dc.contributor.authorBerger, Anika
dc.contributor.authorO'Shea, Kristen
dc.contributor.authorEvangelista, Paul
dc.contributor.authorWoodward, Brian
dc.coverage.spatialCoastal California and Southern Oregonen_US
dc.coverage.spatialLatitude: 33.2° to 42.7°, Longitude: -116.3° to -125.1°en_US
dc.coverage.temporal2000-06-01-2022-08-31en_US
dc.date.accessioned2022-09-15T22:21:13Z
dc.date.available2022-09-15T22:21:13Z
dc.date.issued2022
dc.descriptionFog and low cloud cover (FLCC) provide critical moisture for ecosystems along the Pacific Coast during the summer months and it is currently un- clear how climate change has affected FLCC occurrence. Additionally, FLCC impacts visibility and transportation safety for both road and air traffic. As such, a method for the monitoring of FLCC is necessary to inform land management decisions for fog-obligate species and to improve transportation safety, among other applications. Several gaps exist in current FLCC detection methodologies and while the Moderate Resolution Spectroradiometer (MODIS) sensor has previously been used to detect fog, its utility has not been validated. In this study, we create a 20 year (2000–2020) FLCC dataset using the Terra MODIS cloud flags and examine its effectiveness in detecting daily and monthly FLCC presence along the California and southern Oregon coast for the summer months (June–September). We validate the accuracy of this method using an existing FLCC dataset derived from Geostationary Operational Environmental Satellite (GOES) observations collected at 15 min intervals from 2000 to 2009. The two FLCC datasets have a strong linear relationship for FLCC frequency for each summer month, with an average r2 of 0.82 and p-value of <0.01. This strong relationship demonstrates the ability of Terra MODIS to reliably and accurately detect FLCC. Finally, we demonstrate a case study application of our FLCC dataset in a time series analysis over five coast redwood (Sequoia sempervirens) state parks in the Big Sur region of coastal California. This case study highlights the benefits provided by a 1 km resolution FLCC dataset for ecological applications and for monitoring spatiotemporal FLCC pat- terns across summer months over two decades. Our case study results showed that the number of foggy days fluctuates considerably year-to-year with no discernible positive or negative trend occurring between 2000 and 2020. Finally, we present a freely-accessible Google Earth Engine application to view and download the monthly FLCC data for all summer months for the years 2000–2020. The methods and dataset presented in this paper provide a means for efficient, daily FLCC monitoring at 1 km2 resolution, as well as the capacity for historical FLCC analyses.en_US
dc.descriptionGraduate Degree Program in Ecology
dc.descriptionNatural Resource Ecology Laboratory (NREL)
dc.description.abstractThe MODIS Monthly Fog and Low Cloud Cover Rasters 2000-2022 dataset contains fog and low cloud cover (FLCC) observations summarized into days per month along the California and Southern Oregon Coast from 2000-2022. This dataset accompanies the publication https://doi.org/10.1016/j.rsase.2022.100832, which describes the methodology for creating this dataset. The dataset can also be viewed through a Google Earth Engine web application https://christopherchoi98.users.earthengine.app/view/modis-fog-detection-app.en_US
dc.description.sponsorshipNASA DEVELOP National Program (Contract Number: NNL16AA05C).en_US
dc.format.mediumZIP
dc.format.mediumTIFF
dc.format.mediumTXT
dc.identifier.urihttps://hdl.handle.net/10217/235754
dc.identifier.urihttp://dx.doi.org/10.25675/10217/235754
dc.languageEnglishen_US
dc.language.isoengen_US
dc.publisherColorado State University. Librariesen_US
dc.relation.ispartofResearch Data
dc.relation.isreferencedbyZackary Werner, Christopher Tsz Hin Choi, Anna Winter, Anthony G. Vorster, Anika Berger, Kristen O'Shea, Paul Evangelista, Brian Woodward, MODIS sensors can monitor spatiotemporal trends in fog and low cloud cover at 1 km spatial resolution along the U.S. Pacific Coast, Remote Sensing Applications: Society and Environment, Volume 28, 2022, 100832, ISSN 2352-9385, https://doi.org/10.1016/j.rsase.2022.100832.en_US
dc.rights.licenseThe material is open access and distributed under the terms and conditions of the Creative Commons Public Domain "No rights reserved" (https://creativecommons.org/share-your-work/public-domain/cc0/).
dc.rights.urihttps://creativecommons.org/share-your-work/public-domain/cc0/
dc.subjectFLCCen_US
dc.subjectFogen_US
dc.subjectNASAen_US
dc.subjectEvangelistaen_US
dc.subjectNRELen_US
dc.subjectEarth Observationen_US
dc.subjectRemote Sensingen_US
dc.subjectGISen_US
dc.subjectMODISen_US
dc.subjectTerraen_US
dc.subjectCaliforniaen_US
dc.subjectOregonen_US
dc.subjectRedwooden_US
dc.subjecthistoricalen_US
dc.subjectGOESen_US
dc.titleMODIS Monthly Fog and Low Cloud Cover Rasters 2000-2022en_US
dc.typeDataseten_US

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