MODIS Monthly Fog and Low Cloud Cover Rasters 2000-2022
Date
2022
Authors
Werner, Zackary
Choi, Christopher Tsz Hin
Winter, Anna
Vorster, Anthony G.
Berger, Anika
O'Shea, Kristen
Evangelista, Paul
Woodward, Brian
Journal Title
Journal ISSN
Volume Title
Abstract
The 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.
Description
Fog 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.
Graduate Degree Program in Ecology
Natural Resource Ecology Laboratory (NREL)
Graduate Degree Program in Ecology
Natural Resource Ecology Laboratory (NREL)
Rights Access
Subject
FLCC
Fog
NASA
Evangelista
NREL
Earth Observation
Remote Sensing
GIS
MODIS
Terra
Citation
Associated Publications
Zackary 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.