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JavaScript code for retrieval of MODIS Collection 6 NDSI snow cover at SNOTEL sites and a Jupyter Notebook to merge/reprocess data
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| Type: | Resource | |
| Storage: | The size of this resource is 16.5 KB | |
| Created: | Jan 15, 2021 at 5:50 p.m. | |
| Last updated: | Feb 11, 2022 at 2:38 a.m. | |
| DOI: | 10.4211/hs.d287f010b2dd48edb0573415a56d47f8 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 1219 |
| Downloads: | 33 |
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| Comments: | No comments (yet) |
Abstract
This JavaScript code has been developed to retrieve NDSI_Snow_Cover from MODIS version 6 for SNOTEL sites using the Google Earth Engine platform. To successfully run the code, you should have a Google Earth Engine account. An input file, called NWM_grid_Western_US_polygons_SNOTEL_ID.zip, is required to run the code. This input file includes 1 km grid cells of the NWM containing SNOTEL sites. You need to upload this input file to the Assets tap in the Google Earth Engine code editor. You also need to import the MOD10A1.006 Terra Snow Cover Daily Global 500m collection to the Google Earth Engine code editor. You may do this by searching for the product name in the search bar of the code editor.
The JavaScript works for s specified time range. We found that the best period is a month, which is the maximum allowable time range to do the computation for all SNOTEL sites on Google Earth Engine. The script consists of two main loops. The first loop retrieves data for the first day of a month up to day 28 through five periods. The second loop retrieves data from day 28 to the beginning of the next month. The results will be shown as graphs on the right-hand side of the Google Earth Engine code editor under the Console tap. To save results as CSV files, open each time-series by clicking on the button located at each graph's top right corner. From the new web page, you can click on the Download CSV button on top.
Here is the link to the script path: https://code.earthengine.google.com/?scriptPath=users%2Figarousi%2Fppr2-modis%3AMODIS-monthly
Then, run the Jupyter Notebook (merge_downloaded_csv_files.ipynb) to merge the downloaded CSV files that are stored for example in a folder called output/from_GEE into one single CSV file which is merged.csv. The Jupyter Notebook then applies some preprocessing steps and the final output is NDSI_FSCA_MODIS_C6.csv.
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Related Resources
| This resource is referenced by | Garousi-Nejad, I. and Tarboton, D. (2022), "A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States", Hydrological Processes, https://doi.org/10.1002/hyp.14469 |
| The content of this resource is derived from | Hall, D. K. and G. A. Riggs. 2016. MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6. [NDSI_Snow_Cover]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/MODIS/MOD10A1.006, accessed through Google Earth Engine |
| Title | Owners | Sharing Status | My Permission |
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| Data for a comparison of national water model retrospective analysis snow outputs at SNOTEL sites across the Western U.S. | Irene Garousi-Nejad · David Tarboton | Published | Open Access |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| Utah Water Research Laboratory | Graduate Student Research Assistantship for I Garousi-Nejad | |
| National Science Foundation | Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis | OAC-1664061 |
| National Science Foundation | Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis | OAC-1664018 |
| National Science Foundation | Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis | OAC-1664119 |
Contributors
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
| Name | Organization | Address | Phone | Author Identifiers |
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| Mahyar Aboutalebi | Utah State University |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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