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This data set contains 50 m gridded snow depths derived from airborne light detection and ranging, or lidar, measurements of surface elevations. The data were collected as part of the NASA/JPL Airborne Snow Observatory (ASO) aircraft survey campaigns.
Data Set ID:
ASO_50M_SD
ASO L4 Lidar Snow Depth 50m UTM Grid, Version 1
This is the most recent version of these data.
Version Summary:
Initial release
Overview
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Citing These Data
As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.
Painter, T. 2018. ASO L4 Lidar Snow Depth 50m UTM Grid, Version 1. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/STOT5I0U1WVI. [Date Accessed].Documentation
Created:
23 May 2018
Last modified:
10 March 2020
Data Description
This data set is a collection of 50 m resolution snow depth maps, measured by the Airborne Snow Observatory (ASO), a coupled imaging spectrometer and scanning lidar system created by NASA/JPL. The imaging spectrometer is used to quantify spectral albedo, broadband albedo, and radiative forcing by dust and black carbon in snow. The scanning lidar measures snow depth using the differential altimetry approach of subtracting snow-free gridded elevation data from snow-covered gridded elevation data (Deems et al., 2013). The 50 m gridded snow depth data in this data set were aggregated from the original 3 m snow depth measurements, which are provided in the ASO L4 Lidar Snow Depth 3m UTM Grid data set.
Parameters
The data product featured in this data set is snow depth in meters. An example is shown in Figure 1.
File Information
Format
Data are provided as GeoTIFF (.tif) formatted files. Each GeoTIFF file is paired with an associated XML file, which contains additional metadata.
Naming Convention
Data files are named after the following naming convention and as described in Table 1.
ASO_50M_SD_CCSCBC_YYYMMDD.tif
Example file name:
ASO_50M_SD_USCATB_20170727.tif
| File Designator | Description |
|---|---|
ASO_50M_SD |
Data set ID |
CC |
Two digit country code, e.g. US = United States |
SC |
Two digit US state code, e.g. CA = California |
BC |
Two digit basin (site) code, e.g. TB = Tuolumne Basin. See the ASO basins spreadsheet for a list of basins and basin codes. |
YYYYMMDD |
Data acquisition date |
.tif |
GeoTIFF formatted file |
Spatial Information
Coverage
Spatial coverage for this data set includes several basins listed in the ASO basins spreadsheet. Figure 2 depicts four California basins as an example.
Resolution
50 m x 50 m grid
Geolocation
- Datum: WGS84 Ellipsoidal
- UTM zones: 10N, 11N, 12N, 13N
- EPSG codes: 32610, 32611, 32612, 32613
Temporal Information
Coverage
03 April 2013 to 16 July 2019
ASO Snow-Off campaigns typically occur between August and October, while the Snow-On campaigns are typically conducted between February and June.
Resolution
Varies by seasonal campaigns. In general, given the rapidly changing nature of snow cover presence, depth, and surface properties that modulate its melt, ASO flies target basins on a weekly basis from mid-winter through complete snowmelt.
Data Acquisition and Processing
Processing Steps
The reader is referred to Painter et al. (2016) for details on the processing steps used to generate these data.
Quality, Errors, and Limitations
The reader is referred to Painter et al. (2016) for more information on the quality of the data.
Instrumentation
Lidar System
The Riegl LMS-Q1560 airborne laser scanner (ALS) measures surface elevations from which snow depths are calculated. The Q1560 uses dual lasers at 1064 nm wavelength, each with a 60° scan angle (±30° across-nadir) and a 14° angle relative to the cross-track axis, producing an up to 8° fore/aft look angle (off-nadir in the along-track direction). A 1064 nm wavelength system is used because of its relatively small laser penetration depth in snow and relatively high snow reflectance at that wavelength, as well as greater penetration through vegetation canopies.
Note: Current processing uses some data from the CASI 1500 imaging spectrometer data to discriminate processing steps, but the bulk of the snow depth information comes from the Riegl Q1560 airborne laser scanner.
The required level of geolocation accuracy is achieved through the use of a single lidar-integrated Trimble Applanix POS/AV 510 GPS and Inertial Measurement Unit (IMU). The IMU has angular uncertainties of 0.005° in roll, 0.005° in pitch, and 0.008° in true heading after post-processing, and a resultant attitude uncertainty of 0.011°.
For more detailed information see Deems et al. (2013) and Painter et al. (2016).
Software and Tools
Software that recognizes the GeoTIFF file format is recommended for these images, such as the GIS software QGIS and ArcGIS. See also the libGeoTIFF and GDAL websites for more information.
Related Data Sets
ASO L4 Lidar Snow Depth 3m UTM Grid
ASO L4 Lidar Snow Water Equivalent 50m UTM Grid
NASA SnowEx data sets at NSIDC
Related Websites
Airborne Snow Observatory Project at NASA/JPL
SnowEx Project at NASA
iSWGR - NASA International Snow Working Group Remote Sensing
Contacts and Acknowledgments
Thomas Painter
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
Acknowledgments
Funding for the Airborne Snow Observatory was provided by NASA, the California Department of Water Resources, JPL investments, Colorado Water Conservation Board, City of San Francisco Public Utilities Commission, Turlock Irrigation District, Modesto Irrigation District, and USDA Agricultural Research Service.
References
Deems, J. S., Painter, T. H., & Finnegan, D. C. (2013). Lidar measurement of snow depth: a review. Journal of Glaciology, 59(215), 467–479. https://doi.org/10.3189/2013jog12j154
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J. S., Gehrke, F., … Winstral, A. (2016). The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo. Remote Sensing of Environment, 184, 139–152. https://doi.org/10.1016/j.rse.2016.06.018
