This data set reports the maximum snow cover extent during an eight-day period in 1200 km x 1200 km tiles. Tiles are generated by compositing 500 m observations from the MODIS/Aqua Snow Cover Daily L3 Global 500m Grid (MYD10A1) data set. Bit flags are also provided that report the eight-day snow/no-snow chronology for each 500 m cell.
Hall, D. K. and G. A. Riggs. 2016. MODIS/Aqua Snow Cover 8-Day L3 Global 500m Grid, Version 6. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/MODIS/MYD10A2.006. [Date Accessed].
Snow cover in this data set is derived from the Normalized Difference Snow Index (NDSI). Snow covered land typically has a very high reflectance in visible bands and very low reflectance in the shortwave infrared; the NDSI reveals the magnitude of this difference.
Maximum snow cover extent is generated by reading 8 days of 500 m resolution MYD10A1 tiles. If snow is observed in a cell on any day in the period, the cell is mapped as snow. If no snow is found, the cell is filled with the clear-view observation that occurred most often (e.g. snow free land, lake, etc.). Cloud cover is only reported if the cell was cloud-obscured for all eight days in the period. Each cell's snow/no snow chronology is recorded using bit flags and provided as a separate variable.
Data files range in size from approximately 100 KB to 2.0 MB.
¹Includes 2 or 3 days from the next year.
To retrieve the bit flags, convert the integer stored in the SDS into its binary representation. For example, if a cell contains the value 229, expressing that number in binary yields: 11100101.
Read from right to left, the flags indicate that snow was observed in the cell on days 1, 3, 6, 7, and 8 of the compositing period, while no snow was observed on days 2, 4, and 5.
Fifteen of the 20 band 6 detectors on the Aqua MODIS failed shortly after launch, a 75 percent signal loss that has precluded using this band for snow detection. However, a Quantitative Image Restoration (QIR) technique was recently developed (Gladkova et al., 2012) that restores Aqua MODIS band 6 data to scientific quality. Version 6 incorporates this technique to produce an intermediate, calibrated radiances product with band 6 restored (MYD02HKM_QIR). When generating the swath-level data set for Aqua (MYD10_L2), the snow detection algorithm computes the Normalized Difference Snow Index (NDSI) (Hall and Riggs, 2011) from MYD02HKM_QIR. Aside from this step, the snow detection algorithm is the same for Aqua and Terra.
MYD10A1, the input to this data set, is generated by mapping a best MYD10_L2 observation of the day into each cell of the MODIS Sinusoidal Grid (see the MYD10A1 documentation for details). Maximum snow extent for the period is mapped by compositing eight days of MYD10A1 snow cover. The algorithm reads eight days of MYD10A1 tiles, and if snow cover with a value of NDSI > 10 is found in a cell for any day in the period, the cell is mapped as snow in the Maximum_Snow_Extent SDS. To reduce snow commission errors and produce a more spatially consistent snow map, values in the range of 0 < NDSI ≤ 10 are interpreted as uncertain and not counted toward maximum snow cover.
If no snow is found in the period, the algorithm maps the cell with the clear-view observation that occurred most often. Cells are only mapped to cloud if all eight days in the period are cloud-obscured. Although this logic minimizes cloud cover extent, it biases the output toward clear views because only clear views are used to determine composite observations. To catch any unexpected conditions, cells are mapped to no decision if a composite observation is not determined in the algorithm.
The snow/no snow chronology for each cell is written to the Eight_Day_Snow_Cover SDS. Each of the eight bits in this variable represents one day in the compositing period. On days that snow is observed the cell, the bit corresponding to that day is set to on (1). Days with no snow, cloud cover, and missing data are left unset (0). Days are ordered chronologically across the byte from right to left, from day 1 (bit 0) to day 8 (bit 7).
The NDSI technique has proven to be a robust indicator of snow cover. Numerous investigators have utilized MODIS snow cover data sets and reported accuracy in the range of 88% to 93%. The eight day snow cover extent data set is intended to provide users with a map of maximum snow cover extent during the eight-day window and a record of days on which snow cover was observed. Although this data set's accuracy is typically similar to its MYD10A1 input, compositing snow commission errors over eight days into a single tile can increase the error percentage spatially and temporally, even after uncertain snow detections are filtered as previously described.
Accuracy and errors in MYD10A1 originate in the MYD10_L2 swath product. In the eight-day snow maps, errors associated with MYD10_L2 snow/cloud confusion typically manifest as snow in locations and seasons where snow is impossible or very unlikely. These errors accumulate from each day and occur in different locations on different days, thus increasing the spatial extent of errors when composited into the eight-day snow cover maps.
Finally, anomalies in the input data to MYD10_L2 propagate to the output. Table 3 in the MYD10_L2 documentation lists the products that are used as input to the snow detection algorithm. For a more detailed discussion of potential sources of error, including examples, consult the MODIS Snow Products Collection 6 User Guide.
No product specific QA is provided with this data set.