1. Igor Appel Alexander Kokhanovsky
Estimate of Moderate Resolution Optical Fractional Snow Cover April 13, 2018
Igor Appel
Alexander Kokhanovsky

2. Snow Fraction Provides Enhanced Information for Numerous Application
Most satellite-based snow cover products provide only a "binary" map, whereby each pixel is classified as either "snow" or "not snow".
The fractional snow cover, in comparison, presents significant innovation and progress.
The snow cover maps, including snow fraction derived from satellite observations, present the best possible information relevant to numerous applications, in particular, as input for hydrologic and land surface models.

3. Snow Fraction is Required for Downstream Products
Snow products used for a wide range of different applications, including agricultural and water management, require a quantitative understanding of the errors.
Comprehensive validation of fractional snow cover is a critically important means to improve retrieval algorithms, making the emphasis on the development of robust methodologies to estimate the uncertainty in fractional snow cover retrieval
Most of the land and atmospheric products should be considered downstream products for retrievals
Snow Fraction is Required for Downstream Products

4. Existing Commonly Used Approach to Validate Fractional Snow Product
In many cases the same fractional snow algorithm was applied to high-resolution and moderate resolution observations. The difference between the aggregated fine resolution data and the moderate resolution product was viewed as an indicator of the algorithm uncertainty.
The validity of such an estimate of the algorithm uncertainty is very doubtful, because performance of practically any algorithm will be excellent if estimated by this “methodology.”

5. Fraction Snow Cover Algorithm - Purpose and Requirements
Provide product developers, reviewers and users with the MODIS, VIIRS, Sentinel-3 - OLCI/SLSTR Fractional Snow Cover product
Fractional snow is understood as the viewable snow fraction, not the fraction of snow on the ground.
Geographic Coverage
Horizontal Resolution
Measurement Uncertainty
Data Latency
Global
0.375 km
10% of snow-covered area
30 min after data is available

6. Analogy betwen Recommended Approaches to Snow Fraction Validation
Similar nominal spatial resolution for MODIS, VIIRS and OLCI/SLSTR in the range m.
Intention to use high resolution satellite information to validate fractional snow cover products
Similar spatial resolution provided by Landsat and Sentinel - 2 satellites
The presentation is relevant to Sentinel-3 since VIIRS and OLCI/SLTR have similar bands
Analogy betwen Recommended Approaches to Snow Fraction Validation

7. Snow Fraction Algorithm Validation
General approach
Use matched in time VIIRS and Landsat scenes
Binary classify Landsat pixels as snow / non-snow
Exactly co-register data sets to make Landsat and VIIRS information completely comparable
Aggregate estimates within larger grid cells (500m- 5km) to reduce the effect of data spatial mismatch
Compare snow fraction derived from Landsat classifications with VIIRS snow fraction estimates

8. Processing High Resolution Data for Algorithm Validation
Landsat binary snow cover classification
Location of Landsat scene false color image
(path - 156, row – 37)

9. Classification of High Resolution Pixels
Pixels presented in the spectral space defined by visible (X axis) and short-wave infrared (Y axis) reflectances characterize snow and non-snow
False color images clearly identify snow (cyan), vegetation (green) and bare ground (brown-reddish)
non-snow
Shortwave IR reflectance
snow
Visible reflectance

10. Aggregation and Coregistration of Images
Location of Landsat scene
Landsat Snow Fraction
VIIRS fraction
(path - 41, row – 33) % %

11. Statistics of VIIRS Snow Fraction Validation
Results demonstrate high accuracy of VIIRS snow fraction estimates

12. Summary of Quantitative Assessment of Algorithm Performance
Average correlation coefficient is 94% despite a couple of low magnitudes
Typical intercept of linear regression line is on the order of 1%
Average slope of linear regression line is more than 0.9
Average bias of data is 2 %
Average standard deviation is 10%

13. Stratified Assessment of Algorithm Performance
Comparison of ground truth with NDSI algorithm results (thick lines) and trends (thin lines) for intermediate fractions demonstrates stratified performance for individual scenes
NDSI-based fraction
Landsat fraction

14. Role of Quantitative Assessment of Algorithm Performance
The careful detailed validation of snow fraction for a variety of conditions provides valuable information on the reasons of retrieval errors and helps identify the directions to improve the snow products.
The purposeful methodical validation is a promising way to improve fractional snow cover algorithms and to create unbiased and consistent information on snow cover distribution required for global studies, regional and local scale applications.

15. Inter-comparison of Different Fractional Snow Cover Algorithms
Typically the validation of moderate resolution data requires extensive works on the algorithm implementation making inter-comparison complicated by different viewing and illumination conditions.
The inter-comparison and validation also requires enormous work on data collection and processing as well as on coordination between scientists.

16. Conclusion Recommendations
However in many cases such inter-comparison could be made without significant problems - even without moderate resolution observations.
This simple approach to validation is a very good option to compare and choose better algorithms
It can be recommended to the Sentinel-3 mission
The inter-comparison of the results provided by different approaches to fractional snow retrieval is considered as a desirable stage in the development of the fractional snow cover algorithm.