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

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.

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

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.”

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

Analogy betwen Recommended Approaches to Snow Fraction Validation Similar nominal spatial resolution for MODIS, VIIRS and OLCI/SLSTR in the range 300 - 500 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

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

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

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

Aggregation and Coregistration of Images Location of Landsat scene Landsat Snow Fraction VIIRS fraction (path - 41, row – 33) 0% 100%

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

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%

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

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.

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.

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.