Multi-sensor fusion using random forests for daily fractional snow cover at 30 m

In addition to providing water for nearly 2 billion people, snow drives resource selection by wildlife and influences the behavior and demography of many species. Because snow cover is highly spatially and temporally variable, mapping its extent using currently available satellite data remains a challenge. At present, there are no sensors acquiring daily data of Earth's entire surface at fine spatial resolutions (< 30 m) in wavelengths required for snow cover retrieval, namely: visible, near-infrared, and shortwave infrared. Fine scale observations at 30 m from Landsat are available at 16-day intervals since 1982 and at 8-day intervals since 1999. However, over this duration, snow can accumulate, ablate, or both, making the Landsat data ineffective for many applications. Conversely, the Moderate Resolution Imaging Spectroradiometer (MODIS) atmospherically corrected daily reflectance data, have a coarse spatial resolution of 463 m and thus, are not ideal for snow cover mapping either. This spatial and temporal resolution tradeoff limits the use of these data for a wide range of snow cover applications and indicates a pressing need for data fusion. To address this need, we use a physically-based, spectral-mixture-analysis approach for mapping fractional snow cover (fSCA) and a two-stage random forest algorithm to produce daily 30 m fSCA. We test our algorithm in the US Sierra Nevada and find MODIS fSCA is the most important predictor. We cross validate using 170 Landsat scenes and while snow cover varies immensely in time we find little variation in errors between seasons, a small bias of 0.01, and an overall accuracy of 0.97 with slightly higher precision than recall. This technique for accurate, daily, high-resolution snow cover retrievals could be applied more broadly for analyses of regional energy budget, validating snow cover in global and regional models, and for quantifying changes in the availability of biotic resources in ecosystems. •Data from two satellites were fused for daily 30 m fractional snow cover (fSCA)•Satellite data was combined using a two-stage random forest algorithm•fSCA from MODIS is the most important variable for classification and prediction•Fusion has an accuracy of 97% with similar performance throughout the year