Land and snow-covered area classification method based on the background noise for satellite photon-counting laser altimeters.

Abstract

The changing of snow and glaciers in mountainous areas is a sensitive signature to global warming, and satellite photon-counting laser altimeters provide an effective way to monitor the changing thickness of the snow and ice. Based on the background noise difference between snow/ice-covered areas and bare lands, we proposed a classification method to distinguish snow-covered areas from the raw photons measured by photon-counting laser altimeters in mountainous areas. First, a theoretical noise model was established considering the influence of the sunlight incident direction, the terrain slope, and reflection characteristics of different surfaces. Second, the dynamic thresholds from the proposed theoretical model and the trained thresholds were calculated and tested to classify the along-track land-cover types for the Ice, Cloud, and Elevation Satellite-2 (ICESat-2) photon-counting laser altimeter. Then, the study areas in Aksai Chin in autumn and near Pamirs plateau in winter were selected and the classification method was verified to achieve an overall accuracy of over 93% for both thresholds and areas. Our method utilized the "useless" noise photons that are enormous in quantity and easy to extract compared to the signal photons. More significantly, this method reduces the requirements of the optical images (that are used as the priori knowledge), as it can perform well even without priori knowledge.