Improving the assessment of ICESat water altimetry accuracy accounting for autocorrelation

Abstract

International audiencedata with saturation correction of ICESat waveforms, spatial filtering to avoid measurement disturbance from the land-water transition effects on waveform saturation and data selection to avoid trends in water elevations across space. Initially this paper analyzes 237 collected ICESat transects, consistent with the available hydrometric ground stations for four of the Great Lakes. By adapting a geostatistical framework, a high frequency autocorrelation between successive shot elevation values was observed and then modeled for 45% of the 237 transects. The modeled autocorrelation was therefore used to estimate water elevations at the transect scale and the resulting uncertainty for the 117 transects without trend. This uncertainty was 8 times greater than the usual computed uncertainty, when no temporal correlation is taken into account. This temporal correlation, corresponding to approximately 11 consecutive ICESat shots, could be linked to low transmitted ICESat GLAS energy and to poor weather conditions. Assuming Gaussian uncertainties for both reference data and ICESat data upscaled at the transect scale, we derived GLAS deviations statistics by averaging the results at station and lake scales. An overall bias of -4.6 cm (underestimation) and an overall standard deviation of 11.6 cm were computed for all lakes. Results demonstrated the relevance of taking autocorrelation into account in satellite data uncertainty assesment

Monitoring the Fluctuation of Lake Qinghai Using Multi-Source Remote Sensing Data

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Variations in water level and glacier mass balance in Nam Co lake, Nyainqentanglha range, Tibetan Plateau, based on ICESat data for 2003-09

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Potential of Space-Borne LiDAR Sensors for Global Bathymetry in Coastal and Inland Waters

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