This paper evaluates the accuracy of shoreline positions obtained from the infrared (IR) bands of Landsat 7, Landsat 8, and Sentinel-2 imagery on natural beaches. A workflow for sub-pixel shoreline extraction, already tested on seawalls, is used. The present work analyzes the behavior of that workflow and resultant shorelines on a micro-tidal (<20 cm) sandy beach and makes a comparison with other more accurate sets of shorelines. These other sets were obtained using differential GNSS surveys and terrestrial photogrammetry techniques through the C-Pro monitoring system. 21 sub-pixel shorelines and their respective high-precision lines served for the evaluation. The results prove that NIR bands can easily confuse the shoreline with whitewater, whereas SWIR bands are more reliable in this respect. Moreover, it verifies that shorelines obtained from bands 11 and 12 of Sentinel-2 are very similar to those obtained with bands 6 and 7 of Landsat 8 (−0.75 ± 2.5 m; negative sign indicates landward bias). The variability of the brightness in the terrestrial zone influences shoreline detection: brighter zones cause a small landward bias. A relation between the swell and shoreline accuracy is found, mainly identified in images obtained from Landsat 8 and Sentinel-2. On natural beaches, the mean shoreline error varies with the type of image used. After analyzing the whole set of shorelines detected from Landsat 7, we conclude that the mean horizontal error is 4.63 m (±6.55 m) and 5.50 m (±4.86 m), respectively, for high and low gain images. For the Landsat 8 and Sentinel-2 shorelines, the mean error reaches 3.06 m (±5.79 m).

ElsevierPriego De Los Santos, E. (2012). Rain pattern analysis and forecast model based on GPS estimated atmospheric water vapor content. Atmospheric Environment. 49:85-93. doi:10.1016Environment. 49:85-93. doi:10. /j.atmosenv.2011.019. Document downloaded from:This paper must be cited as: RAIN PATTERN ANALYSIS AND FORECAST MODEL BASED ON GPS 19 ESTIMATED ATMOSPHERIC WATER VAPOR CONTENT

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For improved water resource management and forecasting of risks associated with hydrological processes, it is fundamental to improve the knowledge of rainfall as a natural process. Atmospheric water vapour content is one of the key variables in precipitation. The distribution and evolution of atmospheric water vapour is critical for the functioning of hydrological processes, being one of the essential climate variables as defined in the Global Climate Observing System. Improving understanding of atmospheric water vapour content and distribution is essential for climate change studies because water vapour is the main greenhouse gas, contributing around 70% of global temperature rise (Solomon et al., 2007). Water vapour is also a major component in controlling atmospheric stability, because it is involved actively in the evolution and propagation of convective storm systems. Until recently, atmospheric water vapour could not be observed particularly well due to the absence of instruments capable of measuring it at high‐resolution temporal and spatial scales. However, in recent years the increase in the number of permanent GNSS (Global Navigation Satellite System) reference stations worldwide has led to a major breakthrough in the monitoring of atmospheric integrated water vapour (IWV), with almost 2000 sites in Europe alone contributing near real‐time atmospheric delay estimates. The present study focuses on the relationship between variations in IWV observed using delays in GNSS signals with meteorological variables such as atmospheric pressure and precipitation, in a long‐term study for the period 2000–2012 in the area of Valencia, Spain. Fluctuations in IWV fields correlate well with approaching frontal rainfall, and a combined rise in IWV and fall in atmospheric pressure act together as a precursor to heavy precipitation.

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