Number Of Overpasses Research Articles

Time variability of hydrodynamics and potential use of surface water and ocean topography mission in estuarine macrotidal system: example of Seine estuary

This work was supported by the National Center for Space Studies in the framework of the future mission surface water and ocean topography (SWOT). The estuarine zones with their complex hydrodynamics represent a great challenge for the future SWOT mission. The Seine estuary was selected by the SWOT Science Definition Team for the calibration/validation of the mission. Thus the aim of this study is to investigate the temporal hydrodynamic variability in the Seine estuary and the SWOT ability to reproduce this variability and hydrodynamic phenomena. For this, we used several statistical techniques and frequency analyses applied on the tide-gauge measurements of the Seine estuary and on SWOT simulated data. SWOT samples (SWOT simulated data) have been extracted synthetically from observations using the number of overpasses per repeat cycle [21 days (d)] in order to study the ability of SWOT to reproduce the time variability of the hydrodynamics. Results have demonstrated similar frequencies of multiyear, annual, and intraannual along the estuary from the Seine discharge to the Cherbourg sea level. The 11- to 22-d frequency has been only observed for estuarine and coastal time series suggesting the tide effect in these contexts. The distribution of levels has shown a high influence of tides and discharges at the downstream and the upstream, respectively. Low frequencies of water level are explained by the climate patterns of North Atlantic Oscillation with a mean range of 65%, whereas the high frequencies are associated to the periods of flooding and stormy events. Finally, simulated SWOT samples of water level show a better restitution of observations for high overpasses number. The results have shown a better restitution of hydrodynamic variability by SWOT in the river and upstream of estuary than in the downstream of estuary and coastal zone: in the estuary downstream and the coast, the annual mode is underexpressed while the 3-month (m) mode is overexpressed, which means the annual mode is underestimated and the 3-m mode is amplified by SWOT.

On the Investigation of the Sea-Level Variability in Coastal Zones Using SWOT Satellite Mission: Example of the Eastern English Channel (Western France)

The future mission of surface water and ocean topography (SWOT), launched in 2020 over a period of 3–5 years, will be designated to address the issue of combining surface water hydrology with physical oceanography aiming to present new perspectives of applications for coastal areas. The extent to which the synthetic SWOT measurements can reproduce the temporal variability of the sea level was investigated. The eastern English Channel (NW France) was considered as a case of application. The hourly sea-level records were filtered from the aliased harmonic tides by classic harmonic analyses to obtain the nontidal residual. This residual was used to simulate synthetically the satellite samples based on the number of overpasses per repeat cycle at each geographical station. Both real and synthetic SWOT measurements were compared by the use of different approaches of inference statistics and wavelets. The statistical behavior, deduced from the functions of probability density (pdf) and cumulative distribution (cdf), shows correlations between 65% and 75% for hourly measurements, which increase to 85% for monthly average ones. The frequency of positive and negative extreme values is under-estimated with an order less than 25%. The potential use of SWOT depends on the number of measurements and the sampling interval between SWOT overpasses per repeat orbit. In the time–frequency domain, the wavelet multiresolution analysis of the nontidal sea level displays four components: 1) 1 year; 2) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 4–7 month; 3) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 2–3 month; and 4) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt;2$</tex-math></inline-formula> month bands. Such modes seem to be well manifested by SWOT samples with a mean explained variance more than 75%. The aliasing frequency of the altimeter generates a dispersion and an overexpression of the energy spectrum, which increases with the number of overpasses per repeat orbit and the high frequency ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim $</tex-math></inline-formula> 2–3 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt;2$</tex-math></inline-formula> month bands). The reconstructed wavelet components evidence the capacity of SWOT to estimate the annual and the seasonal variability of the nontidal sea level. In particular, SWOT is able to reproduce the most of extreme storm surges in the English Channel. The main finding of this research clearly shows the utility of SWOT satellite altimetry in observing and understanding the sea-level variability and storm surges, complementing tide-gauge observations for the validation and improvement of coastal models.

Some observations on crop profile modelling

Abstract LANDSAT data are typically available for a number of overpasses during a growing season. There is currently considerable interest in modelling the so-called crop profile for such multitemporal data by a non-linear profile function of some (spectral) index for the spectral bands. The derived coefficients are used in a subsequent allocation procedure. This paper outlines some results obtained from an evaluation of the approach for crop data from the wheatbelt of Western Australia. Specifically, the degree of separation of crop classes from pasture classes, as measured by the discriminant root, is compared for analyses based on the original bands, on various spectral indices and on fitted coefficients from a crop profile function describing the temporal change in these indices. For the data considered, a marked loss of discrimination is found for analyses based on various spectral indices, when compared with those based directly on the corresponding discriminant functions (where the linear combinati...