Computing advective velocities from satellite images of sea surface temperature

1992 ◽  
Vol 30 (1) ◽  
pp. 166-176 ◽  
Author(s):  
Q.X. Wu ◽  
D. Pairman ◽  
S.J. McNeill ◽  
E.J. Barnes
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Images ◽  
Sea Surface

scholarly journals CO2 flux behavior in the maritorium of San Andres Islands on 2019

Respuestas ◽  
2020 ◽  
Vol 25 (3) ◽  
Author(s):  
Juan Guillermo Popayán-Hernández ◽  
Orlando Zúñiga-Escobar
Keyword(s):  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Images ◽  
Co2 Flux ◽  
Sea Surface ◽  
Remote Sensors ◽  
Partial Pressures ◽  
Thermodynamic Relationship ◽  
San Andres

This document estimated the behavior of the CO2 flux in the San Andrés Islas maritime for the first half of 2019. This behavior was established based on the thermodynamic relationship between the sea surface temperature, the partial pressures of CO2 in the atmosphere and the water column, this from data derived from remote sensors. The satellite data were derived from the MODIS aqua sensors and the MERRA model for sea surface temperature and wind speed respectively. Satellite images were obtained from NASA databases, subsequently processed and specialized in ArcGis 10.1. Finally, the behavior of the CO2 flux is shown for the San Andrés Islas maritime, finding that it does not have a tendency to capture CO2, so acidification processes are discarded for the selected study period.

scholarly journals Optimization of Sea Surface Current Retrieval Using a Maximum Cross-Correlation Technique on Modeled Sea Surface Temperature

2017 ◽  
Vol 34 (10) ◽  
pp. 2245-2255 ◽  
Author(s):  
Céline Heuzé ◽  
Gisela K. Carvajal ◽  
Leif E. B. Eriksson
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Western Europe ◽  
Satellite Images ◽  
Cross Correlation ◽  
Surface Current ◽  
Sea Surface ◽  
Time Interval ◽  
Correlation Technique ◽  
Ocean Surface Current

AbstractUsing sea surface temperature from satellite images to retrieve sea surface currents is not a new idea, but so far its operational near-real-time implementation has not been possible. Validation studies are too region specific or uncertain, sometimes because of the satellite images themselves. Moreover, the sensitivity of the most common retrieval method, the maximum cross correlation, to the parameters that have to be set is unknown. Using model outputs instead of satellite images, biases induced by this method are assessed here, for four different seas of western Europe, and the best of nine settings and eight temporal resolutions are determined. The regions with strong currents return the most accurate results when tracking a 20-km pattern between two images separated by 6–9 h. The regions with weak currents favor a smaller pattern and a shorter time interval, although their main problem is not inaccurate results but missing results: where the velocity is too low to be picked by the retrieval. The results are not impaired by the restrictions imposed by ocean surface current dynamics and available satellite technology, indicating that automated sea surface current retrieval from sea surface temperature images is feasible, for pollution confinement, search and rescue, and even for more energy-efficient and comfortable ship navigation.

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