Please use this identifier to cite or link to this item: https://doi.org/10.1109/IGARSS.2007.4422910
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dc.titleOn the possible retrieval of windwave states from optical and nearIR remote sensing imagery of the ocean
dc.contributor.authorSalinas, S.V.
dc.date.accessioned2014-12-12T07:15:56Z
dc.date.available2014-12-12T07:15:56Z
dc.date.issued2007
dc.identifier.citationSalinas, S.V. (2007). On the possible retrieval of windwave states from optical and nearIR remote sensing imagery of the ocean. International Geoscience and Remote Sensing Symposium (IGARSS) : 769-772. ScholarBank@NUS Repository. https://doi.org/10.1109/IGARSS.2007.4422910
dc.identifier.isbn1424412129
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/115465
dc.description.abstractSince the pioneering work of Cox & Munk , who first investigated the probability distribution of liquid surface facets using the analysis of aerial photographs of the Sun glitter, it has been widely recognized that the dominant parameter that determines the sea surface roughness is the mean square slope (MSS) of the sea surface which is mainly related to short wind waves and other surface effects in a lesser degree. Liquid surface reflection has been studied experimentally over the years and several empirical algorithms have been formulated relating wind speed directly to the MSS. Using these empirical formulations, the reflectivity of the liquid surface can be computed using a normal probability distribution of water surface facets and applying Fresnel relations to each surface facet. However, these approaches do not include the influence of wave states or the physical properties of the sea surface on the computation of the MSS. In a recent work, Salinas [1] has developed a radiative transfer model that incorporates a new windwave description for the MSS of the sea surface [2] . This model includes wind interaction and windwave states, such as wave age, as the main factors contributing to surface roughness. In this work, we apply the above mentioned radiative transfer model to the analysis of remote sensing data obtained from TERRA-MODIS satellite imagery taken near Sun glint regions. As as ancillary step, wind speed data, corresponding to TERRA-MODIS date of measurements and obtained from the SeaWinds instrument on the QuikScat satellite, are used to constrain the range of wind speed in our calculations. © 2007 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/IGARSS.2007.4422910
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCTR FOR REM IMAGING,SENSING & PROCESSING
dc.description.doi10.1109/IGARSS.2007.4422910
dc.description.sourcetitleInternational Geoscience and Remote Sensing Symposium (IGARSS)
dc.description.page769-772
dc.description.codenIGRSE
dc.identifier.isiut000256657300196
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