|Copyright||2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).|
|Author(s)||Zabolotskikh Elizaveta1, Mitnik Leonid2, Chapron Bertrand3|
|Affiliation(s)||1 : Russian State Hydrometeorol Univ, Satellite Oceanog Lab, St Petersburg 195196, Russia.
2 : VI Ilichev Pacific Oceanol Inst, Vladivostok 690041, Russia.
3 : IFREMER, Ctr Brest, F-29280 Plouzane, France.
|Source||Remote Sensing (2072-4292) (Mdpi Ag), 2014-03 , Vol. 6 , N. 3 , P. 2317-2342|
|WOS© Times Cited||11|
|Keyword(s)||calibration, geophysical model, numerical simulation, SSMIS, AMSR-E, satellite passive microwave|
|Abstract||In this study, we considered the geophysical model for microwave brightness temperature (BT) simulation for the Atmosphere-Ocean System under non-precipitating conditions. The model is presented as a combination of atmospheric absorption and ocean emission models. We validated this model for two satellite instruments-for Advanced Microwave Sounding Radiometer-Earth Observing System (AMSR-E) onboard Aqua satellite and for Special Sensor Microwave Imager/Sounder (SSMIS) onboard F16 satellite of Defense Meteorological Satellite Program (DMSP) series. We compared simulated BT values with satellite BT measurements for different combinations of various water vapor and oxygen absorption models and wind induced ocean emission models. A dataset of clear sky atmospheric and oceanic parameters, collocated in time and space with satellite measurements, was used for the comparison. We found the best model combination, providing the least root mean square error between calculations and measurements. A single combination of models ensured the best results for all considered radiometric channels. We also obtained the adjustments to simulated BT values, as averaged differences between the model simulations and satellite measurements. These adjustments can be used in any research based on modeling data for removing model/calibration inconsistencies. We demonstrated the application of the model by means of the development of the new algorithm for sea surface wind speed retrieval from AMSR-E data.|
Zabolotskikh Elizaveta, Mitnik Leonid, Chapron Bertrand (2014). An Updated Geophysical Model for AMSR-E and SSMIS Brightness Temperature Simulations over Oceans. Remote Sensing, 6(3), 2317-2342. Publisher's official version : http://doi.org/10.3390/rs6032317 , Open Access version : http://archimer.ifremer.fr/doc/00190/30101/