Title :
Retrieval of Sea Surface Temperature from AMSR-E and MODIS in the Northern Indian Ocean
Author :
Han Zhen ; Huo Wenjuan ; Wang Song
Author_Institution :
Coll. of Marine Sci., Shanghai Ocean Univ., Shanghai, China
Abstract :
The sea surface temperature (SST) is an important parameter of ocean environment. The remote sensing technology is an effective method to retrieve the sea surface temperature. In this paper, we studied the retrieval of sea surface temperature by using the brightness temperature data obtained from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the infrared data from the Moderate-resolution Imaging Spectroradiometer (MODIS) and in situ SST data from the Global Ocean Data Assimilation Experiment in the Northern Indian Ocean. The original brightness temperature data of the polarization channels from AMSR-E L2A and the original MODIS L1B thermal infrared data were preprocessed firstly, and then the retrieval model of AMSR-E SST was built on the multi-parameters linear regression, which based on the correlation of the AMSR-E brightness temperature and the in situ sea surface temperature. The MODIS SST was retrieved by Multichannel algorithm. Finally, we obtained the SST from the AMSR-E brightness temperature and MODIS SST by an AMSR-E and MODIS SST retrieval model developed by the multi-parameters linear regression. This retrieval model mainly relied on the AMSR-E brightness temperature while making the MODIS surface temperature subsidiary. Compared with the in situ SST, the root mean square error of retrieved result is 0.3240°C.
Keywords :
ocean temperature; oceanographic regions; radiometry; remote sensing; AMSR-E SST retrieval model; AMSR-E brightness temperature; AMSR-E observation; Advanced Microwave Scanning Radiometer-Earth Observing System; Global Ocean Data Assimilation Experiment; MODIS L1B thermal infrared data; MODIS observation; MODIS surface temperature subsidiary; Moderate-resolution Imaging Spectroradiometer; Northern Indian Ocean; SST data; infrared data; multichannel algorithm; multiparameter linear regression; ocean environment; original brightness temperature data; remote sensing technology; root mean square error; sea surface temperature; Brightness temperature; MODIS; Ocean temperature; Remote sensing; Sea surface; Temperature sensors;
Conference_Titel :
Remote Sensing, Environment and Transportation Engineering (RSETE), 2012 2nd International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-0872-4
DOI :
10.1109/RSETE.2012.6260714