Atmospheric correction of Landsat data for the retrieval of sea surface temperature in coastal water

来源 :Acta Oceanologica Sinica | 被引量 : 0次 | 上传用户:guider_zq
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A mono-window algorithm was introduced to retrieve sea surface temperature (SST) using Landsat data in coastal waters. In this algorithm, the effective mean air temperature and the water vapor content of air column were estimated with the local meteorological parameters of air temperature and relative humidity, based on the facts that in the troposphere, (1) air temperature decreases linearly with the altitude, and (2) water vapor content lapses exponentially with the altitude. The sea-truth temperature data and MODIS Terra SST product were used to validate the SST retrieved from Landsat TM and ETM+ thermal infrared (TIR) data with the algorithm. The results show that the algorithm can improve the spatial temperature contrast which is often masked due to water vapor effects, and the temperature derived from the algorithm is closer to the sea-truth SST. When applying the algorithm, the initial parameters of air temperature and relative humidity can be easily collected from local meteorological stations, and there is no need to identify the model of air profile. A this mono-window algorithm was introduced to retrieve sea surface temperature (SST) using Landsat data in coastal waters. In this algorithm, the effective mean air temperature and the water vapor content of air column were estimated with the local meteorological parameters of air temperature and relative humidity, based on the facts that in the troposphere, (1) air temperature decreases linearly with the altitude, and (2) water vapor content lapses exponentially with the altitude. The sea-truth temperature data and MODIS Terra SST product were used to validate the SST retrieved from Landsat ™ and ETM + thermal infrared (TIR) ​​data with the algorithm. The results show that the algorithm can improve the spatial temperature contrast which is often masked due to water vapor effects, and the temperature derived from the algorithm is closer to the sea-truth SST. When applying the algorithm, the initial parameters of air temperature and relative humidity can be easily collected from local meteoro logical stations, and there is no need to identify the model of air profile.
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