Abstract
In order to evaluate the skin surface temperature (SSST) estimation accuracy with MODIS data, 84 of MODIS scenes together with the match-up data of NCEP/GDAS are used. Through regressive analysis, it is found that 0.305 to 0.417 K of RMSE can be achieved. Furthermore, it also is found that band 29 is effective for atmospheric correction (30.6 to 38.8% of estimation accuracy improvement). If single coefficient set for the regressive equation is used for all the cases, SSST estimation accuracy is around 1.969 K so that the specific coefficient set for the five different cases have to be set.
Highlights
The required skin sea surface temperature estimation accuracy is better than 0.25K for radiation energy budget analysis, global warming study and so on [1]
Skin sea surface temperature (SSST) is defined as the temperature radiation from the sea surface and is distinct with the Mixed layer sea surface temperature (MSST) based on the temperature radiation from the sea surface and is different from the Bulk sea surface temperature (BSST) which is based on the temperature radiation from just below the skin [2]
In order to estimate SSST, (a) atmospheric influences which are mainly due to water vapor followed by aerosols for the atmospheric window channels [3], (b) cloud contamination, (c) emissivity changes mainly due to white caps, or forms followed by limb darkening due to changes of path length in accordance with scanning angle changes should be corrected [4]-[8]
Summary
The required skin sea surface temperature estimation accuracy is better than 0.25K for radiation energy budget analysis, global warming study and so on [1]. One of the atmospheric corrections is split window method which is represented by Multi Channel Sea Surface Temperature (MCSST) estimation method [9]. Through a regressive analysis between the estimated SSST with acquired channels of satellite onboard thermal infrared radiometer (TIR) data and the corresponding match-up truth data such as buoy or shipment data (Bulk temperature) with some errors, all the required coefficients for the regressive equation are determined. RTE is expressed with Fred-Holm type of integral equation with a variety of parameters Such RTE can be solved with linear and/or Non-Linear inversion methods. The method for regressive analysis is described followed by the procedure of the preparation of match-up data derived from NCEP/GDAS and MODIS data together with cloud masking.
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