Pre- analysis assessment of Sea Surface Temperature (SST) products in the region of Malaysian coastal water

8th IGRSM International Conference and Exhibition on Geospatial & Remote Sensing

地球科学;计算机科学

Aziz, M.A.H.^1 ; Omar, K.M.^1 ; Din, A.H.M.^1,2 ; Reba, M.N.M.^2

Geomatic Innovation Research Group (GnG), Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Johor, Johor Bahru

81310, Malaysia^1

Geoscience and Digital Earth Centre (INSTEG), Universiti Teknologi Malaysia, Johor, Johor Bahru

81310, Malaysia^2

关键词: Global distribution;    Intercomparisons;    Interpolated points;    Moderate resolution imaging spectroradiometer satellites;    Optimal interpolation;    Root mean square errors;    Sea surface temperature (SST);    Southwest monsoon;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/37/1/012017/pdf
DOI  :  10.1088/1755-1315/37/1/012017

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

This paper presents the pre-analysis of validation between the acquisition satellite data and in situ data. To carry out this assessment, Sea Surface Temperature (SST) data are acquired to be regressed with SST In situ. With the launch of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite with a sensor on the Terra spacecraft, data sets of the global distribution of sea surface temperature are retrieved, and need to be validated and analyzed. Radar Altimeter Database System (RADS) also has an archived data of Optimal Interpolation SST (OISST) that can be retrieved based on satellite track of altimeter. The aim of this paper is to present intercomparison study between pixel based (MODIS SST) and point based (RADS SST). The value of root mean square error (rmse) is computed to see the performance of the data product. It is an assessment and evaluation to see the performance for both data. The objective of this paper is to evaluate Malaysian coastal area through validation with in situ data. To achieve the objective, we perform pre-analysis study of the MODIS products and RADS SST to see the performance of both data in terms of spatial value during seasonal changes. However, the scope of this analysis covers only on the spatial MODIS pixel value and the OISST point value during the southwest monsoon daytime. From the result, RADS SST/RADS show higher root mean square error (rmse) at 0.731/0.677 (before calibration) and 0.6951/0.476 (after calibration). From the rmse result, we could deduce that the RADS SST has random error arising from the fact that the interpolated points are based on the track.