Reconstruction of chlorophyll-a data by using DINEOF approach in Sepanggar Bay, Malaysia

Abstract

Loss of spatial data with a long gap is a significant limitation for remote sensing analyses using satellite-based monitoring of oceanography. This limitation could not be ignored as it may affect the subsequent analysis and modeling of the data. Hence, this gap needs to be improved by filling the spatial gap in the satellite datasets. In this research, Data Interpolating Empirical Orthogonal Functions (DI-NEOF) is applied to fill the spatial gap and has successfully worked in the reconstruction of missing data of chlorophyll-a for monitoring harmful algal blooms (HABs) in Sepanggar Bay located at coastal water of Kota Kinabalu, Malaysia. The original chlorophyll-a pixels are used to assess the accuracy of the predicted data. Then, the DINEOF model is compared with the Spatio-temporal Kriging model for validation purposes. The results obtained show that the DINEOF model has the highest Pearson correlation coefficient, 0.9940 and the smallest values of Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD) which are 0.2770 and 0.0155 respectively. Therefore, this proved that the DINEOF model is more effective for filling spatial long gaps.