b b International Journal of Mathematics and M Computer Science, 16(2021), no. 1, 345–356 CS Reconstruction of Chlorophyll-a Data by Using DINEOF Approach in Sepanggar Bay, Malaysia Fatin Nadiah Binti Mohamed Yussof1, Normah Binti Maan1, Mohd Nadzri Bin Md Reba2 1Department of Mathematics Faculty of Science and Technology Universiti Teknologi Malaysia 81310 Skudai, Johor, Malaysia 2Faculty of Geoinformation and Real Estate Universiti Teknologi Malaysia 81310 Skudai, Johor, Malaysia email:
[email protected],
[email protected] (Received July 7,2020, August 7, 2020) Abstract Loss of spatial data with a long gap is a significant limitation for remote sensing analyses using satellite-based monitoring of oceanog- raphy. This limitation could not be ignored as it may affect the sub- sequent 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 wa- ter 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 vali- dation purposes. The results obtained show that the DINEOF model Key words and phrases: Chlorophyll-a, DINEOF, Spatial Long Gaps, Spatio-temporal Kriging. AMS (MOS) Subject Classifications: 62D10. ISSN 1814-0432, 2021, http://ijmcs.future-in-tech.net 346 F.