Publications

Yan, XT; Gao, ZK; Jiang, YT; He, JY; Yin, JJ; Wu, JP (2023). Application of Synthetic DINCAE-BME Spatiotemporal Interpolation Framework to Reconstruct Chlorophyll-a from Satellite Observations in the Arabian Sea. JOURNAL OF MARINE SCIENCE AND ENGINEERING, 11(4), 743.

Abstract
Chlorophyll-a (Chl-a) concentration is an indicator of phytoplankton pigment, which is associated with the health of marine ecosystems. A commonly used method for the determination of Chl-a is satellite remote sensing. However, due to cloud cover, sun glint and other issues, remote sensing data for Chl-a are always missing in large areas. We reconstructed the Chl-a data from MODIS and VIIRS in the Arabian Sea within the geographical range of 12-28 degrees N and 56-76 degrees E from 2020 to 2021 by combining the Data Interpolating Convolutional Auto-Encoder (DINCAE) and the Bayesian Maximum Entropy (BME) methods, which we named the DINCAE-BME framework. The hold-out validation method was used to assess the DINCAE-BME method's performance. The root-mean-square-error (RMSE) and the mean-absolute-error (MAE) values for the hold-out cross-validation result obtained by the DINCAE-BME were 1.8824 mg m(-3) and 0.4682 mg m(-3), respectively; compared with in situ Chl-a data, the RMSE and MAE values for the DINCAE-BME-generated Chl-a product were 0.6196 mg m(-3) and 0.3461 mg m(-3), respectively. Moreover, DINCAE-BME exhibited better performance than the DINEOF and DINCAE methods. The spatial distribution of the Chl-a product showed that Chl-a values in the coastal region were the highest and the Chl-a values in the deep-sea regions were stable, while the Chl-a values in February and March were higher than in other months. Lastly, this study demonstrated the feasibility of combining the BME method and DINCAE.

DOI:
10.3390/jmse11040743

ISSN:
2077-1312