JMSE, Vol. 11, Pages 743: Application of Synthetic DINCAE−BME Spatiotemporal Interpolation Framework to Reconstruct Chlorophyll−a from Satellite Observations in the Arabian Sea

JMSE, Vol. 11, Pages 743: 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 doi: 10.3390/jmse11040743

Authors: Xiting Yan Zekun Gao Yutong Jiang Junyu He Junjie Yin Jiaping Wu

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° N and 56–76° 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.