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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. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) is one of the main greenhouse gases leading to global warming, and the ocean is the largest carbon reservoir on the earth that plays an important role in regulating CO2 concentration on a global scale. The column-averaged dry-air mole fraction of atmospheric CO2 (XCO2) is a key parameter in describing ocean carbon content. In this paper, the Data Interpolation Empirical Orthogonal Function (DINEOF) and the Bayesian Maximum Entropy (BME) methods are combined to interpolate XCO2 data of Orbiting Carbon Observatory 2 (OCO-2) and Orbiting Carbon Observatory 3 (OCO-3) from January to December 2020 occurring within the geographical range of 15–45°N and 120–150°E. At the first stage of our proposed analysis, spatiotemporal information was used by the DINEOF method to perform XCO2 interpolation that improved data coverage; at the second stage, the DINEOF-generated interpolation results were regarded as soft data and were subsequently assimilated using the BME method to obtain improved XCO2 interpolation values. The performance of the synthetic DINEOF–BME interpolation method was evaluated by means of a five-fold cross-validation method. The results showed that the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Bias of the DINEOF-based OCO-2 and OCO-3 interpolations were 2.106 ppm, 3.046 ppm, and 1.035 ppm, respectively. On the other hand, the MAE, RMSE, and Bias of the cross-validation results obtained by the DINEOF–BME were 1.285 ppm, 2.422 ppm, and −0.085 ppm, respectively, i.e., smaller than the results obtained by DINEOF. In addition, based on the in situ measured XCO2 data provided by the Total Carbon Column Observing Network (TCCON), the original OCO-2 and OCO-3 data were combined and compared with the interpolated products of the synthetic DINEOF–BME framework. The accuracy of the original OCO-2 and OCO-3 products is lower than the DINEOF–BME-generated XCO2 products in terms of MAE (1.751 ppm vs. 2.616 ppm), RMSE (2.877 ppm vs. 3.566 ppm) and Bias (1.379 ppm vs 1.622 ppm), the spatiotemporal coverage of XCO2 product also improved dramatically from 16% to 100%. Lastly, this study demonstrated the feasibility of the synthetic DINEOF–BME approach for XCO2 interpolation purposes and the ability of the BME method to be successfully combined with other techniques. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is an important oceanography attribute that has been used to study ocean climatic conditions, ocean dynamics and air-sea interactions. In the present work, the Bayesian maximum entropy (BME) method was used to interpolate a FY-3 C /VIRR satellite dataset in the region with coordinates 120°-150°W and 45°-60°S and during January 2020. A novel approach of constructing valuable soft data was developed by combining BME interpolation with highly correlated SST day and night data differences. The BME interpolation accuracy was assessed by cross-validation, and the results showed that the average RMSE (root mean squared error) was 0.700 and the average bias was 0.441°C. Furthermore, using the Argo data as a basis of comparison, the coverage and accuracy of the BME interpolation of the FY-3 C/VIRR satellite SST dataset were compared numerically with those of the Ordinary Kriging (OK) interpolation of the FY-3 C/VIRR satellite SST dataset and the Optimum Interpolation Sea Surface Temperature (OISST) of the AVHRR satellite SST dataset. It was found that BME had the best SST interpolation performance among the three methods with the lowest average bias and the largest correlation coefficient. Although OISST had a full product coverage rate overall (due to its use of more perfect treatment means), BME's coverage rate (97.5%) considerably improved that of the FY-3 C/VIRR SST data. Also, both the BME and OK products maintained a 12hrs temporal resolution and a 0.05 decimal degrees longitude/latitude spatial resolution, which is an improvement over OISST data with a 24hrs time resolution and a 0.25 decimal degrees longitude/latitude spatial resolution. Another advantage of BME is that because of its broad theoretical support its performance in practice can be improved further as more knowledge sources become available (which can only be incorporated by BME). [ABSTRACT FROM AUTHOR]