Retrieval of Ultraviolet Diffuse Attenuation Coefficients From Ocean Color Using the Kernel Principal Components Analysis Over Ocean

Underwater ultraviolet radiation (UVR), which plays a significant role in photobiological and photochemical processes, is one of the key factors in marine ecosystems. A new algorithm KpcaUV, based on kernel principal component analysis (KPCA) and multiple linear regression (MLR), was proposed in this study for the retrieval of the UVR diffuse attenuation coefficient $ {K_{\mathrm {d}}(\lambda)}$ from remote sensing reflectance $ {R_{\mathrm {rs}}(\lambda)}$ in the global ocean. KPCA can be applied in all areas that principal components analysis (PCA) can be used. More importantly, KPCA can help mapping data into high dimensions and reducing the nonlinearity between inputs and outputs, which will improve the performance and robustness of algorithms when deriving large dynamic ranges parameters. Compared with SeaUVc, which is one of the most successful $ {K_{\mathrm {d}}(\lambda)}$ retrieval algorithms in UVR, the results showed that KpcaUV (with $ {R^{2}}$ : 0.970 and RMSE: 14.0%) performed similar to SeaUVc (with $\boldsymbol {R^{2}}$ : 0.963 and RMSE: 15.6%) when implemented with high-quality data. Nevertheless, KpcaUV was more robust and consistent than SeaUVc when implemented on the satellite images with different levels of quality control. The RMSD of SeaUVc had a significant reduction from 26.8% (QA ≥ 0.6) to 12.7% (QA = 1.0), and the RMSD of KpcaUV varied less than SeaUVc from 14.6% (QA ≥ 0.6) to 10.1% (QA = 1). Hence, considering its good nonlinear-problem-solving ability and robustness when applied to multiple satellites, KpcaUV proposed by this study can be used to obtain ${K_{\mathrm {d}}(380)}$ for the continuous observation of the large area.