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The new generation photon-counting lidar on Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can obtain the subsurface optical properties of sea waters. Recent studies highlight the applications of deriving the lidar attenuation coefficient $K_{\mathrm {lidar}}$ and then substituting $K_{\mathrm {lidar}}$ into the bio-optical model to obtain more information of sea waters. As commonly used bio-optical models are built for the diffuse attenuation coefficient $K_{d}$ that are traditionally derived by passive ocean color sensors, whether $K_{\mathrm {lidar}}$ derived from ICESat-2 can be directly used as $K_{d}$ is a fundamental question. Given that $K_{d}$ is an apparent optical property (AOP) in the water column rather than an inherent optical property (IOP), $K_{d}$ is closely related to the zenith angle of the incident light. The zenith angle of the incident light of the sunlight is normally tens of degrees for ocean color sensors, while the maximum laser off-nadir angle is ~1.5° for the ICESat-2 lidar. To demonstrate this issue, we select hundreds of ground tracks of ICESat-2 in both open ocean and coastal sea waters and compare the derived $K_{\mathrm {lidar}}$ with their corresponding Moderate Resolution Imaging Spectroradiometer (MODIS)-derived $K_{d}$. The results indicate that the corrected results of $1.2\times K_{\mathrm {lidar}}$ , instead of the direct results of $K_{\mathrm {lidar}}$ , are more consistent with MODIS $K_{d}$. This study is of great significance to the better fusion of active lidar data and passive optical data in ocean observations. [ABSTRACT FROM AUTHOR]

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 Kd(λ) from remote sensing reflectance Rrs(λ) 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 Kd(λ) retrieval algorithms in UVR, the results showed that KpcaUV (with R2 : 0.970 and RMSE: 14.0%) performed similar to SeaUVc (with R2 : 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 Kd(380) for the continuous observation of the large area. [ABSTRACT FROM AUTHOR]

An optical algorithm is developed for the retrieval of the downwelling diffuse attenuation coefficient $K_{d}$ (490) in the Red Sea using a comprehensive hydrolight simulated data set ($N = 5000$). We found a robust relationship between the $K_{d}$ (490) and the ratio of remote sensing reflectance $R_{\text {rs}}$ (443)/ $R_{\text {rs}}$ (555), with an excellent determination coefficient ($R^{2} = 0.999$) and a low root-mean-square error (RMSE = 0.00033). The performance of the developed algorithm is evaluated with in situ data collected in the Red Sea by comparing obatined model output with existing empirical (NASA, Morel et al., Zhang and Fell, Tiwari and Shanmugam) and semianalytical (Lee et al.) algorithms. On the used in situ data from the Red Sea, the new algorithm shows good retrievals of $K_{d}$ (490) with a low bias, and a low RMSE compared to that of the existing algorithms. For satellite application, we applied our algorithm to selected MODIS–Aqua images acquired over the Red Sea, which captured spatial features of phytoplankton blooms and physical processes (e.g., cyclonic and anticyclonic circulations) in the Red Sea. The new algorithm has the potential to improve our understanding of water transparency and photosynthetic processes that rely on the availability of solar radiation. [ABSTRACT FROM AUTHOR]

Addressing critical earth science questions often requires time scales beyond the life of any single satellite sensor. Overlap between satellite-based datasets allows for the quantification of continuity (and discrepancies) between sensors. Toward that end, collocated matchups between Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imager Radiometer Suite (VIIRS) water clarity data from the Gulf of Mexico were analyzed at simultaneous, daily, and monthly time scales. Simultaneous data indicated strong agreement between sensors, with unbiased percent difference (UPD) generally less than 10% for both SeaWiFS/MODIS and VIIRS/MODIS matchups, with no apparent temporal trends. Spatially, UPD was highest near frontal boundaries and at high sensor zenith angles, while bias showed nearshore/offshore trends. UPD and bias statistics did not diminish for daily matchups; however, large degradation was seen for comparisons of monthly means between sensors, particularly SeaWiFS/MODIS matchups. Data coverage represented an important factor contributing to uncertainties in monthly mean data, as higher UPD was observed when fewer valid satellite measurements were recorded. Requiring a minimum of 15 samples per pixel per month minimizes the uncertainties in monthly mean products, with UPD between satellites roughly equivalent to that for simultaneous matchups. Overall, these findings demonstrate high consistency between three satellite instruments for most locations, while several “hot spots” of inconsistency are also revealed, which should be avoided in time-series studies. The findings also highlight the need to quantify uncertainties in often-used satellite products (particularly monthly mean composites) as well as the need to have a sufficient number of observations to assure the fidelity of monthly means. [ABSTRACT FROM PUBLISHER]

Despite the importance of diffuse attenuation coefficient of downwelling irradiance (K d) to physical, chemical and biological processes in the euphotic zone, accurate determination of this apparent optical property from satellite ocean color data for coastal oceanic waters is still challenging with most of the existing algorithms. This paper presents a simple and robust algorithm developed using the relationship (R² = 0.995) of the ratio of remote sensing reflectances at 490 and 670 nm to the K d at a wavelength of 490 nm derived from a large number of radiometric profiles from various regional waters. When tested with three independent data sets from relatively clear and turbid waters, accuracy of K d (490) prediction by the present algorithm is statistically good compared to that of the existing algorithms. The effectiveness of the present algorithm is also tested on MODIS-Aqua images from complex waters around India. It is found that the present algorithm makes better retrievals of Kd (490) capturing the spatial characteristics of bloom and turbid water features. These results suggest that the present algorithm has the potential to provide more accurate Kd (490) from satellite ocean color data for coastal oceanic waters. [ABSTRACT FROM PUBLISHER]

Remote estimation of the vertically averaged diffuse attenuation coefficient over a water layer \overlineKd(\lambda) is of great importance in understanding and modeling the physical, chemical, and biological processes in water bodies. A semianalytical algorithm was previously proposed to remotely estimate \overlineKd( \lambda) based on the retrieval of the total absorption and backscattering coefficients ( a and bb ). The algorithm has been effectively applied to clear and slightly turbid oceanic waters, but its applicability in turbid inland waters is still unknown. In this study, the relationship between a and bb on the one hand with \overlineKd(\lambda) used in the semianalytical algorithm was first validated by the measured a and bb. Second, the semianalytical algorithm was combined with the so-called QAA_Turbid (quasi-analytical algorithm for turbid waters) to remotely estimate \overlineKd(\lambda) for highly turbid waters. We tested the performance of the combined algorithm at wavelengths of 443, 556, and 669 nm by using a data set collected from a turbid lake in Japan. The validation results demonstrated that it could estimate \overlineKd(\lambda) (ranging from 1.94 to 9.47 \m^ - 1) with root-mean-square error and relative error values of 0.17 and 12.96%, respectively. These results indicate the great potential of the semianalytical algorithm to accurately monitor the spectra of \overlineKd(\lambda) for turbid inland waters from satellite observations. [ABSTRACT FROM PUBLISHER]