Improvement of Atmospheric Correction of Satellite Sentinel-3/OLCI Data for Oceanic Waters in Presence of Sargassum

The invasive species of brown algae Sargassum gathers in large aggregations in the Caribbean Sea, and has done so especially over the last decade. These aggregations wash up on shores and decompose, leading to many socio-economic issues for the population and the coastal ecosystem. Satellite ocean color data sensors such as Sentinel-3/OLCI can be used to detect the presence of Sargassum and estimate its fractional coverage and biomass. The derivation of Sargassum presence and abundance from satellite ocean color data first requires atmospheric correction; however, the atmospheric correction procedure that is commonly used for oceanic waters needs to be adapted when dealing with the occurrence of Sargassum because the non-zero water reflectance in the near infrared band induced by Sargassum optical signature could lead to Sargassum being wrongly identified as aerosols. In this study, this difficulty is overcome by interpolating aerosol and sunglint reflectance between nearby Sargassum-free pixels. The proposed method relies on the local homogeneity of the aerosol reflectance between Sargassum and Sargassum-free areas. The performance of the adapted atmospheric correction algorithm over Sargassum areas is evaluated. The proposed method is demonstrated to result in more plausible aerosol and sunglint reflectances. A reduction of between 75% and 88% of pixels showing a negative water reflectance above 600 nm were noticed after the correction of the several images.

Retrieval and Uncertainty Analysis of Land Surface Reflectance Using a Geostationary Ocean Color Imager

Land surface reflectance (LSR) is well known as an essential variable to understand land surface properties. The Geostationary Ocean Color Imager (GOCI) be able to observe not only the ocean but also the land with the high temporal and spatial resolution thanks to its channel specification. In this study, we describe the land atmospheric correction algorithm and present the quality of results through comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data for GOCI-II. The GOCI LSR shows similar spatial distribution and quantity with MODIS LSR for both healthy and unhealthy vegetation cover. Our results agreed well with in-situ-based reference LSR with a high correlation coefficient (>0.9) and low root mean square error (<0.02) in all 8 GOCI channels. In addition, seasonal variation according to the solar zenith angle and phenological dynamics in time-series was well presented in both reference and GOCI LSR. As the results of uncertainty analysis, the estimated uncertainty in GOCI LSR shows a reasonable range (<0.04) even under a high solar zenith angle over 70°. The proposed method in this study can be applied to GOCI-II and can provide continuous satellite-based LSR products having a high temporal and spatial resolution for analyzing land surface properties.

Red Tide Detection Method for HY−1D Coastal Zone Imager Based on U−Net Convolutional Neural Network

Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium–high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium–high spatial resolution satellite data is required. This study proposes the red tide detection U−Net (RDU−Net) model by considering the HY−1D Coastal Zone Imager (HY−1D CZI) as an example. RDU−Net employs the channel attention model to derive the inter−channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi−feature dataset including the HY−1D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU−Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1−score and Kappa are 0.87. Compared with the existing method, the F1−score is improved by 0.07–0.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF−1 Wide Field of View 2 (WFV2) images.

Decadal Measurements of the First Geostationary Ocean Color Satellite (GOCI) Compared with MODIS and VIIRS Data

The first geostationary ocean color data from the Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) have been accumulating for more than ten years from 2010. This study performs a multi-year quality assessment of GOCI chlorophyll-a (Chl-a) and radiometric data for 2012–2021 with an advanced atmospheric correction technique and a regionally specialized Chl-a algorithm. We examine the consistency and stability of GOCI, Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) level 2 products in terms of annual and seasonal climatology, two-dimensional frequency distribution, and multi-year time series. Overall, the GOCI agrees well with MODIS and VIIRS on annual and seasonal variability in Chl-a, as the central biological pattern of the most transparent waters over the western North Pacific, productive waters over the East Sea, and turbid waters over the Yellow Sea are reasonably represented. Overall, an excellent agreement is remarkable for western North Pacific oligotrophic waters (with a correlation higher than 0.91 for Chl-a and 0.96 for band-ratio). However, the sporadic springtime overestimation of MODIS Chl-a values compared with others is notable over the Yellow Sea and East Sea due to the underestimation of MODIS blue-green band ratios for moderate-high aerosol optical depth. The persistent underestimation of VIIRS Chl-a values compared with GOCI and MODIS occurs due to inherent sensor calibration differences. In addition, the artificially increasing trends in GOCI Chl-a (+0.48 mg m−3 per 9 years) arise by the decreasing trends in the band ratios. However, decreasing Chl-a trends in MODIS and VIIRS (−0.09 and −0.08 mg m−3, respectively) are reasonable in response to increasing sea surface temperature. The results indicate GOCI sensor degradation in the late mission period. The long-term application of the GOCI data should be done with a caveat, however; planned adjustments to GOCI calibration (2022) in the following GOCI-II satellite will essentially eliminate the bias in Chl-a trends.

From Validation Statistics to Uncertainty Estimates: Application to VIIRS Ocean Color Radiometric Products at European Coastal Locations

Uncertainty estimates are needed to assess ocean color products and qualify the agreement between missions. Comparison between field observations and satellite data, a process defined as validation, has been the traditional way to assess satellite products. However validation statistics can provide only an approximation for satellite data uncertainties as field measurements have their own uncertainties and as the validation process is imperfect, comparing data potentially differing in temporal, spatial or spectral characteristics. This study describes a method to interpret in terms of uncertainties the validation statistics obtained for ocean color remote sensing reflectance RRS knowing the uncertainties associated with field data. This approach is applied to observations collected at sites part of the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) located in coastal regions of the European seas, and to RRS data from the VIIRS sensors on-board the SNPP and JPSS1 platforms. Similar estimates of uncertainties σVRS (term accounting for non-systematic contributions to the uncertainty budget) are obtained for both missions, decreasing with wavelength from the interval 0.8–1.4 10−3 sr−1 in the blue to a maximum of 0.24 10−3 sr−1 in the red, values that are at least twice (but up to 8 times) the uncertainties reported for the field data. These uncertainty estimates are then used to qualify the agreement between the VIIRS products, defining the extent to which they agree within their stated uncertainty. Despite significant biases between the two missions, their RRS products appear fairly compatible.

An Objective Method with a Continuity Constraint for Improving Surface Velocity Estimates from the Geostationary Ocean Color Imager

Mapping surface currents with high spatiotemporal resolution over a wide coverage is crucial for understanding ocean dynamics and associated biogeochemical processes. The most widely used algorithm for estimating surface velocities from sequential satellite observations is the maximum cross-correlation (MCC) method. However, many unrealistic vectors still exist, despite the utilization of various filtering techniques. In this study, an objective method has been developed through the combination of MCC and multivariate optimum interpolation (MOI) analysis under a continuity constraint. The MCC method, with and without MOI, is applied to sequences of simulated sea surface temperature (SST) fields with a 1/48° spatial resolution over the East China Sea continental shelf. Integration of MOI into MCC reduces the average absolute differences between the model’s ‘actual’ velocity and the SST-derived velocity by 19% in relative magnitude and 22% in direction, respectively. Application of the proposed method to Geostationary Ocean Color Imager (GOCI) satellite observations produces good agreement between derived surface velocities and the Oregon State University (OSU) regional tidal model outputs. Our results demonstrate that the incorporation of MOI into MCC can provide a significant improvement in the reliability and accuracy of satellite-derived velocity fields.

Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning

Remote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90–120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sunglint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20–30 angles across all bands (i.e., five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected.

Evolution of Ocean Color Atmospheric Correction: 1970–2005

Retrieval of water properties from satellite-borne imagers viewing oceans and coastal areas in the visible region of the spectrum requires removing the effect of the atmosphere, which contributes approximately 80–90% of the measured radiance over the open ocean in the blue spectral region. The Gordon and Wang algorithm originally developed for SeaWiFS (and used with other NASA sensors, e.g., MODIS) forms the basis for many atmospheric removal (correction) procedures. It was developed for application to imagery obtained over the open ocean (Case 1 waters), where the aerosol is usually non-absorbing, and is used operationally to process global data from SeaWiFS, MODIS and VIIRS. Here, I trace the evolution of this algorithm from early NASA aircraft experiments through the CZCS, OCTS, SeaWiFs, MERIS, and finally the MODIS sensors. Strategies to extend the algorithm to situations where the aerosol is strongly absorbing are examined. Its application to sensors with additional and unique capabilities is sketched. Problems associated with atmospheric correction in coastal waters are described.

Light propagation, coefficient attenuation, and the depth of one optical depth in different water types

The ocean color satellite can only sense a water column up to one optical depth. However, literatur regarding the depth of one optical depth is very limited to none. This study aimed to determine light propagation, attenuation coefficient (Kd), and the depth of one optical depth in different water types. We used in situ data of downwelling irradiance (Ed) with depths taken using the instrument of submersible marine environmental radiometer (MER) in the northeastern gulf of mexico (NEGOM) in April 2000. We also used SeaWiFS data such as water leaving radience (Lw ), remote sensing refectance (Rrs), and chlorophyll-a concentration (Chla). The results showed that the light propagation pattern generally decreased with increasing depth. The reduction in light intensity with depth was very strong in the red wavelengths, lower in the green wavelengths, and the lowest in the blue wavelengths. In contrast, Kd values were generally found the lowest at the blue wavelengths, slightly increase at the purple and green wavelengths, and the highest at the red wavelengths. The depth of one optical depth in the case-1 waters was found as deep as 39.79 m (λ=475 nm), followed by intermediate water of 31.79 m (λ=475 nm), and in the case-2 waters of 16.08 m (λ=490 nm). Both Kd (490) in situ and modelled results showed a good correlation (r=0.83-0.84) and R2 values of 0.68-0.71.