Assessment of Normalized Water-Leaving Radiance Derived From Goci Using AERONET-OC Data

The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric correction procedure

A Bidirectional Subsurface Remote Sensing Reflectance Model Explicitly Accounting for Particle Backscattering Shapes

The subsurface remote sensing reflectance (rrs, sr−1), particularly its bidirectional reflectance distribution function (BRDF), depends fundamentally on the angular shape of the volume scattering functions (VSFs, m−1 sr−1). Recent technological advancement has greatly expanded the collection, and the knowledge of natural variability, of the VSFs of oceanic particles. This allows us to test the Zaneveld\u27s theoretical rrs model that explicitly accounts for particle VSF shapes. We parameterized the rrs model based on HydroLight simulations using 114 VSFs measured in three coastal waters around the United States and in oceanic waters of North Atlantic Ocean. With the absorption coefficient (a), backscattering coefficient (bb), and VSF shape as inputs, the parameterized model is able to predict rrs with a root mean square relative error of ∼4% for solar zenith angles from 0 to 75°, viewing zenith angles from 0 to 60°, and viewing azimuth angles from 0 to 180°. A test with the field data indicates the performance of our model, when using only a and bb as inputs and selecting the VSF shape using bb, is comparable to or slightly better than the currently used models by Morel et al. and Lee et al. Explicitly expressing VSF shapes in rrs modeling has great potential to further constrain the uncertainty in the ocean color studies as our knowledge on the VSFs of natural particles continues to improve. Our study represents a first effort in this direction

An underwater image enhancement model for domain adaptation

Underwater imaging has been suffering from color imbalance, low contrast, and low-light environment due to strong spectral attenuation of light in the water. Owing to its complex physical imaging mechanism, enhancing the underwater imaging quality based on the deep learning method has been well-developed recently. However, individual studies use different underwater image datasets, leading to low generalization ability in other water conditions. To solve this domain adaptation problem, this paper proposes an underwater image enhancement scheme that combines individually degraded images and publicly available datasets for domain adaptation. Firstly, an underwater dataset fitting model (UDFM) is proposed to merge the individual localized and publicly available degraded datasets into a combined degraded one. Then an underwater image enhancement model (UIEM) is developed base on the combined degraded and open available clear image pairs dataset. The experiment proves that clear images can be recovered by only collecting the degraded images at some specific sea area. Thus, by use of the scheme in this study, the domain adaptation problem could be solved with the increase of underwater images collected at various sea areas. Also, the generalization ability of the underwater image enhancement model is supposed to become more robust. The code is available at https://github.com/fanren5599/UIEM

Comparison of Typhoon Locations over Ocean Surface Observed by Various Satellite Sensors

Abstract: In this study, typhoon eyes have been delineated using wavelet analysis from the synthetic aperture radar (SAR) images of ocean surface roughness and from the warm area at the cloud top in the infrared (IR) images, respectively. Envisat SAR imagery, and multi-functional transport satellite (MTSAT) and Feng Yun (FY)-2 Chinese meteorological satellite IR imagery were used to examine the typhoons in the western North Pacific from 2005 to 2011. Three cases of various typhoons in different years, locations, and conditions have been used to compare the typhoon eyes derived from SAR (on the ocean surface) with IR (at the cloud-top level) images. Furthermore, the best track data from the Joint Typhoon Warning Center (JTWC), Chinese Meteorological Administration (CMA), and the Japan Meteorological Agency (JMA) are checked for the calibration. Because of the vertical wind shear, which acts as an upright tilt, the location of the typhoon eye on the ocean surface differs from that at the top of the clouds. Consequently, the large horizontal distance between typhoon eyes on the ocean surface and on the cloud top implies that the associated vertical wind shear profile is considerably more complex than generally expected. This result demonstrates that SAR can be a useful tool for typhoon monitoring study over the ocean surface. Remote Sens. 2013, 5 317

Surface Upwelling off the Zhoushan Islands, East China Sea, from Himawari-8 AHI Data

The summer upwelling around the Zhoushan Islands is well-known. The previous concise review of (mostly) observational studies reveals that the present knowledge of the Zhoushan upwelling is unsatisfactory and has focused on seasonal variations. In this study, a sea surface temperature (SST) gradient-based upwelling detection algorithm was used. The Level 3 daily and hourly SST data from the geostationary satellite Himawari-8 were used to explore statistical features, seasonal variations, and short-term variations of the Zhoushan upwelling. Despite the duration period being like in previous studies, there is a new finding that the location of the upwelling center has a significant monthly migration. The statistical results show that the potential upwelling spots are clustered in the location with large topographic gradients and can be divided into four aggregation areas: between Gouqi Island and Lvhua Island, off Shengsi Island, around the Zhongjieshan Islands, and off the Taohua-Liuheng Islands. The core area of the Zhoushan upwelling is located at 122°E–123°E, 29.5°N–31.15°N with an irregular ellipse extending from southwest to northeast. The continuous cloud-free satellite images display that the lifecycle of the short-term variations was about 24 h and included two stages: intensification and decay. Meanwhile, the surface upwelling center has onshore–offshore movement under the advective transport of local tidal currents. A preliminary discussion suggests that the quasi-24 h periodic variations may be caused by the competing effect between tidal mixing and the stratification in the water column

Surface Upwelling off the Zhoushan Islands, East China Sea, from Himawari-8 AHI Data

The summer upwelling around the Zhoushan Islands is well-known. The previous concise review of (mostly) observational studies reveals that the present knowledge of the Zhoushan upwelling is unsatisfactory and has focused on seasonal variations. In this study, a sea surface temperature (SST) gradient-based upwelling detection algorithm was used. The Level 3 daily and hourly SST data from the geostationary satellite Himawari-8 were used to explore statistical features, seasonal variations, and short-term variations of the Zhoushan upwelling. Despite the duration period being like in previous studies, there is a new finding that the location of the upwelling center has a significant monthly migration. The statistical results show that the potential upwelling spots are clustered in the location with large topographic gradients and can be divided into four aggregation areas: between Gouqi Island and Lvhua Island, off Shengsi Island, around the Zhongjieshan Islands, and off the Taohua-Liuheng Islands. The core area of the Zhoushan upwelling is located at 122°E–123°E, 29.5°N–31.15°N with an irregular ellipse extending from southwest to northeast. The continuous cloud-free satellite images display that the lifecycle of the short-term variations was about 24 h and included two stages: intensification and decay. Meanwhile, the surface upwelling center has onshore–offshore movement under the advective transport of local tidal currents. A preliminary discussion suggests that the quasi-24 h periodic variations may be caused by the competing effect between tidal mixing and the stratification in the water column

A Robust Underwater Multiclass Fish-School Tracking Algorithm

State-of-the-art multiple-object tracking methods are frequently applied to people or vehicle tracking, but rarely involve underwater-object tracking. Compared with the processing in non-underwater photos or videos, underwater fish tracking is challenging due to variations in light conditions, water turbidity levels, shape deformations, and the similar appearances of fish. This article proposes a robust underwater fish-school tracking algorithm (FSTA). The FSTA is based on the tracking-by-detection paradigm. To solve the problem of low recognition accuracy in an underwater environment, we add an amendment detection module that uses prior knowledge to modify the detection result. Second, we introduce an underwater data association algorithm for aquatic non-rigid organisms that recombines representation and location information to refine the data matching process and improve the tracking results. The Resnet50-IBN network is used as a re-identification network to track fish. We introduce a triplet loss function based on a centroid to train the feature extraction network. The multiple-object tracking accuracy (MOTA) of the FSTA is 79.1% on the underwater dataset, which shows that it can achieve state-of-the-art performance in a complex real-world marine environment