High-resolution optical satellite sensors, such as Multispectral Instrument (MSI) and the Operational Land Imager (OLI), show excellent performance in capturing fine-scale sea surface wave motion and spatial patterns. Their inter-band time lag offers the potential to resolve wave directional ambiguity and parameter estimation. Here, we investigate the spatial resolution and channel time lag of optical sensor, based on cross-spectral analysis of multi-channel imagery, to clarify their requirement in ocean wave monitoring. The analysis, validated by matched buoy data and optical imagery, demonstrates that the minimum detectable time lags for 180° directional ambiguity removal are 0.47s, 0.52s, and 0.74s for MSI 10 m, 20 m, and OLI 30 m resolution data, respectively. All three resolutions effectively detect wave system wavelengths ranging from 60 m to 300 m, with minimum detection limits of 20 m, 40 m, and 60 m, respectively. Additionally, the optimal statistics window size for consistent wave detection is about 8 km. These findings not only highlight the strengths of current satellite sensors but also provide references for future high-resolution optical sensor design in ocean wave monitoring.

Scatterometer wind data are critical for meteorological and oceanographic applications. The differences between scatterometer winds and buoy reference winds largely depend on the type of reference wind used in the fitting of the scatterometer’s geophysical model function (GMF). This study evaluates scatterometer winds using advanced buoy reference winds, specifically stress-equivalent winds (U10S), and equivalent-neutral winds (U10N). We utilized HSCAT-B scatterometer wind products retrieved using both NSCAT-4 and NSCAT-5 GMFs). NSCAT-4 winds were corrected to stress-equivalent winds, and these scatterometer winds were compared with various buoy reference winds, including true buoy winds, stress-equivalent winds, and equivalent-neutral winds. The results show that in extratropical regions, stress-equivalent winds provided a closer match to scatterometer winds, while in tropical regions, equivalent-neutral winds exhibited smaller errors. Scatterometer winds derived from the NSCAT-5 GMF demonstrated superior consistency with buoy reference winds, further reducing wind speed biases compared to NSCAT-4. An extended triple collocation (ETC) analysis was conducted to address the uncertainties arising from differences in spatial resolution between scatterometer and buoy measurements. The findings emphasize the importance of using appropriate wind correction methods for scatterometer wind validation, particularly in regions with significant atmospheric variability.

Ocean surface sunglint reflection is very important for detecting ocean surface roughness, oil spills, oceanic internal waves, and so on. Although statistical sunglint models (i.e., Cox–Munk model) have been used successfully on coarse-resolution spaceborne optical images for several decades, it is still a challenge to apply it to high spatial resolution images due to scale effects of the optical remote sensing. In this study, a sea wave model and the Pinhole camera model were employed to simulate the sunglint reflection images with different spatial resolutions and viewing angles. Coefficient of variation (CV) of sunglint reflection collected from various images indicates that there is uncertainty in sunglint reflection in images with different spatial resolutions. This study presents the applicability of sunglint statistical models in images with different spatial resolutions and viewing angles.

Smokes from biomass burning can contribute substantial amounts of hazardous substances and carbon to the atmosphere. These substances can be transported seaward and deposited on the ocean surface. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to map the relative smoke concentration over the ocean between November 8 and 11, 2018 from the recent California fires, with the ultimate goal of developing a generally applicable approach to map smokes over oceans. Because both biomass-burning smokes and clouds can produce strong backscattering signals, two key differences are used to separate them: 1) water-vapor absorption in certain wavelengths only occurs in clouds and 2) cumulus and cirrus clouds occur at different altitudes, therefore, bearing different thermal signatures. Based on these observations, a decision-tree method is developed to separate smokes from clouds. First, MODIS top-of-atmosphere (TOA) reflectance at 936 nm is used to detect both clouds and smokes over oceans. Then, brightness temperature derived from the 9730-nm band is used to separate cirrus from others. Finally, a water absorption depth (WAD) index is used to distinguish cumulus clouds from smokes, whose relative concentration in each image pixel is estimated from the MODIS TOA reflectance at 859 nm. Such derived smoke distribution and concentration are validated using concurrent Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) data, which provide the fine mode aerosol optical thickness (AOT) of smokes. Test of the approach over the recent Australia fires shows promising results, suggesting that the approach might be implemented by operational agencies to monitor and quantify smokes from biomass burning on a routine basis.

Optical remote sensing is applied in the identification, classification, and quantification of weathered oil spills. The automatic detection of oil spills through optical imaging is yet a challenge, because various oils under different sunglint reflections have complex optical image characteristics. Generally, there are two types of weathered oil spills, namely, non-emulsified oil slicks (NEOS) and oil emulsions (OE), which show different image characteristics under various sunglint reflections. The Coastal Zone Imager (CZI) onboard China’s HaiYang-1C/D (HY-1C/D) satellites can provide multispectral images with high spatial resolution and wide coverage for operational monitoring of oil spills. In this study, we applied an adaptive dynamic detector incorporating a built in oil–water mixture distribution classifier, specifically for different sunglint reflections, to automatically extract oil spills from CZI images. The spatial heterogeneity distribution of various oil spills could be quantified using a novel separability index, and then, the optimal oil–water segmentation proportion and scale could be obtained. Oil spills are discriminated and extracted under different sunglint reflections, with the variable scale detector implemented by tiling sliding windows of classifiers on detection images, from which respective volumes are derived with lower uncertainties. This approach also uses spatial and spectral ancillary information to improve weathered oils extraction confidence. The results show stable variable-scale extraction accuracies of approximately 90% and 80% for NEOS and EO, respectively. Therefore, the spatio–spectral–distribution comprehensive feature provides a new approach for the automatic extraction of oil spills from optical remote sensing images.

The ability to detect oil spills remotely is important in marine environmental monitoring. The optical polarization remote sensing has the unique advantage of inversion of refractive index of spilled oils which is the key parameter for calculation of sunglint reflectance. Compared to nonpolarization optical image, the degree of linear polarization (DOLP) of spilled oil's sunglint depends on the refractive index and viewing angles but not on the surface roughness. Accurate correction of sunglint reflectance can promote optical estimation of spilled oils. In this article, a polarized optical model was used to calculate equivalent refractive index of Deepwater Horizon (DWH) spilled oils using space-borne Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) images covering Gulf of Mexico (GOM) in 2010. When the angle (θm) between the direction of the flat surface specular reflection and that of observation is less than 20°, the PARASOL-derived and modeled DOLPs agree well, and the atmospheric polarization effects can be neglected. The equivalent refractive index of the spilled oil area, which implies the relative proportions of seawater and spilled oil in each pixel, could be estimated using polarized remote sensing under sunglint. Furthermore, if the relationship between the equivalent refractive index and remote sensing reflectance (Rrs) of spilled oils in the remote sensing images can be given, it might be used to correct the sunglint effect on various spilled oils, thereby leading to an improvement for optical quantifying spilled oil volume.

Thermal remote sensing is an effective technique for marine oil slick detection. However, many factors, such as the oil type, slick thickness, sensor capability, and the background environment, can together have an impact on the remotely sensed thermal imagery. These cross-coupling effects can usually be clarified by ground-based experiments. In this letter, four different types of oil slicks on water bodies were prepared and their brightness temperatures (BTs) measured periodically in an outdoor experiment. The results indicated that there are obvious differences in the BTs between the different types of oil, especially between crude and refined oil. Defined BT time-changing contrast coefficient of different type of oil slicks numerically displays these significant difference in different observed periods. These results imply that thermal sensors may be used to discern the type of oil slick and that time series of thermal observations will be able to help with oil-type detection in the future. Moreover, the optimal strategy is to make a series of observations covering the cooling period from noon (the optimal detection time) to around sunset.