Sea surface Fresnel reflections difference driven de-glint algorithm for airborne optical images.

This paper presents a glint correction algorithm for high spatial resolution optical remote sensing imagery captured by the ER-2 Airborne Visual Infrared Imaging Spectrometer (AVIRIS). The algorithm employs linear and differential techniques to mitigate sun glint and sky glint effects, encompassing statistical glint reflections resulting from variations in imaging angles within strips and inter-strip variations due to Fresnel reflectance disparities. It aims to diminish Fresnel reflectance diversity on water surfaces and mitigate the distortions induced by glint reflectance during spectral and ocean color inversion. A comparative analysis of spectral and ocean color information in AVIRIS images before and after correction reveals enhanced accuracy following the glint correction. By systematically addressing multiple glint reflections and their ramifications, this method offers a valuable framework for correcting water surface glint in diverse high spatial resolution optical imagery.

Effect of viewing angle difference on spaceborne optical estimation of floating Ulva prolifera biomass in the Yellow Sea.

Optical remote sensing provides optimal technical support for the detection and quantification of floating macroalgae. Although the spatial scale effect on optical estimation of floating macroalgae coverage or biomass from different images has been clarified, the directional effect on them has not been investigated until now. In this study, synchronous multi-angle imaging spectroradiometer (MISR) and MODIS images were collected to investigate the multi-angle remote sensing of green tides. A dual thresholding method, based on the difference vegetation index (DVI) and scaled algae index, was employed to determine algae pixels. In addition, piecewise empirical models were developed for MISR and MODIS images to estimate the total biomass of green tides based on laboratory measurements and DVI values. Comparative analysis of DVI histograms and total biomass shows that the sensor zenith angle has a significant impact on the quantification of green tides. Under the same solar conditions, as the sensor zenith angle increases, the optical signals received from algae pixels weaken, resulting in a decrease in the quantification of green tides. In future research, the observation geometry (including the solar/sensor zenith angle and the solar/sensor azimuth angle) needs to be considered to improve the accuracy of optical remote detection and quantification of floating macroalgae.

Correction of multi-scale sunglint reflections from the water surface in airborne high-spatial resolution optical images.

Airborne optical images (AOI) are often with complex sunglint reflections, which brings a certain influence to watercolor retrieval. This includes the sunglint reflection with water surface statistical distribution characteristics caused by imaging viewing angles differences, with high spatial resolution surface discrete characteristics sharing similar viewing angles, and the surface Fresnel reflection sunglint differences caused by the skylight difference during the flight of unmanned aerial vehicles. Aiming at the multiscale optical characteristics of sunglint reflection in high spatial resolution AOI, based on multi-path optical radiation transmission, the sunglint reflection interference from three different imaging processes is clarified. We developed a correction method to eliminate these different sunglint reflections on water surfaces and improve the reflectivity accuracy. The comparison with the in situ measured remote sensing reflectance of water indicated that the root mean square error (RMSE) was reduced from 0.0009 sr-1 to 0.0004 sr-1, and the mean relative error (MRE) decreased from 21.8% to 15.7%. This method has also been applied to correct the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) images, showing good applicability. The method is fast, effective, and without auxiliary parameters, which provides a correction reference for different surface sunglint corrections of various AOI.

Ultraviolet remote sensing of marine oil spills: a new approach of Haiyang-1C satellite.

Haiyang-1C (HY-1C) is the first operational ocean color satellite of China, which is intended to obtain daily global ocean color data. The Ultraviolet Imager (UVI) onboard provides a potential novel detector for the detection of marine oil spills. Although airborne UV sensors have shown great efficiency for the detection of spilled oils, the capability of spaceborne UV sensor is not yet clear. In this study, we designed a ground-based experiment to interpret the UV characteristics of various weathered oils, and found that very thin oil films are quite sensitive to the UV radiation due to the surface interference light. Moreover, by comparing spaceborne and airborne UV images of spilled oils collected from HY-1C UVI and AVIRIS, the scale effect of ultraviolet remote sensing has been interpreted clearly. The interference light and sunglint reflection play different roles in the imaging process of spilled oils, leading them to appear radical different features (brighter or darker than the background oil-free seawater) in ground, airborne and spaceborne observation, which deserves further research. Ultraviolet remote sensing, therefore, can work as a new approach and improve the detection and monitoring of marine oil spills.

Uncertainty in the optical remote estimation of the biomass of Ulva prolifera macroalgae using MODIS imagery in the Yellow Sea.

A laboratory experiment was conducted to obtain a floating algae index (FAI) of the floating macroalgae (Ulva prolifera), corresponding to various values of biomass per unit area (BPA). A piecewise empirical model was used to fit the statistical relationships between BPA and FAI, corresponding to FAI ≤ 0.2 (BPA ≤ 1.81kg/m2) and FAI ˃ 0.2 (BPA ˃ 1.81 kg/m2). Spectral mixing derived results show that a linear relationship between FAI and BPA is maintained when the BPA of endmembers is less than 1.81 kg/m2. However, when the BPA of the endmembers exceeds 1.81 kg/m2, there is substantial uncertainty in the optical remote estimation of biomass. Although the MODIS-derived FAI of Ulva prolifera is often less than 0.2, it is very difficult to determine whether the FAI results from low BPA (≤ 1.81kg/m2) of the endmembers, or from a low area ratio including high BPA (˃ 1.81 kg/m2), due to pixel mixing. If it is assumed that the unit biomass distribution of pure endmembers is a standard Gaussian distribution, then the uncertainty in the biomass estimation of Ulva prolifera from MODIS data can be expressed. This results in the uncertainty of ~36% in total biomass estimation, ~43% of which was contributed by a few pixels (10% of total pixels) with high FAI (˃ 0.05). The uncertainty in BPA caused by high FAI (˃ 0.05) pixels is about 7.2 times that for low FAI (≤ 0.05) pixels. In future research, the spatial distribution characteristics of the FAI of pure endmembers need to be considered in order to improve the accuracy of optical remote estimation of floating Ulva prolifera.

Optical quantification of oil emulsions in multi-band coarse-resolution imagery using a lab-derived HSV model.

Oil emulsions can harm marine and coastal environments for extended periods. Timely identification and quantification of oil emulsions are essential for oil spill response. Although SAR is the most commonly used technique in detecting oil presence, it has limits in oil quantification. In contrast, optical remote sensing can fill this gap with more spectral bands. Hyperspectral remote sensing is capable of achieving this purpose. However, it is challenging to use multi-band coarse-resolution imagery due to the fewer bands and mixed pixel effect. Through laboratory measurements, numerical simulation, and Hue-Saturation-Value (HSV) model, we illuminate the multispectral mixed characteristics of oil emulsions and demonstrate Hue's role in characterizing the mixture features and oil concentration trends. Hue-based oil emulsion classification and oil concentration segmentation (OCS) methods are proposed and applied to Landsat-5 images under quantified uncertainties. This approach is expected to expand its application in multispectral remote sensing.