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.

Improving sea surface floating matter identification from Sentinel-2 MSI imagery using optical radiative simulation of neighborhood difference.

The reflectance difference (ΔR) between a floating matter pixel and a nearby water reference pixel is a method of atmospheric radiation unmixing. This technique unveils target signals by referencing the background within the horizontal neighborhood. ΔR is effective for removing the mixed-pixel effect and partial atmospheric path radiance. However, other atmospheric interference sources in the difference pixel, including atmospheric extinction and sunglint, need to be clarified. To address these challenges, we combined in situ floating matter endmember spectra for simulation and Sentinel-2 Multispectral Instrument (MSI) sensors for validation. We focused on radiative transfer simulation of horizontal neighborhood and vertical atmospheric column, investigating the bilateral conversion of ΔR between bottom-of-atmosphere (BOA) and top-of-atmosphere (TOA) signals, and clarifying how the atmosphere affects the difference pixel (ΔR) and floating matter identification. Results showed that direct use of TOA ΔR works in discriminating algae from non-algae floating matters under weak sunglint, and is a suitable candidate for no bother with atmospheric correction, least uncertain, and wider coverage. And then, sunglint interference is also inevitable, whether serious or not.

Sunglint reflection facilitates performance of spaceborne UV sensor in oil spill detection.

Ultraviolet Imager (UVI) onboard Haiyang-1C/D (HY-1C/D) satellites has been providing ultraviolet (UV) data to detect marine oil spills since 2018. Although the scale effect of UV remote sensing has been preliminarily interpreted, the application characteristics of spaceborne UV sensors with medium spatial resolution in oil spill detection deserve further investigation, especially the role of sunglint in the process of detection. In this study, the performance of the UVI is thoroughly assessed by the following aspects: image features of oils under sunglint, sunglint requirement for spaceborne UV detection of oils, and the stability of the UVI signal. The results indicate that in UVI images, it is sunglint reflection that determines the image features of spilled oils, and the appearance of sunglint can strengthen the contrast between oils and seawater. Besides, the required sunglint strength in spaceborne UV detection has been deduced to be 10-3 - 10-4 sr-1, which is higher than that in the VNIR wavelengths. Moreover, uncertainties in the UVI signal can meet the demand to discriminate between oils and seawater. The above results can confirm the capability of the UVI and the critical role of sunglint in spaceborne UV detection of marine oil spills, and provide new reference for spaceborne UV remote sensing.

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.

Chronic oiling in global oceans.

Ocean oil slicks can be attributed to natural seepages or to anthropogenic discharges. To date, the global picture of their distribution and relative natural and anthropogenic contributions remains unclear. Here, by analyzing 563,705 Sentinel-1 images from 2014-2019, we provide the first global map of oil slicks and a detailed inventory of static-and-persistent sources (natural seeps, platforms, and pipelines). About 90% of oil slicks were within 160 kilometers of shorelines, with 21 high-density slick belts coinciding well with shipping routes. Quantified by slick area, the proportion of anthropogenic discharges was an order of magnitude greater than natural seepages (94 versus 6%), in contrast to the previous estimate quantified by volume during 1990-1999 (54 versus 46%). Our findings reveal that the present-day anthropogenic contribution to marine oil pollution may have been substantially underestimated.

HY-1C ultraviolet imager captures algae blooms floating on water surface.

Some species of algae such as cyanobacteria can vertically migrate through water during a day, which is a notable floating feature of harmful algae blooms. To date, this process has been observed and quantified using visible and near-infrared (VNIR) bands from spaceborne sensors with high temporal resolution (i.e., Geostationary Ocean Color Imager; GOCI). In this study, we conducted an in-situ measurement at Taihu Lake in China to investigate the ultraviolet (UV) reflection spectra of floating cyanobacteria blooms, and identified that they have significant UV reflection features (higher than that of background water) associated with their floating status. This has been demonstrated using spaceborne UV images at both 355 and 385 nm from the Ultraviolet Imager (UVI) onboard Haiyang-1C (HY-1C) of China. Compared with synchronous optical images from the Chinese Ocean Color and Temperature Scanner (COCTS), we found that UVI has a special ability to distinguish cyanobacteria floating on water surface. Additionally, the intensity of the UV signals obtained is positively correlated with the cyanobacterial equivalent density. Ultraviolet remote sensing, therefore, can work as a new approach for the detection of harmful algae blooms and help determine the floating status of them, which deserves further research.

A novel deep learning method for marine oil spill detection from satellite synthetic aperture radar imagery.

Oil spill discharges from operational maritime activities like ships, oil rigs and other structures, leaking pipelines, as well as natural hydrocarbon seepage pose serious threats to marine ecosystems and fisheries. Satellite synthetic aperture radar (SAR) is a unique microwave instrument for marine oil spill monitoring, as it is not dependent on weather or sunlight conditions. Existing SAR oil spill detection approaches are limited by algorithm complexity, imbalanced data sets, uncertainties in selecting optimal features, and relatively slow detection speed. To overcome these restrictions, a fast and effective SAR oil spill detection method is presented, based a novel deep learning model, named the Faster Region-based Convolutional Neural Network (Faster R-CNN). This approach is capable of achieving fast end-to-end oil spill detection with reasonable accuracy. A large data set consisting of 15,774 labeled oil spill samples derived from 1786C-band Sentinel-1 and RADARSAT-2 vertical polarization SAR images is used to train, validate and test the Faster R-CNN model. Our experimental results show that the proposed method exhibits good performance for detection of oil spills with wide swath SAR imagery. The Precision and Recall metrics are 89.23% and 89.14%, respectively. The average Precision is 92.56%. The effects of environmental conditions and sensor parameters on oil spill detection are analyzed. The expected detection results are obtained when wind speeds and incidence angles are between 3 m/s and 10 m/s, and 21° and 45°, respectively. Furthermore, the computer runtime for oil spill detection is less than 0.05 s for each full SAR image, using a workstation with NVIDIA GeForce RTX 3090 GPU. This suggests that the present approach has potential for applications that require fast oil spill detection from spaceborne SAR images.

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.

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.

Temperature prediction of ultrasonic vibration-assisted milling.

Machining temperature is a key factor in ultrasonic vibration-assisted milling as it can significantly influence tool wear rate and residual thermal stresses. In current study, a physics-based analytical predictive model on machining temperature in ultrasonic vibration-assisted milling is proposed, without resorting to iterative numerical simulations. As the tool periodically loses contact with the workpiece under vibration, three types of tool-workpiece separation criteria are first examined based on the tool trajectory under ultrasonic vibration. Type I criterion examines whether the relative velocity between tool and workpiece in cutting direction is opposite to the tool rotation direction. Type II criterion examines whether the instantaneous vibration displacement in radial direction is larger than instantaneous uncut chip thickness. Type III criterion examines whether there is overlap between current and previous tool paths due to vibration. If no contact, the instantaneous temperature rise is zero. Otherwise, the temperature rise is predicted under shearing heat source in shear zone and secondary rubbing heat source along machined surface. A mirror heat source method is applied to predict temperature rise, considering oblique band heat sources moving in a semi-infinite medium. The proposed predictive temperature model in ultrasonic vibration-assisted milling is validated through comparison to experimental measurements on Al 6063 alloy. The proposed predictive model is able to match the measured temperature with high accuracy of 1.85% average error and 5.22% largest error among all cases. Sensitivity analysis is also conducted to study the influences of cutting and vibration parameters on temperature. The proposed model is valuable in terms of providing an accurate and reliable reference for the prediction of temperature in ultrasonic vibration-assisted milling.

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.

Refined use of AISA band-differences for oil slick identification beyond brightness contrast reversal under sunglint.

Marine oil slicks show brighter or darker than surrounding oil-free seawater under different sunglint, which can be observed by satellite optical sensors. Although this has been interpreted using a critical angle concept and simulated using the Cox-Munk model, it has not been demonstrated in high spatial resolution images from airborne sensors. In this study, an AISA (airborne imaging spectrometer for applications) image was used to determine the characteristics of non-emulsion oil slicks under sunglint in high spatial resolution images. Although a similar positive or negative contrast between non-emulsion oil slicks and oil-free seawater can be observed, it is difficult to directly model sunglint reflectance due to the different remote sensing scale effect. There are many sun glitter pepper noise produced by various micro-mirror facets of ocean surface in high spatial resolution images. Based on the optical image characteristics, a normalized noise index (ξ) was designed to evaluate the pepper noise in 1830 band-difference results. Then a level segmentation method was used to delineate the oil slicks under various sunglint from a minimum pepper noise image. Our study provides a preliminary reference for airborne optical remote sensing of oil slicks under various levels of sunglint.

[Study on the Distinguishment of Camouflage Paints Based on Depolarization Characteristics].

As an important part in the modern warfare, camouflage technology plays a critical role in the battlefield, and the results of detection of camouflage target directly affect the results of war. However, there is little paper to detect camouflage paint by depolarization characteristics, so it is of great significance to use the depolarization technology to study the distinguishment of camouflage paints. To address this issue, we studied the mechanism of the scattering of electromagnetic wave, and analysed the relationship between the characteristics of depolarization and mechanism of scattering. Jones Matrix and Mueller Matrix were used to set up the physical model, and the Mueller-Jones Matrix was decomposed with the characteristics of polarization, then the depolarization coefficients(ωd) of the surfaces of the samples was acquired. In this experiment, we measured soil and three kinds of camouflage yellow paints in seven different incident angles to analyze the characteristics of depolazation of the soil and three kinds of camouflage yellow paints' surfaces. Finally, we applied the theory of Fresnel formulas to verify the theoretical model. The results showed that: the depolarization coefficients of the samples' surfaces were related to the scattering, and with the increase of the incident angles, the depolarization coefficients were decline. But in the whole measurement process, the depolarization coefficients of the soil were far above the camouflage paints'. Research indicated that: this article was the first paper which used the depolarization coefficients as an important parameter to identify the camouflage targets, and could identify the camouflage yellow paints in the soil-background accurately and effectively. The processes of the experiments were simpler, and the time was shorter. In modern battlefield, it could identify the camouflage targets quickly and easily, and furnish the precious time for the victory of the war. Therefore, the depolarization technology had a great application value, and the paper had very important significance on the development of camouflage recognition technology.

Paths correlation matrix.

Both the Jones and Mueller matrices encounter difficulties when physically modeling mixed materials or rough surfaces due to the complexity of light-matter interactions. To address these issues, we derived a matrix called the paths correlation matrix (PCM), which is a probabilistic mixture of Jones matrices of every light propagation path. Because PCM is related to actual light propagation paths, it is well suited for physical modeling. Experiments were performed, and the reflection PCM of a mixture of polypropylene and graphite was measured. The PCM of the mixed sample was accurately decomposed into pure polypropylene's single reflection, pure graphite's single reflection, and depolarization caused by multiple reflections, which is consistent with the theoretical derivation. Reflection parameters of rough surface can be calculated from PCM decomposition, and the results fit well with the theoretical calculations provided by the Fresnel equations. These theoretical and experimental analyses verify that PCM is an efficient way to physically model light-matter interactions.

Analyzing the effects of particle size on remotely sensed spectra: a study on optical properties and spectral similarity scale of suspended particulate matters in water.

Particle size is an important factor for determining the concentration of suspended particle matter (SPM) in water using optical remotely sensed data. We collected reflectance spectra of five SPM samples with different particle sizes in a controlled laboratory experiment using a spectroradiometer. The theoretical relationship between particle size distributions and backscattering coefficient was deduced based on a spectral reflectance model. The backscattering coefficient of the complete SPM sample can be computed using the linear weighted combination of four percentages of different subsamples. The spectral similarity scale results indicate the optimal optical bands and boundary conditions for particle size and concentration of SPM remote sensing. The particle size can be evaluated by optical remote sensing to improve the applicability and precision of remote sensing models for SPM concentration inversion.

An optical remote sensing model for estimating oil slick thickness based on two-beam interference theory.

Oil slick thickness was an important parameter for estimating oil spill volume. Two-beam interference theory could be used to interpret the behavior of reflected and refracted light in oil slick. A quantitative relationship between thickness and spectral reflectance of oil slick could be established based on this theory. Some parameters have the properties of numerical oscillation and can be ignored in practical application. In addition, numerical approximation results showed that two parameters of the relationship were closely related to the spectral reflectance of background water and the thick oil slick. Therefore, a practical model for estimating oil slick thickness could be derived and proved to be consisted with theoretical relationship.

[Spectral response analysis of offshore thin oil slicks].

Hyperspectral data of thin oil slicks have some spectral response characteristics, and an experiment of offshore thin oil slicks was designed to measure and analyze their reflectance by using ASD hyperspectral instrument. With the oil slick thickness changing, its color varied from rainbow sheen slick to fuchsine sheen slick, kelly sheen slick, aqua sheen slick, silver sheen slick and light sheen slick. The result suggested that different thin oil slicks have different characteristics, some thin oil slicks could change the reflectance at 440 nm which is the spectral absorption peak value of chlorophyll, and the best hyperspectral band to distinguish the different offshore thin oil slicks is located in the range between 350 and 440 nm. The reflectance of thin oil slicks is higher than that of seawater in the hyperspectral data range from 440 to 900 nm, but has no absorption or reflection characteristics compared to seawater's. It could not be detected in the range of near infrared from 900 to 2,500 nm of offshore thin oil slicks because of high noise, low signal and influence of the atmosphere. Finally, the spectral response theory of offshore thin oil slicks was analyzed, and the results indicated that the interference phenomenon of offshore thin oil slicks could add reflected light and change the reflectance of seawater, hence, increase the feasibility of offshore oil slick remote sensing.