This paper addresses the fault-tolerant trajectory tracking control problem of Euler-Lagrange (EL) systems subject to actuator faults and component faults (including system uncertainties). A novel finite-time Second-Order Sliding Mode Observer with Nonlinear feedback (SOSMON) incorporating adaptive fault estimators is proposed. Unlike linear feedback or complex sliding mode designs, the proposed SOSMON’s nonlinear feedback terms effectively handle the unknown high-order nonlinear terms in EL systems. To reconstruct the two faults, an adaptively weighted Radial Basis Function Neural Network (RBF NN) technique is introduced to estimate component faults without prior knowledge of fault characteristics, and an Adaptive actuator Fault Estimator (AFE) is designed. Then, accurately estimated faults allow for the simple design of a Sliding Mode Fault Tolerance Controller (SMFTC). Finite-time convergence of the observer and closed-loop stability are proved via Lyapunov theory. Finally, comparative simulations on an Unmanned Surface Vessel (USV) demonstrate the superiority of the proposed method over conventional observers and the Back Propagation (BP) NN-based estimator regarding estimation accuracy and convergence speed.

The 532-nm green laser light commonly used for airborne laser bathymetry (ALB) can penetrate clear shallow water but is sensitive to turbidity, which could lead to water surface uncertainty. In this study, water surface uncertainty was quantitatively assessed using a photon-counting LiDAR (PCL) with high receiver sensitivity to analyze the effect of turbidity. The qualitative results showed that the water surface heights are generally underestimated, and the surface detection accuracy in turbid water is superior to that in clear water. These findings were confirmed by statistical analysis of representative data in quantitative empirical experiments. The diffuse attenuation coefficient as a metric of water turbidity ranged from 0.14 to 4.80$\text{m}^{-1}$for clear to turbid water. The corresponding underestimated deviation ranged from 0.37 to 0.08 m, and the root-mean-square error (RMSE) was ranged from 0.39 to 0.06 m. In addition, the radiative transfer mechanism underlying the underestimation of water surface heights at different levels as water turbidity varies was determined by comparing the simulated and measured results. On the one hand, there is a high exponential relationship between the underestimation deviation and the diffuse attenuation coefficient when considering only the water optical properties. On the other hand, the presence of direct reflection component from the surface actually has an inhibiting effect on the underestimation. The present study provides reliable evidence for further understanding the interaction of green lasers with the air–water interfaces.

Airborne bathymetry LiDAR, such as Mapper5000, experienced varied water-bottom-return intensities. Under the high bottom-return signal condition, owing to the clear water body, high bottom reflectance, low depth, or high-gain setting of the detector, the peak power of the received signal from the bottom often exceeds the linear dynamic range of the 532-nm detector assembly. The resulting signal saturation distorted the recorded bottom-return waveforms and resulted in undesired depth bias exceeding 2.0 m in certain extreme cases. In this study, we developed a low-complexity peak position restoration method for this “saturation in-water range bias” correction based on an intersection point of double linear approximations of the leading and trailing edges of the saturated bottom waveform double line intersection (DLI). The validation of corrected results with consistent sonar depth measurements indicated that the DLI method demonstrated considerable robustness to the effect of pulse stretching under saturation conditions and reduced the mean absolute depth bias from 1.46 to 0.47 m. Compared with Gaussian and exponentially modified Gaussian (EMG) fitting algorithms, the DLI was faster and the most effective at reducing depth bias under strong saturation conditions. The success of DLI benefited from the small width of the transmitted laser pulse and the sharp slope of the leading edge of the saturated bottom waveform. However, the interference of volume backscattering signals at shallow depths could affect the performance of DLI, which should be carefully removed.

In recent years, various coral reef retrieval methods have experienced considerable progress due to a variety of observational instruments and innovative parameter calculation techniques. However, these labor-intensive methods are deficient in handling high-precision remote sensing mapping of coral reef benthic environments, facing challenges, including enhancing the robustness of scaled coral reef retrieval against varying water depths and complex water column conditions. To overcome these limitations, we propose a novel method for coral reef benthic mapping. Our method primarily consists of two central components: water depth extraction and coral reef information acquisition. Accurate bathymetry is critical for coral reef remote sensing inversion. To obtain more precise water depth, we propose the Bathymetry Transformer model. Our Bathymetry Transformer model aggregates vast amounts of active and passive remote sensing data, generates accurate bathymetry (with a 0.375-m RMSE, ranging from 0- to 12-m water depth) that eliminates the need for in situ examinations, and maintains a harmonious balance between greater bathymetry precision and finer spatial resolution. Based on this, water column corrections are then applied, and the results derived from various active and passive remote sensing processes, along with their respective band calculation outcomes, are fed into the proposed coral reef Transformer (CR Transformer) model to generate high-accuracy coral reef benthic mapping results. Copious experimental outcomes affirm that our CR Transformer surpasses current state-of-the-art (SOTA) methods in computational efficiency and results accuracy. Impressively, the CR Transformer achieves a notable mean intersection over union (mIoU) of 91.25% and an accuracy of 95.71% on the validation dataset.

Unmanned aerial vehicle (UAV)-borne laser scanning systems using photon-counting technology are applied to high-resolution water surface mapping with high efficiency. Affected by vast noise photons in raw data, the detection of surface photons from a weak reflective target like water still faces challenges in low signal-to-noise ratio (SNR) application scenarios. Noise filtering of raw data and surface detection from possible signals are two essential steps for water surface detection. In this letter, a water surface height retrieval algorithm is investigated for characterizing terrain and surface height. The proposed algorithm implements multilevel filtering to minimize noise photons and subsequently extracts the topmost boundary points as water surface photons using a modified alpha-shape to derive the water level elevation. Noise filtering results show that the multilevel filtering approach is effective in preserving signal photons integrity at low SNR. Moreover, the accuracy assessment further substantiates the robustness of the methodology in calm waters, and the root mean square error (RMSE) for the estimated water surface height was 0.02 m compared with percentile heights. Our algorithm provides an efficient solution for high-resolution water surface mapping in UAV-borne photon-counting LiDAR (PCL).

Current efforts for improving the hyperspectral optimization processing exemplar (HOPE) model include further testing of remote-sensing reflectance (Rrs) features containing useful information for bathymetry retrieval via the minimization of the interference stemming from the variability in inherent optical properties and benthic reflectance. In this paper, we found a novel feature originating from the pure water absorption within the narrow spectral region of 570–600 nm. In most coastal regions of clear water, for example, in a coral reefs environment, pure water accounts for the majority of the total absorption in this spectral range. In addition to the depth variation, the spectral behavior of Rrs(570–600) is primarily dominated by a steep increase in pure water absorption with wavelength, whereas the influence of other optical properties such as phytoplankton/CDOM absorption, particle backscattering, and benthic reflectance can be simplified using the spectrally constant shape model. A HOPE pure water (HOPE-PW) algorithm using this feature was developed based on Rrs measurements with a spectral resolution of near 3.5 nm, wherein only four unknown parameters must be resolved. The validation from LiDAR data and comparison with HOPE-BRUCE using PRISM data at 15 sites located in five distinct regions of Palau, Guam, Great Barrier Reef, Hawaiian Islands and Florida Key, confirmed that the HOPW-PW yielded a considerable performance and provided adequate transferability to other sites with varying bottom and water environments. The sensitivity analysis based on Hydrolight-simulated datasets showed that HOPE-PW was less effected by bottom type variations but still had limitations in retrieving water optical properties.

Significant range differences were identified between shallow and deep channels of the Mapper5000 bathymetry light detection and ranging (LiDAR) system with segmented field-of-view (FOV) receivers. Range difference varied with depth and water optical properties. The main feature was the maximum value in range difference curves, which ranged from 0.3 to 0.6 m and usually exceeded the International Hydrographic Organization (IHO) accuracy standards. Sensitivity analyses based on a semianalytical Monte Carlo simulation model revealed that the scattering phase function and laser beam divergence angle played more important roles in causing pulse dispersion and determining the amplitude and position of maximum range difference than absorption and scattering coefficients. A range difference correction method by fitting existing shallow and deep channel data in the overlapping range with a cubic polynomial was proposed to correct the deep channel data in the entire depth range that LiDAR can detect. Depth discontinuity at the junction of the shallow channel and deep channel measurements was successfully removed, and the mean and standard deviation of corrected range differences were within 0.01 and 0.1 m, respectively. A combination of range difference correction and mean bias corrector can be an alternative method for depth bias correction of segmented-FOV LiDAR when referenced sonar data is not available.

The data quality of the satellite-retrieved water-leaving reflectance (Rrs) depends on the accuracy of radiometric calibration and the performance of atmospheric correction. A radiometric calibration scheme (RCS) has been developed to ensure the accuracy of Rrs through the gain adjustment factors (GAFs) to adjust the satellite calibrated data. The GAF is obtained from the ratio of the simulated reflectance at the top of atmosphere to the calibrated values. The simulated reflectance is computed by a satellite image simulation model (SISM) based on a dataset of climatological global Rrs images according to the same geometric angles of the image pixels. The dataset, taken as a kind of the pseudo-invariant calibration sites for in situ measurements, is generated from the average of standard satellite-retrieved Rrs during more than two decades (1997–2019). The SISM inputs the aerosol properties retrieved from the satellite level 1B data (L1B) and uses the same algorithms of the data-processing system. The results show that the accuracy of the calibration of the website downloaded Chinese Ocean Color and Temperature Scanner on the Haiyang-1C satellite (COCTS/HY-1C) is beyond the requirement of the operational data-processing system (higher than 10%). The daily GAFs can be used to recalibrate the L1B data and monitor the daily sensor degradations. The influences of GAFs are assessed on different meteorological conditions, indicating that the values decrease with the increase of the aerosol optical depths (AODs) but the average of the GAF image is little affected by the meteorological conditions. The uncertainty of GAFs was tested by the different inputs of Rrs values and the results show that they are actually little affected by errors of the Rrs inputs. Therefore, the RCS, taking the advantage of vicarious calibration, offers a tool to recalibrate the COCTS/HY-1C L1B data for the data reprocessing system.

Oceanic lidar is an effective tool for detecting water’s vertical structure. Cost-effective design of an oceanic lidar system and processing algorithms requires an effective lidar simulator. In this study, an oceanic lidar simulation tool was developed, which is available to the public (https://github.com/soedchen/OLE), and named the Oceanic Lidar Emulator (OLE). OLE is an improved semianalytic Monte Carlo-based lidar simulator, which has the capability of dealing with the physics of light propagating through wind-driven rough air–water interface into the ocean, being scattered by subsurface phytoplankton and reflected from the sea bottom, and returning to the lidar receiver. OLE has three main features: 1) it can deal with stratified water, while most existing lidar models can only be used for homogeneous water; 2) it can be applied to arbitrary scattering phase function (SPF) (e.g., Fournier–Forand or Petzold), while most existing lidar models can only use the Henyey–Greenstein SPF due to the difficulty of solving the inverse equation for the cumulative distribution function; and 3) it takes general consideration of lidar system observation geometry (e.g., receiver field of view, receiving aperture, altitude, incident angle, and so on) and environmental parameters (e.g., wind-driven rough water surface, vertical structure of water optical properties, and sea bottom albedo). We studied lidar extinction caused by multiple scattering and the effects of the SPF, rough sea surface, and stratified water, by comparing simulation results with measurements. These results demonstrated that our model is effective for oceanic lidar simulation.

The radiance received by satellite sensors viewing the ocean is a mixed signal of the atmosphere and ocean. Accurate decomposition of the radiance components is crucial because any inclusion of atmospheric signal in the water-leaving radiance leads to an incorrect estimation of the oceanic parameters. This is especially true over the turbid coastal waters, where the estimation of the radiance components is difficult. A layer removal scheme for atmospheric correction (LRSAC) has been developed to take the atmospheric and oceanic components as the layer structure according to the sunlight passing in the Sun-Earth-satellite system. Compared with the normal coupled atmospheric column, the uncertainty of the layer structure of Rayleigh and aerosols has a relatively small error with a mean relative error (MRE) of 0.063%. As the aerosol layer was put between Rayleigh and ocean, a new Rayleigh lookup table (LUT) was regenerated using 6SV (Second Simulation of a Satellite Signal in the Solar Spectrum, Vector version 3.2) based on the zero reflectance at the ground to produce the pure Rayleigh reflectance without the Rayleigh-ocean interaction. The accuracy of the LRSAC was validated by in situ water-leaving reflectance, obtaining an MRE of 6.3%, a root-mean-square error (RMSE) of 0.0028, and the mean correlation coefficient of 0.86 based on 430 matchup pairs over the East China Sea. Results show that the LRSAC can be used to decompose the reflectance at the top of each layer for the atmospheric correction over turbid coastal waters.

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.

Detecting the vertical profile of optical properties is an important task in the remote sensing of the upper ocean, especially for 3-D reconstruction. Ocean color remote sensing can only provide surface information, while the light detection and ranging (lidar) technique can provide depth-resolved data. Lidar can provide global-scale observations of the upper ocean for days and nights with minimal atmospheric correction errors. Unfortunately, due to the strong multiple scattering effects that occur when light propagates in seawater, the simple lidar equation may cause some deviations between the actual measurements and the simulation of the lidar signals. In this paper, we present a shipborne oceanic lidar, which was developed to detect the optical properties of seawater. For evaluating the performance of the lidar system, a Monte Carlo (MC) model was established to simulate lidar signals based on the simultaneous in situ inherent optical properties of seawater. The lidar measurements and the MC simulation can provide both the lidar signals and the retrieved lidar attenuation coefficient α. The results of the comparison indicate that the lidar-measured signals correspond well with the MC-simulated signals at different experiment stations in the Yellow Sea and at various receiving fields of view (FOVs). We also observed strong correlations between the lidar-measured α and MC-simulated α at different stations (r = 0.95) and at various FOVs (r = 0.96). The results indicate the reliability of the developed lidar system.

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.

Polarization response is an important factor influencing the accuracy of radiance measurement for satellite ocean color sensors, which would change with on-orbit time. In this paper, a practical method is proposed for on-orbit estimation of polarization response. First, the linear polarization components of the Stokes vector entering the sensor are estimated by a vector radiative transfer model of the coupled ocean-atmosphere system. Second, the real radiance entering the sensor is estimated by another high-accuracy ocean color sensor using the cross-calibration method. Finally, based on the estimated linear polarization components and real radiance, the polarization response coefficients are derived by the least squares method. The proposed method is tested by applying it to the Moderate Resolution Imaging Spectroradiometer on board the Aqua satellite, and the derived polarization factors are consistent with the prelaunch values, indicating the reliability of the proposed method. In addition, our results reveal that the contribution of aerosol scattering should be included in the estimation of the linear polarization components of the Stokes vector at the top of atmosphere, particularly for long wavelengths.

This paper presents a new approach to multiscale segmentation of satellite multispectral imagery using edge information. The Canny edge detector is applied to perform multispectral edge detection. The detected edge features are then utilized in a multiscale segmentation loop, and the merge procedure for adjacent image objects is controlled by a separability criterion that combines edge information with segmentation scale. The significance of the edge is measured by adjacent partitioned regions to perform edge assessment. The present method is based on a half-partition structure, which is composed of three steps: single edge detection, separated pixel grouping, and significant feature calculation. The spectral distance of the half-partitions separated by the edge is calculated, compared, and integrated into the edge information. The results show that the proposed approach works well on satellite multispectral images of a coastal area.