Ocean LiDAR technology is of high interest and particularly promising for ocean applications. However, its application in complex oceanic environments is often limited by traditional elastic scattering mechanisms. Most existing ocean LiDAR simulation techniques focus primarily on elastic scattering, with limited attention given to inelastic scattering. This article presents a novel semianalytical Monte Carlo (SAMC) simulation method that integrates photon tracking algorithms with polarization state simulation, incorporating nonelastic scattering processes—such as Raman scattering, Brillouin scattering, and fluorescence—and polarization effects. By combining analytical solutions with numerical LiDAR simulations, the proposed semianalytical method improves both the precision and efficiency of simulations. Additionally, the method constructs models for fluorescence, Raman scattering, Brillouin scattering, and polarization scattering. These models were used to conduct a detailed analysis of nonelastic scattering and polarization scattering echo signals in stratified water, as well as the effects of chlorophyll concentration on these signals. Compared to traditional MC methods, the semianalytical approach offers obvious advantages in computational efficiency and accuracy. The study also investigates the impact of multiple scattering, stratified water, particle size distribution, field of view (FOV), and spectral bandwidth on nonelastic and polarization scattering echo signals. It highlights the significant influence of multiple scattering on signal detection accuracy and the critical role of changes in optical properties within stratified water. Nonelastic and polarization scattering signals play a crucial role in ocean LiDAR research, and the SAMC model developed in this article offers new insights and tools for the understanding and simulation of these signals.

This paper presents a novel Semi-Analytical Monte Carlo (SAMC) method developed for simulating the radiative transfer of full-polarization marine LiDAR signals within complex marine environments. Traditional marine LiDAR techniques, which predominantly rely on the intensity and linear polarization of backscattered signals, encounter limitations in complex settings. To acquire more detailed particulate morphological information, full-polarization LiDAR was introduced by incorporating circular polarization observation capabilities in recent years. However, the comprehensive calculation of full Stokes vectors brings about several inherent drawbacks, such as computational inefficiency and slow convergence. To address these issues, the SAMC method combines photon tracing with semi-analytical full polarization state tracking. It innovatively introduces polarization direction and phase difference variables into the radiative transfer process, thereby enabling the simulation and modeling of circular polarization echo signals. Based on the SAMC model, this study carried out simulation experiments regarding the transmission of lasers with different initial polarization states in water bodies. The impacts of LiDAR parameters, chlorophyll concentration, particle size range, and solar background light on full-polarization backscattered signals were analyzed. The results indicate that changes in polarization states are closely related to chlorophyll concentration and particle size, and multiple scattering effects have a significant influence on full-polarization signals. Moreover, the study explored the influence of the receiver field of view on signal quality and the discrimination ability of full - polarization channels for particles of different sizes. The unique advantages of circular polarized light in resisting solar background interference were also analyzed. This research not only conducts an in-depth analysis of the underwater full-polarization light transmission mechanism but also provides scientific support for the calibration of full - polarization LiDAR backscattered signals. The capacity to distinguish between different particle sizes using full-polarized channels offers a promising approach for the optical remote sensing of marine particle size distributions. The circular polarization's resistance to solar interference and its ability to maintain signal integrity under bright sunlight conditions represent significant advantages for marine remote sensing applications.

Ocean lidar technology is pivotal for observing the vertical structure of marine ecosystems. However, the accuracy of conventional inversion algorithms is often constrained in optically complex waters, as they rely ona prioriassumptions for key parameters such as the lidar ratio (r), which is highly variable and difficult to determine. This issue, coupled with uncorrected multiple scattering effects, limits the retrieval accuracy. To address these limitations, this study introduces a robust framework for a novel shipborne single-photon lidar (SPL). The core of this framework is an innovative dual-iterative hybrid inversion algorithm that adapts to various water types by simultaneously optimizing both therand the logarithmic backscatter-to-attenuation ratio (η), thus eliminating the need for prior assumptions. Furthermore, a semi-analytical model was integrated to correct for multiple scattering, a major source of error. This framework was applied to a large-scale dataset of over 58,000 lidar profiles collected along a 2,500 km track around Hainan Island. The retrieved inherent optical properties (IOPs) revealed spatial patterns that were consistent with known oceanographic features. Validation against in situ measurements demonstrated excellent performance: the diffuse attenuation coefficient (Kd) achieved a correlation coefficient (R) of 0.89 and a mean absolute percentage deviation (MAPD) of 11.38%, while the particulate backscattering coefficient (bbp) yielded anRof 0.93 and aMAPDof 16.82%. The multiple scattering correction proved to be critical, reducing theMAPDof the retrievedKdagainst satellite data from 28.66% to 9.06%. Our findings establish that this advanced SPL system, coupled with our novel inversion framework, provides a validated, reliable, and effective tool for large-scale, high-resolution monitoring of subsurface ocean optical structures.

Oceanic polarization LiDAR is a powerful tool for profiling marine optical properties, yet traditional simulation frameworks often neglect polarization effects or require excessive computational resources. Our model departs from these methods by integrating a semi-analytical approach with full Stokes vector tracking, allowing for efficient and accurate simulation of polarization evolution in multiple scattered light fields, particularly the depolarization ratio, which encodes critical information about waterborne particles and scattering processes. However, conventional Monte Carlo (MC) models suffer from computational inefficiency and insufficient handling of polarization dynamics. In this study, we propose a novel semi-analytical Monte Carlo model that significantly enhances simulation efficiency while accurately modeling the multiple scattering of polarized light in seawater. By integrating stochastic and deterministic approaches, the model captures polarization evolution via Stokes vector transformations and Mie scattering theory. Our simulations explore the influence of LiDAR system parameters, and the model’s predictions are consistent with trends observed in previous empirical studies, providing a basis for future experimental validation, inherent optical properties (IOPs), and particle characteristics on the depolarization ratio. The results reveal that multiple scattering is the dominant mechanism driving depolarization and that scattering coefficients and particle size distributions have profound effects on polarization states. The proposed model provides a robust framework for interpreting polarized LiDAR signals and supports the development of inversion algorithms for marine particle characterization.

In recent years, oceanic profiling lidar has emerged as an essential tool for detecting seawater’s detailed vertical structure. The challenge, however, lies in the necessity to comprehend the impact of polarized multiple scattering on lidar signals. This aspect presents considerable difficulties when addressed using conventional Monte Carlo (MC) models due to their mathematical complexities associated with Muller matrix computations and time-intensive procedures. In response to this, our study introduces a unique semi-analytical method, amalgamating improved stochastic and analytical techniques to model polarized multiple-scattering lidar signals. A preliminary result indicates a thousandfold increase in operational efficiency in comparison to traditional MC models. Additionally, our method holistically contemplates the coupled effects of environmental parameters and the observation geometry of the lidar system on signals. Our research scrutinizes the footprint of multiple scattering on the time-resolved polarization state of lasers under variable environmental factors such as complex stratified structures, scattering phase functions, and particle size distribution in seawater, as well as differing lidar observation conditions like transmitter height, incident angle, field of view, and receiver detection area. Our findings reveal that multiple scattering significantly depolarizes the backscatter return from seawater particulate matter. We have pioneered the proposal of a quantitative correlation between oceanic lidar multiple scattering and depolarization ratio for the first time. Our methodology’s specific application is to enhance lidar inversion algorithms through the correction of multiple scattering from lidar depolarization measurements in future applications.

Satellite-derived bathymetry (SDB) methods have been traditionally hindered by the need for in situ reference bathymetric points. However, the light detection and ranging (LiDAR) instruments on the new ICESat-2 satellite have revolutionized SDB by providing high-precision reference bathymetric point cloud datasets (RBPCDs) in shallow water. While the density-based spatial clustering of applications with noise (DBSCAN) has been effective in photon cloud processing, it has been challenging to determine key parameters due to the complexity of terrain changes. Furthermore, ICESat-2 is unable to measure deep water depths greater than 50 m, which would be less efficient if it has to process the entire track data. To overcome these challenges, we have developed an adaptive ellipse denoising algorithm with adjustable key parameters and a shallow-water feature photon (SWFP) extraction method. These innovative techniques were applied to the Caribbean Sea and the South China Sea, resulting in impressive datasets consisting of 848 395 and 438 643 RBPCDs, respectively. The mean absolute error (MAE) of RBPCDs was found to be within 0.6 m, and the RBPCDs were consistent with in situ data. By combining RBPCDs with Sentinel-2 data using a neural network (NN)-based SDB method, we have created detailed bathymetry maps over 15 islands in the Caribbean Sea. Our adaptive method has great potential for large-scale nearshore RBPCD construction, and these RBPCDs will undoubtedly enhance SDB implementations in the future.

High-resolution ocean surface current observations over large areas are critical for understanding complex earth system processes and supporting marine applications. Several methods have been developed to retrieve surface currents by tracking the motion of tracers (e.g., sea surface temperature (SST) and chlorophyll-${a}$concentration) in continuous imagery. However, directly deriving surface currents from tracers is sensitive to data quality, often leading to numerous spurious vectors. To address the challenge, this study focuses on SST tracers and proposes an objective analysis method, named the optimal interpolation based on decorrelation scales analysis (DSOI), to estimate surface currents. We first investigate the decorrelation scales of global surface currents using 11 years of reanalysis data. The results show that the zonal decorrelation scale of the U component is typically between 1° and 2° (100–200 km), approximately twice the meridional decorrelation scale, while the V component is exactly the opposite. Based on these findings, we redesign the covariances between observation points of the optimal interpolation (OI) method and evaluate the performance of our novel method in optimizing surface currents using model and reanalysis data. Data from different areas, and times, with different initial methods, are investigated. The results show the performance of the DSOI method is stable, outperforming OI optimization in all cases. In various regional current systems with differing temperature gradients, the method effectively filters out erroneous vectors and enhances the accuracy of initial currents, particularly when relying on gradient-sensitive initial methods. Applying the DSOI method to reconstruct reanalysis of SST-derived currents aligns well with real observations, effectively resolving small-scale structures.

We present a novel method using satellite and biogeochemical Argo (BGC-Argo) data to retrieve the 3-D structure of chlorophyll$a$(Chla) in the northern Indian Ocean (NIO). The random forest (RF)-based method infers the vertical distribution of Chla using the near-surface and vertical features. The input variables can be divided into three categories: 1) near-surface features acquired by satellite products; 2) vertical physical properties obtained from temperature and salinity profiles collected by BGC-Argo floats; and 3) the temporal and spatial features, i.e., day of the year, longitude, and latitude. The RF-model is trained and evaluated using a large database including 9738 profiles of Chla and temperature-salinity properties measured by BGC-Argo floats from 2011 to 2021, with synchronous satellite-derived products. The retrieved Chla values and the validation dataset (including 1948 Chla profiles) agree fairly well, with$R^{2} = 0.962$, root-mean-square error (RMSE) = 0.012, and mean absolute percent difference (MAPD) = 11.31%. The vertical Chla profile in the NIO retrieved from the RF-model is more accurate and robust compared to the operational Chla profile datasets derived from the neural network and numerical modeling. A major application of the RF-retrieved Chla profiles is to obtain the 3-D Chla structure with high vertical resolution. This will help to quantify phytoplankton productivity and carbon fluxes in the NIO more accurately. We expect that RF-model can be used to develop long-time series products to understand the variability of 3-D Chla in future climate change scenarios.

L-band passive microwave remote sensing of ocean surfaces is hampered by uncertainties due to the contribution of precipitation. However, modeling and correcting rain effect are complicated because all the precipitation contributions from the atmosphere and sea surface, including the rain effect in the atmosphere, water refreshing, rain-perturbed sea surface, and rain-induced local wind, are coupled. These rain effects significantly alter L-band microwave satellite measurements. This study investigates the impact of precipitation on the satellite measured brightness temperature (TB) and proposes a correction method. The results show that the rain-induced TB increase is approximately 0.5–1.4 K in the atmosphere, depending on the incidence angle and polarization. Moreover, the rain effect on sea surface emissions is more significant than that in the atmosphere. The result shows that rain effects on sea surface emissions are higher than 3 K for both polarizations when the rain rate is higher than 20 mm/h. We validate the rain effect correction model based on Soil Moisture Active Passive (SMAP) observations. The results show that the TBs at the top of the atmosphere (TOA) simulated by the model are in a good agreement with the SMAP observations, with root-mean-square errors (RMSEs) of 1.137 and 1.519 K for the horizontal and vertical polarizations, respectively, indicating a relatively high accuracy of the established model. Then, a correction model is applied to sea surface salinity (SSS) retrieval for analysis, and the results show that the developed model corrects the underestimation in SSS retrieval. Finally, the rain effect correction model is validated with Aquarius observations in three regions, and it is found that the RMSEs of the corrected TOA TBs range from 1.013 to 1.608 K, which is higher than those without rain effect correction (RMSEs range from 2.078 to 3.894 K). Overall, the model developed in this study provides relatively a good accuracy for rain effect correction.

In this letter, we propose a downscaling approach to improve the spatial resolution of sea surface salinity (SSS) in estuarine areas using combined microwave and ocean color data. The model established a relationship between SSS and normalized sea surface emissivity and colored dissolved organic matter (CDOM). The model was validated byin situmeasurements conducted in the East China Sea (ECS) and Mississippi River Estuary (MRE). The model showed relatively good agreement within situSSS measurements and illustrated enhanced SSS at high (4 km) resolution compared with low (40 km) resolution, with root mean square errors (RMSEs) of 1.55 versus 2.58 psu in the ECS and 0.39 versus 1.49 psu in the MRE. Overall, the proposed downscaling approach enhances the spatial resolution and accuracy of satellite SSS observations over estuarine areas, which should be helpful for ocean dynamic and biogeochemical studies in these regions.

We present a statistical analysis of how residual error from satellite remote sensing reflectance ($R_{\mathrm {rs}}$) depends on the environmental factors. Our results indicate that image coverage affects the data quality, and the residual error in high-quality data correlates significantly with the residual error in data heavily contaminated with stray light. Due to imperfectly corrected bidirectional reflectance, we found many residual error anomalies in the covarying relationship between residual error and illumination–observation geometry even though high-quality$R_{\mathrm {rs}}$data appeared more homogeneous than low-quality data. Additionally, we found underestimates of$R_{\mathrm {rs}}$in very oligotrophic tropical waters, and these residual errors positively covaried with sensor zenith angles at the edge of the scans because of strong atmospheric multi-scattering effects. Furthermore, we found that due to their strong correlation with$R_{\mathrm {rs}}$, the spectral relationship of residual errors should be dynamically reinitialized with the image, which is key to an inherent optical properties (IOPs) data processing system (such as IDAS) removing the residual errors from the$R_{\mathrm {rs}}$data. Doing so, and applying the IDAS algorithm, stray-light-contaminated$R_{\mathrm {rs}}$was recovered, obtaining comparable results to those achieved considering high-quality measurements. This permits to relax the corresponding quality check for generation of the Level-3 global area coverage$R_{\mathrm {rs}}$, largely improving the satellite spatiotemporal coverage and consequently the product accuracy.

The polarization characteristics of water-leaving radiation, which contain rich information on oceanic constituents, have often been neglected. Due to the lack of suitable instruments and practical difficulties in removing strong contamination by polarized skylight, direct measurement of the polarization of water-leaving radiation remains a challenge. In this study, we designed an above-water instrument (named POLWR) to directly measure the polarization of water-leaving radiation and examined its field application in Qiandao Lake, China. Results showed that the Stokes components of water-leaving radiance ($L_{w}$) measured by POLWR were consistent with the radiative transfer (RT) simulations, with a determination coefficient ($R^{2}$) and mean relative error of 0.67 and 18.86%, respectively. The Qiandao Lake results revealed that the degree of polarization (DOP) of$L_{w}$varied from 0.05 to 0.5 within the 412–865-nm range. Moreover, a good relationship between the polarized remote sensing reflectance ($R_{\mathrm {rsp}}$), and DOP and chlorophyll-a (Chla) concentration was found at 368 nm in this productive lake, indicating great potential for the inversion of oceanic constituents from polarization signals. With its small size and direct measurement ability, the POLWR instrument should be widely applicable and could help improve our understanding of the polarization characteristics of water-leaving radiation and the underwater light field.

Fast and effective responses are required when a natural disaster (e.g., earthquake and hurricane) strikes. Building damage assessment from satellite imagery is critical before relief effort is deployed. With a pair of predisaster and postdisaster satellite images, building damage assessment aims at predicting the extent of damage to buildings. With the powerful ability of feature representation, deep neural networks have been successfully applied to building damage assessment. Most existing works simply concatenate predisaster and postdisaster images as input of a deep neural network without considering their correlations. In this article, we propose a novel two-stage convolutional neural network for building damage assessment, called BDANet. In the first stage, a U-Net is used to extract the locations of buildings. Then, the network weights from the first stage are shared in the second stage for building damage assessment. In the second stage, a two-branch multiscale U-Net is employed as the backbone, where predisaster and postdisaster images are fed into the network separately. A cross-directional attention module is proposed to explore the correlations between predisaster and postdisaster images. Moreover, CutMix data augmentation is exploited to tackle the challenge of difficult classes. The proposed method achieves state-of-the-art performance on a large-scale dataset—xBD. The code is available athttps://github.com/ShaneShen/BDANet-Building-Damage-Assessment.

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.

Sea surface salinity (SSS) retrieval from satellite-based microwave radiometer is hampered by uncertainties due to atmospheric and surface scattering contributions. This study presents a comprehensive vector radiative transfer model (VRTM) for estimation of brightness temperature (TB) (for SSS retrieval) from L-band radiometry based on a matrix-operator method. It includes an efficient two-scale model (TSM) that combines a geometrical optics (GO) model and a small-perturbation model (SPM) for computing both the small- and large-scale scattering components of the sea surface. Moreover, it considers the influence of rain effects on TB by including the radiation extinction term (scattering and attenuation). The simulation results using the VRTM were validated with those obtained from the RT4 model for flat sea surface conditions and the RTTOV model for rough sea surface conditions. The relative difference in the estimated TB among the models was small (<; 1%) for low wind speeds (<; 1 m/s) and increased up to 3% for high wind speeds and observation angles. Simulations on the influence of wind speed on TB with various parameterizations were further examined. Compared with SMOS-MIRAS and Aquarius measurements, the VRTM simulations agreed well with satellite measurements for both vertically and horizontally polarized TBs with biases of less than 2.2 K for observation angles from 20° to 65°. The binned TBs showed even better results, with a standard deviation of less than 1.45 K and an absolute mean error of less than 1.2 K.

Deep-learning-based methods, such as convolution neural network (CNN), have demonstrated their efficiency in hyperspectral image (HSI) classification. These methods can automatically learn spectral-spatial discriminative features within local patches. However, for each pixel in an HSI, it is not only related to its nearby pixels but also has connections to pixels far away from itself. Therefore, to incorporate the long-range contextual information, a deep fully convolutional network (FCN) with an efficient nonlocal module, named ENL-FCN, is proposed for HSI classification. In the proposed framework, a deep FCN considers an entire HSI as input and extracts spectral-spatial information in a local receptive field. The efficient nonlocal module is embedded in the network as a learning unit to capture the long-range contextual information. Different from the traditional nonlocal neural networks, the long-range contextual information is extracted in a specially designed criss-cross path for computation efficiency. Furthermore, using a recurrent operation, each pixel's response is aggregated from all pixels of HSI. The benefits of our proposed ENL-FCN are threefold: 1) the long-range contextual information is incorporated effectively; 2) the efficient module can be freely embedded in a deep neural network in a plug-and-play fashion; and 3) it has much fewer learning parameters and requires less computational resources. The experiments conducted on three popular HSI data sets demonstrate that the proposed method achieves state-of-the-art classification performance with lower computational cost in comparison with several leading deep neural networks for HSI.

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.

A new segmentation approach for high-resolution remotely sensed imagery that combines the global edge and region information is developed from a new scheme to monitor the best conditions for each growing object to obtain the corresponding meaningful image object during multiscale analysis. The approach, which is an extension of the image object detection approach, includes new algorithms for determination of region-growing criteria, edge-guided image object detection, and assessment of edges. The method consists of two stages: In the first stage, edges are acquired from edge detection with embedded confidence and stored in an R-tree, and initial objects are segmented by eCognition and organized in the region adjacency graph; in the second stage, meaningful image objects are obtained by incorporating multiscale segmentation and analyzing the edge completeness curve. The evaluation results of edge completeness are obtained within the process of multiscale segmentation, and the assessment for the segmentation results shows its merit in coastal remote sensing. Images containing plenty of weak edges or distributing scene objects with various sizes and shapes can fully embody the strength of this method.

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.

In order to make improved use of the altimetry waveform data near the China coastal area, we present a new retracker (OceanCS) consists of waveform Classification and Sub-waveform extraction based on coastal altimetry waveform analysis and existing retracking algorithm comparison. The OceanCS retracker combines the advantages of Ocean retracker and OCOG retracker. The new retracker is further compared to other retrakers (Ocean, Ice-2, OCOG, Threshold, Beta5, and GDR product) using one year (Mar. 2006 to Feb. 2007) Jason1 measurement and in-situ tide gauge station Sea Surface Height and buoy Significant Wave Height measurements. Comparison shows that the OceanCS retracker performs better than other trackers. Furthermore, Calculating the SWH by combining two retrackers is a way to compare different retracking algorithms without taking into account the error of the atomosphere and sea state corrections.