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.
In estuarine and coastal ecosystems, the majority of previous studies have considered coupled nitrification-denitrification (CND) processes to be exclusively sediment based, with little focus on ...suspended particulate matter (SPM) in the water column. Here, we present evidence of CND processes in the water column of Hangzhou Bay, one of the largest macrotidal embayments in the world. Spearman's correlation analysis showed that SPM was negatively correlated with nitrate (rho = -0.372, P = 0.018) and marker genes for nitrification and denitrification in the water column were detected by quantitative PCR analysis. The results showed that amoA and nir gene abundances strongly correlated with SPM (all P < 0.01) and the ratio of amoA/nir strongly correlated with nitrate (rho = -0.454, P = 0.003). Furthermore, aggregates consisting of nitrifiers and denitrifiers on SPM were also detected by fluorescence in situ hybridization. Illumina MiSeq sequencing further showed that ammonia oxidizers mainly belonged to the genus Nitrosomonas, while the potential denitrifying genera Bradyrhizobium, Comamonas, Thauera, Stenotrophomonas, Acinetobacter, Anaeromyxobacter, Sulfurimonas, Paenibacillus and Sphingobacterium showed significant correlations with SPM (all P < 0.01). This study suggests that SPM may provide a niche for CND processes to occur, which has largely been missing from our understanding of nitrogen cycling in estuarine waters.
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 <inline-formula> <tex-math notation="LaTeX">\text{m}^{-1} </tex-math></inline-formula> 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.
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.
Defining highly variable freshwater plume area from space is important for characterizing the dynamics of biogeochemical properties and understanding the effects of climate change and human ...activities on plume‐related processes. The absorption coefficient of colored dissolved organic matter (aCDOM) from satellite ocean color data can be used to estimate the salinity and thus the plume area in coastal oceans if a robust conservative salinity and aCDOM relationship and an accurate satellite aCDOM algorithm can be established. In this paper, tight relationships between surface water salinity and in situ aCDOM were found during several cruises covering all seasons and the full salinity range in the East China Sea. Thus, a salinity inversion model from aCDOM was developed and validated with an independent data set, in which 73.6% of the data were within the absolute salinity error of ±1 and 87.1% were within ±1.5. Factors influencing the conservative behavior of colored dissolved organic matter are analyzed, with a particular focus on the effect of the phytoplankton‐induced autochthonous colored dissolved organic matter. In addition, several satellite aCDOM algorithms were compared and validated with our in situ data. Monthly satellite‐derived salinity images were mapped in August from 2008 to 2010 and showed the significant interannual variability in the plume coverage. This study demonstrated that the salinity derived from satellite‐derived aCDOM can provide a reliable and good synoptic view of the plume area, and help with biogeochemical studies, in particular, those properties related to the interannual variability of plume coverage, although the development of a localized satellite algorithm of aCDOM is still desirable.
Key Points
Relationship between salinity and CDOM was analyzed using nine cruises data
A salinity inversion model applicable to all seasons was developed in the ECS
Satellite CDOM algorithms were validated by in situ data in the ECS
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 complex scenarios with low signal-to-noise ratio (SNR). 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, 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.
The East China Sea (ECS) is well known for its high concentration of total suspended matter (TSM). Some regions of the ECS have concentrations higher than 5000gm−3, exceeding the valid ranges of many ...TSM remote sensing algorithms. To overcome the limitation of the existing algorithms, a new TSM model, the “complex proxy TSM model” (CPTSM), is developed in this study. The model is established on the basis of a complex proxy of remote sensing reflectance. The proxy is designed to convert the non-linear relationship between reflectance and TSM to a quasi-linear function over the entire range of TSM concentrations. This proxy is deduced from four indices defined by combinations of the reflectance at different bands. The four indices take advantage of the different relationships between the band combinations of the reflectance and total TSM concentrations. The band selections and model parameters are based on correlation coefficients and regression analysis between the indices and TSM. The results show that the correlation coefficient of 0.912 between the proxy and TSM is higher than that between any individual index and TSM. To validate the CPTSM model, TSM, turbidity, and reflectance data were collected in the ECS during 4 cruises in 2006 and 2007. The actual TSM concentration was measured by weighing the samples collected on filter papers. Turbidity was measured by a Seapoint Turbidity Meter. The turbidity data with values higher than 750FTU were re-calibrated using an empirical equation. All turbidity values were converted to TSM concentrations using a linear equation. The in situ reflectance was measured using the above-water method at 459 stations and the in-water method at 146 stations. A total of 87 pairs of reflectance measured by both methods were used for inter-comparison with a relative difference of 4.5%. The reflectance values were used to retrieve TSM concentrations using the CPTSM model. A comparison with in situ measurements gave a mean relative error of 23%. Applying the CPTSM model to the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data and analyzing the errors from a match-up dataset of SeaWiFS and in situ data, we found that the average relative error was 24.5%. We propose to use the CPTSM model to map TSM concentrations from satellite data in the ECS.
► A new remote sensing algorithm for total suspended matter (TSM) is developed. ► This algorithm combines a semi-analytical approach with empirical approaches. ► The algorithm is based on a complex proxy from four indices of reflectance. ► The performance is evaluated using a large number of in situ measurements. ► The algorithm is suitable for coastal waters with a wide range of TSM.
Conventional bathymetric inversion approaches require bathymetric data as ground truth to obtain shallow water depth from high spatial resolution remote sensing imagery. Thus, bathymetric mapping ...methods that do not require inputs from in situ measurements are highly desirable. In this paper, we propose a dual-band model improvement method and evaluate the performance of this novel dual-band model approach to obtain the underwater terrain around a coastal island by using four WorldView-2/3 imageries. Then, we validate the results through changing water column properties with the Kd multiple linear regression model simulated by Hydrolight. We multiply the best coefficient and blue–green band value with different substrates on the pixels, which sample along the coastal line and isobath. The results show that the mean bias of inversed depth ranges from 1.73 to 2.96 m in the four imageries. The overall accuracy of root mean square errors (RMSEs) is better for depths shallower than 10 m, and the average relative error is 11.89%. The inversion accuracy of this new model is higher than Lee’s classical Kd model and has a wider range of applications than Chen’s dual-band model. The no-ground-truth dual-band algorithm has higher accuracy than the other log-ratio methods mentioned in this paper.
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 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.
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