Evaluating the effectiveness of coastal environmental management policies in China: The case of Bohai Sea.

With marine pollution issues becoming serious and widespread, a series of coastal environmental managemental policies are being carried out worldwide, the effectiveness of which requires comprehensive evaluation. Taking the Bohai Sea (BS) of China as an example, which has been plagued by serious ecological and environmental issues for decades due to terrestrial pollution discharge, this study explored and quantified, for the first time to our best knowledge, the variability of water quality after initiating a dedicated 3-year pollution control action (Uphill Battle for Integrated Bohai Sea Management, UBIBM, 2018-2020) implemented by China's central government, with two water quality indexes of water color (Forel-Ule index, FUI) and transparency (Secchi disk depth, ZSD, m) from satellite observations. During the UBIBM, a significant improvement in water quality was detected, characterized by a clearer and bluer BS, with ZSD and FUI improved by 14.1% and 3.2%, respectively, compared with the baseline period (2011-2017). In addition, an abrupt drop in the long-term record (2011-2022) of the coverage area of highly turbid waters (ZSD≤2 m or FUI≥8) was found in 2018, which coincided with the start of the UBIBM, indicating that the water quality improvement may be attributed to the pollution alleviation of the UBIBM. Independent data of land-based pollution statistics also supported this deduction. (3) Compared with the previous two pollution control actions in the first decade of 21st century, UBIBM was proved to be the most successful one in terms of the achieved highest transparency and lowest FUI during the past two decades. Reasons for the achievement and implications to future pollution control are discussed for a more sustainable and balanced improvement in the coastal environment. This research provides a valuable example that satellite remote sensing can play a vital role in the management of coastal ecosystems by providing effective evaluation of pollution control actions.

Decade-low aerosol levels over the Bohai and Yellow Seas amid the COVID-19 lockdown.

Coronavirus disease 2019 (COVID-19) has crucially influenced anthropogenic activities, which in turn impacts upon the environment. In this study, we investigated the variations on aerosol optical depth (AOD) at 550 nm over the Bohai Sea and Yellow Sea during the COVID-19 lockdown (from February to March in 2020) of China mainland based on Moderate-resolution Imaging Spectroradiometer (MODIS) observation by comparing with historical AOD records (2011-2019). Our results show that with the lockdown implementation, the decade-low AOD levels are achieved in February and March 2020 (0.39 ± 0.18 and 0.37 ± 0.19, respectively), which are 22% and 28% (p < 0.01) lower than the average AOD between 2011 and 2019 (0.50 ± 0.08 and 0.52 ± 0.05, respectively). After the lockdown restrictions were relaxed and industrial production gradually resumed, the AOD in April 2020 rebounded to the historical average level. Besides, compared with historical observations (2011-2019), the AOD temporal variability from February to April 2020 showed different pattern, with the decade-high increase from March to April (+0.11) and decade-low increase from February to March (-0.01). Independent observations and simulation, including fine particulate matter (PM2.5) from ground-based measurements, wind field from Cross-Calibrated Multi-Platform, satellite-derived aerosol type, and back trajectories calculation by Hybird Single Paricle Lagrangian Intergrated Trajectory (HYSPLIT) model, indicated that the above abnormal AOD variation can be attributed to reduction of anthropogenic emissions during the COVID-19 lockdown period. The results of this paper, therefore, indicate that aerosols over the Bohai and Yellow Seas are strongly influenced by human activities, and the public health events such as the epidemic may alter the intensity of human activities and thus the spatio-temporal pattern of aerosol over ocean. With the global spread of the epidemic and the corresponding significant changes in human behavior patterns (restrictions on human activities, etc.), more studies should be carried out in the future about the aerosol variability and its potential impact on the marine environment.

Super-resolution optical mapping of floating macroalgae from geostationary orbit.

The spatial resolution of an observation from a geostationary orbiting satellite is usually too coarse to track small scale macroalgae blooms. For macroalgae mapping to benefit from a geostationary orbit's staring monitoring and frequent revisit intervals, we introduced a super-resolution method that reconstructs a high-resolution (HR) image of a region from a sequence of raw geostationary low-resolution images of the same region. We tested our method with GF-4 images at 50 m spatial resolution and demonstrated that the spatial resolution increased to 25 m. In addition, the derived HR image had better image quality characterized by a higher signal-to-noise ratio, clarity, and contrast. The increased spatial resolution and improved image quality improved our ability to distinguish macroalgae patches from the surrounding waters, especially tiny patches of macroalgae, and to precisely delineate the patch boundaries. Lastly, we more accurately estimated the areal coverage of the patches by reducing underestimation of the coverage of tiny patches and overestimation of the coverage of large patches.

Out-of-Band Response for the Coastal Zone Imager (CZI) Onboard China's Ocean Color Satellite HY-1C: Effect on the Observation Just above the Sea Surface.

The out-of-band (OOB) response is one of the key specifications for satellite optical sensors, which has important influences on quantitative remote sensing retrieval. In this paper, the effect of OOB response on the radiometric measurements made just above the sea surface is evaluated for the three broad visible bands (i.e., blue, green, and red) of the Coastal Zone Imager (CZI) onboard China's ocean satellite HY-1C to be launched in September 2018. For the turbid coastal (Case 2) waters whose optical properties are mainly dominated by suspended sediment and colored dissolved organic material, the OOB effect can be neglected (<2%) for all three CZI visible bands. For the phytoplankton-dominated (Case 1) waters which are mainly distributed in the clear open ocean, a significant (>2%) OOB effect was found in the green band over oligotrophic waters (chlorophyll a concentration ≤~0.1 mg/m³), and accordingly a model based on the CZI blue-green band ratio is proposed to correct this effect. The OOB influence on the CZI ocean color retrieval is discussed. This research highlights the importance of the comprehensive pre-launch radiometric characterization and the OOB effect correction for the broad band space-borne sensor, in order to achieve a high-quality quantitative ocean product.

A New Approach of Oil Spill Detection Using Time-Resolved LIF Combined with Parallel Factors Analysis for Laser Remote Sensing.

In hope of developing a method for oil spill detection in laser remote sensing, a series of refined and crude oil samples were investigated using time-resolved fluorescence in conjunction with parallel factors analysis (PARAFAC). The time resolved emission spectra of those investigated samples were taken by a laser remote sensing system on a laboratory basis with a detection distance of 5 m. Based on the intensity-normalized spectra, both refined and crude oil samples were well classified without overlapping, by the approach of PARAFAC with four parallel factors. Principle component analysis (PCA) has also been operated as a comparison. It turned out that PCA operated well in classification of broad oil type categories, but with severe overlapping among the crude oil samples from different oil wells. Apart from the high correct identification rate, PARAFAC has also real-time capabilities, which is an obvious advantage especially in field applications. The obtained results suggested that the approach of time-resolved fluorescence combined with PARAFAC would be potentially applicable in oil spill field detection and identification.

A multi-band semi-analytical algorithm for estimating chlorophyll-a concentration in the Yellow River Estuary, China.

In this study, two sample semi-analytical algorithms and one new unified multi-band semi-analytical algorithm (UMSA) for estimating chlorophyll-a (Chla) concentration were constructed by specifying optimal wavelengths. The three sample semi-analytical algorithms, including the three-band semi-analytical algorithm (TSA), four-band semi-analytical algorithm (FSA), and UMSA algorithm, were calibrated and validated by the dataset collected in the Yellow River Estuary between September 1 and 10, 2009. By comparing of the accuracy of assessment of TSA, FSA, and UMSA algorithms, it was found that the UMSA algorithm had a superior performance in comparison with the two other algorithms, TSA and FSA. Using the UMSA algorithm in retrieving Chla concentration in the Yellow River Estuary decreased by 25.54% NRMSE (normalized root mean square error) when compared with the FSA algorithm, and 29.66% NRMSE in comparison with the TSA algorithm. These are very significant improvements upon previous methods. Additionally, the study revealed that the TSA and FSA algorithms are merely more specific forms of the UMSA algorithm. Owing to the special form of the UMSA algorithm, if the same bands were used for both the TSA and UMSA algorithms or FSA and UMSA algorithms, the UMSA algorithm would theoretically produce superior results in comparison with the TSA and FSA algorithms. Thus, good results may also be produced if the UMSA algorithm were to be applied for predicting Chla concentration for datasets of Gitelson et al. (2008) and Le et al. (2009).

A refined deep-learning-based algorithm for harmful-algal-bloom remote-sensing recognition using Noctiluca scintillans algal bloom as an example.

Harmful algal blooms (HABs) are challenging to recognize because of their striped and uneven biomass distributions. To address this issue, a refined deep-learning algorithm termed HAB-Ne was developed for the recognition of HABs in GF-1 Wide Field of View (WFV) images using Noctiluca scintillans algal bloom as an example. First, a pretrained image super-resolution model was integrated to improve the spatial resolution of the GF-1 WFV images and minimize the impact of mixed pixels caused by the strip distribution. Side-window convolution was also explored to enhance the edge features of HABs and minimize the effects of uneven biomass distribution. In addition, a convolutional encoder-decoder network was constructed for threshold-free HAB recognition to address the dependence on thresholds in existing methods. HAB-Net effectively recognized HABs from GF-1 WFV images, achieving an average precision of 90.1% and an F1-score of 0.86. HAB-Net showed more fine-grained recognition results than those of existing methods, with over 4% improvement in the F1-Score, especially in the marginal areas of HAB distribution. The algorithm demonstrated its effectiveness in recognizing HABs in different marine environments, such as the Yellow Sea, East China Sea, and northern Vietnam. Additionally, the algorithm was proven suitable for detecting the macroalga Sargassum. This study demonstrates the potential of deep-learning-based fine-grained recognition of HABs, which can be extended to the recognition of other fine-scale and strip-distributed objects, such as oil spills and Ulva prolifera.

Retrieval of absorption and backscattering coefficients from HJ-1A/CCD imagery in coastal waters.

A simple semi-analytical model (SAB) was developed for computing a(560) and b(b)(550) from HJ-1A/CCD images. By comparison with field measurements, the SAB model produces 5.3-23.5% uncertainty for a(560) and b(b)(550) retrievals. The a(560) and b(b)(550) are also retrieved from satellite images. The match-up analysis results indicate that a(560) and b(b)(550) may be derived from the HJ-1A/CCD images with respective uncertainties of 29.84 and 21.35%. These findings imply that, provided that an atmospheric correction scheme for the green bands is available, the extensive database of HJ-1A/CCD imagery may be used for the quantitative monitoring of optical properties in coastal waters.

A semi-analytical total suspended sediment retrieval model in turbid coastal waters: a case study in Changjiang River Estuary.

A simple semi-analytical model to estimate total suspended sediment matter (3S) was established for estimating TSM concentrations in Changjiang River Estuary. The results indicate that 3S model with near-infrared wavelengths provide good estimates of TSM concentrations in the study region. Furthermore, the applicability of 3S model was evaluated using an independent data set taken from Oujiang river estuary during September 2012. The results indicate that providing an available atmospheric correction scheme for satellite imagery, the 3S model could be used for quantitative monitoring of TSM concentration in coastal waters, even though local bio-optical information is still needed to reinitialize the model.

Spectral variability of sea surface skylight reflectance and its effect on ocean color.

In this study, sea surface skylight spectral reflectance ρ(λ) was retrieved by means of the non-linear spectral optimization method and a bio-optical model. The spectral variability of ρ(λ) was found to be mainly influenced by the uniformity of the incident skylight, and a model is proposed to predict the ρ(λ) spectral dependency based on skylight reflectance at 750 nm. It is demonstrated that using the spectrally variable ρ(λ), rather than a constant, yields an improved agreement between the above-water remote sensing reflectance R(rs)(λ) estimates and concurrent profiling ones. The findings of this study highlight the necessity to re-process the relevant historical above-water data and update ocean color retrieval algorithms accordingly.

Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties.

Lidar techniques present a distinctive ability to resolve vertical structure of optical properties within the upper water column at both day- and night-time. However, accuracy challenges remain for existing lidar instruments due to the ill-posed nature of elastic backscatter lidar retrievals and multiple scattering. Here we demonstrate the high performance of, to the best of our knowledge, the first shipborne oceanic high-spectral-resolution lidar (HSRL) and illustrate a multiple scattering correction algorithm to rigorously address the above challenges in estimating the depth-resolved diffuse attenuation coefficient Kd and the particulate backscattering coefficient bbp at 532 nm. HSRL data were collected during day- and night-time within the coastal areas of East China Sea and South China Sea, which are connected by the Taiwan Strait. Results include vertical profiles from open ocean waters to moderate turbid waters and first lidar continuous observation of diel vertical distribution of thin layers at a fixed station. The root-mean-square relative differences between the HSRL and coincident in situ measurements are 5.6% and 9.1% for Kd and bbp, respectively, corresponding to an improvement of 2.7-13.5 and 4.9-44.1 times, respectively, with respect to elastic backscatter lidar methods. Shipborne oceanic HSRLs with high performance are expected to be of paramount importance for the construction of 3D map of ocean ecosystem.

[Retrieve of red tide distributions from MODIS data based on the characteristics of water spectrum].

After comparing the spectral differences between red tide water and normal water, we developed a method to retrieve red tide distributions from MODIS data based on the characteristics of red tide water spectrum. The authors used the 119 series of in situ observations to validate the method and found that only one observation has not been detected correctly. The authors then applied this method to MODIS data on April 4, 2005. In the research areas three locations of red tide water were apparently detected with the total areas about 2 000 km2. The retrieved red tide distributions are in good agreement with the distributions of high chlorophyll a concentrations. The research suggests that the method is available to eliminating the influence of suspended sediments and can be used to retrieve the locations and areas of red tide water.

[Spectrum simulation based on data derived from red tide].

The present paper utilizes the absorption data of red tide water measured during the growing and dying course to retrieve imaginary part of the index of refraction based on Mie theory, carries out the simulation and analysis of average absorption efficiency factors, average backscattering efficiency factors and scattering phase function. The analysis of the simulation shows that Mie theory can be used to reproduce the absorption property of Chaetoceros socialis with an average error of 11%; the average backscattering efficiency factors depend on the value of absorption whose maximum value corresponds to the wavelength range from 400 to 700 nanometer; the average backscattering efficiency factors showed a maximum value on 17th with a low value during the outbreak of red tide and the minimum on 21th; the total scattering, weakly depending on the absorption, is proportional to the size parameters which represent the relative size of cell diameter with respect to the wavelength, while the angle scattering intensity is inversely proportional to wavelength.

[Spectral modeling of marine suspended particulate matter: comparison between ADA and Mie computations].

The present paper aimed at exploring the feasibility of modeling optical properties of marine suspended particulate matter (SPM) with anomalous diffraction approximation (ADA) method, which had advantage of computation speed over the classic Mie theory. Based on the marine SPM characteristics of refractive index and particle size, attenuation efficiency Q(c) absorption efficiency Q(a) and scattering efficiency Q(b) of SPM were computed by ADA and Mie to analyze their relative error delta(c), delta(a), delta(b) and the variations. The variations of delta(c), delta(a), delta(b) with the particle size, real and imaginary part of refractive index were different and complex. The minimum particle size R(min), with which ADA can be used to give computation results with enough accuracy (relative difference less than 5%), was determined and shown to be influenced by the refractive index and wavelength Classic Mie method, rather than ADA method, was recommended for the spectral modeling of ocean suspended particulate matter.

[Enteromorpha prolifera underwater spectral research based on simulation of radiation transmission].

The accumulation of Enteromorpha prolifera in huge amount in the Yellow sea in June, 2008 draws the attention from all over the world. It is an urgent requirement to monitor the wide range of Enteromorpha prolifera distribution by remote sensing. As to the Enteromorpha prolifera floating on the sea surface, effective monitoring by optical remote sensing has been basically achieved. As far as the underwater suspended Enteromorpha prolifera is concerned, the present paper carried out the radiative transfer simulation research on the above water spectral response, its variation with the suspending depth, the water turbidity and environmental conditions. It was found that with the increase in Enteromorpha prolifera suspending depth and water turbidity as well as the decrease in the thickness of Enteromorpha prolifera, the Enteromorpha prolifera information contained in the surface spectra would decrease. The influence of environmental factors such as water-gas interface roughness, cloud cover extent and sun zenith angle on the underwater suspended Enteromorpha prolifera spectra can be ignored. The maximum Enteromorpha prolifera depth that can cause surface spectrum changes is about 30 m in clean water and about 1 m in turbidity water.

Remote sensing estimation of the biomass of floating Ulva prolifera and analysis of the main factors driving the interannual variability of the biomass in the Yellow Sea.

Since 2007, green tide blooms with Ulva prolifera as the dominant species have occurred every summer in the Yellow Sea. Biomass is a critical parameter used to describe the severity of green tide blooms. In this study, we analyzed the relationships between several indices (normalized difference vegetation index (NDVI), floating algae index (FAI), ratio vegetation index (RVI), enhanced vegetation index (EVI), ocean surface algal bloom index (OSABI), Korea Ocean Satellite Center (KOSC) approach) and the biomass per unit area of Ulva prolifera by using the in situ measurements from a water tank experiment. EVI, NDVI, and FAI showed strong exponential relationships with Ulva prolifera biomass per unit area. In order to apply the relationships to satellite remote sensing data, the impacts of the atmosphere (different aerosol optical depth at 550 nm) and mixed pixels to the relationships were analyzed. The results show that atmosphere has little effect on the relationship between EVI and Ulva prolifera biomass per unit area with R2 = 0.94 and APD (the average percentage deviation) = 19.55% when EVI is calculated from Rrc (Rayleigh-corrected reflectance), and R2 = 0.95 and APD = 17.53% when EVI is calculated from Rtoa (top-of-atmosphere reflectance). Due to the low sensitivity to the atmosphere, the EVI relationship can be directly utilized in the top-of-atmosphere (TOA) reflectance without atmospheric correction. In addition, the EVI was slightly affected by mixed pixels with the APD only increased by ~10%. The EVI relationship was then applied to a long MODIS image time series to obtain the maximal total biomass of floating Ulva prolifera in the Yellow Sea from 2007 to 2016. The results showed that the maximum and minimum total biomass occurred in 2016 (~1.17 million tons) and 2012 (~0.074 million tons), respectively. The main factors that caused the inter-annual biomass variability were analyzed. The total amount of nutrients from Sheyang River which was the largest river on the northern coast of Jiangsu Province, and Porphyra cultivation in the Radial Sand Ridges of Jiangsu Province had both strong correlation with Ulva prolifera total biomass.

Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign.

The world's largest green tide originated from the Jiangsu Shoal of the Yellow Sea was due to fast reproduction of floating green macroalgae (Ulva prolifera). It brought significant impacts on marine environment and ecosystem in the Yellow Sea. In this study, we examined the expansion of green tide from the Jiangsu Shoal during the period from 29 April to 25 June 2016. Using high-resolution satellite images, we revealed a declined growth rate during the northward drifting of early-stage green tide for the first time, i.e., the green tide had higher growth rate (up to 25% per day) in the turbid waters of the Jiangsu Shoal in May and a lower growth rate (low to 3% per day) in the relatively clear waters in the middle of the western Yellow Sea in June, which suggests that water clarity might not be the key factor controlling the growth rate of the floating macroalgae in the surface waters under natural conditions. The high growth rate led to shortened time windows for controlling the green tide by employing macroalgae collecting campaigns at the initial sites of the green tide, which was no more than 14 days in the 2016 case.

[Study of red tide spectral characteristics and its mechanism].

In situ spectral data of different red tide, whose dominant species are leptocylindrus danicus, chattonella marina, skeletonema costatum, and mesodinium rubrum, were acquired by above water method utilizing spectrometer manufactured by FieldSpec Dual VNIR (USA). It is emphasized that the characteristic reflectance peak lying between 687 and 728 nm can be used to distinguish between red tide and normal sea water. Also the spectral discrepancy between different dominant species of red tide is pointed out, which could be utilized to identify certain red tide species by remote sensing technique. Mechanisms of phytoplankton red tide spectra peaks and vales are given. Spectral characteristics of mesodinium rubrum, a kind of protozoan, may be related to its symbiotic alga in its body and phytoplankton pigment crumb. So, research on ingestion preference, symbiotic property with algae, and fluorescence emission character of such symbiotic algae under normal temperature may be helpful for the deep understanding of mechanism of mesodinium rubrum spectra.

[Research on airborne hyperspectral identification of red tide organism dominant species based on SVM].

Airborne hyperspectral identification of red tide organism dominant species can provide technique for distinguishing red tide and its toxin, and provide support for scaling the disaster. Based on support vector machine(SVM), the present paper provides an identification model of red tide dominant species. Utilizing this model, the authors accomplished three identification experiments with the hyperspectral data obtained on 16th July, and 19th and 25th August, 2001. It is shown from the identification results that the model has a high precision and is not restricted by high dimension of the hyperspectral data.

A Neural Network Model for K(λ) Retrieval and Application to Global Kpar Monitoring.

Accurate estimation of diffuse attenuation coefficients in the visible wavelengths Kd(λ) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability of a semi-analytical Kd(λ) retrieval model (SAKM) and Jamet's neural network model (JNNM), and then develop a new neural network Kd(λ) retrieval model (NNKM). Based on the comparison of Kd(λ) predicted by these models with in situ measurements taken from the global oceanic and coastal waters, all of the NNKM, SAKM, and JNNM models work well in Kd(λ) retrievals, but the NNKM model works more stable and accurate than both SAKM and JNNM models. The near-infrared band-based and shortwave infrared band-based combined model is used to remove the atmospheric effects on MODIS data. The Kd(λ) data was determined from the atmospheric corrected MODIS data using the NNKM, JNNM, and SAKM models. The results show that the NNKM model produces <30% uncertainty in deriving Kd(λ) from global oceanic and coastal waters, which is 4.88-17.18% more accurate than SAKM and JNNM models. Furthermore, we employ an empirical approach to calculate Kpar from the NNKM model-derived diffuse attenuation coefficient at visible bands (443, 488, 555, and 667 nm). The results show that our model presents a satisfactory performance in deriving Kpar from the global oceanic and coastal waters with 20.2% uncertainty. The Kpar are quantified from MODIS data atmospheric correction using our model. Comparing with field measurements, our model produces ~31.0% uncertainty in deriving Kpar from Bohai Sea. Finally, the applicability of our model for general oceanographic studies is briefly illuminated by applying it to climatological monthly mean remote sensing reflectance for time ranging from July, 2002- July 2014 at the global scale. The results indicate that the high Kd(λ) and Kpar values are usually found around the coastal zones in the high latitude regions, while low Kd(λ) and Kpar values are usually found in the open oceans around the low-latitude regions. These results could improve our knowledge about the light field under waters at either the global or basin scales, and be potentially used into general circulation models to estimate the heat flux between atmosphere and ocean.