最新刊期
卷 27 ， 期 1 ， 2023
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摘要：The HY-1C and HY-1D satellites are the first operational ocean color constellation in China. HY-1C and HY-1D was launched in September 7, 2018 and Jun 11, 2020, respectively. Chinese Ocean Color and Temperature Scanner (COCTS), Coastal Zone Imager (CZI), Ultra-Violet Imager (UVI), Satellite-based Calibration Spectrometer (SCS) and Automatic Identification System (AIS) are the key payloads onboard HY-1C and HY-1D. The HY-1C/D constellation has been detecting the global ocean color twice and sea surface temperature (SST) four times every day, and high- resolution (50m) China’s coastal zone and offshore environments twice every three days.In this article, the HY-1C/D satellites and the specifications of the payloads, flow-process, product levels and distribution of data products are introduced. The overpass times are 10:30 am±30 min and 1:30 pm±30 min of local time at the descending node for HY-1C and ascending node for HY-1D, respectively. The HY-1C/D satellites complete processing chains for generating raw data, radiances, ocean color and Sea Surface Temperature (SST) from the payloads by Geo-location, radiometric and atmospheric correction, geophysical parameters retrieval, data gridding and merging. There are different data products associated with the five levels of processing (Level 0 to Level 4). Data products of Level-1 (radiances), 2 (ocean color parameters and SST), 3 (gridded products) and 4 (merged gridded products) are available from the China Ocean Satellite Data Service Center to the general public for free. The HY-1C/D satellites product structure system is complete and data product processing and distribution are efficient.The typical products of Chlorophyll-a concentration and SST, the applications on floating green tides algae Ulva prolifera, sea ice, offshore aquaculture, inland water and tropical cyclones are also presented in this article. Maps of global daily gridded chlorophyll-a concentration and SST (L3A products) from COCTS onboard the HY-1C and HY-1D are presented to show their temporal and spatial coverage. The CZI remote sensing images have being used to extract the distribution location and area of offshore green tide, sea ice and offshore aquaculture facilities. The process of ice shelf falling off and drifting around Antarctic continent has been monitored by using a long time series CZI remote sensing images. In addition to offshore and coastal environmental monitoring applications, HY-1C/D remote sensing data products have also been applied in inland water algal bloom, typhoon cloud map and its moving path monitoring. The typical products and application shown in this article indicates that the sensors and the data products of HY-1C/D have high quality and great application potential in marine and coastal environmental monitoring.The planned future satellites including the next generation of Chinese ocean color satellites and geostationary coastal zone and ocean environment monitoring satellite and their key specifications are briefly introduced. The new generation satellites with more spectral channels, broader spectrum range, and higher temporal and spatial resolution will have greater application potential in monitoring of ocean color and coastal zone ecology, resources and environment.关键词：HY-1C/D satellites;ocean color;products structure system;applications of satellite- 1971
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发布时间：2023-02-17
Review
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摘要：A large number of global observation data was obtained by HY-1C COCTS since its launch in September 2018. The comprehensive evaluation of the HY-1C/COCTS products is important for further applications. In this study, we used the global in-situ data from AERONET-OC to evaluate the performance of the remote sensing reflectance (Rrs) products of the HY-1C COCTS. Firstly, the AERONET-OC dataset were divided into four optical water types (A, clean water; B, relatively clean water; C, slightly turbid water; D, turbid water) based on a spectral normalization method. Secondly, the AERONET-OC Rrs data and HY-1C/COCTS retrieved Rrs data were matched according to the defined spatial-temporal windows (5×5 box and 1 hour). Finally, the performances of the HY-1C/COCTS Rrs products were quantitatively evaluated in the four optical water types. As a result, good correlation between satellite and in-situ Rrs data was indicated as R values among four types water ranged from 0.680 to 0.879. In type A water, the relatively good consistency between satellite and in-situ Rrs data was observed as average percent difference (PD) at 6.79%, and the average absolute percent difference (APD) at 38.79%. In type B water, slight overestimation of satellite data occurred with PD at 18.73% and APD less than 45%. Underestimation of satellite data was reported in type C water, as negative remote sensing Rrs data in 412 nm and 443 nm bands were with PD at -14.38% and APD at 47.14%. Similarly, in type D water, negative Rrs in 412 nm and 443 nm bands were with PD at -32.35%, and APD at 47.14%, indicating significant underestimation from the satellite data. In addition, difference of accuracy performance of Rrs products in different bands of COCTS for four water types was also observed. Rrs presented good consistency between in situ and COCTS data in band 412 nm and 443 nm for type A water. Better consistency in band 520 nm and 565 nm was observed for type C, D water than type A water while significant underestimation of COCTS Rrs were reported in all four types of water compared to in situ data. Overall, COCTS and in situ Rrs data showed good consistency in clean water, but remained relatively inconsistent in turbid water. Our results also reported that the COCTS inversed Rrs products are slightly overestimated compared with the AERONET-OC in situ data in type A and B water. In contrast, COCTS products slightly underestimated Rrs for type C water but significantly underestimated for type D water. Attention should also be paid to enlarge the evaluated errors, as the AERONET-OC in-situ spectral data was linearly interpolated to match COCTS band. In the future, the hyperspectral in-situ Rrs data should be used to further evaluate the performance of COCTS.关键词：HY-1C;COCTS;remote sensing reflectance;AERONET-OC;validation- 828
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发布时间：2023-02-17 -
摘要：Since the successful launch of HY-1C in September, 2018, China has entered a new era of operational global ocean color remote sensing. As one of its three major payloads, the Chinese Ocean Color and Temperature Scanner (COCTS) measures the radiances scattered from the atmosphere, ocean and sea surface in eight visible and near-infrared bands and two thermal infrared bands across a swath of over 2500 km. As many businesses such as marine environment observation, marine ecological monitoring and marine disaster prevention and mitigation are putting higher demands on quantitative ocean color products, we here validate operational L2A products retrieved from COCTS data across coastal waters across China and Europe, and also compare them with widely accepted L2 products of MODIS onboard AQUA satellite. Validation practice uses in-situ data obtained in field campaigns in the East and South China Sea in 2018 and 2020, and those derived by automated sun-photometers (SeaPRISM, CIMEL Inc.) deployed in the East China Sea and in the Adriatic Sea in Europe. Confined by that SeaPRISM can only produces remote-sensing reflectance and aerosol optical thickness directly, we only validate operational L2A products provided by COCTS, which include Remote Sensing Reflectance and Aerosol Optical Thickness. Match-up practice follows those protocols widely accepted in the ocean color community. It is found that SeaPRISM can provide in-situ data in a more efficient way and the data covers a larger temporal range, which facilitates characterizing temporal and spatial uncertainty of ocean color products. Validation results show that both remote-sensing reflectance and aerosol optical products of COCTS L2A products agree well with in-situ measurements, but their comparison shows larger deviation. The average relative percentage difference (RPD) between COCTS L2A product and field measurement across various coastal waters are -5.6%, 11.8% and 101.4% in blue, green and red bands respectively, while the average Absolute Percentage Ddifference (APD) are 46.8%, 53.0% and 173.9% respectively. Meanwhile, for aerosol optical thickness, the average RPD and APD between COCTS L2A product and field measurement across these waters are -14.2% and 79.5%, respectively. Remote sensing reflectance of COCTS shows similar spectral shape to that of in-situ data, but their similarity varies across different waters. Specially, remote sensing reflectance in the South China Sea demonstrates similar spectral shape but shows large overestimation; high spectral shape similarity exists in the East China Sea but COCTS gives underestimation; there appears large deviation in spectral shape in the Adriatic Sea, namely, overestimation in blue but underestimation in the blue and red. As compared with the validation results of MODIS L2 products following same criteria, it is found that MODIS performs better than COCTS in quantitative retrieval of remote-sensing reflectance, but shows no obvious advantage in quantitative retrieval of aerosol optical thickness. Although HY-1C COCTS operational products have contributed to wide range of applications, this practice suggests there is still great space in improving quantitative retrieval of ocean color products, at least L2A products.关键词：HY-1C Satellite;COCTS;L2A product;validation;coastal waters around China;coastal waters around Europe;remote-sensing reflectance;aerosol optical thickness- 597
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发布时间：2023-02-17 -
摘要：The HY-1D satellite is equipped with a Coastal Zone Imager (CZI), which uses both field-of-view-butting and optical-butting. Due to the CZI's super large 63° field of view, the edge of the camera is distorted obviously, which makes the side-slither images distorted observably nonlinearly. The Lines on side-slither images are not slanted lines close to 45°, but irregular curves with a straight middle and bent edges. The traditional processing algorithm for side-slither images based on LSD (Line Segment Detector) is not suitable for the side-slither image of the HY-1D CZI. Aiming at the structural characteristics of the HY-1D CZI, this paper proposed a method for automatic standardization of side-slither images based on the neighbor relationship of detectors, which effectively resolves the problem of the nonlinear distortion of the HY-1D CZI’s 90 degree side-slither image. Firstly, primary correction is performed on the side-slither image according to the traditional side-slither image processing method; Secondly, starting from the first column of the primary corrected image, the mean square error between the current column and the next column with a sliding window with a certain step size in the row direction is calculated, and the step corresponding to the minimum mean square error is taken as the relative value of the next column. Based on the offset of the current column, the overall offset of each column is determined in turn; Finally, according to the offset calculated in the second step, the enhanced corrected side-slither image is obtained by adjusting the primary corrected image, and then the relative radiometric calibration coefficient of the camera is obtained through histogram matching on images. The relative radiometric calibration experiments are carried out on the normal push-broom images, and the comparison experiments are implemented using the on-orbit statistical method. After using the method we proposed, for land scenes, the maximum streaking coefficient of all sensors in the land scene is smaller than 0.33%, the average streaking coefficient is smaller than 0.04%, and the median streaking coefficient is smaller than 0.03%; For ocean scenes, the maximum streaking coefficient of all the sensors is smaller than 0.48%, the average streaking coefficient is smaller than 0.07%, and the median streaking coefficient is smaller than 0.06%. The experiment shows that all indicators are better than the on-orbit statistical method based on massive statistical data of normal push-broom images, our method effectively improves the relative radiometric quality of the HY-1D CZI.关键词：side-slither data;HY-1D (Coastal Zone Imager) CZI;automatic standardization processing;relative radiometric calibration- 873
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发布时间：2023-02-17 -
摘要：The Coast Zone Imager (CZI) onboard the Chinese first marine aqua-color satellite HY-1C started operational operations in June 2019. The data acquired by CZI have the characteristics of medium resolution, large width and high revisit period and taking into account the requirements of ocean water color, terrestrial ecology and polar glaciers. Therefore, the large amount of coastal, land, and ocean data acquired by CZI is of great significance for marine disaster and environmental monitoring research. However, related studies have shown that clouds cover an average of 68% of the earth's surface. CZI data is severely affected by cloud, which will then have a strong impact on its subsequent applications. The effective identification of clouds in remote sensing images is extremely important for the application of CZI images. Most of the existing cloud detection algorithms are based on RGB images or multi-spectral images including thermal infrared band. There are few researches on cloud detection algorithms for RGB-NIR four-band remote sensing images, such as HY-1C CZI. The objective of this paper is thus to propose an unsupervised cloud detection method for HY-1C CZI remote sensing images that makes full use of NIR band information. The method includes four processes: training samples selection, feature extraction, Support Vector Machine (SVM) classification, and post-processing. In the selection of training samples, combining dark channel reflectivity, normalized vegetation index and whiteness index of the image, this paper proposes an automatic training sample extraction algorithm, which uses the whiteness index to obtain detail information, and accurately extract cloud/non-cloud samples through a gradual refinement process. For feature extraction, the spatial spectrum feature information of CZI remote sensing image is selected, including reflectance, spectral index, texture and structure features, to characterize remote sensing image features, and maximize the feature difference between cloud and non-cloud regions. Based on the above automatically extracted sample and its feature description, SVM is used to initially classify the CZI remote sensing data, and then the guided filtering, hole filling and geometric judgment post-processing are performed to obtain the final high-precision cloud detection results. This paper applies the algorithm to four typical scenarios (vegetation, soil, wetland, and ice and snow scenarios), and compares and analyzes it with the currently popular unsupervised cloud detection algorithms. Compared with other cloud detection algorithms, the qualitative analysis results show that the cloud detection results in this paper are in good agreement with real cloud distribution image labeled by human. In addition, the most commonly used error rate metric is also used to quantitatively evaluate the cloud detection results. It shows that the error rates of the proposed algorithm in vegetation, soil, wetland, ice and snow scenes are 0.027, 0.064, 0.026, and 0.049, respectively; and that has the lowest error rate in four scenarios. Through the above comparative analysis, the detection results of the proposed algorithm in different scenarios are more accurate, which demonstrates the effectiveness of the proposed algorithm for cloud detection from HY-1C CZI data.关键词：HY-1C;Coast Zone Imager (CZI);cloud detection;Whiteness Index;unsupervised- 1003
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发布时间：2023-02-17 -
摘要：China has specifically planned a series of ocean observation satellites in the Medium and Long-term Development Plan for National Civil Space Infrastructure (2015—2025) to establish a more complete three-dimensional monitoring system for the marine environment through a network from a single test satellite to a constellation. Satellite observations place high demands on the degree of quantification of ocean satellite ground data processing systems due to the influence of the atmosphere and the typically low contribution of ocean parameters at the top of the atmosphere. Since the launch of CZCS water color satellite in 1970 s, the United States has accumulated many years of experience in water color satellite data processing system. In this paper, we develop HY-1C/1D offline data processing system (OffLine-COCPS), which realizes the whole chain from L1B data (after geometric positioning and radiometric calibration) to the production of remote sensing reflection ratio of water bodies and various water color products for HY-1C/1D Chinese Ocean Color and Temperature Scanner (COCTS).Based on NASA's mature open-source SeaDAS Ocean Color Science SoftWare package and HY-1C/1D COCTS format, we develop and recompile the software package to support COCTS L1B data. Using a vector sea-air coupled radiative transfer model developed based on the successive scattering method, HY-1C/1D COCTS specific atmospheric related lookup table are generated.The results show that the independently established HY-1C/1D satellite COCTS sensor atmospheric correction related lookup table basically meets the quantitative application on a global scale. The global inversion chlorophyll products of HY-1C/1D COCTS are obtained based on statistical projection, indicating that the system can realize support for HY-1C/1D dual-satellite missions, and then the overall distribution trend is also consistent with MODIS when compared with the official MODIS chlorophyll product scatter plot released by NASA for the same period. Through this study, the extension of the domestic water color satellite sensor and the independent inversion algorithm has been successfully realized, and the atmospheric correction algorithm and other water color inversion algorithms can be extended subsequently, while more work is needed to evaluate the accuracy of atmospheric correction and the accuracy of water color inversion products.关键词：ocean remote sensing;water color satellite;HY-1C/1D;processing system;SeaDAS- 737
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发布时间：2023-02-17
Data Processing and Calibration/Validation
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Multitype inland water atmospheric correction and water quality estimation based on HY-1C CZI images
摘要：The Coastal Zone Imager (CZI) on HY-1C has great potential in the application of water color remote sensing for inland water. At present, few studies exist on the atmospheric correction and water quality estimation of HY-1C CZI images in inland water, and problems, such as the lack of atmospheric correction and water quality estimation models applicable to different types of inland water, still need to be solved. Therefore, in this study, a synchronization experiment was carried out on five lakes and reservoirs with different turbidity degrees in the North China Plain: Xiaolangdi Reservoir, Guanting Reservoir, Danjiangkou Reservoir, Baikushan Reservoir, and Baiyangdian Lake. The surface remote sensing reflectance spectra and typical water quality parameters of 85 sampling points were obtained. The relative atmospheric correction algorithm for HY-1C CZI images based on Sentinel-2 MSI images and system calibration model were developed. The average unbiased relative errors (AUREs) of remote sensing reflectance estimation in blue, green, red, and near-infrared bands of HY-1C CZI are 14.7%, 11.2%, 28.9%, and 41.7%, respectively. The atmospheric correction accuracy of blue, green, and red bands is relatively high. In addition, the mean value of correlation coefficient between atmospheric correction and measured spectra is 0.978, and the mean value of spectral angle distance is 0.109, indicating that the shape of the reflectance spectra of atmospheric correction is consistent with that of the measured spectra. The estimation models of chlorophyll-a concentration and Secchi disk depth were established on the basis of the measured data. The AURE of chlorophyll-a concentration estimation from HY-1C CZI images is 33.8%, and the root-mean-square error (RMSE) is 4.8 μg/L. The AURE and RMSE of Secchi disk depth estimation are 25.0% and 34.9 cm, respectively. The results show that HY-1C CZI images can be applied to the water quality estimation of multiple inland water bodies in the North China Plain.This method solved the problem of water atmospheric correction when HY-1C lacks short wave infrared band by borrowing Sentinel-2 MSI data. And realized the bottleneck of high-precision water remote sensing reflectance calculation of 4-band multispectral images, and improves the quantitative processing and the application level of water color remote sensing of HY-1C data.关键词：HY-1C CZI;inland water;atmospheric correction;chlorophyll-a;secchi disk depth- 817
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发布时间：2023-02-17 -
摘要：Oceanic whitecaps, generated from wind-wave breaking process, are the medium of air-sea exchange and the indicator of sea surface state. Due to the intense reflection and scattering of incident light, whitecaps can be effectively recognized and distinguished in in-situ photos or videos. Whitecap coverage (W), defined as the proportion of ocean surface covered by whitecaps, is an important parameter for the quantification of whitecaps. Of course, oceanic whitecaps can also be discriminated in high spatial resolution optical remote sensing images, such as Sentinel-2 MSI and Landsat-8 OLI data. This can provide a new research direction in marine environment observation, and may be further used for wind speed monitoring. However, how to estimate oceanic whitecap coverage from these optical remote sensing images is still a challenge. In this study, the formula of whitecap coverage is obtained by converting the form of radiative equation related with the constant and image reflectance of Sentinel-2 MSI data under some assumptions. The background signal of seawater and atmosphere was eliminated by data optimization, and the signal of whitecaps can be distinguished using regional filtering method. Then, whitecap coverage can be estimated. The identification and estimation results indicate that whitecap coverage derived from Sentinel-2 MSI images are consistent with previous studies using in situ observations in the order of magnitude, and can invert sea surface wind speed using a statistical model. Coarse spatial resolution wind speed images converted from MSI inversion were validated by ERA5 wind speed products from European Centre for Medium-Range Weather Forecasts (ECMWF). In addition, whitecap coverage can imply the modulation of other marine environmental dynamic factors (e.g., water mass, ocean fronts, ocean eddies and internal waves). Moreover, it should be noted that sunglint reflection is a non-negligible issue for optical remote sensing of oceanic whitecaps whose signal should be effectively eliminated. Examining the MSI-derived results to HY1-C/D CZI, it indicates that whitecaps can be identified in CZI images when wind speed is greater than 9 m/s, and the reflectance difference between whitecaps and background seawater is 5.8%—8.3%. We hope the above results can be used to improve the accuracy of atmospheric correction, and provide new reference for using high spatial resolution optical remote sensing in sea surface wind speed estimation and marine environmental dynamic factors monitoring. This will hopefully expand the research and application fields of ocean color remote sensing.关键词：optical remote sensing;oceanic whitecaps;sunglint;HY-1C/D;Sentinel-2;MSI;CZI- 1179
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发布时间：2023-02-17 -
摘要：Artemia is a kind of small crustacean that lives in high salinity water, which can be used as an excellent fishery feed and an important component of carbon flux and biological chain in salt lakes. Because of its nonnegligible ecological and economic value, it is of great significance to develop a high-precision extraction method of Artemia based on remote sensing data for biological resource monitoring and reasonable fishing. Taking Ebinur Lake as an example, this paper proposed an automatic method to extract Artemia based on the HY-1C Coastal Zone Imager (CZI) images and deep learning technology. Firstly, the spectral characteristics of HY-1C CZI and Landsat-8 OLI sensors in the Artemia endmember were analyzed and the Spectral Band Adjustment Factors (SBAF) were used to eliminate the response differences between the two sensors to construct the Artemia-water dataset containing 837 effective samples of 64×64 size. Secondly, 70% of the dataset was used to train the U-Shaped Fully Convolutional Neural Network (U-Net) with a depth of 5, and the remaining 20% and 10% of the data were used to verify and test the algorithm, respectively. The model iterated 6700 times in the training process, which took 35 minutes. During this period, we used the adaptive moment estimation (Adam) optimizer with an initial learning rate of 1×10-4, and the binary cross entropy as the loss function. The training batch size was set to 4 since the equipment limitation. Whenever the loss value of the verification dataset did not decline within the last 3 epochs, the learning rate was halved. The training would be terminated automatically if it did not decline within the last 10 epochs. Finally, the impact factors and application potential of this method were further analyzed and discussed. The experimental results demonstrated that, compared with the Support Vector Machine (SVM), the Maximum likelihood Classification (MLC), and the Normalized Difference Water index (NDWI) algorithms, the extraction Precision and F1 score of U-Net were 92.02% and 90.55%, respectively, which were about 11%—23% higher than other methods. Even in the face of complex water background interference, the proposed method showed better robustness since the extraction error of the U-Net was only 3.3%. In addition, the maximum extraction area of Artemia slicks in 10 CZI images from 2019 to 2021 was 9.27 km2, 5.8 times larger than the minimum. So was the water area of Ebinur Lake, which varied sharply between 497.34 km2 and 330.93 km2. The violent difference in Artemia extraction may be due to the variations of water area and other natural or human factors such as temperature, wind speed, precipitation, pollution, and overfishing. It is necessary to conduct a profound study on their correlation relationship. Therefore, the future effort still needs to establish further a more representative and inclusive remote sensing dataset of Artemia, and develop more reliable and practical algorithms to carry out more long time series and large-scale studies in Artemia extraction.关键词：HY-1C Satellite;Coastal Zone Imager;Artemia slicks;Ebinur Lake;U-Net- 1095
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发布时间：2023-02-17 -
摘要：Shallow water depth of islands and reefs is an important marine element. The islands and reefs in the South China Sea are located far from the mainland, which makes it difficult to assess long-term changes in underwater topography owing to the low efficiency and difficulty in field investigations. Satellite remote sensing imagery capable of large coverage and high frequency is urgently needed. The double satellite network of HY-1C/D greatly improves the coverage frequency. The Coastal Zone Imager (CZI) can provide fast operational remote sensing services for underwater detection of islands and reefs. To fully explore the depth detection capabilities of the two satellites, this study used Yongle Atoll as the research area, with HY-1C/D CZI multispectral remote sensing imagery as the data source. Combined semi-analytical and log-ratio models were used to perform water depth inversion independent of in situ data. The objective is to access the application potential of HY-1C/D CZI imagery for shallow water depth inversion of islands and reefs. This study combined a semi-analytical and logarithmic ratio model ( called L-S model) based on satellite remote sensing imagery of HY-1C/D CZI, which includes four bands from visible to near-infrared. The strong linear relationship between water depth and the relevant spectral parameters of the logarithmic ratio model were used to globally restrict the semi-analytical model. After preprocessing the HY-1C/D CZI domestic multispectral imagery, which included geographic projection, geometric precision correction, calculation of the top of atmosphere reflectance, radiometric correction, sun glint correction, and atmospheric correction, a shallow water depth inversion experiment was carried out in Yongle Atoll, independent of in situ water depth or any other priori knowledge based on the L-S model. The water depth inversion results after tidal height correction using the OSU tidal prediction software in Yongle Atoll were compared with the in situ data and cross-compared with the inversion results based on GeoEye-1 remote sensing imagery. Compared with the in situ water depth, the mean absolute errors of HY-1C/D CZI were 1.60 m and 1.85 m, and the relative errors were 22.48% and 26.23%, respectively. The mean absolute error of the water depth inversion result of GeoEye-1 was 0.78 m and the relative error was 10.86%. Compared with the results of GeoEye-1, the mean average absolute deviation of HY-1C/D CZI were 1.65 m and 1.81 m, and the relative deviations were 22.33% and 23.83%, respectively, which were basically consistent in different sate llite sensors. Although the overall accuracy is lower than that of high-spatial-resolution satellite images, the mean absolute error of the inversion results of HY-1C/D CZI can be controlled within 2.0 m, with a high reference value. This solves the problem of the lack of in situ water depth data during shallow water depth inversion at a large scale. In addition, a cross-comparison of the inversion results between HY-1C and HY-1D showed that this method is robust when applied to different satellite sensors. This indicates that HY-1C/D CZI have the advantages of a short revisiting period and large imaging coverage, which can quickly and repeatedly obtain large-scale optical image data of the ocean and perform shallow water depth mapping of islands and reefs, thereby realizing high-frequency monitoring of underwater terrain changes. HY-1C/D CZI imagery and high-spatial-resolution satellite imagery complement each other and compensate for the shortcomings of field measurements. Therefore, based on the HY-1C/D CZI imagery, the water depth information can be retrieved in the range of 0—20 m stably and accurately, which has a wide range of application potential in the shallow water depth inversion of global islands and reefs.关键词：HY-1C/D;Coastal Zone Imager (CZI);island shallow water depth;Yongle Atoll;L-S model- 769
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发布时间：2023-02-17 -
摘要：Phytoplankton is a significant producer of global primary productivity and influences the ocean’s biological cycle and energy conversion. Understanding and detecting the phytoplankton biomass is important to grasp the variations in the marine environment. However, observing the changes of phytoplankton taxa remains a great challenge on spatial and temporal scales. Recent developments in ocean color sensors have enabled large-scale and long time-series remote sensing retrieval of phytoplankton biomass. HaiYang-1C and HaiYang-1D (HY-1C/D) satellites, as the main members of the Chinese ocean color satellite series, can provide ocean color products with a larger observation range, higher accuracy, and resolution, with great application potential.In this study, we collect in situ data, including the pigment concentration with the high-performance liquid chromatography method (HPLC) and remote sensing reflectance (Rrs), from four cruises in the Bohai Sea and the Yellow Sea from 2016 to 2018. Then, we obtain eight typical phytoplankton taxa concentrations through CHEMTAX (CHEMical TAXonomy) software based on these pigment data. We found the sum of the relative contributions of diatoms, cryptophytes, cyanobacteria, and chlorophytes to total chlorophyll a (TChla) accounted for a large proportion (79%). In addition, the spatial distribution of the CHEMTAX-calculated phytoplankton taxa showed a trend of higher nearshore concentration than offshore by spatial interpolation analysis.We used the singular value decomposition (SVD) method to construct a link between Rrs and phytoplankton concentrations. The matrix U obtained from SVD was used to build four models by multiple linear regression methods, to estimate four phytoplankton taxa concentrations. We carried out validation independently based on the measured and estimated concentrations, and the result showed relatively high consistent between diatoms, cryptophytes, cyanobacteria, and chlorophytes and the measured values (determination coefficients (R2): 0.44, 0.70, 0.70 and 0.71 (p<0.001); median percent error (ME): 44.81%, 45.34%, 51.20% and 62.80%; Root Mean Squared Error (RMSE): 0.23 mg/m3, 0.24 mg/m3, 0.11 mg/m3 and 0.06 mg/m3, respectively). The established model was further applied to China Ocean Color & Temperature Scanner (COCTS) Rrs data on the HY-1C/D L1A to demonstrate the spatial distribution of four major phytoplankton taxa in the Bohai Sea. The satellite results are consistent with previous studies that showed decreasing concentrations from nearshore to offshore.Finally, this study applies the same modeling approach (SVD) to MODIS and GOCI sensor bands. A comparison of model performance and satellite applications between the three sensors showed that the new model established by COCTS bands outperformed the GOCI-Ⅱ model and was similar to the MODIS-Aqua model. Also, the satellite application of COCTS is superior to the other two sensors. Generally, this study can provide a methodological foundation for understanding the spatial-temporal evolution of the phytoplankton community in the Bohai Sea. Meanwhile, this study shows the great potential of HY-1C/D in models establishing and phytoplankton community monitoring.关键词：Phytoplankton taxa concentrations;CHEMTAX;SVD;HY-1C/D;The Bohai Sea- 947
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发布时间：2023-02-17
Satellite Remote Sensing for Water Environment and Resources
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摘要：The green tide in the Southern Yellow Sea have appeared 1.5 decades since 2007, resulting in a great losses to the marine ecological environment and government finance. With the help of multiple satellite images, remote sensing technique play an import part in monitoring green tide outbreak process. Yet selecting an appropriate sensor is a precondition in quantifying the severity of green tide. HY-1C satellite is great superior to other sensors for its relatively high spatial resolution (50 m), wide swath width (950 km) as well as short revisit time (3 days). Here, we introduce the green tide in GF-6 WFV images (16 m) to evaluate the capability of CZI images in the monitoring of green tide, and then compare the CZI results with the traditional MODIS images (250 m). And the GF-6 WFV images is also applied to evaluate the omission rate and accuracy of green tide extraction in the CZI and MODIS images. Based on the convert parameter between MODIS and CZI green tide mapping result, we get high frequency green tide outbreak process in 2019, 2020 and 2021.In this study, dynamic threshold is introduced to extract green tide from the DVI results and the coverage area of green tide will be obtained by adding up the area of pixel. Besides, the ratio of coverage area to affected area is used as the aggregation density of green tide. The relationship between the ratio of coverage area of green tide from MODIS and CZI images and aggregation density of green tide is also analyzed. And we give the linear relationship of coverage area of green tide obtained from satellite images with different resolution.Results indicate that the average omission rate of CZI images (6.64%) is much lower than that of MODIS images (34.08%). In addition, the coverage area of green tide acquired from MODIS and CZI images is high linearly correlated, so both of them can be linear transformed. With the combination of CZI images and MODIS images, the daily coverage areas of green tide in the Yellow Sea in 2019, 2020 and 2021 are retrieved. The CZI-based maximum daily coverage areas of green tide were 2290 km², 336 km² and 1949 km², respectively, which are consistent with the evolutions in the countermeasures adopted by managers to control the green tide.This study shows that, CZI image has the advantages of lower omission rate and higher accuracy of coverage area in monitoring of green tide in contrast to the MODIS image. And the defect of observation frequency in CZI image will be improved by the linear conversion of coverage area of green tide from MODIS and CZI images. Then, the high-frequency and high-precision of the observation of green tide will be realized.关键词：green tide;Ulva prolifera;HY-1 CZI;MODIS;coverage area;omission rate;the Yellow Sea- 1013
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发布时间：2023-02-17 -
摘要：In recent years, various types of floating algal blooms have been reported in oceans and lakes around the world. These include “green tides” of Ulva, “golden tides” of Sargassum, “red tides” of Noctiluca, and cyanobacterial blooms, among others. These floating algal blooms not only represent hazards to the marine and lake ecosystems, but also cause economic losses and human health problems. The launch of new satellite ocean color sensors can improve the monitoring capability of existing satellites for floating algal blooms.to evaluate the capacity of HY-1C/D CZI sensors (50 m resolution, 3-day revisit) in detecting and differentiating various types of floating algal blooms, and to analyze the spectral characteristics of blooms of several major floating algae using CZI data. Based on the types and distributions of major floating algal blooms in the global oceans and lakes, CZI data are obtained and analyzed for their spectral characteristics from the visually identified bloom features in the CZI false-color Red-Green-Blue images. The spectral characteristics are presented from the difference spectra between the identified image features and the surrounding waters, thus minimizing the impact of atmospheric signals and water signals on the spectra. All floating algal blooms show elevated reflectance in the near infrared wavelengths. In the visible wavelength range, the spectral shapes of Ulva prolifera, Sargassum horneri, and red Noctiluca scintillas are different and therefore can be differentiated from each other. Other blooms, for example Trichodesmium blooms or Microcystis blooms, can also be differentiated once some ancillary information is available.Based on the spectral features and combined with the environmental characteristics, the multi-band Hy-1C/D CZI images are found to be able to detect and differentiate different floating algal blooms. This capacity is important for monitoring floating algal blooms in the Yellow Sea and East China Sea where “multi-tide outbreaks” of Ulva, Sargassum, and Noctiluca may occur at the same time. Because of the high spatial and temporal resolutions, such a capacity is expected to make HY-1C/D CZI an important satellite sensor in near real-time monitoring as well as in quantitative analysis of floating algal blooms in the near future.关键词：floating algae;HY-1C/D CZI;Ulva;Sargassum;Noctiluca;cyanobacteria- 904
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发布时间：2023-02-17 -
摘要：Intense cyanobacterial blooms often occur in eutrophic lakes, satellite data with high spatial resolution often has low temporal frequency, and nearly daily revisiting satellite data has coarse spatial resolution, limiting the monitoring of floating blooms in small lakes. The Coastal Zone Imager (CZI) onboard HY-1C/1D satellite provides a new data source to monitor the cyanobacterial bloom of inland lakes with 50 m spatial resolution, revisit time of 3 d. An index, namely the Adjusted Floating Algae Height (AFAH), is developed based on the difference between red band (Rrc(650)), and a baseline between green band (560 nm) and NIR band (near infrared, 825 nm). AFAH was then applied in four eutrophic lakes, for instance, Lake Taihu, Lake Chaohu, Lake Dianchi, and Lake Xingyun, and its advantages and uncertainties in monitoring cyanobacterial blooms were evaluated. The results showed that: (1) In cloudless conditions, AFAH, NDVI (Normalized Difference Vegetation Index), DVI (Difference Vegetation Index), and VB-FAH (Virtual baseline floating macroAlgae Height) have high accuracy larger than 0.93. AFAH has advantages over NDVI, EVI, and VA-FAH, as it is less sensitivity to the solar/viewing geometry, aerosol type and thickness, thin cloud, and sun glint. (2) The maximum gradient method is used to derive the AFAH threshold to extract bloom pixels, total of 180 images with cyanobacterial blooms are used to calculate the mean (0.041) and standard deviation (0.013) of AFAH threshold values. (3) The spatial distribution of cyanobacterial blooms from July, 2019 to July, 2021 in Lake Taihu, Lake Chaohu, Lake Dianchi, and Lake Xingyun is accordance with the previous studies. The areas with bloom frequency larger than 5% are 609.05 km2, 134.43 km2, 20.91 km2, and 14.50 km2 for Lake Taihu, Lake Chaohu, Lake Dianchi, and Lake Xingyun. The Zhushan Bay, Meiliang Bay, and western part of Lake Taihu, the western part of Lake Chaohu, and most part of Lake Dianchi and Lake Xingyun have high frequency of floating blooms. This study indicated that AFAH has good performance in detecting floating blooms using satellite data with only one NIR band, and multi-source satellite data should be combined in the following study in order to improve the temporal frequency of bloom monitoring.关键词：water color remote sensing;eutrophic lakes;cyanobacterial bloom;HY-1C/D satellite;Coastal Zone Imager (CZI);baseline subtraction method- 854
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发布时间：2023-02-17 -
摘要：Remote sensing is an important tool for monitoring floating macroalgae over the sea surface. In this study, three pairs of images obtained by a drone and high-resolution satellites (Sentinel-2 MSI, GF-6 WVF, HY-1C/D CZI) were selected to monitor the movements of floating green tide macroalgae (Ulva prolifera) in the Yellow Sea with multiple spatial scales. Based on the HY-1C/D double-star network, the point-to-point tracking of patches with a resolution level of 50 m within 2.45 hours is realized. During the observation period, the direction of wind and ocean current is relatively consistent and the superposition of the two effects makes the movement speed of macroalgae relatively high, with an average speed of 0.380 m/s. On the 10 m resolution MSI and WVF images, the water mass front or convergence area of macroalgae patches at different observation angles will lead to more or less bright and dark areas of the incoming pupil solar flare. This kind of convergence area characterized by solar flare anomaly has continued to exist 30 minutes, and has undergone significant movement, with an average velocity of about 0.2 m/s. Based on centimeter-level ultra-high-resolution UAV images, the movement observation of floating macroalgae can be realized from seconds to minutes, with an average speed of 0.066 m/s. Influenced by wind, small patches of macroalgae are distributed in a chain shape along the wind direction, and the angle between them is less than 15°. The results suggested that the orientations of macroalgae patches were consistent with wind directions, indicating the influence of wind on the movement of floating macroalgae. The convergent zones and the position changes of macroalgae patches identified from sun glitter reflected the influence of ocean hydrodynamics on the movement of macroalgae patches. This study demonstrates that high-resolution optical images can be applied for accurately monitoring the movement of floating macroalgae, as well as the investigation of the corresponding physical dynamic mechanisms.关键词：HY-1C/D CZI;GF-6;Sentinel-2;drone;movement;floating macroalgae;green tide;Ulva prolifera;the Yellow Sea- 765
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发布时间：2023-02-17 -
摘要：After an oil spill accident, it is essential to quickly detect the location, spatial coverage, pollution type, and amount of oil spills, so as to measure the impacts of different oil spill types, clean up the oil, and help the ocean recover. Although the mechanism and characteristics of optical remote sensing of oil spills have been basically clarified, the research on automatic oil spill extraction algorithms is still insufficient, which still needs to be addressed. The main challenge is that the significant variation of sunglint is helpful to the oil spill identification, but also brings many uncertainties to the extraction. Hence, the marine remote-sensing community is always committed to developing remote-sensing methods for improving the performance of aspects, such as preprocessing, segmentation, and classification. The scale of extraction is deemed the key to eliminating sunglint interference. As the conventional extraction method cannot be applied on the optical images directly for the reasons outlined in the context, a new man-machine interactive oil spill extraction method (more specific an oil–water mixture detector), which is able to eliminate sunglint interference was introduced. It accomplishes this by splitting the oil spill global region into adjacent sub-windows, as the sunglint can be considered constant in a small region. The proposed method and density-based clustering is used cooperatively in the method. The detector first discretizes different types of oil spills in images based on the specific optical detection principle of oil spills. Then the clustering uses the auxiliary information of multispectral images to achieve high confidence clustering output. The Coastal Zone Imager (CZI) onboard China’s HaiYang-1C/D (HY-1C/D) satellites can provide multispectral images with high spatial resolution and wide coverage for operational monitoring of oil spills. The proposed method was applied to HY/CZI oil spill dataset and other optical images of several oil spill events. The spatial differentiation of sunglint and remote sensing response characteristics of different oil spills in CZI were analyzed, and the accurate identification and extraction of oil spills in CZI images were realized. The results show that the optical remote sensing extraction method considering the variation of sunglint can effectively identify and extract the oil spills, and has good anti-interference ability. Based on testing of CZI data covering different areas, the method was proved to be effective in removing interference caused by sunglint, image interference (e.g. cloud, rough surface texture, ship wakes, illumination, shadowing), and so on. In addition, the method can further distinguish oil slicks and oil emulsions under the condition of weak sunglint, showing the ability to identify different oil spills, which can provide a reference for the operational application in oil spill monitoring. The results show stable variable-scale extraction accuracies of approximately 90.24% and 80.55% for oil slicks and oil emulsions, respectively. It is also applicable for the optical images with lower resolution, but the effect is inferior to that in CZI because of the influence of mixed pixels. As aforementioned, the accurate results are attributed to appropriate parameter adjustment under different spatial resolutions, oil spil types, and sunglint reflections. The satisfactory transfer applicability is mainly attributed to the variable-scale detector for sunglint reflection differences. In summary, the precondition for accurate oil spill extraction is to eliminate the sunglint interference, which depends on not only the sunglint model but also appropriate local scale, not global. Without a doubt, to what extent the coordination and utilization of sunglint and spectral information is the key to breakthrough for accurate oil spill extraction and quantification.关键词：HY-1C/D;sunglint reflection;oil spills;optical remote sensing;segmentation scale- 1112
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发布时间：2023-02-17
Satellite Remote Sensing for Ecological Disasters
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