Conference Agenda

Altimeter and radiometer are import means for marine dynamic phenomenon monitoring by remote sensing. In this paper we will present a comprehensive comparison of marine remote sensing products e.g. wave height, mesoscale eddy, sea ice, and sea surface salinity. The performance and accuracy of remote sensing production are validated.

For oceanic dynamic production, sea surface height and significant wave height of Sentinel-3A/3B SRAL are compared at their self-crossovers and mutual-crossovers, and biases, trends and precisions of these data are analyzed. Furthermore, mesoscale eddy detection applications based on unified Sentinel-3A/3B SRAL data are analyzed by comparing with the mesoscale eddy detection results of the Jason-2/Jason-3 series satellites.

For sea surface salinity, a new method is introduced to estimate the representativeness error and apply it to the triple collocation data set of Argo, SMAP and SMOS for the year of 2015-2017. The spatial-temporal scales of all three data sets are studied and the representativeness error as well as the random errors is obtained.

For sea ice, ice freeboard extracted by CryoSAT-2 and Sentinel-3 are compared at different waveform retracking methods, snow models and retracking thresholds. Accuracy of two kinds of ice freeboard productions is also validated by Operation IceBridge data.

With an increasing number of SAR systems in space that are operated at different frequencies
and provide different imaging modes, there is a need to re-evaluate strategies for operational sea
ice mapping and for scientific process studies such as interactions between atmosphere, sea ice,
and ocean. In our presentation we will focus on aspects of using images acquired at different
radar frequencies, different polarizations, and spatial resolutions. Examples will be shown that
demonstrate how the anaylsis of sea ice conditions can be improved for a given problem such
as thin ice detection and classification or identification of ice deformation structures. For this purpose
we used L-, C-, and X-band SAR images from different satellites and from airborne campaigns
acquired over different ice regimes. We address the additional gain of complementary data sources
such as optical and thermal images and model simulations of ice growth.

Sea ice is a fundamental component of the Earth climate system since it influences directly the albedo of our planet and regulates the heat exchange between the atmosphere and the ocean. The launch of the EC/ESA's CryoSat-2 and Sentinel-3 missions offer the opportunity to observe the near real-time sea ice thickness information. However, the characteristics of individual radar types differ for the available altimeter missions. Hence, it is important and our goal to study the consistency between single sensors in order to develop long and consistent time series. Here, a comprehensive comparison between freeboard measurements of the CryoSat-2 and Sentinel-3 is tested. We first examine the relationship between snow depth and ice and radar freeboard by comparing in situ snow depth measurements from OIB missions. And then move to the intercomparison of freeboards from CryoSat-2 and Sentinel-3 to investigate the penetration of the radar signal into the snow layer. We also compare total ice freeboard calculated from CryoSat-2 and Sentinel-3 with OIB data. Further we also investigate whether we can improve the accuracy of the freeboard retrieval from CryoSat-2 and Sentinel-3 by changing the threshold used in the retracking process, and examine the merits of adjusting the retracking threshold in a quest for an accurate sea-ice freeboard estimation.

The main detection modes of the existing sea-ice microwave remote sensors are normal incidence (0°, represented by the altimeter) and medium-angle incidence (20°-60°, represented by the scatterometer and SAR). With the development of the remote sensing technology, the low-incidence-angle microwave detectors have been put into use increasingly, which are represented by the Precipitation Radar (PR, Global Precipitation Measurement program) and the Surface Wave Investigation and Monitoring (SWIM, CFOSAT Satellite). Both of the Dual-frequency Precipitation Radar (DPR) and the SWIM can observe the Pole sea ice and have the potential of the sea-ice detection. SWIM is now in the in-orbit test phase whose data has not been released yet. Therefore, the microwave scattering characteristics of the sea ice at the low-incidence angles will be studied, and the sea-ice detection capability of the DPR will been assessed based on the CryoSat-2 and DPR scatterometer data of the Antarctic Weddell Sea from 2014 to 2019 in this paper. Firstly, the Cryosat-2 and DPR backscattering coefficients are matched for the resolution and the temporal-spatial information, the type information of the objects (including three types of the sea ice, sea water and leads) provided by the Cryosat-2 L2I products are also added in. It is ensured that each point in the study area contains both the backscattering coefficients of the two remote sensors and the type information; Secondly, intra- and inter-annual analysis of the sea-ice backscattering coefficients derived from the Cryosat-2 and DPR are carried out. For example, during the period of the sea-ice freezing, developing and melting from January to December of each year, the microwave scattering characteristics of the sea-ice at the low-incidence angles are studied based on the DPR data, which are compared with the Cryosat-2 normal incidence mode. Thirdly, intra- and inter-annual analysis of the different types for the backscattering coefficients of the Cryosat-2 and DPR are carried out. For example, during the freezing period of the sea ice, the DPR backscattering characteristics of the sea ice, sea water and leads are compared, which are compared with those of the Cryosat-2. Finally, the sea-ice detection capability of the DPR at low-incidence angles is evaluated. In the next step, we will add the sea-ice thickness information for research and analysis.

The Sentinel-3A and Sentinel-3B satellites, which were equipped with SAR Rader Altimeter (SRAL), were launched by ESA on February 26, 2016 and April 25, 2018, respectively. The simultaneous observations of Sentinel-3A/3B satellite altimeters will increase the spatial and temporal coverage of altimeter global observations and improve their data application. In this study, observation data (Sea Surface Height, Significant Wave Height and backscattering coefficient) and corrections data (wet troposphere correction, ionosphere correction and sea state bias correction) of Sentinel-3A/3B SRAL are compared at their self-crossovers and mutual-crossovers, and biases, trends and precisions of these data are analyzed. Data unification method of Sentinel-3A/3B SRAL data is given based on their comparisons. Furthermore, mesoscale eddy detection applications based on unified Sentinel-3A/3B SRAL data are analyzed by comparing with the mesoscale eddy detection results of the Jason-2/Jason-3 series satellites, and the joint application capabilities of Sentinel-3A/3B SRAL data are summarized.

Sea surface salinity (SSS) is one of the key parameters for us to understand the oceans better. Since the L-band radiometers SMOS, Aquarius and SMAP have observed the SSS from space for years, the scientific community have devoted enormous efforts to the validation of the retrieved SSS data, based on the “double match” procedure between the in-situ and remote sensed measurements. However, the direct comparison based on the double match procedure has its limitations. Firstly, it assumes the in-situ data is error free and only give the relative accuracy of remote-sensed SSS data. To resolve this problem, the triple match method, which uses three independent SSS data sources to develop a triple collocation data set, can estimate the random error of all three data sets. Secondly, the in-situ data present the “point” measurements and its spatial-temporal scale is clearly smaller than the space borne SSS data which is the spatial average within the antenna footprint with the typical value of 100 km. Consequently, the in-situ data contain the true small-scale SSS variation information which cannot be resolved by radiometer retrieved SSS data. The effect of this small-scale SSS is neglected in the direct comparison method and it is regarded as a part of remote sensed SSS error which leads to an overestimation of retrieved SSS error. In the triple match method, researchers introduce the representativeness error to describe the effect of this small-scale SSS variations in high resolution SSS data. However, the estimation of representativeness error remains challenging. In this study, we introduce a new method to estimate the representativeness error and apply it to the triple collocation data set of Argo, SMAP and SMOS for the year of 2015 ~2017. The spatial-temporal scales of all three data sets are studied and the representativeness error as well as the random errors is obtained. It is founded that the ascending order of spatial-temporal scale of all three data sources is Argo, SMAP and SMOS. The representativeness error (SSS variations observed by Argo and SMAP but not by SMOS) is 0.093 psu². The random error of Argo in-situ measurements is better than 0.21 psu which is superior to the other data sources. At the spatial-temporal scale of SMOS, the random error of SMOS (0.41 psu) is better than SMAP (0.45 psu). But at the spatial-temporal scale of SMAP, SMAP has the lower random error (0.32 psu) than SMOS (0.51 psu).

he amplitude of internal solitary waves is one of the key techniques for parameters inversion on remote sensing images. The relationship is established, which aims to match optical remote sensing characteristic parameters with internal solitary wave factors. In this paper, based on the mechanism of optical remote sensing imaging, combined with the dimension analysis method and similarity principle of hydrodynamics, an optical remote sensing detection system for internal solitary waves is constructed in the laboratory, as shown in Figure 1. The optical remote sensing imaging characteristics of internal solitary waves under the condition of two-layer fluid are studied. Internal solitary waves are generated in a three-dimensional straight flume by gravity collapse method. Optical platform and observation recording equipment are set up to complete the continuous synchronous observation of optical remote sensing images and internal solitary wave factor images in the field of view. The optical remote sensing response of internal solitary waves is detected in the laboratory.

The convergence and divergence of surface are modulated by the propagation of internal solitary waves in the pycnocline. In addition to bright-dark pattern distance, the grayscale change is also a significant phenomenon in the experiment. Figure 2 shows the synchronous response of optical remote sensing of internal solitary waves extracted by time series method. Therefore, the relationship between gray difference of optical remote sensing images and amplitude of internal solitary waves is explored in the laboratory. The amplitude of the incident internal solitary wave is setting by the height of gravity collapse, the water stratification is setting by the thickness ratio of upper and lower fluid. The experimental results show that the gray difference caused by internal solitary waves on optical remote sensing images increases with the increase of amplitude. The linear fitting for the scatters is shown in Figure 3. The relationship between gray difference and amplitude of all collapse heights under the same stratification is Δgray=0.57A+0.80. With the increase of the proportion of upper water depth to total water depth, the gray difference caused by internal solitary waves with the same amplitude decreases, as shown in Figure 4. The quantitative expression is k_Δgray-A=4.00exp(-8.13h₁/h). The change of grayscale coincides with the fact that the upper layer of the real ocean is thicker in winter and internal solitary waves with small amplitude are difficult to be observed.

Since the large-scale bloom in 2008, green tide, as a marine natural disaster, happens every year along the coast of Qingdao. It brings huge economic losses to society every year. So, obtaining the real time dynamic information about green tide distribution becomes very urgent. Generally, researches on green tide are mainly focused on the coverage area. For Operational Application of Disaster Emergency Response,the influence rangeof the green tide is what people isconcerned about. The influence rangeof the green tide can not only give information about the gaps between small green tide patches but also show the trend of development of greed tide. Our research is mainly about the influence range of the green tide. Wedesigned an algorithm for extracting the green tide distribution boundaries automatically.Principle of the algorithmis based on mathematical morphology dilation/erosion operation. Several issues such as the division of green tide regions, the extraction of basic distributions, the abnormity of distribution contour, and the elimination of islands, are solved in the paper. Since green tide mainly bursts along the Qingdao Coast and there is no established system so far, a green tide monitoring system is built. The system is based on IDL/GIS secondary development technology in the integrated environment of RS and GIS. It has the abilities of RS monitoring and information extraction. Optical remote sensing and microwave remote sensing are employed in this system. Special processing flow and information extraction algorithm are designed according to the different characteristics of these data. Without using this system, a complete data process from beginning to endingneeds 2 hours, but it can be finished in 10-15 minutes now in our system. The system runs smoothly and successfully in the State Oceanic Administration for three years till now.

Mesoscale eddies are rotating coherent structures of ocean currents, which generally refer to ocean signals with spatial scales from tens to hundreds of kilometers and time scales from days to months. Eddies can be found nearly everywhere in the world ocean, and dominate the ocean’s kinetic energy. Over the recent decades, with the advancements in remote sensing satellites and the abundance of in-situ observations data, people find that mesoscale eddies can transport water, heat, salt, and energy as they propagate in the ocean. By combining satellite altimetry and Argo profiling float data, the analysis of eddy three-dimensional structure becomes an important part of studying the oceanic eddy.

The South China Sea (SCS) is the largest marginal sea in the tropics and has a maximum depth of over 5000 m. Mesoscale eddies are an important phenomenon in the SCS, that change dynamic conditions within the ocean and play an important role in the transport of heat, salt and other chemical substances. SCS climate is part of the East Asia monsoon system. In winter, the SCS is dominated by the strong northeasterly monsoon, whereas in summer the winds reverse direction to southwesterly. The alternating monsoons in winter and summer lead to the transformation of the upper circulation and formation of several seasonal eddies in the SCS. The higher eddy kinetic energy (EKE) centers in the SCS are observed to the east of Vietnam and to the west of Taiwan, areas that are also characterized as having high mesoscale eddy activity.

In this paper, we investigated mean properties and the spatiotemporal variability of eddies in the SCS, identified using the winding angle method and 25 years of satellite altimetry data. A series of statistical analysis methods were used to study the statistical characteristics of the mesoscale eddies in the region, e.g., eddy number and lifetime, geographical distribution of eddies, evolution of eddy properties, and seasonal variation of eddy activities. Then, based on Argo profile data and climatology data, the eddy synthesis method is used to construct the three-dimensional temperature and salt structure of the eddy in SCS. The growth and decay of long-lived eddies and characteristics and mechanism of temporal variation in eddy activity are incompletely documented.

During the last decade the radar altimetry has entered in its golden age as demonstrated by the different number of missions (Jason-2/-3, CryoSat-2, Saral/Altika, Sentinel-3) currently operating and the forthcoming ones (Sentinel-6). The relatively new operational synthetic aperture radar (SAR) mode in CryoSat-2 and Sentinel-3 missions, opens a new paradigm in the capabilities that can offer an altimetric radar mission. In this line, a scientific proposal within the DRAGON-4 tries to exploit the lessons learned from classical 2-D SAR focusing to evaluate the imaging-like capability of delay-Doppler altimetric radar mounted on Sentinel-3 over coastal areas. In this way, the altimetric product gets closer to the conventional SAR imaging data, but in the altimeter case a “strip-like” image is obtained compared to the classical 2D SAR image.

Conventional delay-Doppler processor (DDP) coherently integrates a series of pulses (around some miliseconds) to provide specific Doppler beams focused to a specific location, which after being correctly aligned provide several looks that can be incoherently averaged, increasing the performance in terms of geophysical retrieval and leading to along-track resolutions of ~300 m. The fully focused DDP moves one step ahead and performs a coherent processing over the entire aperture defined by the along-track antenna beamwidth (around some seconds) to get an even higher along-track resolution (~0.5 m) and with higher number of looks for the same resolution as the conventional DDP.

The main objective of the scientific proposal within the DRAGON-4 is to evaluate the potential capabilities offered by the fully focused DDP (FF-DDP), specifically when exploiting state-of-the-art Sentinel-3 operational synthetic aperture radar (SAR) mode. This will confer the SAR altimetric product a very high resolution (in the order of 0.6 m) of great interest for Coastal Altimetry (being able to get closer to the coastline), providing much higher number of looks that can be averaged to improve the altimetric performance as anticipated by Raney in [RD-1].

The core of this presentation is to show the operation of the FF-DDP and its evaluation using simulated data. The current implementation of the processor, based on a backprojection algorithm, will be revisited. The potential capability of the FF-DDP in terms of resolution will be verified using point target simulations. This exercise will be complemented by processing open ocean scenario simulations with a comparison on the retrieved waveforms against conventional DDP. ESA Sentinel-6 simulated data with its two operation modes (RAW and RMC) will be exploited as testbed; these data sets might be complemented with simulations from the new altimetric mission proposal PICE (polar ice).

References:

[RD- 1] Raney, R. K., 1998. The delay/Doppler radar altimeter. IEEE Transactions on Geoscience and Remote Sensing, 36, 1578–1588. doi:10.1109/36.718861.

[RD- 2] Egido, A., & Smith, W. H. F., 2017. Fully Focused SAR Altimetry: Theory and Applications, in IEEE Transactions on Geoscience and Remote Sensing, 55, 1, 392-406, Jan. 2017. doi: 10.1109/TGRS.2016.2607122

[RD- 3] E. Makhoul, M. Roca, R. Escolà, A. Garcia-Mondejar, G. Moyano, P. Garcia, M. Fornari, M. Kuschnerus, R. Cullen. S6 P4 GPP: Fully Focused Delay-Doppler Processing applied on RAW and RMC data- preliminary results, in Ocean Surface Topography Science Team Meeting, 27-28 September 2018.

It is presented the recent progresses of ocean surface winds and typhoons remote sensing including (1) GF-3 SAR ocean wind retrieval: the first view and preliminary assessment; (2) Preliminary analysis of Chinese GF-3 SAR quad-polarization measurements to extract winds in each polarization; (3) Assessments of ocean wind retrieval schemes and geophysical model functions used for Chinese GF-3 SAR data at each polarization; (4) Combined co- and cross-polarized SAR measurements under extreme wind conditions; (5) Sea Surface Wind Speed Retrieval and Validation of the Interferometric Imaging Radar Altimeter Aboard the Chinese Tiangong-2 Space Laboratory; (6) Top cloud motion field of Typhoon Megi–2016 revealed by GF-4 Images.

Thanks to the Satellite Hurricane Observation Campaign (SHOC) initiative, the ESA Sentinel-1 mission planning team allows acquisitions over Tropical Cyclone since 2016. This data collection yielded to a catalogue of about 100 hits over Tropical Cyclone (TC) eyes. In parallel, the hurricane watch program from CSA also organises acquisitions over TC eyes. This study co-analyses data from the two missions and presents performances of our algorithm for ocean surface wind field retrieval at high resolution. As a first step the quality of the Normalized Radar Cross Section (NRCS) for both polarization and sensors is compared and found to be very consistent. The relationship between NRCS, wind speed and direction is analyzed for extreme cases. Then, the wind speed performances are compared to other satellite remote sensing data, airplane measurements and analysis from experts in TC centers (tracks). The impact of rain on the ocean wind measurement is discussed. Finally, to complement the NRCS, other radar parameters such as Doppler Centroid and the energy of the MeAn Cross-Spectra (MACS) high frequency part are also analyzed. In particular, we show how MACS could be used to constrain the wind retrieval.

A new space-borne dataset of global ocean swell, called Environment Monitoring Services (CMEMS) “fireworks” Level-3 product, derived from Sentinel-1A/B Level-2 ocean swell spectra is presented and its performances are assessed. The Level-2 swells inverted from synthetic aperture radars (SAR) images are retro-propagated along the great circle and refocused at their remote origins (coinciding strong storms), producing a higher level product to describe the swell temporal and spatial evolution from origin until land across the oceans. The Level-3 “fireworks” are now operationally produced by Copernicus CMEMS.

Here, we assess their performances using sentinel-1A/B wave mode data for the period from July 2016 to Nov. 2018, based on the “virtual” buoy concept. Reference data are in situ directional wave measurements from two different buoy networks:National Data Buoy Centre (NDBC) and Coastal Data Information Program (CDIP). Comparison results show a good agreement between Sentinel-1 Level-3 swells and buoy measurements, with root mean square error of 48 cm, 45.66 m and 21.21° for swell height, peak wavelength and direction, respectively. Influence of buoy network on the validation results are also examined, revealing better wave directional measurement accuracy for Waverider buoys used in CDIP than in NDBC network.

Sentinel-1 A & B SAR constellation is collecting data in Wave Mode (WV) and ESA is routinely producing and delivering Level-2 Ocean products with ocean surface wind, waves and radial velocities. In particular, the wave component of this product contains the image cross spectra (real and imaginary parts) and the 2D ocean spectrum with associated waves partitions. Here, we focus on the wave measurements originating from Tropical Cyclones. As a first step, we developed a method to filter out the wave partitions with low quality. This quality control procedure is performed for each acquired track and relies on the expected swell consistency between successive acquisitions along any given track. This method is evaluated against model outputs (statistical analysis) and buoys (case study). Then, we analyze the waves properties (wavelength and wave propagation direction) with respect to the Tropical Cyclones properties. The impact of storm size, translation speed and intensity on the extended fetch and waves escaping from the storm source is illustrated and discussed.

Swells from strong storms can spread over very long distances. Ocean currents alter this propagation, with the possible formation of constructive or destructive interference. This effect, still neglected in current models of atmospheric, oceanic, and even wave prediction, is often traced in current measurements, altimetry or even scatterometry / radiometry at medium and high resolution.

Large-scale currents are indirectly measured by satellite. Since the small-scale currents are generally unknown, we propose to consider them as random in wave dynamics simulations. Specifically, the statistical spatial structure of these currents is inferred from large-scale currents through self-similar assumptions. The temporal correlations of the small-scale currents is neglected due to the short-time wave-current interaction. The dispersion ratio is modified and becomes stochastic. From there, we can derive and simulate the random dynamics of wave group the rays. Analytic and semi-analytic solutions have also been derived for simple – though realistic – cases.

Our results not only improve wave simulation capabilities, but also bring new insights about the large wave’s developments at small scales and the wave-current effects on satellite measurements. At longer term, those type of random dynamics will bring new data assimilation procedures for joint wave-current estimations from space.

Nearly ¾ of the world’s mega cities are by the sea and almost 80% of the global population live within 100km from the coast. These numbers make very clear that coastal regions worldwide are not only socially important but also economically critical with e.g. harbours, fish farms and exploitation sites for natural resources (oil/gas rigs or offshore wind farms). Moreover the seaside is important for recreational activities and as a natural habitat for local marine life. With respect to the extreme importance of the coastline, constant monitoring of this region is compulsory. Just like the diversity of perspectives in the coastal areas, the diversity of maritime information is complex. Many parameters and layers of information are needed to obtain a comprehensive picture for a given application.

Driven by the self-evident advantages of Earth observation methods to monitor large areas while keeping costs at a reasonable level, numerous methods have been developed and improved by the Chinese and German Dragon partners to extract maritime information from satellite-based sensors and will be outlined in the presentation. While most of the information is only available separately, the combination of different information layers is needed to generate a holistic maritime situation awareness. For this an integrated platform is needed to simultaneously visualize and generate a synopsis of different types of information, selected according to the respective application.

We present a prototype of a web-based near-real-time information platform to combine information such as sea state, wind information, AIS messages, SAR-based ship detections and sea ice information to obtain a thorough maritime situation awareness. The system can be expanded for oil spill detection and other relevant information and can thus serve as a powerful decision support system for national or international authorities e.g. in catastrophy or disaster management. The platform can help uncover exceptional conditions or behavior and represents an important constituent in sustainable coastal management.

Between 2008 and 2016, there were mass summer blooms of Enteromorpha in the Yellow Sea, China. It covered an area of thousands of square kilometers annually, lasting an average of 90 days. The blooms seriously affected the marine ecological environment and attracted considerable research attention. Remote sensing data, model predictions, and marine environment ecological data measured by ships before, during, and after the Enteromorpha blooms were used in this study of the national aquatic germ plasm resources of Qianliyan Island area. Underwater robots survey trepang, wrinkles abalone, and submarine ecological status. We found that the time taken by Enteromorpha to cover the national aquatic germ plasm resources of Qianliyan Island area was relevant, as were changes in sea surface temperature (SST). The Enteromorpha made a rise in inorganic nitrogen, reactive phosphate, and heavy metals content in upper, middle, and bottom layers of sea water, dissolved oxygen (DO) and pH were reduced; and there were changes in the dominant animal and plant population. Enteromorpha sedimentation during out-breaks was measured by benthos sampling. Considerable growth in starfish number was obtained by underwater robot observation. All of this directly influenced the regional ecological environment. Numbers of trepang and wrinkles abalone were declined over the years. Global warming and SST anomalies are the two main reasons for frequent marine disasters that take place. National aquatic germ plasm resources of Qianliyan should be protected from the blooms.

The simulation of fully nonlinear steady-state large-amplitude internal solitary waves in continuously stratified fluids based on the 2D incompressible Euler equations with Boussinesq approximation is carried out with Gerris, an open source fluid dynamics software. The large-amplitude solitary wave structure and characteristic parameters simulated by the fully nonlinear Gerris implementation of Euler model and by the weakly nonlinear KdV model are compared. The results indicate that high-order nonlinear terms should not be neglected when large-amplitude are concerned for studying internal solitary wave. The results simulated by Gerris reveal that the wavelength of isopycnic surface of a fully nonlinear large-amplitude internal solitary wave varies with depth, which makes it doubtful to retrieve the internal wave amplitude using the distance between two extreme values of internal wave pattern extracted from a spaceborne SAR image based on the analytical solution of the KdV equation. Therefore, the retrieval method is necessary to be reassessed. The validity of the interal solitary wave modeling with Gerris is tested by two sets of in-situ measurements of internal waves.

The internal solitary wave (ISW) amplitude is one of the most important parameters of ISWs. Knowing its variation is helpful for understanding ISW energy transferring, dissipating and mixing processes. Synthetic aperture radar (SAR) has been considered as a powerful instrument for deriving ISW amplitude as it can provide a wide view of ISW evolution independent from daylight, cloud coverage, and weather conditions. However, the derivation of ISW amplitude by SAR images in a two-layer shallow water system is much sensitive to the upper layer thickness. So the accurate estimation of upper layer thickness is crucial for determining the ISW amplitude. In this paper, we present a novel method of finding the best-fit values of upper layer thickness within their reasonable ranges from consecutive SAR images based on the extended Korteweg–de Vries (eKdV) equation, to derive ISW amplitude. An ISW case observed twice by the Chinese C-band SAR GaoFen-3 (GF-3) and the German X-band SAR TerraSAR-X (TS-X) with temporal interval of approximately 11 minutes, in a shallow water depth of around 74 m at the southeast of Hainan Island, is used to demonstrate the method. Compared to the representative amplitude estimation of -4.43 m - 6.99 m derived by classic KdV equation in a continuously stratified ocean, the proposed method yields an amplitude of -4.67 m, which indicates the new method can provide reliable ISW amplitude estimation. To further illustrate its practicability in the case when there were no nearly synchronous in-situ measurements with the satellite observation, the typical climatological datasets World Ocean Atlas 2013 (WOA13) are used to perform the new method in the Hainan case, and the results show the new method has prominent advantages in amplitude estimation in shallow water than the conventional method based on classic KdV theory in a continuously stratified ocean.

The HY-2B satellite is the second dynamic environment satellite in China. It was successfully launched on October 25th, 2018 with a sun-synchronous orbit at an altitude of ~970km. Repeat cycles of 14 days are planned for the first two years with oceanographic purpose and 168 days geodetic cycles will follow for the third year of the mission. The satellite is equipped with a Ku/C bands altimeter and the orbit is determined thanks to SLR, GPS.

The HY-2B satellite altimeter provides sea surface height, significant wave height, sea surface wind speed, and polar ice sheet elevation. First of all, the description of instruments and the instrument parameters will be put forward briefly in this research. And then, the current status of the HY-2B products will be described in detail, including the measurement accuracy.

Comparing with Jason-2 and Jason-3 satellite radar altimeters and on-site buoys, the objects of comparison include significant wave height, sea surface wind speed, and sea level anomaly and so on. It is found that the precision of HY-2B satellite radar altimeter secondary products reaches the same kind of satellite radar altimeter products in the world, and some products are better than Jason-2 and Jason-3 standard products.

During over the past 10 years, the massive green macro-algal bloom has regularly occurred in the Yellow Sea, the spatial coverage of which is mainly derived by the remote sensing community from satellite images with moderate/low resolution (30-m~1000-m), such as the 250-m-resolution MODIS (Moderate Resolution Imaging Spectroradiometer). In this paper, the MODIS estimates are compared for the first time with the concurrent high resolution (3-m) airborne Synthetic Aperture Radar (SAR) data. We find that the MODIS results are overestimated by more than a factor of 3 when each algae pixel is assumed to be pure (i.e. 100% algae cover), whereas the overestimation is significantly reduced to 1.14 when the pure pixel assumption is abandoned and the genuine (fractional) algae coverage is derived with the linear pixel un-mixing method. These results, together with the re-sampling processing of the high resolution images, indicate that the mixed pixel effect, that is inherent with images with moderate and low resolutions, is the key factor for the satellite extraction of the macro-algae coverage, and these findings are further confirmed by the satellite data with different resolutions. Besides, significant correlations (R²>0.9) are found between the macro-algae coverage from 3-m resolution SAR images and those from concurrent satellite images with various resolutions (30-m~1000-m) under the pure pixel assumption, which provides an alternative statistics-based method (in addition to the linear pixel un-mixing) for the accurate macro-algae coverage extraction from satellite images with coarse resolution (e.g. HJ-1 CCD, AQUA MODIS, COMS GOCI). This new method is independently validated with high resolution optical images, and applied to derive the annual maxima of the massive green macro-algal bloom areas (fractional coverage) in the Yellow Sea from 2007 to 2016, which ranges from 45.6~732.9-km² with an average of 247.9 ± 199.3-km².

High-resolution satellite earth observation data are available in large archives, as data collection increases the ability to inspect and investigate each scene becomes impossible due to the scale and quantity of observations. Computer assisted classification, segmentation and description of satellite data over aquatic bodies can provide invaluable information for focusing analysis to experts and the general public on everyday use of water resources.

Convolutional Neural Networks are capable of classifying and segmenting objects across thousands of images in a fraction of the time a human operator would require inspecting these images visually. These Deep Learning networks have previously been applied to classifying both land usage and land cover, they have been shown to be accurate using multi- or hyper-spectral data such as those collected by the Sentinel-2 MultiSpectral Instrument.

In this work, a training dataset consisting of coastal and in-land waters has been assembled from Sentinel-2 imagery covering multiple sites across North America, South Africa and China and extensively labelled to be compatible with Deep Learning methods. Convolutional Neural Networks developed and trained for natural image classification and segmentation have been extended and retrained through transfer learning to detect and segment the extents of Algal Blooms and River Plumes in the imagery.

Current Convolutional Neural Network architectures are evaluated to establish best approaches to leverage spectral and spatial data in the context of water classification. Several spectral data configurations are used to evaluate competency and suitability for generalisation to other Optical Satellite Sensor configurations. The impact of the atmospheric correction technique applied to data is explored to establish the most reliable data for use during training and requirements for pre-processing data pipelines.

Spaceborne SAR has been proved to be a valuable tool in measuring sea surface winds and waves in coastal waters. In this study we present evidence to show that 1) ships and ocean front have impact on SAR wind retrieval and 2) uncertainly wave retrieval exists under the hurricane conditions.

We acquired a large number of Sentinel-1 SAR images in the Yangtze River estuary, China, where the ocean environment is very complicated due to the exchanges of coastal and Changjiang Diluted Waters. The root-mean-square error between SAR wind speeds and buoy measurements reaches up to 3.81 m/s. Analysis of quasi-synchronous SAR images and sea surface temperature (SST) observations shows that the main possible causes for such a large bias are the impact of ships or changes in the atmospheric stability induced by SST change across the ocean front on the sea surface backscatter signal received by radar. A change of 1 °C in SST at low wind conditions may lead to an error of 1~2 dB in the satellite observed normalized radar backscatter cross section (NRCS). The existence of ships at sea surface even results in a falsely high NRCS value. Since the accuracy of wind speed estimation from SAR is strongly dependent on the accuracy of the NRCS measurement, great cautions should be taken when generating or using SAR wind products. Consideration of the above-mentioned effects on the NRCS may improve the accuracy of the estimated wind speeds to a certain extent.

We also investigate the performance of the wave retrieval algorithm (PFSM) when it is applied for dual-polarization C-band Sentinel-1 SAR. SAR-derived significant wave height (SWH) and mean wave period (MWP) are compared with simulation results from the WAVEWATCH-III model. The validation shows a 0.69 m root mean square error (RMSE) of SWH with a -0.01 m bias and a 0.62 s RMSE of MWP with a -0.17 s bias. Although the PFSM algorithm relies on a good quality SAR spectrum, this study confirms the applicability for wave retrieval from Sentinel-1 SAR images. Moreover, it is found that the retrieved results have less accuracy on the right sector of cyclone eyes where swell directly affects strong wind-sea, while the PFSM algorithm works well on the left and rear sectors of cyclone eyes where the interaction of wind-sea and swell is relatively weak.

Within the context of coastal area management, that includes promoting sustainable economy, preserving biodiversity and ensuring population safety, the continuous and effective monitoring of the shoreline is primary need. Nonetheless, a rigorous definition of the shoreline is ambiguous to some extent since it is influenced by bathymetry, tide level, etc. and, in addition, shoreline position continuously changes due to urbanization, deforestation, accretion/erosion, etc. It was shown that remote sensing tools, including optical/microwave satellite sensors and aerial UAV surveys, are valuable information sources to provide systematic observations of the shores to be integrated with ground surveys, i.e., GPS measurements.

The exploitation of spaceborne SAR measurements can improve optical-based shoreline extraction, which is affected by solar illumination and weather conditions. In addition, it was shown that polarimetric information provides extra-benefits for shoreline extraction purposes, i. e., the algorithms are more robust and accurate. Nevertheless, reliable pixel-wise land/sea separation is still a challenging task since the latter is hampered by the several SAR imaging and environmental effects that include inherent speckle noise, limited spatial resolution, bathymetry, high sea state conditions and coastal morphology.

In this framework, in this study, the applicability of patch-based filters to reduce speckle noise in polarimetric SAR imagery is investigated for shoreline extraction purposes. In fact, with respect to standard pixel-wise speckle filters, the patch-based approaches for speckle reduction exploit the measurements redundancy to look for similar local patches within the polarimetric SAR image. Then, according to a statistical-based similarity criterion, a speckle-reduced polarimetric SAR observable, i. e., coherency matrix, is obtained from which land/sea separation can be performed according to a given metric. Hence, in this study, the capability of the patch-based paradigm to be applied on polarimetric SAR images for speckle filtering is investigated and evaluated in terms of accuracy in the shoreline position. Selected showcases will be presented at the conference time to quantitatively evaluate the improvements in shoreline extraction accuracy performance.

SAR measurements have proven to be a very useful tool for tropical cyclone monitoring and forecasting applications. The sea surface wind maps derived from the SAR cross-polarized channel can provide fine-scale information about the tropical cyclone (TC) inner core. A hurricane morphology and sea surface wind vector estimation model (SHEW) based on measurements acquired by the C-band SAR onboard RADARSAT-2 has been recently proposed [1]. A limitation of this model is that it only deals with hurricanes of circular or elliptical-shaped eyewalls. In this study, a new parametric model, which uses SAR observations and allows for asymmetric description of the TC wind structure around the eyewall in storm centric coordinates, is developed. SAR observations from
 TCs in the North Atlantic and East Pacific basins are analyzed to determine the azimuthal and radial asymmetry typical in these mesoscale systems. The new asymmetric directional wind model adjusts the widely used Holland (1980) axis-symmetric model to account for the different azimuthal asymmetries of TC winds. The model will be tested against collocated NOAA hurricane hunter observations (i.e., dropsondes and the Step-Frequency Microwave Radiometer or SFMR) and its performance will be compared with other existing models, such as, the Holland [2], SHEW [1], and Olfateh [3] models. Showcases will also be presented to demonstrate the improvements related to the proposed model.

[1] Zhang, G., W. Perrie, X. Li, and J.A. Zhang. (2017), A Hurricane Morphology and Sea Surface Wind Vector Estimation Model Based on C-Band Cross-Polarization SAR Imagery, IEEE TGRS, 55(3), 1743-1751.

[2] Holland, G. J. (1980), An analytic model of the wind and pressure profiles in hurricanes, Mon. Weather Rev., 108(8), 1212–1218.

[3] Olfateh, M., D. P. Callaghan, P. Nielsen, and T. E. Baldock. (2017), Tropical cyclone wind field asymmetry— Development and evaluation of a new parametric model, J. Geophys. Res. Oceans, 122, 458–469, doi:10.1002/ 2016JC012237.

Wind is a sustainable and alternative resource for producing energy and it has a good reputation of being a green form of electricity. Within this context, wind turbines are widely used at onshore and offshore sites to convert the energy of moving air into electrical power. For this reason, wind turbines are a critical infrastructure whose monitoring is an important issue for both economy and environment protection. Within this context, remote sensing can be an important tool to guarantee an effective and relatively cheaper monitoring. Optical images have the great advantage of being simple to interpret and they are easily obtainable. However, optical radiation is severely affected by cloud cover, solar illumination, and other adverse meteorological conditions. These problems can be solved using radar sensors, which guarantee all-day and almost all-weather acquisitions, together with a wide area coverage. In particular, the Synthetic Aperture Radar (SAR) can be very useful for intertidal zone monitoring purposes, because of its fine spatial resolution.

The objective of this study is quantifying the added-value of polarimetric information to detect metallic targets. On this purpose, a very challenging scenario is considered that consists of mud flat area where wind turbines are present. To make the analysis fair, we selected detection algorithm that are able to work with both full- and partial-polarimetric information, i.e.; Polarimetric Notch Filter (PNF) and the change detection approach proposed in [1] and [2], respectively.

Experiments, undertaken on actual SAR data collected over the intertidal zone near Jiangsu, China, by the C-band RadarSAT-2 and Sentinel-1 missions show that the proposed methodologies, well detect the wind turbines inside mud flat areas. Furthermore, a detailed analysis shows that polarimetric information always guarantees performance better than the single–polarization counterpart.

[1] A. Marino, (2013), “A Notch Filter for Ship Detection With Polarimetric SAR Data", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), 1219-1232.

[2] A. Marino; S.R. Cloude, and J. M. Lopez-Sanchez, (2013), “A New Polarimetric Change Detector in Radar Imagery”, IEEE Transactions on Geoscience and Remote Sensing, 51(5), 2986 -3000.

The study of coastal wetlands is of paramount importance due to both anthropomorphic activities and natural phenomena, which threaten the stability of land and safety of the people.

However, the monitoring of coastal wetlands is not trivial due to the presence of different kind of habitats that include coastal plain, coastal beaches, rocky shorelines, salt marshes, mangrove, seagrass beds, mud flats and sand bars. For this reason, the study of wetlands results very challenging.

Within this context, remote sensing plays an important role for coastal wetlands monitoring. Optical images have the great advantage of being simple to interpret and they are easily obtainable. However, optical radiation is severely affected by cloud cover, solar illumination, and other adverse meteorological conditions. These problems can be solved using radar sensors, which guarantee all-day and almost all-weather acquisitions, together with a wide area coverage. In particular, the Synthetic Aperture Radar (SAR) can be very useful for intertidal zone monitoring purposes, because of its fine spatial resolution.

The main goals of this study are to develop multi-polarimetric and multi-temporal methods to effectively monitor the wetland area of the WWT Caerlaverock in Scotland one of the most important wetland in the United Kingdom. The test site was selected since it is severely affected by coastal erosion that makes the monitoring a very important issue.

For this purpose, two methodologies based on the joint use of co- and cross-polarized channels [1] and on the polarimetric notch filter [2], are used to both extract the profile of the coastal area and to detect the wetlands.

Preliminary results are obtained processing a set of full polarimetric SAR (PolSAR) data collected at C-band from RadarSAT-2 sensor. The results show that PolSAR data can be effectively used to detect both coastline and wetlands.

[1] Nunziata F., Buono A., Migliaccio M., Benassai G. (2016), “Dual-Polarimetric C- and X-Band SAR Data for Coastline Extraction" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-Stars), Volume: 9, Issue: 11, Pages: 4921 - 4928

[2] Marino A. (2013), “A Notch Filter for Ship Detection With Polarimetric SAR Data” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-Stars), Volume: 6, Issue: 3, Pages: 1219 - 1232

Wind speed retrieval is a subject of paramount importance since wind estimation is extremely useful for different meteorological and oceanographic applications: problematics like coastal erosion, climate change and so on are strictly connected with wind parameter. In this context, most of the remote sensing satellite radar can provide sea surface wind field data. In particular, microwave sensors, mainly scatterometer and Synthetic Aperture Radar (SAR), are worldwide recognized as the most suitable sensors for wind field retrieval. Radar backscattered signal contains quantitative information about the condition of the sea surface roughness and, hence, can be used to infer sea surface wind data. One of the most challenging case of wind speed retrieval is represented by the tropical cyclone case. Although, tropical cyclones are among the most dangerous and destructive natural disasters, current models are still not able to give an accurate forecast of their intensity and track. Typically, in literature, scatterometer, and then SAR, data are used to implement Geophysical Model Function (GMF) to extract wind speed information. These functions link the Normalized Radar Cross Section (NRCS) with wind speed and wind direction.

While in this work, a spectral based technique is adopted: the azimuth cut-off approach.

When managing SAR microwave sensors, Doppler misregistration in azimuth occur because of the gravity wave orbital movement. This issue is the major responsible of the imaged spectrum and of a strong cut-off in the azimuthal direction: this is the azimuth cut-off. This technique is used to retrieve wind speed and a few investigations have been carried out improve this approach [1]. More in detail, there is a straight link between λc values and geophysical parameters, similar to wind speed and critical wave tallness. In [1] the ACF-based λc approach has been improved to manage high wind speed routines, e.g.; extreme weather conditions. The key issues that allow to stretch out the technique to high wind regimes concern the tuning of a strategy that takes into account pixel spacing, box size and the homogeneity of the SAR image. In particular, the box size is set to be 1 km × 1 km and the median filter window is set at 90-120 m.

It is revealed in recent study that λc is related with wind speed at strong winds. In this study, we try to retrieve wind speed from Sentinel-1 SAR images in hurricanes and typhoons. The SAR-derived λc is compared with simulated azimuthal cutoff wavelength using the wave spectrum from numeric wave model in the three part of typhoon wave system. The retrieval wind speed is validated against measurements from the Soil Moisture Active Passive (SMAP) radiometer.

[1] V. Corcione, G. Grieco, M. Portabella, F. Nunziata and M. Migliaccio, “A novel azimuth cut-off implementation to retrieve sea surface wind speed from SAR imagery,” IEEE Transaction on Geoscience and Remote Sensing, vol. XXX, no. XXX, pp. XXXX-XXXX, 2018.

Sea oil spill monitoring is of extreme importance for researchers, ecologists, local authorities and a wider set of stakeholders since ocean pollution is a serious threat since, every day, a significant amount of oil is released into the maritime environment due to operational vessel procedures, accidental collisions, land-based discharges and all oil-related human activities. From a scientific perspective, a systematic and reliable support to sea oil pollution monitoring can be found in the exploitation of satellite-based sensors. Among them, it was shown that Synthetic Aperture Radar (SAR) plays a key role due to its almost all-weather capability to provide fine resolution (few meters) imagery with dense revisit time (few days).
The sea oil spill detection by means of SAR imagery is possible due to the damping effects oil slicks have on the wind-driven short sea waves that result, in the intensity images, in more or less homogeneous patches darker than the surrounding sea. Nevertheless, SAR-based sea oil spill detection still remains a challenging task since there exist several natural phenomena that generate the same signature in the intensity SAR imagery. They are termed as oil look-alikes and include internal waves and currents, grease ice, oceanic fronts, algal blooms, phytoplankton, etc. The latter represent false alarms that significantly reduce the performance of oil spill detection and classification algorithms.
In literature, when dealing with single-polarization SAR data, intensity-, texture- and morphologicalbased features, i.e., grey level co-occurrence matrix parameters, backscattering coefficients, shape, size and perimeter of the slick, etc. are usually adopted that often result in non-reliable results or in low detection accuracy. Those issues can be at least partly overcome by exploiting polarimetric information, even though this is paid at the expense of the area coverage thus limiting the operational application of polarimetric SAR architectures.
Hence, in this study, a new spectral method is applied on single-polarization SAR imagery for sea oil spill monitoring: the azimuth cut-off. The latter is a distortive non-linear effect occurring when the observed surface is moving during the SAR acquisition time. This is the case, for instance, of the ocean. In literature, several studies have been carried out to analyze the dependence of azimuth cutoff wavelength on sea surface parameters as wind speed, wave height, etc. In this study, preliminary results obtained on actual C-band SAR measurements collected over certified oil spills and lookalikes are shown that exhibit a different sensitivity with respect to the azimuth cut-off wavelength.

Satellite and Argo observations of dissolved oxygen responses to “Wind Pump” in the Bay of Bengal

Huabing Xu^{1, 3}, Danling Tang^{1, 3*}, Jinyu Sheng², Yupeng Liu^{1, 3}, Yi Sui²

Effects of tropical cyclones (TCs) “Wind pump” on dissolved oxygen (DO) in subsurface waters (20-200 m) over the Oxygen Minimum Zones (OMZs) in the Bay of Bengal (BoB) are examined based on Argo and satellite data. Five TCs (Hudhud, Five, Vardah, Maarutha and Mora) sweeping the central BoB during 2013-2018 are considered. Our analyses reveal three types of DO temporal variability caused by the storm-induced mixing and upwelling. The first type occurred during TC Hudhud features temporal increases of DO in subsurface waters (37-70 m) caused mainly by intense vertical mixing and downwelling. The second type features DO reductions in subsurface waters respectively after four TCs (Hudhud, Five, Maarutha and Mora) attributed to storm-induced upwelling. The third type occurring during TC Vardah features temporal increases of DO at depths between 40-79 m and decreases at depths between 80-150 m due to the combined effect of strong vertical mixing and upwelling. These three types of DO responses can occur in different areas, depending on TC intensity, translational speed and Ekman pumping. The temporal DO variability is also influenced by the shallow oxycline (58.3±16.7 m), mesoscale eddies and biochemical processes. Due to TC intensification, a pre-existing oceanic cyclonic eddy produced a large upwelling and induced a long time of DO decrease in the subsurface layer. This study suggests three different types of DO responses along TC track in the OMZ, which would help us to evaluate the influence of TC on OMZ.

In studies of upwelling usually data from infrared and optical sensors are used which provide information on the sea surface temperature (SST) and the chlorophyll-a (Chl-a) concentration. In this paper, we show that also synthetic aperture radars (SAR) images can also give valuable contribution to such studies. Upwelling regions become detectable by SAR because they are associated with a reduction of the radar backscatter due to 1) a change of the stability of the air-sea interface or/and 2) the presence of biogenic slicks. While the change of the stability of the air-sea interface due to the presence of cold surface water in the upwelling region causes only a small reduction of the radar backscatter, biogenic cause a very strong reduction, usually of more than 10 dB. In areas of strong upwelling, the biological productivity is high due to increased nutrient supply from lower water levels. The biota living in this area secrete surface active material that ascends to the sea surface and forms there biogenic slicks, which often cover large areas in the form as monomolecular layers. The biogenic slicks damp the short-scale surface waves, which are responsible for the radar backscattering, as strongly as mineral oil films, and thus areas covered with the surface films become visible on SAR images as areas of strongly reduced radar backscatter (dark areas). Biogenic slicks often are entrained in the surface current field of eddies and thus render the structure of eddies visible on SAR images.

This paper focuses on upwelling events off the coast of Namibia (caused by northward directed coastal winds), off the coast of South Africa (caused by intensification of the meandering Agulhas Return Current), off the southern coast of Sicily (caused by strong southeastward directed coastal winds, in particular by the Mistral), and off the north coast of Taiwan (caused by the interaction of the Kuroshio Current with shallow bottom topography). These upwelling events are studied using Sentinel-1 and GF-3 SAR images, Modis SST and Chl-a maps and model data of geostrophic surface currents. We show that this synergism yields new insights into upwelling mechanism. In particular, we show that upwelling events are often associated with the generation of filaments, internal waves, and small-scale eddies, which are detectable by SAR.

Rain is one common phenomenon usually observed on SAR images. Its signatures on C-band images are often composed of very bright patches and adjacent dark patches. It is caused by the radar backscattering or attenuation from rain-induced structures on the sea surface (ring waves, splash products, and turbulence) and the hydrometeors in the atmosphere (liquid water or ice). Although some models have been developed and laboratory experiments have been conducted to explain rain impact at C-band, the vertical non-uniform distribution of raindrops and the presence of ice aloft are often ignored. In this study, we co-analyze Sentinel-1 C-band SAR data together with high temporal and spatial resolution weather radar NEXRAD. NEXRAD provide different precipitation products, including rain rate (1 hour/0.25 km), basic reflectivity (less than 5 min /0.5 km) and hydrometeor classification (less than 5 min/0.25 km). More than 747 SAR images in both co- and cross-polarization have been collocated to provide statistics of NRCS under rain, at 1 km resolution and less than 5 min between SAR and NEXRAD. Our results evidence that NRCS at both VV and VH increase with rain rate, for low to moderate wind regimes. In addition, the very bright patches obtained at both polarizations are found to be in relation with melting ice particles in the atmosphere, by examining the NEXRAD hydrometeor classification. Our analysis also reveals the possible importance of rain impact after the rainfall, with a decrease of the backscatter observed after intense rainfall. This indicates the need to analyze the rain event history to take into account rain effect persistency after the rain event. Finally, examples in the specific case of hurricane are discussed.

.

A semi-automated pocket FerryBox monitoring system was installed on a cargo ship in October 2015 to continuously record in situ chemical oceanographic data along a transect between two cities across the Bohai Strait, China for one year. In 2016, 2 cruises by research vessels that had an automated FerryBox system installed covered the Bohai Strait in the summer and winter. This report summarizes the outcome of this one-year campaign that used FerryBox as a water quality monitoring tool in the Bohai Strait. Temperature, salinity, dissolved oxygen, turbidity and saturation were the 5 parameters selected to describe the seasonal patterns in the Bohai Strait during the one-year period. Evidence of a short-term spring bloom event was found between the FerryBox transects, and the hydrodynamic factors that are the main controlling forces of the water quality in the strait are discussed. The importance of using FerryBox as a valuable and robust tool in future coastal operational observation networks is emphasized

Sand excavation can transport sediments into surrounding waters and thus raise the suspended sediment concentration. However, assessment of the area that is influenced by sand excavation and the strength of this influence is not easy due to the temporal and spatial variability of the background suspended sediment concentrations. Remote sensing can provide data before and after sand excavation activities and thus provides a possibility to estimate the variation in suspended sediment resulting from sand excavation. Sand excavation generally occurs in rivers or estuaries. The medium resolution of ocean color satellite data makes it difficult to obtain appropriate information in estuaries or rivers because of the spatial resolution of the sensors, the narrow spans of the rivers and the effects from the adjacent land. Sand excavation in the Pearl River Estuary has become frequent in recent years due to the development of urbanization in China. The Hong Kong-Zhuhai-Macau Bridge crosses the Pearl River Estuary and is the largest bridge and tunnel project in the world. The suspended sediment generated by upstream sand excavation was doubted to have a significant impact on the suspended sediment in the tunnel region. In this paper, we assessed the impact of upstream sand excavation on the suspended sediment in the Hong Kong-Zhuhai-Macau Bridge construction area using Landsat OLI, ETM+ and TM data. Regional suspended sediment algorithms were developed for Landsat using a symbolic regression method based on data from 25 cruises in the study area from 2003 to 2014. A band shift was conducted on the remote sensing reflectance data from Landsat ETM+ and OLI to produce a time series of suspended sediment that was internally consistent with that of Landsat TM data. The suspended sediment distribution was extracted and used to compare two different conditions, with and without sand excavation. The correlation of suspended sediment in different regions in the surrounding waters, including the correlation between the construction regions and the sand excavation regions, was calculated. Our results indicate that the sand excavation in the upstream region of the Pearl River Estuary has limited impact on the surface suspended sediment concentrations in the Hong Kong-Zhuhai-Macau Bridge tunnel area.