Conference Agenda

Our project aims to develop and validate innovative products for inland, transitional and coastal waters to support and improve the water ecosystem services, sustainable management and security. We have made some progress on the algorithms and applications of optical remote sensing images on oil spill detecting, harmful algal blooms monitoring, and water quality retrieving.

We developed several kinds of methods to detect oil spills based on different kinds of satellite data. Firstly, we assessed the performance of Ultraviolet Imager (UVI) onboard Haiyang-1C/D satellites by the following aspects: image features of oils under sunglint, sunglint requirement for spaceborne UV detection of oils, and the stability of the UVI signal. The results indicated that in UVI images, it is sunglint reflection that determines the image features of spilled oils, and the appearance of sunglint can strengthen the contrast between oils and seawater. Secondly, we proposed an object-based spectra comparison approach to extract emulsified oil slicks from Balikpapan Bay, Indonesia, using optical imagery from Sentinel-2 Multispectral Instrument (MSI) and PlanetScope. We used optical imagery from Landsat-8 OL to detect oil slicks on the ocean surface through spatial analysis and spectral diagnosis in the northern South China Sea. We demonstrated the capability of medium-resolution optical imagery in monitoring regional oil spills. Thirdly, through laboratory measurements, numerical simulation, and Hue-Saturation-Value model, we illuminated the multispectral mixed characteristics of oil emulsions and demonstrated Hue's role in characterizing the mixture features and oil concentration trends. Then, we proposed the Hue-based oil emulsion classification and oil concentration segmentation methods, and applied to Landsat-5 images under quantified uncertainties.

We developed several kinds of methods to detect Harmful Algal Blooms (HABs) based on satellite data and ground-based data. Firstly, we investigated the ultraviolet (UV) reflection spectra of cyanobacteria blooms using the Haiyang-1C/D UVI data, and identified that the blooms have significant UV reflection features associated with the floating status. Secondly, we developed a deep learning-based automatic extraction of cyanobacterial blooms from Sentinel-2 MSI satellite data, and indicated the high potential of the cyanobacterial blooms extraction model based on deep learning in further high-precision and automatic extraction of cyanobacterial blooms from large-scale water bodies. Thirdly, we applied the ground-based multispectral remote sensing data to detect cyanobacteria blooms, and proposed a new technical method to dynamically monitoring cyanobacteria blooms, which can operate under cloud cover and provide accurate and continuous spatiotemporal patterns of the blooms.

We developed several algorithms for retrieving water quality parameters, including water color, chlorophyll-a, and water clarity. Firstly, we applied Sentinel-2 MSI data to monitor changes in water color in two optically complex river systems: the Yangtze and Danube using the Forel-Ule Index (FUI). The results revealed contrasting water color patterns in the two rivers on both spatial and seasonal scales. Secondly, we proposed an optical classification algorithm to exclude highly turbid waters, and then to estimate Chla in the less turbid waters only. We constructed an exponential estimation model based on Rrs(NIR)/Rrs(red), and applied the model to Landsat TM and OLI images in Lake Taihu to analyze its Chla spatiotemporal distribution. Thirdly, we also proposed a modified model of the quasi-analytical algorithm to retrieve the water clarity of inland waters across Hainan Island, China using Sentinel-2 multispectral instrument data. Based upon this, the first spatiotemporal analysis of recent water clarity in Hainan Island was conducted.

We developed an empirical algorithm for identifying blooms of Alexandrium minutum in coastal waters of Spain from Sentinel-2 MSI and Sentinel-3 OLCI using in-situ radiometric data and biogeooptical data. The algorithm was calibrated and validated using in situ data from two sources: dedicated field campaigns, and the weekly monitoring program conducted by the Technological Institute for the Control of the Marine Environment of Galicia (INTECMAR). Here we also present some first results from our recent campaigns in the Black Sea to characterise its bio-optical properties and assess satellite products for the area.

The project aims at exploiting microwave satellite measurements to generate innovative added-value products to observe coastal areas characterized by harsh environments, even under extreme weather conditions. The following added-values products are addressed: water pollution, estimation of oil slick thickness using model-based NN methods, intertidal area monitoring, observation of green algae, detection and tracking of icebergs. The following activities have been addressed:

Water pollution

Previous activities: Theoretical scattering models (under monostatic and bistatic configurations) have been developed to predict sea surface scattering with or without surfactants. In the monostatic case, theoretical predictions have been contrasted with actual measurements collected by the Synthetic Aperture Radar. A model has been developed to shed light in the prediction of oil-sea contrast using different combinations of scattering (AIEM and two scale BPM) and damping (Marangoni and MLB) models.

New activities: The AIEM model has been used to train a NN to infer info about sea oil thickness. A dual-polarization method is developed to exploit time-series of dual-polarimetric SAR data for sea oil pollution observation.

Target detection

Previous activities: Multi-polarization backscattering from a known ship observed at different incidence angles. The analysis is carried on using metrics based on both power and phase information.

New activities: A new metric is defined, namely the polarization signature of the degree of polarization, that can be used to better asses the scattering variability at the variance of incidence angle for both sea and targets.

The polSAR backscatter from PAZ imagery acquired over the Robin Riggs wind farm is analysed to estimate blade rotation using sub-aperture analysis.

Intertidal area monitoring

Previous activities: A data set that consists of X-band (CosmoSkyMed and PAZ), L-band (ALOS-2) and C-band (RadarSAT-2 and Sentinel-1) polarimetric SAR scenes has been acquired in the Scottish Solway Firth intertidal area to discuss the variability of the polarimetric scattering against SAR frequency and incidence angle over a common area.

New activities: A classification scheme, which includes polarimetric and intensity info, is developed to partition the intertidal area according to the scattering classes.

Observation of green algae

New activities: A multi-polarization analysis to infer scattering information about green algae at sea is proposed and tested using Sentinel-1 C band SAR imagery and optical measurements.

Iceberg detection and tracking

New activities: The C33 iceberg which, calved from the Antarctica continental ice, drifted in the Terra Nova Bay for more than 1 month has been observed using dual-polarimetric SAR measurements.

All this matter will be detailed in the proposed piece of study.