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 and water quality retrieving.

Firstly, we have made some progress on optical remote sensing image preprocessing. We developed an OWT (Optical Water Types) based method for flagging land-affected signal. The developed method improved the retrieval of water quality parameters. Results show a seasonality in the land-affected signal driven mainly by sun geometry and land cover. Besides, we tested different atmospheric correction models against in-situ hyperspectral data and evaluated their performance over coastal waters.

Secondly, we applied different kinds of satellite data to detect oil spills. We assessed the performance of Ultraviolet Imager (UVI) onboard Haiyang-1C/D (HY-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. Besides, we proposed an object-based spectra comparison (OBSC) 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 (NSCS). We demonstrated the capability of medium-resolution optical imagery in monitoring regional oil spills.

Thirdly, we developed several algorithms for retrieving water quality parameters, including CDOM (Colored Dissolved Organic Matter), Chla (chlorophyll-a), and water clarity. We proposed a blended CDOM algorithm based on OWT classification. Results showed that the blended algorithm has higher accuracy in CDOM estimating than a single algorithm for all waters. We also 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. 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.

Coastal regions are vital places for the economy, sustainability, and environmental care of entire nations with severe impacts on a global scale. However, coastal regions are vulnerable to natural disasters. The coastal regions are particularly exposed to extreme events and the effects of global climate change. Remote sensing (RS) technologies play a significant role for: i) monitoring disturbances of public/private infrastructures, ii) helping cultural/natural heritage preservation, iii) handling and maintain effective and updated disaster risk management plans, and iv) managing efficient agriculture processes. In this context, the joint European Space Agency (ESA) – Ministry of Science and Technology of China (MOST) DRAGON V GREENISH project was designed to develop and apply conventional and new algorithms for the detection and mapping of flooded areas, the analysis of urban climate-related threats and the anthropogenic disasters (e.g., ground subsidence in coastal areas and over reclaimed-land platforms), to improve the knowledge and develop innovative RS methods. GREENISH is the result of international cooperation between some European and Chinese research centers that operate in the remote sensing (RS) sector. The main project goals are: i) to detect and study the ground deformations in coastal/deltaic regions using conventional and novel interferometric synthetic aperture radar approaches; ii) to monitor changes through coherent and incoherent change detection analyses; iii) to study coastal erosion, using high-resolution optical and SAR images; iv) to assess sea level rise (SLR) and hydrogeological risks in urban coastal areas; v) to train Young Scientists (YS).

Within this framework, SAR remote sensing is a valuable tool for detecting and monitoring flood phenomena, allowing the differentiation between inundated and non-inundated areas. This work and the presentation planned at the next D5 symposium aim to summarizes the project's key achievements during the recent years and provide insights on the forthcoming activities. A special focus will be on the application/derivation of new RS techniques, also aided with artificial intelligence tools and methods. More specifically, starting from a sequence of calibrated, co-registered SAR acquisitions, the family of used methodologies for change detection analyses of Earth’s surface consists of different modules that span from the generation of proper change detection indices to the integration of these pieces of information with those achievable using novel interferometric SAR approaches, also aided by AI and multi-grid techniques. Moreover, methods about evaluation of regional disaster reduction risk capacity are also developed. Accessibility and location of emergency shelters in coastal mega city under extreme waterlogging disasters are also analyzed.