Upwelling is a phenomenon which involves wind-driven motion of dense, cool, and usually nutrient-rich deep water towards the ocean surface replacing the warmer usually nutrient-depleted surface water. The deeper water is rich in nutrients, favoring the growth of seaweed and phytoplankton, and is characterized by high Chlorophyll-a (Chl-a) concentrations. Upwelling regions are considered as the most fertile fishing grounds and a so fundamental economic resource. In this paper, an approach for satellite monitoring of coastal upwelling regions is proposed based on Sea Surface Temperature (SST), and Chl-a information from Sentinel-3 OLCI Level-2 products, as well as, wind information from Copernicus Marine Environment Monitoring Service global product. The approach consists of the following parts. Firstly, using wind information the time periods of upwelling-favorable wind were identified. For these time periods, a thermal map is produced from Sentinel-3 SST products using a clustering approach. From the clustering result a vector file which contains the cold patches of upwelled water is generated. Lastly, Chl-a concentration information is parsed in the vector file. The approach was tested over the Benguela Upwelling System. The results are satisfactory and the proposed methodology is capable of detecting and monitoring the upwelling spatial extent and variations, as well as Chl-a concentration changes in the upwelling regions. The proposed methodology will be utilized within the framework of SEO-DWARF H2020 programme (MSCA-RISE-691071), in order to create the relevant metadata for Sentinel-3 OLCI Level-2 products.
SEO-DWARF (Semantic Earth Observation Data Web Alert and Retrieval Framework) is a project funded by the European Union Horizon 2020 research and innovation programme. The main objective of the project is to realize the content-based search of Earth Observation (EO) images on an application specific basis. The satellite images, which come from EO satellites such as Sentinels 1, 2 and 3, as well as ENVISAT, are distributed with few correlated meta-data which do not describe the phenomena and the objects included in the image. Innovative approaches to process remote sensing images can extract relevant information which semantically describes the land type, the region area border, objects and events such as oil spill. This information can be modeled as structured information through ontologies to be processed by algorithms to perform information retrieval and filtering. The proposed system is aware of the semantic elements which are relevant for final user and will be able to answer natural language queries such as “Show me the images of the Mediterranean Sea which include an algal bloom”. The possibility to retrieve a specific set of land images starting from a query expressed by a final user can quickly increase the interoperability and the diffusion of applications able to efficiently use EO data. In this work, we present a brief overview of the most successful application of this formalization strategy focusing on the tools and approaches for creating a robust and efficient domain geo-ontology. Furthermore, we describe the approach adopted to define the specific ontology used in the SEO-DWARF project, including the strategy adopted for implementing and populating it.
SAR spaceborne capability to detect marine oil spills through damping of short gravity-capillary waves has been
extensively demonstrated during past years. In contrast, it has not yet been found the optimal use of VIS/NIR imaging
sensors for detection and monitoring of oil polluted areas. We propose the use of Modis images acquired in sun glint
conditions to reveal smoothed regions such as those affected by oil pollution. According to Cox and Munk model, the
physical mechanism that allows detection of oil slicks under sun glint imaging of clear sea surface is based on the
modification of the wind-generated wave slopes distribution due the action of mineral oils.
The methodology is demonstrated for a number of case studies occurred in the Mediterranean Sea and North Atlantic
from 2001 to 2004. For each case, the oil slicks were detected by ERS SAR imaging and the corresponding Modis
images were acquired within a few hours the SAR passage under sun glint conditions.
The implemented procedure compares the water-leaving Cox and Munk sun glint reflectance with the reflectance
measured by Modis at the top of the atmosphere (TOA). To accomplish the task, the Modis imaging parameters and an
estimate of the wind vector are provided as input. The ECMWF analysis wind fields are considered for the purpose. It
was found that the ratio between the TOA reflectance and the C&M reflectance enhances the capability to detect oil
slicks. Moreover, an extensive analysis of the atmospheric effects on oil slick detection has been carried out by
performing simulations using the 6S code. Preliminary results show that atmosphere contribution to the reflectance has
little impact on oil slick detection, so that implementation of a surveillance procedure could be envisaged.
SAR spaceborne capability to detect marine oil spills through damping of wind-generated short gravity-capillary waves has been extensively demonstrated during past years. In contrast, it has not yet been found the optimal use of optical/NIR imaging sensors for detection and monitoring of polluted areas. We propose the use of Modis images acquired in sun glint conditions to reveal smoothed regions such as those affected by oil pollution. The underlying physical mechanism is based on the modification of the surface slopes distribution composing the roughened sea due to the action of mineral oils. The methodology is demonstrated for selected case studies in the Mediterranean Sea and North Atlantic where spills were detected by ERS SAR imaging. The corresponding Modis images acquired within a few hours were under sun glint conditions according to satellite imaging geometry and wind field distribution over the selected areas. Results of a detailed study about the effective applicability of the method is discussed. The importance of these results are based on the possible extensive exploitation of combined Modis and SAR data in view of the high repetitive coverage (about two times a day).