scholarly journals Satellite monitoring of flods by C-band radar data

2018 ◽  
pp. 4-12
Author(s):  
Dmytro Mozgovoy
Keyword(s):  
Image Processing ◽  
Spatial Resolution ◽  
Radar Data ◽  
Satellite Monitoring ◽  
Radar Images ◽  
Automated Image Processing ◽  
Monitoring Area ◽  
Flooded Areas ◽  
Sentinel 2

Automated image processing methodology is proposed for all-weather satellite monitoring of floods based on C-band radar data, which allows to determine the boundaries and areas of flooded areas when assessing the magnitude, dynamics and consequences of floods. Processing results comparison of medium spatial resolution scanner and radar images from Sentinel-1 and Sentinel-2 satellites is made. The advantages of a radar survey with cloudiness in the monitoring area are shown.

scholarly journals SATELLITE RADAR MONITORING OF OIL POLLUTION IN THE WATER AREAS BETWEEN ANAPA AND GELENDZHIK IN 2018–2020

2021 ◽  
Vol 49 (1) ◽  
pp. 163-185
Author(s):  
N. A. Knyazev ◽  
O. Yu. Lavrova ◽  
A. G. Kostianoy
Keyword(s):  
Black Sea ◽  
Oil Pollution ◽  
Radar Data ◽  
Oil Products ◽  
Satellite Monitoring ◽  
The Black Sea ◽  
Northeastern Part ◽  
Radar Images ◽  
Data Archives ◽  
Satellite Radar

The paper presents the results of satellite monitoring of oil pollution in the northeastern part of the Black Sea in the area between Anapa and Gelendzhik in 2018–2020. The monitoring was carried out using the archives of radar data obtained by SAR-C radars installed on the Sentinel-1A and -1B satellites. The work with the data archives was carried out using the tools of the “See the Sea” (STS) information system developed at the Space Research Institute of the Russian Academy of Sciences. The conducted satellite monitoring revealed the main sources of sea surface pollution with oil products in the study area. The overwhelming pollution (85%) is associated with discharges of water containing oil products from moving vessels. With the help of STS tools, a map of oil pollution detected on radar images was compiled, on the basis of which the main areas of oil pollution were identified. These include the main shipping routes to the Novorossiysk Sea Port, the anchorage of ships and the water areas of the Tsemes (Novorossiysk) Bay and Gelendzhik Bay. Seasonal and interannual variability of oil pollution was determined on the basis of satellite information for the area between Anapa and Gelendzhik. The results of the 2018–2020 monitoring were compared with those obtained during similar monitoring carried out in 2006–2010. It was concluded that there has been no reduction in the amount of detected pollution, which negatively affects the ecological state of the northeastern part of the Black Sea.

scholarly journals Combining Optical and Radar Satellite Imagery to Investigate the Surface Properties and Evolution of the Lordsburg Playa, New Mexico, USA

2021 ◽  
Vol 13 (17) ◽  
pp. 3402
Author(s):  
Iyasu G. Eibedingil ◽  
Thomas E. Gill ◽  
R. Scott Van Pelt ◽  
Daniel Q. Tong
Keyword(s):  
New Mexico ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Land Surface ◽  
Spectral Unmixing ◽  
Radar Images ◽  
Linear Spectral Unmixing ◽  
Radar Satellite ◽  
Sentinel 2

Driven by erodible soil, hydrological stresses, land use/land cover (LULC) changes, and meteorological parameters, windblown dust events initiated from Lordsburg Playa, New Mexico, United States, threaten public safety and health through low visibility and exposure to dust emissions. Combining optical and radar satellite imagery products can provide invaluable benefits in characterizing surface properties of desert playas—a potent landform for wind erosion. The optical images provide a long-term data record, while radar images can observe land surface irrespective of clouds, darkness, and precipitation. As a home for optical and radar imagery, powerful algorithms, cloud computing infrastructure, and application programming interface applications, Google Earth Engine (GEE) is an invaluable resource facilitating acquisition, processing, and analysis. In this study, the fractional abundance of soil, vegetation, and water endmembers were determined from pixel mixtures using the linear spectral unmixing model in GEE for Lordsburg Playa. For this approach, Landsat 5 and 8 images at 30 m spatial resolution and Sentinel-2 images at 10–20 m spatial resolution were used. Employing the Interferometric Synthetic Aperture Radar (InSAR) techniques, the playa’s land surface changes and possible sinks for sediment loading from the surrounding catchment area were identified. In this data recipe, a pair of Sentinel-1 images bracketing a monsoon day with high rainfall and a pair of images representing spring (dry, windy) and monsoon seasons were used. The combination of optical and radar images significantly improved the effort to identify long-term changes in the playa and locations within the playa susceptible to hydrological stresses and LULC changes. The linear spectral unmixing algorithm addressed the limitation of Landsat and Sentinel-2 images related to their moderate spatial resolutions. The application of GEE facilitated the study by minimizing the time required for acquisition, processing, and analysis of images, and storage required for the big satellite data.

scholarly journals Aplicación de imágenes Sentinel-1 y Sentinel-2 en la detección y delineación de información de crisis de desastres naturales en el marco de los servicios Copernicus EMS

2017 ◽  
pp. 49 ◽  
Author(s):  
U. Donezar-Hoyos ◽  
A. Larrañaga Urien ◽  
A. Tamés-Noriega ◽  
C. Sánchez-Gil ◽  
L. Albizua-Huarte ◽  
...  
Keyword(s):  
European Space Agency ◽  
Earth Observation ◽  
High Spectral Resolution ◽  
Radar Images ◽  
Space Agency ◽  
Optical Images ◽  
Different Types ◽  
Burnt Areas ◽  
Flooded Areas ◽  
Sentinel 2

<p>This study shows the inclusion of Sentinel-1 and Sentinel-2 images in the workflows to obtain of crisis information of different types of events and their applicability in the detection and monitoring of those events. Sentinel is an Earth Observation (EO) program that is currently being developed by the European Space Agency (ESA) in the scope of the Copernicus program operative since April 2012, formerly known as Global Monitoring for Environment and Security (GMES). This program comprises six missions, out of which three are active, Sentinel-1 that provides radar images, Sentinel-.2 providing High Resolution optical images and Sentinel-3 developed to support GMES ocean, land, atmospheric, emergency, security and cryospheric applications. The present paper describes the use of Sentinel-1 radar to detect and delineate flooded areas, and the MultiTemporal Coherence (MTC) analysis applied with pre and post-event images to delimit and monitor burnt areas and lava flows. With respect to Sentinel-2, its high spectral resolution bands allowed the delineation of burnt areas by calculating differences of vegetation and burnt indices using pre and postevent images. Results using Sentinel-1 and Sentinel-2 data were compared with results using higher spatial resolution images, both optical and radar. In all cases, the usability of Sentinel images was proven.</p>

scholarly journals The Complementary Use of Optical and SAR Data in Monitoring Flood Events and Their Effects

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 644 ◽  
Author(s):  
Melpomeni Zoka ◽  
Emmanouil Psomiadis ◽  
Nicholas Dercas
Keyword(s):  
Flash Flood ◽  
Radar Data ◽  
Flood Events ◽  
Threshold Method ◽  
Landsat 7 ◽  
Flooded Area ◽  
Earth Observation Satellites ◽  
Flooded Areas ◽  
Water Indices ◽  
Sentinel 2

This paper describes the synergetic use of earth observation satellites optical and radar data to detect flooded areas and explore the impacts of the flood event. A flash flood episode took place in May 2016, in the central-eastern part of West Thessaly (Central Greece). A Landsat-7 ETM+ and a Sentinel-1 SAR image were acquired. For Landsat-7 several water indices were applied and for the Sentinel-1 a threshold method was implemented. Furthermore, Sentinel-2 images were utilized so as to record the land use/cover of the flooded area. The inundated areas and the affected cultivations were delineated with high precision, and the financial effects were evaluated.

Fusion of rain radar images and wind forecasts in adeep learning model applied to rain nowcasting

2021 ◽  
Author(s):  
Anastase Charantonis ◽  
Vincent Bouget ◽  
Dominique Béréziat ◽  
Julien Brajard ◽  
Arthur Filoche
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Weather Forecast ◽  
Forecast Model ◽  
Radar Data ◽  
Learning Model ◽  
Rainfall Forecasting ◽  
Radar Images ◽  
Deep Learning Model ◽  
Rain Radar

&lt;p&gt;Short or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risks monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 minutes. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls. These results can also be found in Bouget, V., B&amp;#233;r&amp;#233;ziat, D., Brajard, J., Charantonis, A., &amp; Filoche, A. (2020). Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting. arXiv preprint arXiv:2012.05015&lt;/p&gt;

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