Water is an indispensable resource not only for humans but for all living being on earth. Conservation and management of water resources helps sustain and thrive life and also prevent its destruction. Water management can include activities such as monitoring the changing course of rivers and streams, regional planning, flood management, agriculture, and so on, all of which requires survey and planning, including accurate mapping of water bodies. Hence, extraction of water bodies from remote sensing data is critical to record how this dynamic changes and map their current forms. The remote sensing data used here is SAR, which is a powerful imagery for information extraction, as it is unaffected by cloud cover, acquires images overnight, enables all-weather imaging, and it is cost effective compared to other imageries. This deep learning model can be used to automate the task of extracting water bodies from SAR imagery.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band Sentinel-1 C band SAR GRD VH polarization band raster.OutputBinary raster representing water and non-water classesApplicable geographiesThe model is expected to work well in the United States.Model architectureThe model uses the DeepLab model architecture implemented in ArcGIS API for Python.Accuracy metricsThe model has a precision of 0.945, recall of 0.92 and F1-score of 0.933.Training dataThis model is trained on manually classified training dataset. Labels were created by using Sentinel-1 C band SAR GRD VH polarization imagery using histogram based thresholding method, followed by QA and manual cleaning to get water masks.Sample resultsHere are few results from the model.

Groundwater is the water that soaks into the ground from rain and can be stored beneath the ground. Groundwater floods occur when the water stored beneath the ground rises above the land surface. The Synthetic Aperture Radar (SAR) Seasonal Flood Maps shows observed peak flood extents which took place between Autumn 2015 and Summer 2021. The maps were made using Synthetic Aperture Radar (SAR) images from the Copernicus Programme Sentinel-1 satellites. SAR systems emit radar pulses and record the return signal at the satellite. Flat surfaces such as water return a low signal. Based on this low signal, SAR imagery can be classified into non-flooded and flooded (i.e. flat) pixels.Flood extents were created using Python 2.7 algorithms developed by Geological Survey Ireland. They were refined using a series of post processing filters. Please read the lineage for more information.The flood maps shows flood extents which have been observed to occur. A lack of flooding in any part of the map only implies that a flood was not observed. It does not imply that a flood cannot occur in that location at present or in the future.This flood extent are to the scale 1:20,000. This means they should be viewed at that scale. When printed at that scale 1cm on the maps relates to a distance of 200m.They are vector datasets. Vector data portray the world using points, lines, and polygons (areas). The flood extents are shown as polygons. Each polygon has information on the confidence of the flood extent (high, medium or low), a flood id and a unique id.

Dates of Images:9/20/2022 - 10/31/2022Date of Next Image:Varies by region, typically 12 days since previous pass. Set time slider to most recent interval and click on area of interest to identify date of last pass.Summary:The Alaska Satellite Facility has developed false color Red, Green, Blue (RGB) and Radiometrically Terrain-Correct (RTC) composites of the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument which assigns the co- and cross-polarization information to a channel in the composite. When used to support a flooding event, areas in blue denotes water present at the time of the satellite overpass before or after the start of the flooding event.Sentinel-1 RGB Decomposition of RTC VV and VH imagery over United States coastlines. Blue areas have low returns in VV and VH (smooth surfaces such as calm water, but also frozen/crusted soil or dry sand), Green areas have high returns in VH (volume scatterers such as vegetation or some types of snow/ice), and Red areas have relatively high VV returns and relatively low VH returns (such as urban or sparsely vegetated areas).To identify the date of an image either set the time slider to a 1 day interval or zoom in to AOI, set time slider to desired range and click on the imagery. The name will contain a full date and time in a format like this example:S1A_IW_20210705T231453_DVR_RTC30_G_gpufed_27F4The date and time would be 07/05/2021 at 23:14:53 UTC.Additional SAR InformationSuggested Use:In this image, water appears in blue, vegetated areas in shades of green and urban areas in bright orange. It is recommended to use this product with ancillary information to derive flooded areas. Satellite/Sensor: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1A/B satelliteNOTE: Sentinel-1B is no longer acquiring data and is only available into December 2021Resolution:30 metersCredits: Sentinel data used in this derived product, contains modified Copernicus Sentinel data (2019-2022), processed by ESA, Alaska Satellite Facility.Esri REST Endpoint:RGB Product:https://asf.img.arcgis.com/arcgis/rest/services/ASF_RTC/ASF_S1_RGB/ImageServerRTC Product:https://asf.img.arcgis.com/arcgis/rest/services/ASF_RTC/ASF_S1_RTC/ImageServerData Download: Data can be downloaded via the Pop-Ups in the Web Map or through each service's attribute table.

The dataset used in this study is designed to facilitate the monitoring and detection of Harmful Algal Blooms (HABs) using Synthetic Aperture Radar (SAR) remote sensing and deep learning models. The dataset includes Sentinel-1 SAR C-band (TIF), Sentinel-2 MSI (TIF), and Water indices (TIF) that were utilized as input dataset in the deep learning model. The dataset used in this study originates from external sources and is not the property of the authors. If reused, proper attribution to the original sources is required in accordance with their respective citation guidelines. The authors have modified the dataset for research purposes.

Ship detection plays an important role in port management, in terms of ship traffic, maritime rescue, cargo transportation and national defense. Satellite imagery provides data with high spatial and temporal resolution, which is useful for ship detection. SAR data has advantages over optical data, as microwaves are capable of penetrating clouds and can be used in all types of weather. SAR data is also useful for locating ships during storms for rescue missions. Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputSentinel-1 C band SAR VV polarization band raster.OutputFeature class containing detected ships as polygons.Model architectureThis model uses the Faster R-CNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe model has an average precision score of 0.70 on our validation dataset.Training dataThe deep learning model was trained using the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) which is prepared using Sentinel-1 imagery.Sample resultsHere are a few results from the model. To view more, see this story.

The map shows observed peak flood extents which took place between Autumn 2016 and Summer 2017. The map was made using Synthetic Aperture Radar (SAR) images from the Copernicus Programme Sentinel-1 satellites. SAR systems emit radar pulses and record the return signal at the satellite. Flat surfaces such as water return a low signal. Based on this low signal, SAR imagery can be classified into non-flooded and flooded (i.e. flat) pixels.Flood extents were created using Python 2.7 algorithms developed by Geological Survey Ireland. They were refined using a series of post processing filters. Please read the lineage for more information.The flood map shows flood extents which have been observed to occur. A lack of flooding in any part of the map only implies that a flood was not observed. It does not imply that a flood cannot occur in that location at present or in the future.This flood extent map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas). The flood extents are shown as polygons. Each polygon has information on the confidence of the flood extent (high, medium or low), a flood id and a unique id.

Dates of Images:8/21/2019; 8/23/2019; 8/25/2019; 8/26/2019; 8/31/2019; 9/2/2019; 9/3/2019; 9/4/2019; 9/5/2019; 9/6/2019; 9/7/2019; 9/8/2019; 9/9/2019Date of Next Image:UnknownSummary:The Alaska Satellite Facility has developed a false color Red, Green, Blue (RGB) composite image of the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument which assigns the co- and cross-polarization information to a channel in the RGB composite. When used to support a flooding event, areas in blue denotes water present at the time of the satellite overpass before or after the start of the flooding event. Suggested Use:In this image, water appears in blue, vegetated areas in shades of green and urban areas in bright orange. It is recommended to use this product with ancillary information to derive flooded areas. Satellite/Sensor: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1A/B satellite; 30 m resolution Credits: Sentinel data used in this derived product, contains modified Copernicus Sentinel data (2019), processed by ESA, Alaska Satellite Facility, and NASA Marshall Space Flight Center.Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/hurricane_dorian_2019/sentinel1_rgb/MapServer/WMSServer

Delineation of flooded areas obtained from SAR images (COSMO-Sky Med / Sentinel-1) for selected case studies occurred in Massa, Vilanova i la Geltrù and Oasoladea. These maps were use in SCORE WP 8 to validate the effectiveness of a Digital Twin of the three cities developped in SCORE.

Deliverable 8.11 titled 'Early warning and spatial Digital Twin Assessment Report'

Date of Image:2/8/2023, 2/9/2023, 2/10/2023Date of Next Image:UnknownSummary:2/8/2023 (EOS-RS, JAXA ALOS-2)The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Turkiye (Turkey) and Syria due to the M7.8 and M7.5 earthquakes that occurred on 6 Feb 2023. This map was derived from synthetic aperture radar (SAR) images acquired by the ALOS-2 satellite operated by the Japan Aerospace Exploration Agency (JAXA) before (7 Apr 2021 and 6 Apr 2022) and after (8 Feb 2023) the event.2/9/2023 (EOS-RS, Copernicus Sentinel-1)The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Turkiye (Turkey) and Syria due to the M7.8 and M7.5 earthquakes that occurred on 6 Feb 2023. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (12 Oct 2022 to 28 Jan 2023) and after (9 Feb 2023) the event.2/10/2023 (EOS-RS, Copernicus Sentinel-1)The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Turkiye (Turkey) and Syria due to the M7.8 and M7.5 earthquakes that occurred on 6 Feb 2023. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (13 Oct 2022 to 29 Jan 2023) and after (10 Feb 2023) the event.Suggested Use:Standard Version: The color variation from pale yellow to red indicates increasingly more significant surface change (drop in radar reflection coherence). Preliminary validation was done by comparing with high-resolution optical imagery and media reports.Color Vision Deficiency (CVD) Version: The color variation from light blue to dark blue indicates increasingly more significant surface change. Preliminary validation was done by comparing with high-resolution optical imagery and media reports.NOTE: This damage proxy map could be used as a guidance to identify damaged areas, and may be less reliable over vegetated areas. The time intervals between the ALOS-2 acquisitions are up to a year apart, so the accuracy of the DPM may be lower in areas of vegetation, such as in the mountains. Scattered pixels over vegetated areas may be false positives, and a lack of colored pixels over vegetated areas may not mean no damage.Satellite/Sensor:Japan Aerospace Exploration Agency (JAXA) ALOS-2 PALSAR-2Copernicus Sentinel-1 Synthetic Aperture Radar (SAR)Resolution:30 metersCredits:Original data ALOS-2 PALSAR-2 Product - JAXA (2021-2023). Data were provided by Sentinel Asia and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) in collaboration with NASA-JPL and Caltech.The product contains modified Copernicus Sentinel data (2022-2023), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) in collaboration with NASA-JPL and Caltech, using the Advanced Rapid Imaging and Analysis (ARIA) system originally developed at NASA's Jet Propulsion Laboratory, California Institute of Technology, and modified at EOS-RS. Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/turkey_earthquake_2023/dpm/MapServer/WMSServerData Download:http://eos-rs-products.earthobservatory.sg/EOS-RS_202302_Turkiye_Syria_Earthquake/

NOTE: This damage proxy map could be used as a guidance to identify damaged areas particularly in more urban areas, and may be less reliable over vegetated areas and in the mountains. Scattered pixels over vegetated areas may be false positives, and a lack of colored pixels over vegetated areas may not mean no damage.Date of Image:9/11/2023Date of Next Image:UnknownSummary:The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this Damage Proxy Map (DPM) depicting areas that are likely damaged in Morocco due to the M6.8 earthquake that occurred on 8 Sep 2023. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (26 May 2023 and 30 Aug 2023) and after (11 Sep 2023) the event.Suggested Use:Standard Version: The color variation from pale yellow to red indicates increasingly more significant surface change (drop in radar reflection coherence). Preliminary validation was done by comparing with high-resolution optical imagery and media reports.Color Vision Deficiency (CVD) Version: The color variation from light blue to dark blue indicates increasingly more significant surface change. Preliminary validation was done by comparing with high-resolution optical imagery and media reports.NOTE: This damage proxy map could be used as a guidance to identify damaged areas, and may be less reliable over vegetated areas and in the mountains. Scattered pixels over vegetated areas may be false positives, and a lack of colored pixels over vegetated areas may not mean no damage.Satellite/Sensor:Copernicus Sentinel-1 Synthetic Aperture Radar (SAR)Resolution:30 metersCredits:Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2023), Validated with FirstLook Imagery © 2023 MaxarEsri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/morocco_earthquake_202309/eos_dpm/MapServer/WMSServerData Download:https://eos-rs-products.earthobservatory.sg/EOS-RS_202309_Morocco_Earthquake/

The active satellite remote sensing market is experiencing robust growth, driven by increasing demand for high-resolution imagery and data across various sectors. A projected Compound Annual Growth Rate (CAGR) suggests a significant expansion in market value over the forecast period (2025-2033). This growth is fueled by several key factors: the rising adoption of advanced technologies such as Synthetic Aperture Radar (SAR) and LiDAR, increasing government investments in space-based infrastructure, and the burgeoning need for real-time data across applications like precision agriculture, environmental monitoring, urban planning, and disaster management. The market is witnessing a shift towards more sophisticated data analytics capabilities, with companies investing heavily in Artificial Intelligence (AI) and Machine Learning (ML) to extract actionable insights from the vast amounts of data generated by active satellite systems. Furthermore, the miniaturization of satellite technology and the emergence of constellations are lowering the barrier to entry, fostering innovation and competition within the market. Despite the promising outlook, the market faces certain challenges. High initial investment costs associated with satellite development and launch remain a barrier, particularly for smaller companies. Data security concerns and the need for robust data processing infrastructure also present hurdles. However, the overall market trend indicates a steady upward trajectory. The continued advancement of technology, coupled with increased public and private sector funding, is poised to drive further growth and wider adoption of active satellite remote sensing solutions across diverse industries in the coming years. Major players like Thales Group, Airbus Defence and Space, and Lockheed Martin are at the forefront of these developments, shaping the future landscape of this dynamic market. The competitive landscape is characterized by both large, established companies and innovative startups, leading to continuous product and service diversification.

The position of the terminus of Hansbreen is derived with very high frequency in the period 1991–2015. Over 160 multispectral and Synthetic Aperture Radar (SAR) data were used: LANDSAT 5, LANDSAT 7, LANDSAT 8, Terra ASTER, Alos AVNIR, SPOT 5, ERS-1, ERS-2, ENVISAT, Alos PALSAR, TerraSAR-X, TanDEM-X, and Sentinel-1. Terra ASTER images were orthorectified with use of 2008 DEM SPOT and geocoded in PCI Geomatica and ArcGIS software. Multispectral, already terrain-corrected images were rectified in ArcGIS software. SAR data were provided at the Single Look Complex level and that both radiometric and geometric corrections were applied using the same methods, and with the same digital elevation model (2008 DEM SPOT). The SAR data were processed in BEAM (http://www.brockmann-consult.de/cms/web/beam). Sentinel data downloaded from the Sentinel’s Data Hub were already processed. The satellite data were digitized manually to obtain the front position. The database is the supplement to the paper: M. Błaszczyk, J.A. Jania, M. Ciepły, M. Grabiec, D. Ignatiuk, L. Kolondra, A.Kruss, B. Luks, M. Moskalik, T. Pastusiak, A. Strzelewicz, W. Walczowski, T. Wawrzyniak, Factors controlling terminus position of Hansbreen, a tidewater glacier in Svalbard, Journal of Geophysical Research: Earth Surface, DOI: 10.1029/2020JF005763

Dates of Images:Post-Event: 9/1/2021, 9/3/2021Pre-Event: 3/12/2021, 3/13/2021Date of Next Image:None ExpectedSummary: These UAVSAR false color RGB images provide a unique look at the Earth's surface that can be used for identifying flooding and inundation under tree canopy. The overlay of the intensities of these three polarization channels allows user to visually classify a scene by its backscattering mechanism, such as surface scattering (strong HH and VV return), volume scattering (strong HV return) and double-bounce scattering (strong HH return). Areas dominated by green (HV) intensity are typically vegetated areas. Areas dominated by shades of pink (HH+HV) intensity are typically inundated forests or vegetated fields. Black and dark grey areas are usually smooth surface (roads, open water, smooth bare ground) where there is very little radar backscatter.Suggested Use: The polarimetric color composite is well suited for identifying inundation under tree canopies. This product is especially useful in mapping inundation extent in regions covered with vegetation where the ground is not visible in optical imagery.Open water can be seen as dark blue/black, areas with inundation under tree canopy can be seen in pink-like colorsSatellite/Sensor: UAVSAR airborne L-band synthetic aperture radar (SAR) aboard a NASA Gulfstream C-20A jet. Resolution: 10 meters Credits:UAVSAR data courtesy of NASA/JPL-CaltechData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2021/hurricane_ida/uavsar/Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/hurricane_ida_2021/uavsar_rgb/MapServer/WMSServer

This dataset contains satellite-borne synthetic aperture radar (SAR) images from offshore Louisiana, 2004-2012. SAR images provided by the Center for Spatial Technologies and Remote Sensing (CSTARS), the European Space Agency (ESA), and the National Aeronautics and Space Administration (NASA). Images associated with Gulf oil spills were processed using either the Texture Classifying Neural Network Algorithm (TCCNA) or the digitizing tool in ArcGIS to manually identify oil slicks. This dataset supports the publication: Asl, S.D., Amos, J., Woods, P., Garcia-Pineda, O., and MacDonald, I.R. (2015). Chronic, Anthropogenic Hydrocarbon Discharges in the Gulf of Mexico. Deep Sea Research Part II: Topical Studies in Oceanography 129: 187-195.

About the dataSentinel-1 is a spaceborne Synthetic Aperture Radar (SAR) imaging system and mission from the European Space Agency and the European Commission. The mission launched and began collecting imagery in 2014.The Sentinel-1 RTC data in this collection is an analysis ready product derived from the Ground Range Detected (GRD) Level-1 products produced by the European Space Agency. Radiometric Terrain Correction (RTC) accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return.With the ability to see through cloud and smoke cover, and because it does not rely on solar illumination of the Earth's surface, Sentinel-1 is able to collect useful imagery in most weather conditions, during both day and night. This data is good for wide range of land and maritime applications, from mapping floods, to deforestation, to oil spills, and more.About the appSentinel-1 imagery helps to track and document land use and land change associated with climate change, urbanization, drought, wildfire, deforestation, and other natural processes and human activity.Through an intuitive user experience, this app leverages a variety of ArcGIS capabilities to explore and begin to unlock the wealth of information that Sentinel-1 provides. Some of the key capabilities include:Visual exploration of a dynamic global mosaic of the latest available scenes.On-the-fly band/polarization combinations and indices for visualization and analysis.Interactive Find a Scene by location, time, and orbit direction.Visual change by time and renderings with Swipe and Animation modes.Analysis such as threshold masking and temporal profiles for vegetation, water, land surface temperature, and more.

Date of Images:9/29/2022, 10/2/2022, 10/3/2022, 10/4/2022Date of Next Image:N/ASummary:RADARSAT-2 and MSFC Sentinel-1:Scientists at NASA's Marshall Space Flight Center created these water extents on September 29, 2022 using the RADARSAT-2 Synthetic Aperture Radar (SAR) instrument. These images can be used to see where open water is visible at the time of the satellite overpass. This product shows all water detected and differentiates between normal water areas and some flooded areas. This product was classified using the USDA Crop Data Layer for 2021. It's important to note that all flooded areas may not be captured do to the sensors limitations of not being able to "see" through vegetation and buildings. To determine where additional flooding may have occurred, combine this layer with other data sets.ARIA Flood Proxy Map:This Flood Proxy Map (FPM) depicts areas that are likely flooded in Florida due to Hurricane Ian. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellites operated by the European Space Agency (ESA) before (9/30/2021) and after (10/2/2022) the event.Dartmouth Flood Observatory at the University of Colorado and NASA GSFC PlanetScope, Sentinel-1, and MODIS:Potentially flooded area created using PlanetScope imagery from October 2, 2022, October 3, 2022, and October 4, 2022 using a beta PlanetScope Flood Mapping system created in partnership between NASA GSFC and Dartmouth Flood Observatory at the University of Colorado.Potentially flooded area created using Sentinel-1 SAR data from October 2, 2022. The product is processed by the Dartmouth Flood Observatory at the University of Colorado, from Copernicus/European Space Agency Sentinel 1 SAR data. The NASA Earth Sciences Program provided funding to the University for Colorado for this work.Potentially flooded area created using MODIS data from September 30, 2022, October 2, 2022, and October 3, 2022. The product is processed by the Dartmouth Flood Observatory at the University of Colorado, MODIS instrument on the Terra and Aqua satellites. The NASA Earth Sciences Program provided funding to the University for Colorado for this work.Suggested Use:RADARSAT-2 and MSFC Sentinel-1:This product shows water that is detected by the sensor with different colors indicating different land cover/land use classifications from the USDA Crop Data Layer for 2021 that appear to have water and are potentially flooded.Blue (1): Known WaterRed (2): Anomalous WaterGreen (3): Flooded WetlandsBrown (4): Flooded CroplandsPurple (5): Potentially Flooded Developed Areas (Low Confidence)(0): No DataARIA Flood Proxy Map:Dark red pixels indicate areas that are likely flooded.This flood proxy map should be used as a guide to identify areas that are likely flooded, and is less reliable over urban and vegetated areas.Caveats: the majority of developed areas were filtered out due the capabilities of the sensor to detect urban flooding. As a result, these images may not detect all flooding and some potentially flooded developed areas could be inaccurate.Dartmouth Flood Observatory at the University of Colorado and NASA GSFC PlanetScope, Sentinel-1, and MODIS:In some cases, responders need this information only during the event. In many others, "building back better" requires accurate knowledge of what land areas were flooded, and also how large the event was compared to previous events. Input from disaster responders, flood risk analysts, and all others seeking information of what land was flooded during major events is welcomed. In many cases, Dartmouth Flood Observatory can produce information products tailored to end user GIS systems and analysis objectives. Write to Robert.Brakenridge@Colorado.edu or Albert.Kettner@Colorado.eduSatellite/Sensor:RADARSAT-2 Synthetic Aperture Radar (SAR)Copernicus Sentinel-1 Synthetic Aperture Radar (SAR)PlanetScopeMODISResolution:PlanetScope: 3 metersRADARSAT-2: ~20 metersSentinel-1: 30 metersMODIS: 250 metersCredits:NASA Disasters Program, Dartmouth Flood Observatory at the University of Colorado, NASA MSFC, NASA GSFCRADARSAT-2: This service contains modified RADARSAT-2 data, collected through Hazards Data Distribution System (HDDS)-USGS; post-processing and data product development performed by NASA Marshall Space Flight Center. RADARSAT-2 Data and Products © Maxar Technologies Ltd. (2022) - All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency.Sentinel-1: Sentinel data used in this derived product, contains modified Copernicus Sentinel data (2022), processed by ESA, Alaska Satellite Facility, NASA Marshall Space Flight CenterThe FPM contains modified Copernicus Sentinel data (2021-2022), processed by the European Space Agency and analyzed by the NASA-JPL/Caltech ARIA team. Part of the funding was provided by NASA's Earth Applied Sciences Disasters Program.PlanetScope: Includes copyrighted material of Planet Labs PBC. All rights reserved.Esri REST Endpoint:See URL section on the right side of page.WMS Endpoint: https://maps.disasters.nasa.gov/ags04/services/hurricane_ian_2022/water_extents/MapServer/WMSServer Data Download: DFO PlanetScope (flood extent): https://maps.disasters.nasa.gov/download/gis_products/event_specific/2022/hurricane_ian_2022/planet/dfo_gsfc/ DFO Sentinel 1: https://maps.disasters.nasa.gov/download/gis_products/event_specific/2022/hurricane_ian_2022/sentinel1/dfo/ DFO MODIS: https://maps.disasters.nasa.gov/download/gis_products/event_specific/2022/hurricane_ian_2022/modis/ Radarsat 2: https://maps.disasters.nasa.gov/download/gis_products/event_specific/2022/hurricane_ian_2022/radarsat2/ ARIA FPM: https://maps.disasters.nasa.gov/download/gis_products/event_specific/2022/hurricane_ian_2022/aria/ MSFC Sentinel-1: https://maps.disasters.nasa.gov/download/gis_products/event_specific/2022/hurricane_ian_2022/sentinel1/

The positions of the glacier termini in Hornsund are derived with very high frequency in the period 1991–2018. Over 230 multispectral and Synthetic Aperture Radar (SAR) data were used: LANDSAT 5, LANDSAT 7, LANDSAT 8, Terra ASTER, Alos AVNIR, SPOT 5, ERS-1, ERS-2, ENVISAT, Alos PALSAR, TerraSAR-X, TanDEM-X, and Sentinel-1. SAR data were used to detect any variability in the glacier front during the polar night. The satellite data were digitized manually to obtain the ice cliff position. Multispectral images were orthorectified and geocoded in PCI Geomatica and ArcGIS software. SAR data were usually provided at the SLC level, so that both radiometric and geometric corrections could be applied using the same methods, and with the same digital elevation model (2008 DEM SPOT developed by the IPY-SPIRIT Project; Korona et al., 2009). The SAR data were processed in BEAM (http://www.brockmann-consult.de/cms/web/beam). Sentinel data downloaded from the Sentinel’s Data Hub were already processed. Data not published.

Culminating more than four years of processing data, NASA and the National Geospatial-Intelligence Agency (NGA) have completed Earth's most extensive global topographic map. The mission is a collaboration among NASA, NGA, and the German and Italian space agencies. For 11 days in February 2000, the space shuttle Endeavour conducted the Shuttle Radar Topography Mission (SRTM) using C-Band and X-Band interferometric synthetic aperture radars to acquire topographic data over 80% of the Earth's land mass, creating the first-ever near-global data set of land elevations. This data was used to produce topographic maps (digital elevation maps) 30 times as precise as the best global maps used today. The SRTM system gathered data at the rate of 40,000 per minute over land. They reveal for the first time large, detailed swaths of Earth's topography previously obscured by persistent cloudiness. The data will benefit scientists, engineers, government agencies and the public with an ever-growing array of uses. The SRTM radar system mapped Earth from 56 degrees south to 60 degrees north of the equator. The resolution of the publicly available data is three arc-seconds (1/1,200th of a degree of latitude and longitude, about 295 feet, at Earth's equator). The final data release covers Australia and New Zealand in unprecedented uniform detail. It also covers more than 1,000 islands comprising much of Polynesia and Melanesia in the South Pacific, as well as islands in the South Indian and Atlantic oceans. SRTM data are being used for applications ranging from land use planning to "virtual" Earth exploration. Currently, the mission's homepage "http://www.jpl.nasa.gov/srtm" provides direct access to recently obtained earth images. The Shuttle Radar Topography Mission C-band data for North America and South America are available to the public. A list of complete public data set is available at "http://www2.jpl.nasa.gov/srtm/dataprod.htm" The data specifications are within the following parameters: 30-meter X 30-meter spatial sampling with 16 meter absolute vertical height accuracy, 10-meter relative vertical height accuracy, and 20-meter absolute horizontal circular accuracy. From the JPL Mission Products Summary, "http://www.jpl.nasa.gov/srtm/dataprelimdescriptions.html". The primary products of the SRTM mission are the digital elevation maps of most of the Earth's surface. Visualized images of these maps are available for viewing online. Below you will find descriptions of the types of images that are being generated:

• Radar Image
• Radar Image with Color as Height
• Radar Image with Color Wrapped Fringes
  -Shaded Relief
• Perspective View with B/W Radar Image Overlaid
• Perspective View with Radar Image Overlaid, Color as Height
• Perspective View of Shaded Relief
• Perspective View with Landsat or other Image Overlaid
• Contour Map - B/W with Contour Lines
• Stereo Pair
• Anaglypgh

The SRTM radar contained two types of antenna panels, C-band and X-band. The near-global topographic maps of Earth called Digital Elevation Models (DEMs) are made from the C-band radar data. These data were processed at the Jet Propulsion Laboratory and are being distributed through the United States Geological Survey's EROS Data Center. Data from the X-band radar are used to create slightly higher resolution DEMs but without the global coverage of the C-band radar. The SRTM X-band radar data are being processed and distributed by the German Aerospace Center, DLR.

[From The Landmap Project: Introduction, "http://www.landmap.ac.uk/background/intro.html"]

 A joint project to provide orthorectified satellite image mosaics of Landsat,
 SPOT and ERS radar data and a high resolution Digital Elevation Model for the
 whole of the UK. These data will be in a form which can easily be merged with
 other data, such as road networks, so that any user can quickly produce a
 precise map of their area of interest.

 Predominately aimed at the UK academic and educational sectors these data and
 software are held online at the Manchester University super computer facility
 where users can either process the data remotely or download it to their local
 network.

 Please follow the links to the left for more information about the project or
 how to obtain data or access to the radar processing system at MIMAS. Please
 also refer to the MIMAS spatial-side website,
 "http://www.mimas.ac.uk/spatial/", for related remote sensing materials.

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