1. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Synthetic Aperture Radar (SAR): From R&D to Operations William Pichel 1 (GOVERNMENT PRINCIPAL INVESTIGATOR) and Pablo Clemente-Colón 1 1 NOAA/NESDIS/STAR/SOCD Science Challenges: Improve existing algorithms: 1. To use L-band and X-band data in addition to C-band. 2. Optimize with multiple polarizations – for wind, vessels, ice 3. Investigate use of phase information for wind, vessel, and current direction determination 4. Improve accuracy of high-velocity storm winds 5. Improve calibration stability and accuracy Develop new products: 1. Wave measurements 2. Ocean current measurements 3. Estimates of mixed layer depth 4. Shallow water bathymetric change 5. Surf zone parameters 6. Flood mapping Next Steps: 1. Wind algorithm improvements as part of operational implementation 2. Complete oil spill analysis algorithm development 3. Automated and interactive product systems development 4. Partner with foreign satellite operators in order to obtain future SAR constellation data in near-real-time over areas of interest to NOAA Transition Path: 1. Develop operational SAR product systems (automated and interactive). 2. Begin generating products using available SAR data. 3. Develop operational data path for international SAR constellation data. 4. Tailor products to new SAR constellation data. 5. Begin routine near-real-time operational production of SAR-derived products. End users are: National Weather Service Forecast Offices, National Ocean Service Emergency Response Division, U.S. Coast Guard, NOAA Marine Sanctuaries and Monuments, FEMA, USGS, Minerals Management Service Science: Requirement: The tables below summarize user requirements for SAR-related geophysical measurements. Requirement: Mission Goal (Commerce and Transportation): - Develop the Information and Tools to Make Reliable Decisions in Preparedness, Response, Damage Assessment, and Restoration - Oil Spill Mapping – Milestone from NOAA 5-Year Research Plan: Refine methods for modeling and monitoring dispersed oil/chemical plumes based on current research. - Oil Spill Mapping, Oil Platform Change Detection, Ship Detection – Milestone from NOAA 5-Year Research Plan: Transition field tools to operations that improve the efficiency of oil spill and marine debris assessment. Mission Goal (Commerce and Transportation): - Provide Accurate, Timely, and Integrated Weather Information to Meet Air and Surface Transportation Needs - Coastal Winds, Polar/Great Lakes Ice, Swell Waves – Milestone from NOAA 5-Year Research Plan: Transition research weather-observation prototypes into full operational use. Mission Goal (Mission Support): Increase quantity, quality, and accuracy of satellite data that are processed and distributed within targeted time. Mission Goal (Climate): Understand Impacts of Climate Variability and Change on Marine Ecosystems to Improve Management of Marine Ecosystems - Sea Ice – Milestones from NOAA 5-Year Research Plan: Development of a model to incorporate the effects of climate into living marine resource assessments for the Bering Sea. Initiate a competitive program on Loss of Sea Ice in the Arctic. Science: Oil Spill Mapping – Can an efficient, automated oil spill mapping algorithm be developed that uses SAR and/or other remote sensing data with a minimum of false alarms? Oil Platform Change Detection – Is there a way to rapidly assess the status of offshore oil platforms right after major hurricanes utilizing satellite remote sensing data? What are the constraints of such a procedure? Coastal Winds – What is the optimum way to determine wind direction from a combination of model winds and directions derived from SAR wind aligned features? Can high-velocity winds be measured from multi-polarization SAR data? Swell waves – What is the accuracy of two-dimensional SAR-derived wave spectra measurements and significant wave height? Would the assimilation of SAR wave parameters have any impact on wave forecasts? Great Lakes Ice Classification – How stable are SAR-derived freshwater ice classifications? Can this technique be automated fully? Benefit: 1. Safety of transportation and low-altitude aviation 2. Protection of coastal wetlands and beaches from oil 3. Protection of fisheries and endangered species 4. Wind farm establishment and maintenance 5. Safety and enforcement of fisheries 6. SAR instruments provide observations not possible with other remote sensing instruments, such as high resolution (<100 m) sea and lake ice concentration/age/edge location/motion under all weather conditions and day/night, high resolution (<1 km) coastal winds, swell wave characteristics in two dimensions, and oil spill maps day or night. Opportunity: Beginning in 2012, the European Space Agency, the Canadian Space Agency, and the Japan Aerospace Exploration Agency will launch a constellation of six operational satellites carrying advanced Synthetic Aperture Radar (SAR) instruments These may be joined by a possible NASA SAR mission later in the decade (i.e., DESDynI). For the first time, SAR data will be available non-commercially on an operational basis. STAR is preparing for this opportunity by conducting product and application research and development and working together with NESDIS operational components to develop both automated and interactive SAR operational product systems. RADARSAT Constellation Mission Flight Concept ENVISAT ASAR wind image (north image) March 13, 2007 07:40 UT showing gap winds in the Kennedy entrance to Cook Inlet, Alaska. Radarsat-1 SAR wind image (south image) March 13, 2007 03:57 UT, approximately 4 hours before the ENVISAT wind image. Left: ENVISAT Standard Mode Vessel Position product showing Gulf of Mexico and entrance to Galveston Harbor. Above: Photograph shows the vessels waiting to enter the Galveston Ship Channel. Both product and photo are from September 17, 2008. SAR image © ESA, 2008 TCNNA Analysis of oil pipeline spill in Gulf of Mexico July 26, 2009 ENVISAT ASAR image © ESA, 2009. Output is a GIS Shape File that can be sent to the Emergency Response Division of NOAA and to spill responders in the field. SAR returns from “destroyed” platforms Oil Spill MMS positions with no SAR returns © JAXA, 2008 Experimental swell wave product showing waves south of Unalaska Island in the Aleutian Islands of Alaska. The length of each bar is proportional to swell length; the direction of the bar shows swell wave direction (with 180 deg. ambiguity); the color scale gives the significant wave height in meters. Image is from RADARSAT-1 01/19/2005 04:35 UTC Great Lakes experimental automated lake ice type analysis for Lake Superior. Ice types have been determined by an unsupervised classifier trained with ground truth data collected on winter cruises on Coast Guard ice breakers. Analysis of oil platform changes due to damage sustained in Hurricane Ike (Sept. 2008). Eight of 16 destroyed platforms were correctly identified, the other 8 still had a SAR signature. Procedure has a false alarm rate of about 5%. Image is ALOS PALSAR image from September 18, 2008. Image © JAXA, 2008 Shallow water bathymetric features in Bristol Bay, Alaska. RADARSAT-1 image © CSA. Comparing a recent image with a time composite of a number of SAR images taken in the past can be used to highlight bathymetric changes. High Resolution Sea Surface Winds Algorithm: CMOD5 Scatterometer Algorithm with Thompson HH/VV Polarization Ratio Partners: The Johns Hopkins University Applied Physics Laboratory, General Dynamics Advanced Information Systems, Global Ocean Associates, IMSG at NOAA/NESDIS, and NOAA/NESDIS Product Implementation Branch Vessel Detection Algorithm: Constant False Alarm Rate Algorithm Partners: General Dynamics Advanced Information Systems and Global Ocean Associates Oil Spill Location Algorithm: Texture Classifying Neural Network Algorithm (TCNNA) Partners: Florida State University, NOAA/NESDIS Satellite Analysis Branch, IMSG at NOAA/NESDIS Swell Significant Wave Height Algorithm: European Space Agency ENVISAT Wave Mode Algorithm Partners: CLS (France), General Dynamics Advanced Information Systems Great Lakes Ice Classification Algorithm: Unsupervised Classifier based on ground truth measurements in the field Partner: Great Lakes Environmental Research Laboratory, National Ice Center Oil Platform Change Detection Algorithm: Constant False Alarm Rate Vessel Detection Algorithm Partner: Global Ocean Associates Shallow Water Bathymetric Features Algorithm: SAR / InSAR imaging model “M4S“ by Roland Romeiser Partner: IMSG at NOAA/NESDIS SAR PRODUCTS UNDER DEVELOPMENT FOR TRANSITION TO OPERATIONS ALGORITHM or TECHNIQUE SAMPLE PRODUCT SAR Image Applications SAR imagery is also used for ice analysis, storm analysis, glacier change detection, and ocean feature mapping. Radarsat-1 Katrina 8/27/05 11:29 UT 24.4N, 84.6W 51.4 m/s,940mb © CSA, 2005 Radarsat-1 Katrina 8/28/05 23:49:41 UT, 27.2N, 89.1W, 71.5 m/s, 904 mb © CSA, 2005