Synthetic Aperture Radar (SAR)

"A high-resolution ground-mapping technique that effectively synthesizes a large receiving antenna by processing the phase of the reflected radar return. The along-track resolution is obtained by timing the radar return (time gating) as for ordinary radar. The crosstrack (azimuthal) resolution is obtained by processing the Doppler phase of the radar return. The cross-track dimension of the antenna is a function of the length of time over which the Doppler phase is collected." (National Aeronautics and Space Administration, 2014)

Sources

"Synthetic Aperture Radar (SAR)". NASA Glenn Research Center, National Aeronautics and Space Administration. Last modified June 12, 2014.
https://www.grc.nasa.gov/www/k-12/TRC/laefs/laefs_s.html#SAR.
Accessed February 1, 2019.

Related Content

Article

Interview with Ailin Sol Ortone Lois, Director of SAR Research group, at Universidad Tecnológica Nacional, Specialist at the Argentinian Air Force, Teacher and Researcher at Universidad Nacional de Luján and the UTN

Ailin Sol Ortone Lois is a Remote Sensing specialist at Remote Sensing Center of the Argentinian Air Force, where she applies space technologies to monitor Natural Areas of the Defense. She is the Director of Synthetic Aperture Radar Research Group at the National University of Technology (UTN), where she leads a project related to glacier monitoring and mass balance calculations using free open remote sensing sources. Ailin also teaches physics at UTN and geomatics at the National University of Luján, in Buenos Aires.

Interview with Prof. Wolfgang Wagner

Professor Wagner holds a Ph.D. in remote sensing. He gained his experience at renowned institutions, including academia, space agencies and international organisations. He is the Dean of the Faculty for Mathematics and Geoinformation and cofounder of the Earth Observation Data Centre for Water Resources among other affiliations.

Remote sensing techniques for observing snow and ice

Introduction 

Snow has a crucial contribution to Earth’s climate and helps to maintain the Earth’s temperature. When snow melts, it aids in providing water to people for their livelihood and affects the survival of animals and plants (National Snow and Ice Data Center). Approximately 1.2 billion people - constituting one-sixth of the global population - depend on snowmelt water for both agricultural activities and human consumption (Barnett et al., 2005).

Использование космических технологий для мониторинга загрязнения нефтью морской среды

Разливы нефти являются критической формой загрязнения окружающей среды и имеют масштабные негативные последствия. Они серьезно деградируют морские экосистемы, растворяя в океанах токсичные химические вещества и нанося вред морской флоре и фауне. Разливы нефти имеют отрицательно воздействие на финансовое состояние экологического туризма, а также на коммерчески жизнеспособные виды. Кроме этих негативных последствий, разливы нефти трудно отслеживать и контролировать, учитывая в целом отсутствие наблюдения за мировым океаном. Космические технологии действуют как инструмент, помогающий обнаруживать разливы нефти повсеместно. Для мониторинга разливов нефти доступны две основные технологии: оптическая визуализация и радиолокатор с синтезированной апертурой (SAR). Оптическая визуализация работает примерно так же, как фотосъёмка поверхности Земли, и для получения изображений требуется чистое небо и дневной свет. С другой стороны, изображения SAR основаны на микроволнах для их получения, и это делает возможным производить съемку в любую погоду и в любое время суток. Сочетание этих двух технологий позволило ученым расширить возможности мониторинга загрязнения нефтью, обеспечивая контроль за деятельностью на море. Эти космические технологии помогают в обнаружении различных инцидентов разлива нефти, они особенно эффективны для мониторинга незаконного сброса нефти и сточных вод с судов, так как суда больше не могут незаметно сбрасывать нефтесодержащие трюмные воды в океан в ночное время. К сожалению, обеспечение соблюдения экологического и морского права остается трудновыполнимой задачей, и судовладельцы редко привлекаются к ответственности. Важно, чтобы космические технологии продолжали развиваться и помогали предоставлять доказательства загрязнения морской среды для обеспечения защиты морских экосистем Земли.

From Jakarta to Nusantara: Land subsidence and other pressing water challenges in a sinking mega city

Jakarta, “the sinking city”, is the current capital city of Indonesia. Located on the Java Sea, this coastal city is home to nearly 30 million people within the greater-Jakarta area. Jakarta has grappled with water management issues for decades, leading to several current day water-related crises. Access to a reliable, potable water supply is extremely limited as there is a significant disparity between those with piped water access and those without. Citizens without piped water access have consequently relied heavily on groundwater and have dug thousands of unregulated wells as a result. This has led to a second water crisis – the chronic overextraction of Jakarta’s underground aquifers. Land subsidence is of the utmost concern as this sinking city is placed at high flood risk from the surrounding ocean. Approximately 40% of Jakarta now lies below sea level as a result and predictive models suggest that the entire city will be underwater by 2050 (Gilmartin, 2019). Compounding these problems, the climate crisis has led to significant sea level rise as glaciers and ice caps continue to melt (Intergovernmental Panel on Climate Change, 2019; Lindsey, 2022). As the city of Jakarta continues to sink and sea levels rise, millions of citizens within Jakarta are at extremely high risk of flooding, particularly during monsoon season. Thousands of residents have already been forced to abandon their homes in search of improved conditions and higher ground (Garschagen et al., 2018).

How has space revolutionised subsidence?

Introduction

Land subsidence is a global phenomenon and is defined as:

“a gradual settling or sudden sinking of the Earth's surface due to removal or displacement of subsurface earth materials”  - National Oceanic and Atmospheric Administration (2021)

От Джакарты до Нусантары: проседание грунта и другие насущные проблемы с водой в тонущем мегаполисе

Джакарта, «тонущий город», является нынешней столицей Индонезии. Расположенный на берегу Яванского моря, этот прибрежный город является домом для почти 30 миллионов человек в районе Большой Джакарты. Джакарта десятилетиями боролась с проблемами управления водными ресурсами, что привело к некоторым нынешним кризисам, связанным с водой. Доступ к надежному питьевому водоснабжению крайне ограничен, поскольку существует значительная разница между теми, кто имеет доступ к водопроводной воде, и теми, кто его не имеет. Граждане, не имеющие доступа к водопроводной воде, в значительной степени зависят от грунтовых вод и в результате вырыли тысячи нерегулируемых колодцев. Это привело ко второму водному кризису: постоянному чрезмерному извлечению водоносных горизонтов Джакарты. Проседание грунта вызывает наибольшую обеспокоенность, поскольку этот тонущий город подвергается высокому риску наводнений со стороны окружающего океана. В результате примерно 40 процентов территории Джакарты в настоящее время находится ниже уровня моря, и прогностические модели предполагают, что к 2050 году весь город окажется под водой (Gilmartin, 2019). Эти проблемы усугубляются тем, что климатический кризис привел к значительному повышению уровня моря, поскольку ледники и ледяные шапки продолжают таять (Intergovernmental Panel on Climate Change, 2019; Lindsey, 2022). В связи с тем, что город Джакарта продолжает опускаться, а уровень моря повышается, миллионы жителей Джакарты подвергаются чрезвычайно высокому риску наводнений, особенно в сезон муссонов (рис. 1). Тысячи жителей уже были вынуждены покинуть свои дома в поисках улучшенных условий и возвышенностей (Garschagen et al., 2018).

Enhancing maritime domain awareness through ship detection in satellite imagery

Maritime Domain Awareness (MDA) confronts significant challenges in the maritime domain, leveraging satellite technologies that play a role in enabling extensive and consistent area mapping. In this case, Synthetic Aperture Radar (SAR) stands out for its all-weather capability, serving as a crucial tool for applications ranging from environmental monitoring to defense systems (Ulaby and Long, 2014).

Comment l'espace a révolutionné les affaissements?

 Traduit de l'anglais par Mussa Kachunga Stanis

Introduction


L’affaissement de terrain est un phénomène mondial et se définit comme :

    "Un tassement progressif ou un affaissement soudain de la surface de la Terre dû à l'enlèvement ou au déplacement de matériaux terrestres souterrains" - National Oceanic and Atmospheric Administration (2021)

Using space-based technologies to monitor marine oil pollution

Oil spills are a critical form of environmental pollution that have far-reaching negative impacts. They severely degrade marine ecosystems, introducing toxic chemicals into the oceans and harming sea life. They also have significant financial impacts through the diminishment of ecotourism as well as the killing of commercially viable species. Despite these negative impacts, oil spills are notoriously difficult to track and monitor given the general lack of surveillance over the vastness of the Earth’s oceans. Space-based technologies are evolving as a tool to aid in the detection of oil spills worldwide. Two primary technologies have been optimized for oil spill monitoring: optical satellite imagery and synthetic aperture radar (SAR). Optical satellite imagery functions somewhat like taking a photograph of the Earth’s surface and requires clear skies and daylight to produce imagery. SAR imagery, on the other hand, relies on microwaves to produce images, and therefore can function regardless of weather, as well as at night. The combination of these two technologies has allowed scientists an increased ability to monitor where and when oil pollution is happening, providing an eye-in-the-sky to survey marine activities. While these space-based technologies are aiding in the detection of a variety of oil spill incidents, they are particularly helpful to monitor the illegal dumping of oil and effluent from shipping vessels as ships are no longer able to dump oily bilgewater into the ocean under the veil of darkness. Unfortunately, the enforcement of environmental and marine law remains an issue and ships are rarely prosecuted. It will be important for space-based technologies to continue to evolve and provide evidence of marine pollution in the effort to provide protection for Earth’s marine ecosystems.

SAR backscatter to monitor under tree cover

Forest cover refers to the extent of land area covered by forests. It can be expressed either as a percentage relative to the total land area or in absolute terms measured in square kilometers or square miles (ScienceDirect). As of 2020, globally, forests account for 31 percent of the land area with roughly half of this area considered relatively intact. The total forest coverage is 4.06 billion hectares.

Utilizando tecnologías espaciales para monitorear la contaminación marina por petróleo

Oil spills are a critical form of environmental pollution that have far-reaching negative impacts. They severely degrade marine ecosystems, introducing toxic chemicals into the oceans and harming sea life. They also have significant financial impacts through the diminishment of ecotourism as well as the killing of commercially viable species. Despite these negative impacts, oil spills are notoriously difficult to track and monitor given the general lack of surveillance over the vastness of the Earth’s oceans. Space-based technologies are evolving as a tool to aid in the detection of oil spills worldwide. Two primary technologies have been optimized for oil spill monitoring: optical satellite imagery and synthetic aperture radar (SAR). Optical satellite imagery functions somewhat like taking a photograph of the Earth’s surface and requires clear skies and daylight to produce imagery. SAR imagery, on the other hand, relies on microwaves to produce images, and therefore can function regardless of weather, as well as at night. The combination of these two technologies has allowed scientists an increased ability to monitor where and when oil pollution is happening, providing an eye-in-the-sky to survey marine activities. While these space-based technologies are aiding in the detection of a variety of oil spill incidents, they are particularly helpful to monitor the illegal dumping of oil and effluent from shipping vessels as ships are no longer able to dump oily bilgewater into the ocean under the veil of darkness. Unfortunately, the enforcement of environmental and marine law remains an issue and ships are rarely prosecuted. It will be important for space-based technologies to continue to evolve and provide evidence of marine pollution in the effort to provide protection for Earth’s marine ecosystems.

Interview with Prof. Wolfgang Wagner

Professor Wagner holds a Ph.D. in remote sensing. He gained his experience at renowned institutions, including academia, space agencies and international organisations. He is the Dean of the Faculty for Mathematics and Geoinformation and cofounder of the Earth Observation Data Centre for Water Resources among other affiliations.

Interview with Mina Konaka, Satellite engineer at JAXA

Mina Konaka works at the Japan Aerospace Exploration Agency (JAXA) as a satellite engineer and is currently working on the satellite ALOS-4, which can detect changes in groundwater on land. She attended the International Space University, participating in the project AWARE (Adapting to Water and Air Realities on Earth), in which participants aimed to provide solutions for flood and air quality risks due to climate change, using earth observation data and ground-based sensors. Mina feels strongly about the need to talk more globally about water management solutions, rather than on an individual country basis. Mina also hopes that in the future there will be more female engineers who pursue dreams of space, and that gender balance is no longer an issue.

Interview with Ailin Sol Ortone Lois, Director of SAR Research group, at Universidad Tecnológica Nacional, Specialist at the Argentinian Air Force, Teacher and Researcher at Universidad Nacional de Luján and the UTN

Ailin Sol Ortone Lois is a Remote Sensing specialist at Remote Sensing Center of the Argentinian Air Force, where she applies space technologies to monitor Natural Areas of the Defense. She is the Director of Synthetic Aperture Radar Research Group at the National University of Technology (UTN), where she leads a project related to glacier monitoring and mass balance calculations using free open remote sensing sources. Ailin also teaches physics at UTN and geomatics at the National University of Luján, in Buenos Aires.

Interview with Mina Konaka, Satellite engineer at JAXA

Mina Konaka works at the Japan Aerospace Exploration Agency (JAXA) as a satellite engineer and is currently working on the satellite ALOS-4, which can detect changes in groundwater on land. She attended the International Space University, participating in the project AWARE (Adapting to Water and Air Realities on Earth), in which participants aimed to provide solutions for flood and air quality risks due to climate change, using earth observation data and ground-based sensors. Mina feels strongly about the need to talk more globally about water management solutions, rather than on an individual country basis. Mina also hopes that in the future there will be more female engineers who pursue dreams of space, and that gender balance is no longer an issue.

Capacity Building and Training Material

ARSET - Crop mapping using synthetic aperture radar (SAR) and optical remote sensing

Overview

Monitoring crop growth is important for assessing food production, enabling optimal use of the landscape, and contributing to agricultural policy. Remote sensing methods based on optical and/or radar sensors have become an important means of extracting information related to crops. Optical data is related to the chemical properties of the vegetation, while radar data is related to vegetation structure and moisture. Radar can also image the Earth’s surface regardless of almost any type of weather condition.

ARSET - Mapping crops and their biophysical characteristics with polarimetric SAR and optical remote sensing

Overview

Mapping crop types and assessing their characteristics is critical for monitoring food production, enabling optimal use of the landscape, and contributing to agricultural policy. Remote sensing methods based on optical and/or microwave sensors have become an important means of extracting information related to crops. Optical data is related to the chemical properties of the vegetation, while radar data is related to vegetation structure and moisture. Radar can also image the Earth’s surface regardless of almost any type of weather condition.

Rapid Impact Assessment Using Open-source Earth Observation - on the example of the Kachowka Dam Break

The Jupyter notebook demonstrates how EOdal can be used for disaster relief after the break of the Kachowka using open-source Earth Observation data.

On June 6, 2023, the Kakhovka Dam in Ukraine broke. We do not yet know who or what was responsible for the collapse of the dam. What we do know, however, are the devastating consequences for the region downstream - especially for the local population.

Recommended Practice: Flood Mapping and Damage Assessment using Sentinel-1 SAR data in Google Earth Engine

Floods, as natural disasters, are most commonly caused by storms and torrential rain or by overflowing lakes, rivers or oceans; this type of natural disaster is one of the most common and effects nearly every demographic and area on Earth. As they are wide-ranging disasters, floods leave disaster managers with a wide-range of concerns. The immediate concern during a disaster is that of human life and the infrastructure needed to offer emergency response through. Floods can wash away bridges and buildings, can destroy electricity systems and can even disconnect portions of cities or rural communities from the first responders who need to reach them. Long-term concerns caused by major floods focus on systemic damage; food is often the most serious concern as crops are destroyed and livestock drowns in major flood disasters. This Recommended Practice aims to create important disaster information for both the short- and long-term concerns of floods. The tool produces a flood extent map using Sentitnel-1 SAR images, as well as displays information about cropland and population centers affected in order to address the totality of major concerns that floods cause.

Event

Stakeholder

Stimson Center

The Energy, Water, & Sustainability Program at the Stimson Center addresses important and timely policy issues and technical opportunities concerning energy, water, and sustainable development in the Global South from a multidisciplinary perspective.

Our work on transboundary river basins identifies pathways towards enhancing water security and optimizing tradeoffs between water, energy, and sustainable development options in the Mekong, Ganges-Brahmaputra, Indus, Aral Sea and Euphrates-Tigris river basins.

Person

Photo of Jumpei Takami

Jumpei Takami

Associate Expert in Remote Sensing United Nations Office for Outer Space Affairs

Proficient in Remote Sensing and Geographic Information Systems with Machine Learning approach: Analysis of disaster risk reduction and management associated with climate change using remote sensing and geographic information system technologies and implementation of disaster-oriented projects; landslide, flooding, drought, and land subsidence, optionally with machine learning approaches; forest inventory for canopy height and above ground biomass, and planning, design, construction, and maintenance of civil engineering construction projects.

Photo of Felix Isundwa Kasiti

Felix Kasiti

PhD Researcher University of Stirling

Felix is a PhD researcher at the University of Stirling, Stirling, UK, researching on the use of Synthetic Aperture Radar (SAR) in mapping floods. He recently worked as a hydrologist with SERVIR Eastern and Southern Africa project at the Regional Centre for Mapping of Resources for Development, Nairobi, Kenya from 2019 to 2022. 
 
In 2018, he obtained his M.Sc. degree on Water Science (Policy) from the Pan African University Institute of Water and Energy Sciences (PAUWES). Attained his B.Sc.

Photo of Shaima Almeer

Shaima Almeer

Senior Space Data Analyst Bahrain Space Agency

Shaima Almeer is a young Bahraini lady that works as a senior space data analyst at the National Space Science Agency. At NSSA she is responsible for acquiring data from satellite images and analyzing them into meaningful information aiming to serve more than 21 governmental entities. Shaima is also committed to publishing scientific research papers, aiming to support and spread the knowledge to others.

Space-based Solution

Addressed challenge(s)

Hydrocarbon contamination of water bodies in the Niger Delta

Collaborating actors (stakeholders, professionals, young professionals or Indigenous voices)
Suggested solution

 

Three (3) different space-based solutions have been attempted to solve the problem of hydrocarbon contamination. The first method involves the use of an oilspill detection tab in SNAP. This method has limitations if the user cannot primarily identify the area where the spill occurred. The second method is the use of a script where all the steps which could be done manually are incorporated within the script. This solution also works well with a limitation of understanding and editing scripts. The last solution involves identifying thresholds of pixel values to detect the spill regions. This solution has produced the most desirable results, however it involves lengthy processes.

 

Requirements

Data

  • Pre-spill and During spill Sentinel 1 SAR data from Alaska Satellite Facility ASF Home | Alaska Satellite Facility .You might need to create an account to enable download
  • Click on the “vertex” icon on the left side of the “services” icon. A new page opens
  • On the map interface, click drawing tool (with the letter “A” written in it) under the icon “Area of Interest”. Select “Draw a polygon” and draw a polygon around your area of interest.
  • Click on “Filters” You can import your shapefile if you wish. Move down and enter your search dates (start and end dates)
  • You can add additional filters like the file type. Under the file type, it is advisable to choose the ground products (GRD) which are georeferenced. The Single Look Complex (SLC) products are preferred for Interferometric (InSAR) analyses. You can change the beam mode to IW (Interferometric Wide Swath)
  • You can leave the other parameters under “additional filters” as default to avoid limiting your search results.
  • Then download product

Software

  • The Sentinel Application Platform (SNAP) 11.0 software was used for solutions 1 and 3. SNAP is an open-source software developed by the European Space Agency (ESA) to support the processing and analysing of earth observation data particularly from the Sentinel repository. Link to the Sentinel platform SNAP Download – STEP
  • Scroll down and click on either Windows, Mac or Linux depending on your computer’s operating system requirement.
  • Google Earth Engine (GEE) was used for solution 2. GEE is an open source, cloud-based platform for processing geospatial data. It works with JavaScript and uses codes to pull out the needed satellite data from different repositories
  • Link to GEE platform Google Earth Engine
  • Click on “platform” and then “code editor”. This takes you to the code editor interface where you can write and save your scripts or run them.

Physical

  • Information from the National Oilspill Detection and Response Agency (NOSDRA)was used to validate the occurrence of the spill

 

Outline steps for a solution

1 Manually Download Image- The first step is to manually download two (2) Sentinel 1 images from the Alaska Satellite Facility as described above. One image (archived image) before the spill and the other during the spill taking into consideration the temporal resolution (revisit period) of the sensor. The images will be zipped. There is no need to unzip the images

2 Load Image- Load the images onto the SNAP software. Simply drag and drop the zipped images onto the product explorer interface. Click on the icon on the left of the image file and the metadata and band icons will be visible. Click on the “bands” icon to reveal Amplitude VV, Amplitude VH, and Intensities. Load the Amplitude VV bands for both pre and post spill images and compare

3 Subset Image-Create a subset of your Area of Interest by using the subset tool under the icon “Raster”. This helps to reduce processing time as you will be focusing on a smaller area (area of interest),rather than the whole satellite image.

Image Pre-processing- Preprocessing the image by Radiometric (speckle filtering and calibrating) and Geometric Corrections in SNAP

4.1 Speckle filtering- This is the removal of background noise, which literally appears as speckles from an image. A speckle filtered image always appears less “grainy” than a non-speckle filtered image. In the Radar toolbar, you will find speckle filtering as the third option, select the single product speckle filter sub-option and use the subset file as input. Under processing parameters, choose amplitude VV, which is the best channel for detecting oil spills. Choose the Lee  filter (3*3) and run. Finally, go to file in the menu bar and select tile horizontally, to have pre-speckle filtered image and post speckle filtered image side by side for comparison. After speckle filtering, the result produced on the product explorer space usually has the extension (.spk)

Image showing the “speckle filtering” location in SNAP

 

4.2 Calibration- This is done to improve radiance and reflectance to ensure that digital numbers (DN) accurately represent the reflectance of the physical characteristics of the SAR image. Speckle filtering and calibration are types of radiometric corrections. The calibration tool can be found under the “radar” icon in the SNAP software. Convert the result (sigma vv) from linear to db (decibels). This results in a virtual band. Right click (sigma-vv-db) and convert to band to have a real band. After speckle filtering and calibrating, the result produced on the product explorer interface usually has the extension (.spk cal)

Image showing the “calibration” location in SNAP

 

4.3 Terrain Correction- If the Area of Interest was only the ocean, we might not need to do a terrain correction. However, it will be needed in this case as we are more concerned with the terrestrial environment. Go to “Radar” then “Geometric”, then “Terrain correction”. Choose the Range doppler terrain correction option. Set the options for your Digital Elevation Model (DEM)and run

Image showing “terrain correction” location in SNAP

 

After terrain correction, the result on the product explorer interface will have the extension (Spk.Cal.TC-1)

The three pre-processing steps documented above are required for solutions 1 and 3 

 

Next step for Solution 1

Oilspill detection Tool: Within the Radar toolbar in SNAP, there is a “SAR Applications” window which further opens into “Ocean Applications” and then the oil spill detection tool can be found. After entering the parameters and running the tool, it creates a mask of the spill area and the geometry to aid spill area calculation.

Solution 2

GEE Platform

The steps listed below are the steps coded in the script to delineate areas with oil spills

1 Define Area of interest (AOI): The region of interest must be defined either by using a shapefile or geotag.

2 Define Temporal Scale: The revisit time (temporal resolution) of the Sentinel 1 SAR sensor is twelve (12) days. Dates must be entered to accommodate this temporal resolution

3 Load Sentinel-1 Ground Data:The Sentinel-1 data with the ‘vertical-vertical’ polarization (VV) which is best for oil spill detection is loaded,

4 Speckle filtering to reduce noise: Most Sentinel images come with speckles which may obscure the viewing of important information. Speckle filtering helps to provide a clearer image and better feature extraction.

5 Oil spill Change Mask and Thresholding is applied: This is done using the differences in the band sensitivity between the oil and water areas to delineate the oil spill areas.

6 Validation using the Nigerian Oilspill Detection and Response Agency (NOSDRA) database:

Code will be submitted in a separate document.

Solution 3 (Thresholding)

After the preprocessing steps explained above, the next steps are as follows

1 Layer Stacking-This is done to perform advanced analysis on the bands. Go to Radar, under “Radar”, you find coregistration then “stack tools” then “create stack”. Choose the bands you want to layerstack, this should be the (sigma0-vv-db bands) of the pre spill image and the post spill image.

2 Open RGB image- Now that the bands are stacked, you can do a band combination to have an idea of the areas where the spill occurred. Right click on the stack and “open RGB image”, put the post spill data in red and green and leave the pre spill data in blue, then run. This returns an image with the areas where the spill likely occurred in the image.ee RGB image window below.

 

Image showing “RGB” window in SNAP

The green areas are the likely spill areas. The shape file covers the region of interest but we are interested in the surrounding areas as well.

Measurement of the spill area to ascertain correctness of analysis (2.75+/-9.2*10-3)

 

Histogram Generation to obtain threshold values- Thehistogram shows the region of the oilspill.Load the histogram of the post spill image by highlighting the image in the product explorer window (sigma00-vv-db), click “Analysis”, scroll down and click histogram, then generate histogram. This gives the range of pixel values where the spill can be detected. See histogram below

Histogram of the post-spill image

 

Here, we see that the histogram range is from 0 to -25. However, from -15 to -25 looks like a false peak. So the threshold value chosen is -15 to -17

Also, from the image, if you hover around the green areas and check the pixel info on the “pixel info” tab next to “product explorer”, it ranges from -15 to -25, it is explained above why -15 was chosen.

The last step is to use this chosen value or range of values as the case may be to create an expression using the band maths tool to obtain the final spill area.

Band Maths Expression -Access the band maths tool which is  the first option under  “raster”. Create an expression with your chosen threshold values as seen in the figure below.

 

In the band maths expression above, the pre-spill data (24th May 2023) is subtracted from the post-spill data (17th June 2023) to obtain the spill area.

Results

There is an image with likely spill areas

Describe the impact this solution has on the ground

Related space-based solutions
Keywords (for the solution)
Climate Zone (addressed by the solution)
Habitat (addressed by the solution)
Region/Country (the solution was designed for, if any)
Relevant SDGs