Skip to main content

Landsat #NASA #USGS #Earth Taking Temperatures from ISS

Taking Temperatures from ISS

When remote sensing scientists observe Earth, they often look for heat signatures. Fires, volcanoes, ice, water, and even sunlit or shaded landscapes emit and reflect heat and light—energy—in ways that make them stand out from their surroundings. NASA scientists recently used a new sensor to read some of those signatures more clearly.

Through nearly a year of testing on the International Space Station (ISS), the experimental Compact Thermal Imager (CTI) collected more than 15 million images of Earth, and the results were compelling. Researchers were impressed by the breadth and quality of the imagery CTI collected in 10 months on the ISS, particularly of fires.

For instance, CTI captured several images of the unusually severe fires in Australia that burned for four months in 2019-20. With its 80-meter (260 foot) per pixel resolution, CTI was able to detect the shape and location of fire fronts and how far they were from settled areas—information that is critically important to first responders.

For the past two decades, scientists have generally relied upon coarse resolution (375–1000 m) thermal data from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors to monitor fire activity from above. During its flight test, CTI made observations of fires with 20 times more detail than VIIRS and 190 times more detail than MODIS.

The images above highlight the difference. Both images show CTI's view of large fires burning in the Gondwana Rainforests of New South Wales on November 1, 2019. The right image also includes the VIIRS fire detections (red diamonds) of the same area that day. The data were overlaid on a natural-color image acquired by the Operational Land Imager (OLI) on Landsat 8.

The image below, acquired by the European Space Agency's Sentinel-2 spacecraft on November 1, shows a more detailed view of one of the fire clusters, along with the CTI data.

"CTI's deployment on the space station was primarily a test of how well the hardware would perform in space. It was not initially designed as a science mission," explained Doug Morton, chief of the Biospheric Sciences Laboratory at NASA's Goddard Space Flight Center. "Nonetheless, CTI data proved scientifically useful as we monitored several high-profile fire outbreaks this past summer."

One aspect of CTI's mission that was of particular interest to Morton was the timing of the images. MODIS and VIIRS have polar orbits and make observations over a given area at the same time each day (roughly 10:30 a.m. and 1:30 p.m.). Imagers on the ISS provide more variety and less consistency in timing, as the orbit of the International Space Station is more variable, as is the lighting and angles as it passes over different locations.

"We ended up getting these amazing images of fires at times of the day when we don't usually get them," said Morton. Fire researchers are eager to have more views of fires around dawn and dusk, which are sometimes missed by MODIS and VIIRS. "It was a reminder of how much critical science we could do if we had a whole fleet of sensors like CTI giving us such detailed measurements multiple times a day."

CTI was designed at NASA's Goddard Space Flight Center and installed on the ISS in 2019 as part of the Robotic Refueling Mission 3. It used an advanced detector called a strained layer superlattice (SLS), an improved version of the detector technology that is part of the Thermal Infrared Sensor (TIRS) of Landsat 8 and 9.

"The new SLS technology operates at a much warmer temperature with greater sensitivity and has a broader spectral response than the TIRS technology, resulting in a smaller and less costly instrument to design and build," said Murzy Jhabvala, principal investigator for CTI. "SLS has proved itself. This technology is now a viable candidate for the future Landsat 10 and a variety of other lunar, planetary, and asteroid missions."

NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey, VIIRS data from NASA EOSDIS/LANCE and GIBS/Worldview and the Suomi National Polar-orbiting Partnership, topographic data from the Shuttle Radar Topography Mission (SRTM), and modified Copernicus Sentinel data (2018) processed by the European Space Agency. CTI data courtesy of the CTI team at NASA's Goddard Space Flight Center. The sensor was developed with QmagiQ and funded by the Earth Science Technology Office (ESTO). Story by Adam Voiland.

Read More at:


and/or


#Landsat #NASA #USGS #Earth

....


Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://g.page/vineeshvc

Comments

Popular posts from this blog

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...

Image Transformation

In remote sensing, image transformation is used to create new images by manipulating existing ones, enhancing the information extracted from each pixel. Multi-image manipulation and other transformations allow analysts to highlight specific features, compare time-series data, or reduce data dimensions for better interpretation. 1. Multi-Image Manipulation This technique involves applying mathematical operations to multiple images, typically from different bands, dates, or sensors, to highlight specific features or changes. Common methods include: a. Image Addition Purpose : Highlights areas of similarity or enhances features when the same objects appear in multiple images. How it Works : By adding pixel values from two images, the output image emphasizes areas where brightness is high in both. Example : Enhancing overall brightness or detecting seasonal changes by adding images from different times. b. Image Subtraction Purpose : Identifies differences between images, useful for chan...

Spatial Feature Manipulation and Filtering

1. Spatial Feature Manipulation: Spatial Frequency Spatial frequency describes how quickly brightness changes across an image. It can reveal different levels of detail, based on how often these changes occur: High Spatial Frequency : Areas where brightness changes quickly, like edges, fine textures, or detailed patterns. These parts contain high detail but can also include noise. Low Spatial Frequency : Areas with gradual changes in brightness, like smooth surfaces or broad homogeneous areas. These regions are usually featureless, such as water bodies or large agricultural fields. Zero Spatial Frequency : Represents a completely uniform area with no brightness variation. This area is very smooth and has no texture or detail. By manipulating spatial frequencies, we can enhance or suppress certain features in an image. Spatial filtering is one of the primary methods to perform this manipulation. 2. Spatial Filtering and Spatial Domain Spatial filtering is the process of applying mathe...

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process :    - The algorithm starts by divid...

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature  A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...