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 LINEAR SPECTRAL UNMIXING OF SENTINEL-3 IMAGERY FOR URBAN LAND COVER - LAND SURFACE TEMPERATURE (LST) ANALYSIS: A CASE STUDY OF METRO MANILA, PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 141-148
Author(s):  
C. Cruz ◽  
A. C. Blanco ◽  
J. Babaan ◽  
J. A. Cruz ◽  
R. R. Sta. Ana ◽  
...  
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Impervious Surface ◽  
Spectral Unmixing ◽  
Vegetation Fraction ◽  
Metro Manila ◽  
Linear Spectral Unmixing

Abstract. The advancement of remote sensing technologies is a huge advantage in various environmental applications including the monitoring of the rapid development in an urban area. This study aims to estimate the composition of the different classes (vegetation, impervious surfaces, soil) in Metro Manila, Philippines using a 300-meter spatial resolution Sentinel-3 Ocean and Land Colour Instrument image. The relationship between these land cover fractions with the spatial distribution of land surface temperature at this scale is evaluated. Sentinel-3 image has a higher spectral resolution (i.e. 21 bands), as compared with other Landsat and Sentinel missions, which is a requirement for an accurate cover mapping. Linear Spectral Unmixing (LSU), a sub-pixel classification method, was employed in identifying the fractional components in the image based on their spectral characteristics. Field survey using spectroradiometer was conducted to acquire spectral signatures of an impervious surface, vegetation, and soil which were used as the endmembers in the unmixing process. To assess the accuracy of the resulting vegetation fractional image, this was compared with a separate land cover pixel-based classification result using a 3-meter high spatial resolution PlanetScope image and with another vegetation index product of Sentinel-3. The results indicate that the recently available Sentinel-3 image can accurately estimate vegetation fraction with R2 = 0.84 and 0.99, respectively. In addition, the land surface temperature (LST) retrieved from Climate Engine is negatively correlated with the vegetation fraction cover (R2 = 0.81) and positively correlated with the impervious surface fraction cover (R2 = 0.66). Soil, on the other hand, has no correlation with the LST.

scholarly journals Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery

2019 ◽  
Vol 11 (19) ◽  
pp. 2304 ◽  
Author(s):  
Hanna Huryna ◽  
Yafit Cohen ◽  
Arnon Karnieli ◽  
Natalya Panov ◽  
William P. Kustas ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Near Infrared ◽  
Landsat 8 ◽  
Large Area ◽  
Primary Methods ◽  
Thermal Images ◽  
Spatially Distributed ◽  
The Difference ◽  
Sentinel 2

A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is not suitable for monitoring small fragmented fields. Thermal sharpening is one of the primary methods used to obtain thermal images at finer spatial resolution at a daily revisit time. In the current study, the utility of the TsHARP method to sharpen the low resolution of Sentinel-3 thermal data was examined using Sentinel-2 visible-near infrared imagery. Compared to Landsat 8 fine thermal images, the sharpening resulted in mean absolute errors of ~1 °C, with errors increasing as the difference between the native and the target resolutions increases. Part of the error is attributed to the discrepancy between the thermal images acquired by the two platforms. Further research is due to test additional sites and conditions, and potentially additional sharpening methods, applied to the Sentinel platforms.

scholarly journals Remote Sensing Phenology of Antarctic Green and Red Snow Algae Using WorldView Satellites

2021 ◽  
Vol 12 ◽  
Author(s):  
Andrew Gray ◽  
Monika Krolikowski ◽  
Peter Fretwell ◽  
Peter Convey ◽  
Lloyd S. Peck ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cell Density ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Algal Blooms ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Snow Algae ◽  
Antarctic Snow ◽  
Sentinel 2

Snow algae are an important group of terrestrial photosynthetic organisms in Antarctica, where they mostly grow in low lying coastal snow fields. Reliable observations of Antarctic snow algae are difficult owing to the transient nature of their blooms and the logistics involved to travel and work there. Previous studies have used Sentinel 2 satellite imagery to detect and monitor snow algal blooms remotely, but were limited by the coarse spatial resolution and difficulties detecting red blooms. Here, for the first time, we use high-resolution WorldView multispectral satellite imagery to study Antarctic snow algal blooms in detail, tracking the growth of red and green blooms throughout the summer. Our remote sensing approach was developed alongside two Antarctic field seasons, where field spectroscopy was used to build a detection model capable of estimating cell density. Global Positioning System (GPS) tagging of blooms and in situ life cycle analysis was used to validate and verify our model output. WorldView imagery was then used successfully to identify red and green snow algae on Anchorage Island (Ryder Bay, 67°S), estimating peak coverage to be 9.48 × 104 and 6.26 × 104 m2, respectively. Combined, this was greater than terrestrial vegetation area coverage for the island, measured using a normalized difference vegetation index. Green snow algae had greater cell density and average layer thickness than red blooms (6.0 × 104 vs. 4.3 × 104 cells ml−1) and so for Anchorage Island we estimated that green algae dry biomass was over three times that of red algae (567 vs. 180 kg, respectively). Because the high spatial resolution of the WorldView imagery and its ability to detect red blooms, calculated snow algal area was 17.5 times greater than estimated with Sentinel 2 imagery. This highlights a scaling problem of using coarse resolution imagery and suggests snow algal contribution to net primary productivity on Antarctica may be far greater than previously recognized.

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.

Remote Sensing of Broom Snakeweed (Gutierrezia Sarothrae) with Noaa-10 Spectral Image Processing

1992 ◽  
Vol 6 (4) ◽  
pp. 1015-1020 ◽  
Author(s):  
Albert J. Peters ◽  
Bradley C. Reed ◽  
Marlen D. Eve ◽  
Kirk C. McDaniel
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
New Mexico ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Spectral Image ◽  
Late Season ◽  
Shortgrass Prairie ◽  
Gutierrezia Sarothrae ◽  
Meteorological Satellite

Low-spatial resolution satellite imagery from the NOAA-10 polar-orbiting meteorological satellite was analyzed to determine if central New Mexico grasslands infested by broom snakeweed could be discriminated from unaffected areas. Distinctive phenological characteristics of broom snakeweed, including an early season growth flush and late season flowering, enable moderate to heavily infested areas to be separated from grasslands having few or no weeds present. The procedure used shows promise as a tool for locating and monitoring brown snakeweed and other weeds growing on shortgrass prairie.

scholarly journals Selection of informative features to highlight areas of waste disposal on high spatial resolution satellite imagery

2019 ◽  
pp. 29-44
Author(s):  
A. B. Murynin ◽  
A. A. Richter ◽  
M. A. Shakhramanyan
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Waste Disposal ◽  
High Spatial Resolution ◽  
Ground Truth ◽  
Space Images ◽  
Radar Images ◽  
High Resolution Images ◽  
Interpretive Signs ◽  
Selection Of

The paper deals with the problem of integrated interpretation of waste disposal facilities according to satellite imagery and ground truth monitoring, features of space images of landfills from various points of view: texture analysis, statistical properties, fractal analysis, color features, and the possibility of using machine learning methods. The main visual interpretive signs of landfills on optical and radar images of high spatial resolution are given. The fractal dimension of landfills was calculated for high resolution images using two models.

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