Coseismic displacements of the 14 November 2016 Mw7.8 Kaikoura, New Zealand, earthquake using an optical cubesat constellation

Abstract. Satellite measurements of coseismic displacements are typically based on Synthetic Aperture Radar (SAR) interferometry or amplitude tracking, or based on optical data such as from Landsat, Sentinel-2, SPOT, ASTER, very-high resolution satellites, or airphotos. Here, we evaluate a new class of optical satellite images for this purpose – data from cubesats. More specific, we investigate the PlanetScope cubesat constellation for horizontal surface displacements by the 14 November 2016 Mw7.8 Kaikoura, New Zealand, earthquake. Single PlanetScope scenes are 2–4 m resolution visible and near-infrared frame images of approximately 20–30 km × 9–15 km in size, acquired in continuous sequence along an orbit of approximately 375–475 km height. From single scenes or mosaics from before and after the earthquake we observe surface displacements of up to almost 10 m and estimate a matching accuracy from PlanetScope data of up to ±0.2 pixels (~ ±0.6 m). This accuracy, the daily revisit anticipated for the PlanetScope constellation for the entire land surface of Earth, and a number of other features, together offer new possibilities for investigating coseismic and other Earth surface displacements and managing related hazards and disasters, and complement existing SAR and optical methods. For comparison and for a better regional overview we also match the coseismic displacements by the 2016 Kaikoura earthquake using Landsat8 and Sentinel-2 data.

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Coseismic displacements of the 14 November 2016 <i>M</i><sub>w</sub> 7.8 Kaikoura, New Zealand, earthquake using the Planet optical cubesat constellation

Natural Hazards and Earth System Science ◽

10.5194/nhess-17-627-2017 ◽

2017 ◽

Vol 17 (5) ◽

pp. 627-639 ◽

Cited By ~ 25

Author(s):

Andreas Kääb ◽

Bas Altena ◽

Joseph Mascaro

Keyword(s):

New Zealand ◽

Land Surface ◽

Near Infrared ◽

Sar Interferometry ◽

Optical Methods ◽

Optical Data ◽

Time Interval ◽

Landsat 8 ◽

Surface Displacements ◽

Sentinel 2

Abstract. Satellite measurements of coseismic displacements are typically based on synthetic aperture radar (SAR) interferometry or amplitude tracking, or based on optical data such as from Landsat, Sentinel-2, SPOT, ASTER, very high-resolution satellites, or air photos. Here, we evaluate a new class of optical satellite images for this purpose – data from cubesats. More specific, we investigate the PlanetScope cubesat constellation for horizontal surface displacements by the 14 November 2016 Mw 7.8 Kaikoura, New Zealand, earthquake. Single PlanetScope scenes are 2–4 m-resolution visible and near-infrared frame images of approximately 20–30 km  ×  9–15 km in size, acquired in continuous sequence along an orbit of approximately 375–475 km height. From single scenes or mosaics from before and after the earthquake, we observe surface displacements of up to almost 10 m and estimate matching accuracies from PlanetScope data between ±0.25 and ±0.7 pixels (∼ ±0.75 to ±2.0 m), depending on time interval and image product type. Thereby, the most optimistic accuracy estimate of ±0.25 pixels might actually be typical for the final, sun-synchronous, and near-polar-orbit PlanetScope constellation when unrectified data are used for matching. This accuracy, the daily revisit anticipated for the PlanetScope constellation for the entire land surface of Earth, and a number of other features, together offer new possibilities for investigating coseismic and other Earth surface displacements and managing related hazards and disasters, and complement existing SAR and optical methods. For comparison and for a better regional overview we also match the coseismic displacements by the 2016 Kaikoura earthquake using Landsat 8 and Sentinel-2 data.

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Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network

Remote Sensing ◽

10.3390/rs13081512 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1512

Author(s):

Quan Xiong ◽

Liping Di ◽

Quanlong Feng ◽

Diyou Liu ◽

Wei Liu ◽

...

Keyword(s):

Land Surface ◽

Near Infrared ◽

Similarity Index ◽

Data Availability ◽

Optical Data ◽

Generative Adversarial Network ◽

Spectral Angle Mapper ◽

Adversarial Network ◽

Target Date ◽

Sentinel 2

Sentinel-2 images have been widely used in studying land surface phenomena and processes, but they inevitably suffer from cloud contamination. To solve this critical optical data availability issue, it is ideal to fuse Sentinel-1 and Sentinel-2 images to create fused, cloud-free Sentinel-2-like images for facilitating land surface applications. In this paper, we propose a new data fusion model, the Multi-channels Conditional Generative Adversarial Network (MCcGAN), based on the conditional generative adversarial network, which is able to convert images from Domain A to Domain B. With the model, we were able to generate fused, cloud-free Sentinel-2-like images for a target date by using a pair of reference Sentinel-1/Sentinel-2 images and target-date Sentinel-1 images as inputs. In order to demonstrate the superiority of our method, we also compared it with other state-of-the-art methods using the same data. To make the evaluation more objective and reliable, we calculated the root-mean-square-error (RSME), R2, Kling–Gupta efficiency (KGE), structural similarity index (SSIM), spectral angle mapper (SAM), and peak signal-to-noise ratio (PSNR) of the simulated Sentinel-2 images generated by different methods. The results show that the simulated Sentinel-2 images generated by the MCcGAN have a higher quality and accuracy than those produced via the previous methods.

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

Remote Sensing ◽

10.3390/rs11192304 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2304 ◽

Cited By ~ 3

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.

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Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

Remote Sensing ◽

10.3390/rs12183062 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3062 ◽

Cited By ~ 1

Author(s):

Michel E. D. Chaves ◽

Michelle C. A. Picoli ◽

Ieda D. Sanches

Keyword(s):

Systematic Review ◽

Land Use ◽

Land Cover ◽

Change Detection ◽

Near Infrared ◽

Data Cube ◽

Optical Data ◽

Landsat 8 ◽

Detailed Knowledge ◽

Sentinel 2

Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

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Sharpening the Sentinel-2 10 and 20 m Bands to Planetscope-0 3 m Resolution

Remote Sensing ◽

10.3390/rs12152406 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2406 ◽

Cited By ~ 1

Author(s):

Zhongbin Li ◽

Hankui K. Zhang ◽

David P. Roy ◽

Lin Yan ◽

Haiyan Huang

Keyword(s):

Land Surface ◽

Large Scale ◽

Near Infrared ◽

Detailed Examination ◽

Modulation Transfer ◽

Computationally Efficient ◽

Red Edge ◽

Shortwave Infrared ◽

Sentinel 2 ◽

Surface Changes

Combination of near daily 3 m red, green, blue, and near infrared (NIR) Planetscope reflectance with lower temporal resolution 10 m and 20 m red, green, blue, NIR, red-edge, and shortwave infrared (SWIR) Sentinel-2 reflectance provides potential for improved global monitoring. Sharpening the Sentinel-2 reflectance with the Planetscope reflectance may enable near-daily 3 m monitoring in the visible, red-edge, NIR, and SWIR. However, there are two major issues, namely the different and spectrally nonoverlapping bands between the two sensors and surface changes that may occur in the period between the different sensor acquisitions. They are examined in this study that considers Sentinel-2 and Planetscope imagery acquired one day apart over three sites where land surface changes due to biomass burning occurred. Two well-established sharpening methods, high pass modulation (HPM) and Model 3 (M3), were used as they are multiresolution analysis methods that preserve the spectral properties of the low spatial resolution Sentinel-2 imagery (that are better radiometrically calibrated than Planetscope) and are relatively computationally efficient so that they can be applied at large scale. The Sentinel-2 point spread function (PSF) needed for the sharpening was derived analytically from published modulation transfer function (MTF) values. Synthetic Planetscope red-edge and SWIR bands were derived by linear regression of the Planetscope visible and NIR bands with the Sentinel-2 red-edge and SWIR bands. The HPM and M3 sharpening results were evaluated visually and quantitatively using the Q2n metric that quantifies spectral and spatial distortion. The HPM and M3 sharpening methods provided visually coherent and spatially detailed visible and NIR wavelength sharpened results with low distortion (Q2n values > 0.91). The sharpened red-edge and SWIR results were also coherent but had greater distortion (Q2n values > 0.76). Detailed examination at locations where surface changes between the Sentinel-2 and the Planetscope acquisitions occurred revealed that the HPM method, unlike the M3 method, could reliably sharpen the bands affected by the change. This is because HPM sharpening uses a per-pixel reflectance ratio in the spatial detail modulation which is relatively stable to reflectance changes. The paper concludes with a discussion of the implications of this research and the recommendation that the HPM sharpening be used considering its better performance when there are surface changes.

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GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY

GEODESY CARTOGRAPHY AND AERIAL PHOTOGRAPHY ◽

10.23939/istcgcap2021.94.035 ◽

2021 ◽

Vol 94, 2021 (94) ◽

pp. 35-43

Author(s):

Andriy Babushka ◽

◽

Lyubov Babiy ◽

Borys Chetverikov ◽

Andriy Sevruk ◽

...

Keyword(s):

Forest Fires ◽

Local Governments ◽

Supervised Classification ◽

Near Infrared ◽

Practical Significance ◽

Exclusion Zone ◽

Space Images ◽

Before And After ◽

Normalized Burn Ratio ◽

Sentinel 2

Earth remote sensing and using the satellite images play an important role when monitoring the effects of forest fires and assessing damage. Applying different methods of multispectral space images processing, we can determine the risk of fire distribution, define hot spots and determine thermal parameters, mapping the damaged areas and assess the consequences of fire. The purpose of the work is the severity assessment connected with the post-fire period on the example of the forests in the Chornobyl Exclusion Zone. The tasks of the study are to define the area of burned zones using space images of different time which were obtained from the Sentinel-2 satellite applying the method of a normalized burn ratio (NBR) and method of supervised classification. Space images taken from the Sentinel-2 satellite before and after the fire were the input data for the study. Copernicus Open Access Hub service is a source of images and its spatial resolution is 10 m for visible and near infrared bands of images, and 20 m for medium infrared bands of images. We used method of Normalized Burn Ratio (NBR) and automatically calculated the area damaged with fire. Using this index we were able to identify areas of zones after active combustion. This index uses near and middle infrared bands for the calculations. In addition, a supervised classification was performed on the study area, and signature files were created for each class. According to the results of the classification, the areas of the territories damaged by the fire were also calculated. The scientific novelty relies upon the application of a method of using the normalized combustion coefficient (NBR) and supervised classification for space images obtained before and after the fire in the Chernobyl Exclusion Zone. The practical significance lies in the fact that the studied methods of GIS technologies can be used to identify territories and calculate the areas of vegetation damaged by fires. These results can be used by local organizations, local governments and the Ministry of Emergency Situations to monitor the condition and to plan reforestation. The normalized burned ratio (NBR) gives possibility efficiently and operatively to define and calculate the area which were damaged by fires, that gives possibility operatively assess the consequences of such fires and estimate the damage. The normalized burned ratio allows to calculate the area of burned forest almost 2 times more accurately than the supervised classification. The calculation process itself also takes less time and does not require additional procedures (set of signatures). Supervised classification in this case gives worse accuracy, the process itself is longer, but allows to determine the area of several different classes.

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KappaMask: AI-Based Cloudmask Processor for Sentinel-2

Remote Sensing ◽

10.3390/rs13204100 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4100

Author(s):

Marharyta Domnich ◽

Indrek Sünter ◽

Heido Trofimov ◽

Olga Wold ◽

Fariha Harun ◽

...

Keyword(s):

Land Surface ◽

European Space Agency ◽

Optical Data ◽

Dice Coefficient ◽

Test Dataset ◽

Space Agency ◽

Northern Latitudes ◽

Agricultural Applications ◽

Cloud Mask ◽

Sentinel 2

The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth’s land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%.

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Discharge Estimation Using Harmonized Landsat and Sentinel-2 Product: Case Studies in the Murray Darling Basin

Remote Sensing ◽

10.3390/rs12172810 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2810 ◽

Cited By ~ 1

Author(s):

Zhuolin Shi ◽

Yun Chen ◽

Qihang Liu ◽

Chang Huang

Keyword(s):

Case Studies ◽

Near Infrared ◽

Estimation Methods ◽

Small Rivers ◽

Discharge Data ◽

Murray Darling Basin ◽

Optical Satellite Images ◽

Discharge Estimation ◽

Mean Square Errors ◽

Sentinel 2

Quantifying river discharge is a critical component for hydrological studies, floodplain ecological conservation research, and water resources management. In recent years, a series of remote sensing-based discharge estimation methods have been developed. An example is the use of the near infrared (NIR) band of optical satellite images, with the principle of calculating the ratio between a stable land pixel for calibration (C) and a pixel within the river for measurement (M), applying a linear regression between C/M series and observed discharge series. This study trialed the C/M method, utilizing the Harmonized Landsat and Sentinel-2 (HLS) surface reflectance product on relatively small rivers with 30~100 m widths. Two study sites with different river characteristics and geographic settings in the Murray-Darling Basin (MDB) of Australia were selected as case studies. Two independent sets of HLS data and gauged discharge data for the 2017 and 2018 water years were acquired for modeling and validation, respectively. Results reveal high consistency between the HLS-derived discharge and gauged discharge at both sites. The Relative Root Mean Square Errors are 53% and 19%, and the Nash-Sutcliffe Efficiency coefficients are 0.24 and 0.69 for the two sites. This study supports the effectiveness of applying the fine-resolution HLS for modeling discharge on small rivers based on the C/M methodology, which also provides evidence of using multisource synthesized datasets as the input for discharge estimation.

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Monitoring 10-m LST from the Combination MODIS/Sentinel-2, Validation in a High Contrast Semi-Arid Agroecosystem

Remote Sensing ◽

10.3390/rs12091453 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1453

Author(s):

Juan M. Sánchez ◽

Joan M. Galve ◽

José González-Piqueras ◽

Ramón López-Urrea ◽

Raquel Niclòs ◽

...

Keyword(s):

Spatial Resolution ◽

Land Surface ◽

Near Infrared ◽

Estimation Error ◽

Vegetation Indices ◽

Model Performance ◽

Test Site ◽

Spatio Temporal ◽

Average Estimation Error ◽

Sentinel 2

Downscaling techniques offer a solution to the lack of high-resolution satellite Thermal InfraRed (TIR) data and can bridge the gap until operational TIR missions accomplishing spatio-temporal requirements are available. These techniques are generally based on the Visible Near InfraRed (VNIR)-TIR variable relations at a coarse spatial resolution, and the assumption that the relationship between spectral bands is independent of the spatial resolution. In this work, we adopted a previous downscaling method and introduced some adjustments to the original formulation to improve the model performance. Maps of Land Surface Temperature (LST) with 10-m spatial resolution were obtained as output from the combination of MODIS/Sentinel-2 images. An experiment was conducted in an agricultural area located in the Barrax test site, Spain (39°03′35″ N, 2°06′ W), for the summer of 2018. Ground measurements of LST transects collocated with the MODIS overpasses were used for a robust local validation of the downscaling approach. Data from 6 different dates were available, covering a variety of croplands and surface conditions, with LST values ranging 300–325 K. Differences within ±4.0 K were observed between measured and modeled temperatures, with an average estimation error of ±2.2 K and a systematic deviation of 0.2 K for the full ground dataset. A further cross-validation of the disaggregated 10-m LST products was conducted using an additional set of Landsat-7/ETM+ images. A similar uncertainty of ±2.0 K was obtained as an average. These results are encouraging for the adaptation of this methodology to the tandem Sentinel-3/Sentinel-2, and are promising since the 10-m pixel size, together with the 3–5 days revisit frequency of Sentinel-2 satellites can fulfill the LST input requirements of the surface energy balance methods for a variety of hydrological, climatological or agricultural applications. However, certain limitations to capture the variability of extreme LST, or in recently sprinkler irrigated fields, claim the necessity to explore the implementation of soil moisture or vegetation indices sensitive to soil water content as inputs in the downscaling approach. The ground LST dataset introduced in this paper will be of great value for further refinements and assessments.

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Detection of Liquefaction Phenomena from the 2017 Pohang (Korea) Earthquake Using Remote Sensing Data

Remote Sensing ◽

10.3390/rs11182184 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2184 ◽

Cited By ~ 4

Author(s):

Baik ◽

Son ◽

Kim

Keyword(s):

Satellite Images ◽

Remote Sensing Data ◽

Short Wave ◽

Landsat 8 ◽

Content Increase ◽

Infrared Band ◽

Water Index ◽

Before And After ◽

Optical Satellite Images ◽

Sentinel 2

On 15 November 2017, liquefaction phenomena were observed around the epicenter after a 5.4 magnitude earthquake occurred in Pohang in southeast Korea. In this study, we attempted to detect areas of sudden water content increase by using SAR (synthetic aperture radar) and optical satellite images. We analyzed coherence changes using Sentinel-1 SAR coseismic image pairs and analyzed NDWI (normalized difference water index) changes using Landsat 8 and Sentinel-2 optical satellite images from before and after the earthquake. Coherence analysis showed no liquefaction-induced surface changes. The NDWI time series analysis models using Landsat 8 and Sentinel-2 optical images confirmed liquefaction phenomena close to the epicenter but could not detect liquefaction phenomena far from the epicenter. We proposed and evaluated the TDLI (temporal difference liquefaction index), which uses only one SWIR (short-wave infrared) band at 2200 nm, which is sensitive to soil moisture content. The Sentinel-2 TDLI was most consistent with field observations where sand blow from liquefaction was confirmed. We found that Sentinel-2, with its relatively shorter revisit period compared to that of Landsat 8 (5 days vs. 16 days), was more effective for detecting traces of short-lived liquefaction phenomena on the surface. The Sentinel-2 TDLI could help facilitate rapid investigations and responses to liquefaction damage.

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