scholarly journals Investigating Remote-Sensing Techniques to Reveal Stealth Coronal Mass Ejections

2021 ◽  
Vol 8 ◽  
Author(s):  
Erika Palmerio ◽  
Nariaki V. Nitta ◽  
Tamitha Mulligan ◽  
Marilena Mierla ◽  
Jennifer O’Kane ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Coronal Mass Ejections ◽  
Large Scale ◽  
Geomagnetic Storms ◽  
A Priori ◽  
Source Region ◽  
Remote Sensing Data ◽  
Additional Information ◽  
Remote Sensing Techniques ◽  
Geometric Techniques

Eruptions of coronal mass ejections (CMEs) from the Sun are usually associated with a number of signatures that can be identified in solar disc imagery. However, there are cases in which a CME that is well observed in coronagraph data is missing a clear low-coronal counterpart. These events have received attention during recent years, mainly as a result of the increased availability of multi-point observations, and are now known as “stealth CMEs.” In this work, we analyze examples of stealth CMEs featuring various levels of ambiguity. All the selected case studies produced a large-scale CME detected by coronagraphs and were observed from at least one secondary viewpoint, enabling a priori knowledge of their approximate source region. To each event, we apply several image processing and geometric techniques with the aim to evaluate whether such methods can provide additional information compared to the study of “normal” intensity images. We are able to identify at least weak eruptive signatures for all events upon careful investigation of remote-sensing data, noting that differently processed images may be needed to properly interpret and analyze elusive observations. We also find that the effectiveness of geometric techniques strongly depends on the CME propagation direction with respect to the observers and the relative spacecraft separation. Being able to observe and therefore forecast stealth CMEs is of great importance in the context of space weather, since such events are occasionally the solar counterparts of so-called “problem geomagnetic storms.”

LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes

2019 ◽  
Vol 221 ◽  
pp. 695-706 ◽  
Author(s):  
Jianbo Qi ◽  
Donghui Xie ◽  
Tiangang Yin ◽  
Guangjian Yan ◽  
Jean-Philippe Gastellu-Etchegorry ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Image Simulation ◽  
Simulation Framework ◽  
Sensing Data

scholarly journals Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽  
2019 ◽  
Author(s):  
Juan Carlos Valdiviezo-Navarro ◽  
Adan Salazar-Garibay ◽  
Karla Juliana Rodríguez-Robayo ◽  
Lilián Juárez ◽  
María Elena Méndez-López ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Food Sovereignty ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Multispectral Images ◽  
Crop Fields ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

scholarly journals Automatic calibration of a large-scale sediment model using suspended sediment concentration, water quality, and remote sensing data

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Hugo de Oliveira Fagundes ◽  
Fernando Mainardi Fan ◽  
Rodrigo Cauduro Dias de Paiva
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Suspended Sediment ◽  
Large Scale ◽  
Suspended Sediment Concentration ◽  
Remote Sensing Data ◽  
Sediment Concentration ◽  
Spatial Discretization ◽  
Calibration And Validation ◽  
Sediment Model

ABSTRACT Calibration and validation are two important steps in the application of sediment models requiring observed data. This study aims to investigate the potential use of suspended sediment concentration (SSC), water quality and remote sensing data to calibrate and validate a large-scale sediment model. Observed data from across 108 stations located in the Doce River basin was used for the period between 1997-2010. Ten calibration and validation experiments using the MOCOM-UA optimization algorithm coupled with the MGB-SED model were carried out, which, over the same period of time, resulted in 37 calibration and 111 validation tests. The experiments were performed by modifying metrics, spatial discretization, observed data and parameters of the MOCOM-UA algorithm. Results generally demonstrated that the values of correlation presented slight variations and were superior in the calibration step. Additionally, increasing spatial discretization or establishing a background concentration for the model allowed for improved results. In a station with high quantity of SSC data, calibration improved the ENS coefficient from -0.44 to 0.44. The experiments showed that the spectral surface reflectance, total suspended solids and turbidity data have the potential to enhance the performance of sediment models.

scholarly journals Review of Satellite Interferometry for Landslide Detection in Italy

2020 ◽  
Vol 12 (8) ◽  
pp. 1351 ◽  
Author(s):  
Lorenzo Solari ◽  
Matteo Del Soldato ◽  
Federico Raspini ◽  
Anna Barra ◽  
Silvia Bianchini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Management Strategies ◽  
Back Analysis ◽  
Remote Sensing Data ◽  
Economic Losses ◽  
Interferometric Synthetic Aperture Radar ◽  
Monitoring Tools ◽  
Average Production ◽  
Landslide Mapping ◽  
Remote Sensing Techniques

Landslides recurrently impact the Italian territory, producing huge economic losses and casualties. Because of this, there is a large demand for monitoring tools to support landslide management strategies. Among the variety of remote sensing techniques, Interferometric Synthetic Aperture Radar (InSAR) has become one of the most widely applied for landslide studies. This work reviews a variety of InSAR-related applications for landslide studies in Italy. More than 250 papers were analyzed in this review. The first application dates back to 1999. The average production of InSAR-related papers for landslide studies is around 12 per year, with a peak of 37 papers in 2015. Almost 70% of the papers are written by authors in academia. InSAR is used (i) for landslide back analysis (3% of the papers); (ii) for landslide characterization (40% of the papers); (iii) as input for landslide models (7% of the papers); (iv) to update landslide inventories (15% of the papers); (v) for landslide mapping (32% of the papers), and (vi) for monitoring (3% of the papers). Sixty-eight percent of the authors validated the satellite results with ground information or other remote sensing data. Although well-known limitations exist, this bibliographic overview confirms that InSAR is a consolidated tool for many landslide-related applications.

scholarly journals Quantifying large‐scale ecosystem stability with remote sensing data

2020 ◽  
Vol 6 (3) ◽  
pp. 354-365
Author(s):  
Hannah J. White ◽  
Willson Gaul ◽  
Dinara Sadykova ◽  
Lupe León‐Sánchez ◽  
Paul Caplat ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Ecosystem Stability ◽  
Sensing Data

Time-series processing of large scale remote sensing data with extreme learning machine

2014 ◽  
Vol 128 ◽  
pp. 199-206 ◽  
Author(s):  
Jiaoyan Chen ◽  
Guozhou Zheng ◽  
Cong Fang ◽  
Ningyu Zhang ◽  
Huajun Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Learning Machine

scholarly journals Accomplishments of the MUSICA project to provide accurate, long-term, global and high-resolution observations of tropospheric {H<sub>2</sub>O,<i>δ</i>D} pairs – a review

2016 ◽  
Vol 9 (7) ◽  
pp. 2845-2875 ◽  
Author(s):  
Matthias Schneider ◽  
Andreas Wiegele ◽  
Sabine Barthlott ◽  
Yenny González ◽  
Emanuel Christner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Measurement Techniques ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Horizontal Resolution ◽  
Atmospheric Composition ◽  
Remote Sensing Techniques ◽  
Middle Infrared

Abstract. In the lower/middle troposphere, {H2O,δD} pairs are good proxies for moisture pathways; however, their observation, in particular when using remote sensing techniques, is challenging. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) addresses this challenge by integrating the remote sensing with in situ measurement techniques. The aim is to retrieve calibrated tropospheric {H2O,δD} pairs from the middle infrared spectra measured from ground by FTIR (Fourier transform infrared) spectrometers of the NDACC (Network for the Detection of Atmospheric Composition Change) and the thermal nadir spectra measured by IASI (Infrared Atmospheric Sounding Interferometer) aboard the MetOp satellites. In this paper, we present the final MUSICA products, and discuss the characteristics and potential of the NDACC/FTIR and MetOp/IASI {H2O,δD} data pairs. First, we briefly resume the particularities of an {H2O,δD} pair retrieval. Second, we show that the remote sensing data of the final product version are absolutely calibrated with respect to H2O and δD in situ profile references measured in the subtropics, between 0 and 7 km. Third, we reveal that the {H2O,δD} pair distributions obtained from the different remote sensors are consistent and allow distinct lower/middle tropospheric moisture pathways to be identified in agreement with multi-year in situ references. Fourth, we document the possibilities of the NDACC/FTIR instruments for climatological studies (due to long-term monitoring) and of the MetOp/IASI sensors for observing diurnal signals on a quasi-global scale and with high horizontal resolution. Fifth, we discuss the risk of misinterpreting {H2O,δD} pair distributions due to incomplete processing of the remote sensing products.

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