scholarly journals Comparison of PRISMA Data with Model Simulations, Hyperion Reflectance and Field Spectrometer Measurements on ‘Piano delle Concazze’ (Mt. Etna, Italy)

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7224
Author(s):  
Vito Romaniello ◽  
Malvina Silvestri ◽  
Maria Fabrizia Buongiorno ◽  
Massimo Musacchio
Keyword(s):  
Near Infrared ◽  
Pearson Correlation ◽  
Test Site ◽  
Short Wave ◽  
Space Mission ◽  
Mean Difference ◽  
Atmospheric Transmission ◽  
Complete Evaluation ◽  
Model Simulations ◽  
Mt Etna

In this work, we compare first acquisitions from the ASI-PRISMA (Agenzia Spaziale Italiana-PRecursore IperSpettrale della Missione Applicativa) space mission with model simulations, past data acquired by the Hyperion sensor and field spectrometer measurements. The test site is ‘Piano delle Concazze’ (Mt. Etna, Italy), suitable for calibration purposes due to its homogeneity characteristics. The area measures at about 0.2 km2 and is composed of very homogeneous trachybasalt rich in plagioclase and olivine. Three PRISMA acquisitions, achieved on 31 July and 8 and 17 August 2019, are analyzed. Firstly, spectral profiles of PRISMA top of atmosphere (TOA) radiance are compared with MODerate resolution atmospheric TRANsmission (MODTRAN) simulations. The Pearson correlation coefficient is equal to 0.998 and 0.994 for VNIR (Visible and Near InfraRed) and SWIR (Short-Wave InfraRed) spectral ranges, respectively. PRISMA radiance overestimates values simulated by MODTRAN for all considered days, showing a mean bias of +5.22 and of +0.91 Wm−2sr−1µm−1 for VNIR and SWIR, respectively. The relative mean difference between reflectance values estimated by PRISMA and Hyperion, on the test area, is around +19%, despite the great difference in time acquisition (up to 19 years); PRISMA slightly overestimates Hyperion reflectance with an absolute mean difference of about +0.0083, within the variability of Hyperion acquisitions of ±0.0250 (corresponding to ±2 standard deviation). Finally, FieldSpec measurements also confirm the great quality of PRISMA reflectance estimations. The absolute mean difference results are around +0.0089 (corresponding to a relative error of about +21%). In the study, we investigate only the lower values of reflectance characterizing the test site. A more complete evaluation of PRISMA performances needs to consider other test sites with different optical characteristics.

scholarly journals Ankle-brachial index to monitor limb perfusion in patients with femoral venoarterial extracorporeal membrane oxygenation

2021 ◽  
Author(s):  
Andre Son ◽  
Azad Karim ◽  
Rachel Joung ◽  
Randy Mcgregor ◽  
Tingqing Wu ◽  
...  
Keyword(s):  
Extracorporeal Membrane Oxygenation ◽  
Near Infrared ◽  
Pearson Correlation ◽  
Ankle Brachial Index ◽  
Limb Ischemia ◽  
Mean Difference ◽  
Membrane Oxygenation ◽  
Venoarterial Extracorporeal Membrane Oxygenation ◽  
Limb Perfusion ◽  
Va Ecmo

Background: Limb ischemia is a major complication of femoral venoarterial extracorporeal membrane oxygenation (VA-ECMO). Use of ankle-brachial index (ABI) to monitor limb perfusion in VA-ECMO has not been described. We report our experience monitoring femoral VA-ECMO patients with serial ABI and the relationships between ABI and near infrared spectroscopy (NIRS). Methods: This is a retrospective single-center review of consecutive adult patients placed on femoral VA-ECMO between January 2019 and October 2019. Data were collected on patients with paired ABI and NIRS values. Relationships between NIRS and ABI of the cannulated (E-NIRS and E-ABI) and non-cannulated legs (N-NIRS and N-ABI) along with the difference between legs (D-NIRS and D-ABI) were determined using Pearson correlation. Results: Overall, 22 patients (mean age 56.5±14.0 years, 72.7% male) were assessed with 295 E-ABI and E-NIRS measurements, and 273 N-ABI and N-NIRS measurements. Mean duration of ECMO support was 129.8±78.3 hours. ECMO-mortality was 13.6% and in-hospital mortality was 45.5%. N-ABI and N-NIRS were significantly higher than their ECMO counterparts (ABI mean difference 0.16, 95%CI 0.13-0.19, p<0.0001; NIRS mean difference 2.51, 95%CI 1.48-3.54, p<0.0001). There was no correlation between E-ABI vs. E-NIRS (r=0.032, p=0.59), N-ABI vs. N-NIRS (r=0.097, p=0.11), or D-NIRS vs. D-ABI (r=0.11, p=0.069). Conclusions: ABI is a quantitative metric that may be used to monitor limb perfusion and supplement clinical exams to identify limb ischemia in femorally cannulated VA-ECMO patients. More studies are needed to characterize the significance of ABI in femoral VA-ECMO and its value in identifying limb ischemia in this patient population.

Two-Dimensional Near-Infrared and Shortwave Infrared Molecular Aggregates: Taking Kasha’s Model to the Next Dimension

2019 ◽  
Author(s):  
Arundhati Deshmukh ◽  
Danielle Koppel ◽  
Chern Chuang ◽  
Danielle Cadena ◽  
Jianshu Cao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Photophysical Properties ◽  
Satellite Telemetry ◽  
Transition Dipole Moment ◽  
Short Wave ◽  
Tissue Imaging ◽  
Transition Dipole ◽  
Deep Tissue Imaging ◽  
Excitonic States ◽  
Shortwave Infrared

Technologies which utilize near-infrared (700 – 1000 nm) and short-wave infrared (1000 – 2000 nm) electromagnetic radiation have applications in deep-tissue imaging, telecommunications and satellite telemetry due to low scattering and decreased background signal in this spectral region. However, there are few molecular species, which absorb efficiently beyond 1000 nm. Transition dipole moment coupling (e.g. J-aggregation) allows for redshifted excitonic states and provides a pathway to highly absorptive electronic states in the infrared. We present aggregates of two cyanine dyes whose absorption peaks redshift dramatically upon aggregation in water from ~ 800 nm to 1000 nm and 1050 nm with sheet-like morphologies and high molar absorptivities (e ~ 10<sup>5 </sup>M<sup>-1</sup>cm<sup>-1</sup>). To describe this phenomenology, we extend Kasha’s model for J- and H-aggregation to describe the excitonic states of <i> 2-dimensional aggregates</i> whose slip is controlled by steric hindrance in the assembled structure. A consequence of the increased dimensionality is the phenomenon of an <i>intermediate </i>“I-aggregate”, one which redshifts yet displays spectral signatures of band-edge dark states akin to an H-aggregate. We distinguish between H-, I- and J-aggregates by showing the relative position of the bright (absorptive) state within the density of states using temperature dependent spectroscopy. Our results can be used to better design chromophores with predictable and tunable aggregation with new photophysical properties.

scholarly journals Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

2021 ◽  
Vol 13 (3) ◽  
pp. 536
Author(s):  
Eve Laroche-Pinel ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Véronique Chéret ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Status ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Short Wave ◽  
Spatial And Temporal Scales ◽  
Spectral Bands ◽  
Main Challenge ◽  
Spectral Scale ◽  
Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

scholarly journals Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 2967
Author(s):  
Nicola Acito ◽  
Marco Diani ◽  
Gregorio Procissi ◽  
Giovanni Corsini
Keyword(s):  
Spectral Range ◽  
Near Infrared ◽  
Random Noise ◽  
Synthetic Data ◽  
Regression Function ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Electromagnetic Spectrum ◽  
Great Opportunity ◽  
Atmospheric Compensation

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

Plasmon Coupled Colloidal Gold Nanorods for Near‐Infrared and Short‐Wave‐Infrared Broadband Photodetection

2021 ◽  
pp. 2101217
Author(s):  
Hengyang Xiang ◽  
Zhelu Hu ◽  
Chenghao Xin ◽  
Hung‐Ju Lin ◽  
Lionel Aigouy ◽  
...  
Keyword(s):  
Gold Nanorods ◽  
Colloidal Gold ◽  
Near Infrared ◽  
Short Wave ◽  
Short Wave Infrared

scholarly journals Monitoring Urban Wastewaters’ Characteristics by Visible and Short Wave Near-Infrared Spectroscopy

Water ◽  
2013 ◽  
Vol 5 (4) ◽  
pp. 2026-2036 ◽  
Author(s):  
Ignacio Melendez-Pastor ◽  
M. Almendro-Candel ◽  
Jose Navarro-Pedreño ◽  
Ignacio Gómez ◽  
Mariano Lillo ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Short Wave

Red/near-infrared/short-wave infrared multi-band persistent luminescence in Pr3+-doped persistent phosphors

2017 ◽  
Vol 46 (34) ◽  
pp. 11149-11153 ◽  
Author(s):  
Yanjie Liang ◽  
Feng Liu ◽  
Yafei Chen ◽  
Xianli Wang ◽  
Kangning Sun ◽  
...  
Keyword(s):  
Near Infrared ◽  
Short Wave ◽  
Persistent Luminescence ◽  
Short Wave Infrared ◽  
Multi Band

MgGeO3:Pr3+ and CdSiO3:Pr3+ red/near-infrared/short-wave infrared multi-band persistent phosphors exhibit long-lasting (>120 h) afterglow at 625 nm, 900 nm and 1085 nm.

scholarly journals Forest and land fire smoke detection using GCOM-C data (case study: Pulang Pisau, Central Kalimantan)

2021 ◽  
Vol 893 (1) ◽  
pp. 012068
Author(s):  
K I N Rahmi ◽  
N Febrianti ◽  
I Prasasti
Keyword(s):  
Near Infrared ◽  
Thermal Infrared ◽  
Short Wave ◽  
Forest Land ◽  
Visual Interpretation ◽  
Infrared Band ◽  
Central Kalimantan ◽  
Fire Smoke ◽  
Shortwave Infrared

Abstract Forest/land fire give bad impact of heavy smoke on peatland area in Indonesia. Forest/land fire smoke need to be identified the distribution periodically. New satellite of GCOM-C has been launched to monitor climate condition and have visible, near infrared and thermal infrared. This study has objective to identify fire smoke from GCOM-C data. GCOM-C data has wavelength range from 0.38 to 12 μm it covers visible, near infrared, short-wave infrared and thermal infrared. It is relatively similar to MODIS or Himawari-8 images which could identify forest/land fire smoke. The methodology is visual interpretation to detect forest/land fire smoke using near infrared band (VN08), shortwave infrared band (SW03), and thermal bands (T01 and T02). Hotspot data is overlaid with GCOM-C image to represent the location of fire events. Combination of composite RGB image has been applied to detect forest/land fire smoke. GCOM-C image of VN8 bands and combination of thermal band in composite image could be used to detect fire smoke in Pulang Pisau, Central Kalimantan.

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

2022 ◽  
pp. 096703352110572
Author(s):  
Nicholas T Anderson ◽  
Kerry B Walsh
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Short Wave ◽  
Least Squares Regression ◽  
Machine Learning Methods ◽  
Transmission Geometry ◽  
Modelling Techniques ◽  
Test Sets ◽  
Model Robustness

Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

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