Mapping methane point emissions with imaging spectroscopy satellite missions

2021 ◽  
Author(s):  
Luis Guanter ◽  
Itziar Irakulis-Loitxate ◽  
Elena Sánchez-García ◽  
Javier Gorroño ◽  
Yongguang Zhang ◽  
...  
Keyword(s):  
Imaging Spectroscopy ◽  
Electromagnetic Spectrum ◽  
Spectral Window ◽  
Remote Sensing Technique ◽  
The Earth ◽  
Spatial Coverage ◽  
Airborne Imaging ◽  
Emission Detection ◽  
Shortwave Infrared ◽  
Spectral Channels

<p>Imaging spectroscopy, also known as hyperspectral imaging, is a remote sensing technique in which images of the solar radiation reflected by the Earth are produced in hundreds of spectral channels between the visible and the shortwave infrared part of the electromagnetic spectrum (roughly 400–2500 nm). The 2100-2450 nm spectral window can be used for methane retrievals, as it has been demonstrated over the last years with airborne imaging  spectrometers, and very recently also with space-based instruments. Satellite-based hyperspectral images are acquired with a typical spatial sampling for satellite data of 30 m, a spatial coverage between 30x30 and 60x60 km per scene, and a spectral sampling of 10 nm. In this work, we will present an overview of the state-of-the-art of methane mapping with imaging spectroscopy missions. We will review the characteristics of the available missions, the main retrieval approaches, and will present examples of methane emission detection from a number of missions and locations around the Earth.</p><p> </p>

scholarly journals Spectroscopic imaging of sub-kilometer spatial structure in lower-tropospheric water vapor

2021 ◽  
Vol 14 (4) ◽  
pp. 2827-2840
Author(s):  
David R. Thompson ◽  
Brian H. Kahn ◽  
Philip G. Brodrick ◽  
Matthew D. Lebsock ◽  
Mark Richardson ◽  
...  
Keyword(s):  
Water Vapor ◽  
Spatial Variability ◽  
Imaging Spectroscopy ◽  
Lower Troposphere ◽  
Airborne Lidar ◽  
Order Structure ◽  
Scaling Properties ◽  
Airborne Imaging ◽  
Shortwave Infrared ◽  
Airborne Lidar Data

Abstract. The subgrid spatial variability of water vapor is an important geophysical parameter for modeling tropical convention and cloud processes in atmospheric models. This study maps sub-kilometer spatial structures in total atmospheric column water vapor with visible to shortwave infrared (VSWIR) imaging spectroscopy. We describe our inversion approach and validate its accuracy with coincident measurements by airborne imaging spectrometers and the AERONET ground-based observation network. Next, data from NASA's AVIRIS-NG spectrometer enable the highest-resolution measurement to date of water vapor's spatial variability and scaling properties. We find second-order structure function scaling exponents consistent with prior studies of convective atmospheres. Airborne lidar data show that this total column measurement provides information about variability in the lower troposphere. We conclude by discussing the implications of these measurements and paths toward future campaigns to build upon these results.

scholarly journals Spectroscopic Imaging of Sub-Kilometer Spatial Structure in Lower Tropospheric Water Vapor

2020 ◽  
Author(s):  
David R. Thompson ◽  
Brian H. Kahn ◽  
Philip G. Brodrick ◽  
Matthew D. Lebsock ◽  
Mark Richardson ◽  
...  
Keyword(s):  
Water Vapor ◽  
Spatial Variability ◽  
General Circulation ◽  
Imaging Spectroscopy ◽  
General Circulation Models ◽  
Order Structure ◽  
Scaling Properties ◽  
Observation Network ◽  
Airborne Imaging ◽  
Shortwave Infrared

Abstract. Understanding the subgrid spatial variability of water vapor is important for parameterizing and simulating cloud processes in General Circulation Models (GCMs). This study maps sub-kilometer spatial structures in total atmospheric column water vapor with Visible to Shortwave Infrared (VSWIR) imaging spectroscopy. We describe our inversion approach and validate its accuracy with coincident measurements by airborne imaging spectrometers and the AERONET ground-based observation network. Next, data from NASA’s AVIRIS-NG spectrometer enables the highest resolution measurement to date of water vapor’s spatial variability and scaling properties. We find second order structure function scaling exponents consistent with prior studies of convective atmospheres. Finally, we conclude by discussing the implications of these measurements and paths toward future campaigns to build upon these results.

FlexBRDF: A Flexible BRDF Correction for Grouped Processing of Airborne Imaging Spectroscopy Flightlines

2021 ◽  
Author(s):  
Natalie Queally ◽  
Zhiwei Ye ◽  
Ting Zheng ◽  
Adam Chlus ◽  
Fabian Schneider ◽  
...  
Keyword(s):  
Imaging Spectroscopy ◽  
Airborne Imaging

Calibration and Validation of Infrared Sounders with Moon and Mercury

2021 ◽  
Author(s):  
Martin Burgdorf ◽  
Stefan A. Buehler ◽  
Viju John ◽  
Thomas Müller ◽  
Marc Prange
Keyword(s):  
Large Time ◽  
Angular Resolution ◽  
The Moon ◽  
Photometric Calibration ◽  
The Earth ◽  
Spread Function ◽  
Spectral Channels ◽  
Better Than ◽  
Full Disk

<p>Serendipitous observations of airless bodies of the inner solar system provide a unique means to the calibration of instruments on meteorological research satellites, because the physical properties of their surfaces change very little, even on large time scales. We investigated how certain instrumental effects can be characterised with observations of the Moon and Mercury. For this we identified and analysed intrusions of the Moon in the deep space views of HIRS/2, /3, and /4 (High-resolution Infrared Sounder) on various satellites in polar orbits and as well some images obtained with SEVIRI (Spinning Enhanced Visible Infra-Red Imager) on MSG-3 and -4 (Meteosat Second Generation), which had Mercury standing close to the Earth in the rectangular field of view.</p><p>A full-disk, infrared Moon model was developed that describes how the lunar flux density depends on phase angle and wavelength. It is particularly helpful for inter-calibration, checks of the photometric consistency of the sounding channels, and the calculation of an upper limit on the non-linearity of the shortwave channels of HIRS. In addition, we used the Moon to determine the co-registration of the different spectral channels.</p><p>Studies of the channel alignment are also presented for SEVIRI, an infrared sounder with an angular resolution about a hundred times better than HIRS. As we wanted to check the image quality of this instrument with a quasi-point source as well, we replaced here the Moon with Mercury. We found the typical smearing of the point spread function in the scan direction and occasionally a nearby ghost image, which is three to four times fainter than the main image of the planet. Both effects cause additional uncertainties of the photometric calibration.  </p>

scholarly journals Imaging Spectroscopic Analysis of Biochemical Traits for Shrub Species in Great Basin, USA

2018 ◽  
Vol 10 (10) ◽  
pp. 1621 ◽  
Author(s):  
Yi Qi ◽  
Susan Ustin ◽  
Nancy Glenn
Keyword(s):  
Great Basin ◽  
Land Surface ◽  
Imaging Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Leaf Mass Per Area ◽  
Shrub Species ◽  
Biochemical Traits ◽  
Airborne Imaging

The biochemical traits of plant canopies are important predictors of photosynthetic capacity and nutrient cycling. However, remote sensing of biochemical traits in shrub species in dryland ecosystems has been limited mainly due to the sparse vegetation cover, manifold shrub structures, and complex light interaction between the land surface and canopy. In order to examine the performance of airborne imaging spectroscopy for retrieving biochemical traits in shrub species, we collected Airborne Visible Infrared Imaging Spectrometer—Next Generation (AVIRIS-NG) images and surveyed four foliar biochemical traits (leaf mass per area, water content, nitrogen content and carbon) of sagebrush (Artemesia tridentata) and bitterbrush (Purshia tridentata) in the Great Basin semi-desert ecoregion, USA, in October 2014 and May 2015. We examined the correlations between biochemical traits and developed partial least square regression (PLSR) models to compare spectral correlations with biochemical traits at canopy and plot levels. PLSR models for sagebrush showed comparable performance between calibration (R2: LMA = 0.66, water = 0.7, nitrogen = 0.42, carbon = 0.6) and validation (R2: LMA = 0.52, water = 0.41, nitrogen = 0.23, carbon = 0.57), while prediction for bitterbrush remained a challenge. Our results demonstrate the potential for airborne imaging spectroscopy to measure shrub biochemical traits over large shrubland regions. We also highlight challenges when estimating biochemical traits with airborne imaging spectroscopy data.

Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy

2020 ◽  
Vol 28 (2) ◽  
pp. 70-80 ◽  
Author(s):  
Perez Mukasa ◽  
Collins Wakholi ◽  
Akbar Faqeerzada Mohammad ◽  
Eunsoo Park ◽  
Jayoung Lee ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Imaging Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Successive Projections Algorithm ◽  
Hinoki Cypress ◽  
Sorting System ◽  
Shortwave Infrared ◽  
Successive Projections

The combination of hyperspectral imaging with multivariate data analysis methods has recently been applied to develop a nondestructive technique, required to determine the seed viability of artificially aged vegetable and cereal seeds. In this study, the potential of shortwave infrared hyperspectral imaging to determine the viability of naturally aged seeds was investigated and thereafter a model for online seed sorting system was developed. The hyperspectral images of 400 Hinoki cypress tree seeds were acquired, and germination tests were conducted for viability confirmation, which indicated 31.5% of the viable seeds. Partial least square discriminant analysis models with 179 variables in the wavelength region of 1000–1800 nm were developed with a maximum model accuracy of 98.4% and 93.8% in both the calibration and validation sets, respectively. The partial least square discriminant analysis beta coefficient revealed the key wavelengths to differentiate viable from nonviable seeds, determined based on the differences in the chemical compositions of the seeds, including their lipid and fatty acid contents, which may control the germination ability of the seeds. The most effective wavelengths were selected using two model-based variable selection methods (i.e., the variable importance of projection (15 variables) and the successive projections algorithm (8 variables)) to develop the model. The successive projections algorithm wavelength selection method was considered to develop a viability model, and its application to the raw data resulted in a prediction accuracy of 94.7% in the calibration set and 92.2% in the validation set. These results demonstrate the potential of shortwave infrared hyperspectral imaging spectroscopy as a powerful nondestructive method to determine the viability of Hinoki cypress seeds. This method could be applied to develop an online seed sorting system for seed companies and nurseries.

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