Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing

There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion of multiple different remote sensing datatypes); and overall, sub-meter-level leaf-area index (LAI) and biomass maps have largely been absent. We evaluated the detectability of LAI and biomass patterns at a northern boreal fen (Halssiaapa) in northern Finland with multi-temporal and multi-source remote sensing data and assessed the benefit of hyperspectral drone data. We measured vascular plant percentage cover and height as well as moss cover in 140 field plots and connected the structural information to measured aboveground vascular LAI and biomass and moss biomass with linear regressions. We predicted both total and plant functional type (PFT) specific LAI and biomass patterns with random forests regressions with predictors including RGB and hyperspectral drone (28 bands in a spectral range of 500-900 nm), aerial and satellite imagery as well as topography and vegetation height information derived from structure-from-motion drone photogrammetry and aerial lidar data. The modeling performance was between moderate and good for total LAI and biomass (mean explained variance between 49.8 and 66.5%) and variable for PFTs (0.3-61.6%). Hyperspectral data increased model performance in most of the regressions, usually relatively little, but in some of the regressions, the inclusion of hyperspectral data even decreased model performance (change in mean explained variance between -14.5 and 9.1%-points). The most important features in regressions included drone topography, vegetation height, hyperspectral and RGB features. The spatial patterns and landscape estimates of LAI and biomass were quite similar in regressions with or without hyperspectral data, in particular for moss and total biomass. The results suggest that the fine-scale spatial patterns of peatland LAI and biomass can be detected with multi-source remote sensing data, vegetation mapping should include both spectral and topographic predictors at sub-meter-level spatial resolution and that hyperspectral imagery gives only slight benefits.Peer reviewe

Installation of an aryl boronic acid function into the external section of N-aryl-oxazolidinones : Synthesis and antimicrobial evaluation

N-aryl-oxazolidinones is a prominent family of antimicrobials used for treating infections caused by clinically prevalent Gram-positive bacteria. Recently, boron-containing compounds have displayed intriguing potential in the antibiotic discovery setting. Herein, we report the unprecedented introduction of a boron-containing moiety such as an aryl boronic acid in the external region of the oxazolidinone structure via a chemoselective acyl coupling reaction. As a result, we accessed a series of analogues with a distal aryl boronic pharmacophore on the oxazolidinone scaffold. We identified that a peripheric linear conformation coupled with freedom of rotation and no further substitution on the external aryl boronic ring, an amido linkage with hydrogen bonding character, in addition to a para-relative disposition between boronic group and linker, are the optimal combination of structural features in this series for antimicrobial activity. In comparison to linezolid, the analogue comprising all those features, compound 20b, displayed levels of antimicrobial activity augmented by an eight-fold to a thirty-two-fold against a panel of Gram-positive strains, and a near one hundred-fold against Escherichia coli JW5503, a Gram-negative mutant strain with a defective efflux capability.Peer reviewe

Intercomparison of arctic XH2_{2}O observations from three ground-based Fourier transform infrared networks and application for satellite validation

In this paper, we compare column-averaged dry-air mole fractions of water vapor (XH$_{2}$O) retrievals from the COllaborative Carbon Column Observing Network (COCCON) with retrievals from two co-located high-resolution Fourier transform infrared (FTIR) spectrometers as references at two boreal sites, Kiruna, Sweden, and Sodankylä, Finland, from 6 March 2017 to 20 September 2019. In the framework of the Network for the Detection of Atmospheric Composition Change (NDACC), an FTIR spectrometer is operated at Kiruna. The H$_{2}$O product derived from these observations has been generated with the MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) processor. In Sodankylä, a Total Carbon Column Observing Network (TCCON) spectrometer is operated, and the official XH$_{2}$O data as provided by TCCON are used for this study. The datasets are in good overall agreement, with COCCON data showing a wet bias of (49.20±58.61) ppm ((3.33±3.37) %, R$^{2}$=0.9992) compared with MUSICA NDACC and (56.32±45.63) ppm ((3.44±1.77) %, R$^{2}$=0.9997) compared with TCCON. Furthermore, the a priori H$_{2}$O volume mixing ratio (VMR) profiles (MAP) used as a priori information in the TCCON retrievals (also adopted for COCCON retrievals) are evaluated with respect to radiosonde (Vaisala RS41) profiles at Sodankylä. The MAP and radiosonde profiles show similar shapes and a good linear correlation of integrated XH$_{2}$O, indicating that MAP is a reasonable approximation of the true atmospheric state and an appropriate choice for the scaling retrieval methods as applied by COCCON and TCCON. COCCON shows a reduced dry bias (−14.96 %) in comparison with TCCON (−19.08 %) with respect to radiosonde XH$_{2}$O. Finally, we investigate the quality of satellite data at high latitudes. For this purpose, the COCCON XH$_{2}$O is compared with retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) generated with the MUSICA processor (MUSICA IASI) and with retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). Both paired datasets generally show good agreement and similar correlations at the two sites. COCCON measures 4.64 % less XH2O at Kiruna and 3.36 % less at Sodankylä with respect to MUSICA IASI, whereas COCCON measures 9.71 % more XH$_{2}$O at Kiruna and 7.75 % more at Sodankylä compared with TROPOMI. Our study supports the assumption that COCCON also delivers a well-characterized XH$_{2}$O data product. This emphasizes that this approach might complement the TCCON network with respect to satellite validation efforts. This is the first published study where COCCON XH$_{2}$O has been compared with MUSICA NDACC and TCCON retrievals and has been used for MUSICA IASI and TROPOMI validation

Intercomparison of atmospheric CO2 and CH4 abundances on regional scales in boreal areas using Copernicus Atmosphere Monitoring Service (CAMS) analysis, COllaborative Carbon Column Observing Network (COCCON) spectrometers, and Sentinel-5 Precursor satellite observations

We compare the atmospheric column-averaged dry-air mole fractions of carbon dioxide (XCO$_{2}$) and methane (XCH$_{4}$) measured with a pair of COllaborative Carbon Column Observing Network (COCCON) spectrometers at Kiruna and Sodankylä (boreal areas). We compare model data provided by the Copernicus Atmosphere Monitoring Service (CAMS) between 2017 and 2019 with XCH$_{4}$ data from the recently launched Sentinel-5 Precursor (S5P) satellite between 2018 and 2019. In addition, measured and modeled gradients of XCO$_{2}$ and XCH$_{4}$ (ΔXCO$_{2}$ and ΔXCH$_{4}$) on regional scales are investigated. Both sites show a similar and very good correlation between COCCON retrievals and the modeled CAMS XCO$_{2}$ data, while CAMS data are biased high with respect to COCCON by 3.72 ppm (±1.80 ppm) in Kiruna and 3.46 ppm (±1.73 ppm) in Sodankylä on average. For XCH$_{4}$, CAMS values are higher than the COCCON observations by 0.33 ppb (±11.93 ppb) in Kiruna and 7.39 ppb (±10.92 ppb) in Sodankylä. In contrast, the S5P satellite generally measures lower atmospheric XCH$_{4}$ than the COCCON spectrometers, with a mean difference of 9.69 ppb (±20.51 ppb) in Kiruna and 3.36 ppb (±17.05 ppb) in Sodankylä. We compare the gradients of XCO$_{2}$ and XCH$_{4}$ (ΔXCO$_{2}$ and ΔXCH$_{4}$) between Kiruna and Sodankylä derived from CAMS analysis and COCCON and S5P measurements to study the capability of detecting sources and sinks on regional scales. The correlations in ΔXCO$_{2}$ and ΔXCH$_{4}$ between the different datasets are generally smaller than the correlations in XCO$_{2}$ and XCH$_{4}$ between the datasets at either site. The ΔXCO$_{2}$ values predicted by CAMS are generally higher than those observed with COCCON with a slope of 0.51. The ΔXCH$_{4}$ values predicted by CAMS are mostly higher than those observed with COCCON with a slope of 0.65, covering a larger dataset than the comparison between S5P and COCCON. When comparing CAMS ΔXCH$_{4}$ with COCCON ΔXCH$_{4}$ only in S5P overpass days (slope = 0.53), the correlation is close to that between S5P and COCCON (slope = 0.51). CAMS, COCCON, and S5P predict gradients in reasonable agreement. However, the small number of observations coinciding with S5P limits our ability to verify the performance of this spaceborne sensor. We detect no significant impact of ground albedo and viewing zenith angle on the S5P results. Both sites show similar situations with the average ratios of XCH$_{4}$ (S5P/COCCON) of 0.9949±0.0118 in Kiruna and 0.9953±0.0089 in Sodankylä. Overall, the results indicate that the COCCON instruments have the capability of measuring greenhouse gas (GHG) gradients on regional scales, and observations performed with the portable spectrometers can contribute to inferring sources and sinks and to validating spaceborne greenhouse gas sensors. To our knowledge, this is the first published study using COCCON spectrometers for the validation of XCH$_{4}$ measurements collected by S5P

LISA:a lightweight stratospheric air sampler

We developed a new lightweight stratospheric air sampler (LISA). The LISA sampler is designed to collect four bag samples in the stratosphere during a balloon flight for CO2, CH4 and CO mole fraction measurements. It consists of four multi-layer foil (MLF) sampling bags, a custom-made manifold, and a diaphragm pump, with a total weight of ∼2.5 kg. A series of laboratory storage tests were performed to assess the stability of CO2, CH4 and CO mole fractions in both MLF and Tedlar bags. The MLF bag was chosen due to its better overall performance than the Tedlar bag for the three species CO2, CH4 and CO. Furthermore, we evaluated the performance of the pump under low pressure conditions to optimize a trade-off between the vertical resolution and the sample size. The LISA sampler was flown on the same balloon flight with an AirCore in Sodankylä, Finland (67.368∘ N, 26.633∘ E, 179 m a.s.l.), on 26 April and 4–7 September 2017. A total of 15 stratospheric air samples were obtained during the ascent of four flights. The sample size ranges between 800 and 180 mL for the altitude between 12 and 25 km, with the corresponding vertical resolution ranging from 0.5 to 1.5 km. The collected air samples were analysed for CO2, CH4 and CO mole fractions, and evaluated against AirCore retrieved profiles, showing mean differences of 0.84 ppm for CO2, 1.8 ppb for CH4 and 6.3 ppb for CO, respectively. High-accuracy stratospheric measurements of greenhouse gas mole fractions are useful to validate remote sensing measurements from ground and from space, which has been performed primarily by comparison with collocated aircraft measurements (0.15–13 km), and more recently with AirCore observations (0–30 km). While AirCore is capable of achieving high-accuracy greenhouse gas mole fraction measurements, it is challenging to obtain accurate altitude registration for AirCore measurements. The LISA sampler provides a viable low-cost tool for retrieving stratospheric air samples for greenhouse gas measurements that is complementary to AirCore. Furthermore, the LISA sampler is advantageous in both the vertical resolution and sample size for performing routine stratospheric measurements of the isotopic composition of trace gases

Intercomparison of atmospheric CO2 and CH4 abundances on regional scales in boreal areas using Copernicus Atmosphere Monitoring Service (CAMS) analysis, COllaborative Carbon Column Observing Network (COCCON) spectrometers, and Sentinel-5 Precursor satellite observations

We compare the atmospheric column-averaged dry-air mole fractions of carbon dioxide (XCO2) and methane (XCH4) measured with a pair of COllaborative Carbon Column Observing Network (COCCON) spectrometers at Kiruna and Sodankyla (boreal areas). We compare model data provided by the Copernicus Atmosphere Monitoring Service (CAMS) between 2017 and 2019 with XCH4 data from the recently launched Sentinel-5 Precursor (S5P) satellite between 2018 and 2019. In addition, measured and modeled gradients of XCO2 and XCH4 (Delta XCO2 and Delta XCH4) on regional scales are investigated. Both sites show a similar and very good correlation between COCCON retrievals and the modeled CAMS XCO2 data, while CAMS data are biased high with respect to COCCON by 3.72 ppm (+/- 1.80 ppm) in Kiruna and 3.46 ppm (+/- 1.73 ppm) in Sodankyla on average. For XCH4, CAMS values are higher than the COCCON observations by 0.33 ppb (+/- 11.93 ppb) in Kiruna and 7.39 ppb (+/- 10.92 ppb) in Sodankyla. In contrast, the S5P satellite generally measures lower atmospheric XCH4 than the COCCON spectrometers, with a mean difference of 9.69 ppb (+/- 20.51 ppb) in Kiruna and 3.36 ppb (+/- 17.05 ppb) in So-dankyla. We compare the gradients of XCO2 and XCH4 (Delta XCO2 and Delta XCH4) between Kiruna and Sodankyla derived from CAMS analysis and COCCON and S5P measurements to study the capability of detecting sources and sinks on regional scales. The correlations in Delta XCO2 and Delta XCH4 between the different datasets are generally smaller than the correlations in XCO2 and XCH4 between the datasets at either site. The Delta XCO2 values predicted by CAMS are generally higher than those observed with COCCON with a slope of 0.51. The Delta XCH4 values predicted by CAMS are mostly higher than those observed with COCCON with a slope of 0.65, covering a larger dataset than the comparison between S5P and COCCON. When comparing CAMS Delta XCH4 with COCCON Delta XCH4 only in S5P overpass days (slope = 0.53), the correlation is close to that between S5P and COCCON (slope = 0.51). CAMS, COCCON, and S5P predict gradients in reasonable agreement. However, the small number of observations coinciding with S5P limits our ability to verify the performance of this spaceborne sensor. We detect no significant impact of ground albedo and viewing zenith angle on the S5P results. Both sites show similar situations with the average ratios of XCH4 (S5P/COCCON) of 0.9949 +/- 0.0118 in Kiruna and 0.9953 +/- 0.0089 in Sodankyla. Overall, the results indicate that the COCCON instruments have the capability of measuring greenhouse gas (GHG) gradients on regional scales, and observations performed with the portable spectrometers can contribute to inferring sources and sinks and to validating spaceborne greenhouse gas sensors. To our knowledge, this is the first published study using COCCON spectrometers for the validation of XCH4 measurements collected by S5P

Retrieval of atmospheric CH_4 vertical information from ground-based FTS near-infrared spectra

International audienceThe Total Carbon Column Observing Network (TCCON) column-averaged dry air mole fraction of CH 4 (X CH 4) measurements have been widely used to validate satellite observations and to estimate model simulations. The GGG2014 code is the standard TCCON retrieval software used in performing a profile scaling retrieval. In order to obtain several vertical pieces of information in addition to the total column, in this study, the SFIT4 retrieval code is applied to retrieve the CH 4 mole fraction vertical profile from the Fourier transform spectrometer (FTS) spectrum at six sites (Ny-Ålesund, Sodankylä, Bialystok, Bremen, Orléans and St Denis) during the time period of 2016-2017. The retrieval strategy of the CH 4 profile retrieval from ground-based FTS near-infrared (NIR) spectra using the SFIT4 code (SFIT4NIR) is investigated. The degree of freedom for signal (DOFS) of the SFIT4NIR retrieval is about 2.4, with two distinct pieces of information in the troposphere and in the stratosphere. The averaging kernel and error budget of the SFIT4NIR retrieval are presented. The data accuracy and precision of the SFIT4NIR retrievals, including the total column and two partial columns (in the troposphere and stratosphere), are estimated by TCCON standard retrievals, ground-based in situ measurements, Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS) satellite observations, TCCON proxy data and AirCore and aircraft measurements. By comparison against TCCON standard retrievals, it is found that the retrieval uncertainty of SFIT4NIR X CH 4 is similar to that of TCCON standard retrievals with systematic uncertainty within 0.35 % and random uncertainty of about 0.5 %. The tropospheric and strato-spheric X CH 4 from SFIT4NIR retrievals are assessed by comparison with AirCore and aircraft measurements, and there is a 1.0 ± 0.3 % overestimation in the SFIT4NIR tropospheric X CH 4 and a 4.0 ± 2.0 % underestimation in the SFIT4NIR stratospheric X CH 4 , which are within the systematic uncertainties of SFIT4NIR-retrieved partial columns in the tropo-sphere and stratosphere respectively

Evaluating atmospheric methane inversion model results for Pallas, northern Finland

A state-of-the-art inverse model, CarbonTracker Data Assimilation Shell (CTDAS), was used to optimize estimates of methane (CH4) surface fluxes using atmospheric observations of CH4 as a constraint. The model consists of the latest version of the TM5 atmospheric chemistry-transport model and an ensemble Kalman filter based data assimilation system. The model was constrained by atmospheric methane surface concentrations, obtained from the World Data Centre for Greenhouse Gases (WDCGG). Prior methane emissions were specified for five sources: biosphere, anthropogenic, fire, termites and ocean, of which bio-sphere and anthropogenic emissions were optimized. Atmospheric CH 4 mole fractions for 2007 from northern Finland calculated from prior and optimized emissions were compared with observations. It was found that the root mean squared errors of the posterior esti - mates were more than halved. Furthermore, inclusion of NOAA observations of CH 4 from weekly discrete air samples collected at Pallas improved agreement between posterior CH 4 mole fraction estimates and continuous observations, and resulted in reducing optimized biosphere emissions and their uncertainties in northern Finland