cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48577/jpl.7bskbn
title: Quantifying the impact of the COVID-19 pandemic restrictions on CO, CO2, and CH4 in downtown Toronto using open-path Fourier transform spectroscopy

description: During the global COVID-19 pandemic, anthropogenic emissions of air pollutants and greenhouse gases (GHGs), especially traffic emissions in urban areas, have declined. Long-term measurements of trace gas concentrations in urban areas can be used to quantify the impact of emission reductions on GHG mole fractions. Open-path Fourier transform infrared (OP-FTIR) spectroscopy is a non-intrusive technique that can be used to simultaneously measure multiple atmospheric trace gases in the boundary layer. This study investigates the reduction of surface CO, CO2, and CH4 mole fractions during the stay-at-home periods in downtown Toronto, Canada, which is the fourth largest city in North America. Mean CO mole fraction enhancement above background declined by 51+/-23% and 42+/-24% during the 2020 and 2021 stay-at-home periods, respectively, relative to a reference period before the 2020 restrictions started. The mean afternoon CO2 mole fraction enhancement above background declined by 3.9+/-2.6 ppm (36+/-24%) and 3.5+/-2.8 ppm (33+/-26%) during the stay-at-home periods in 2020 and 2021 relative to the 2020 reference period. CH4 mole fraction enhancement above background did not show significant decline during the 2020 stay-at-home period relative to the 2020 reference period. Diurnal variation in CO during the stay-at-home period in 2020 was also significantly reduced relative to the reference period in 2020. Reductions in trace gas mole fraction enhancements are coincident with the decline of local traffic during the stay-at-home periods, with reduction in CO and CO2 enhancements of 0.74+/-0.15 ppb and 0.18+/-0.05 ppm per percentage decrease in traffic, respectively.
DOI: 10.48577/jpl.8iazuj
title: Diverse biosphere influence on carbon and heat in mixed urban Mediterranean landscape revealed by high resolution thermal and optical remote sensing

description: A fundamental challenge in verifying urban CO2 emissions reductions is estimating the biological influence that can confound emission source attribution across heterogeneous and diverse landscapes. Recent work using atmospheric radiocarbon revealed a substantial seasonal influence of the managed urban biosphere on regional carbon budgets in the Los Angeles megacity, but lacked spatially explicit attribution of the diverse biological influences needed for flux quantification and decision making. New optical and thermal sensors from airborne and satellite platforms simultaneously resolve fine-scale urban land cover, land use (irrigation), phenology and water stress effects required to accurately quantify biological influences on CO2 exchange in complex urban environments. We use remote sensing constraints with an urban land surface model to quantify spatial and seasonal gross primary production (GPP) variability across the Southern California Air Basin (SoCAB) at 30 m resolution. High spatial resolution optical and thermal remote sensing imagery provides attribution of landscape influences related to vegetation type, fragmentation and irrigation across urban and non-urban gradients. Results showed that montane non-urban shrubs and trees dominate the regional carbon budget of SoCAB (80% of GPP). The urban carbon budget is strongly influenced by land use and landscape fragmentation. Irrigated vegetation accounts for only 21% of urban vegetation but 31% of annual GPP, driven by turf grass, and is twice as productive as non-irrigated vegetation during hot and dry Mediterranean summer months. Fragmented vegetation accounts for 25% of urban vegetation but 50% of annual GPP, with cooler interior vegetation enhancing tree and grass GPP in spring and summer, and irrigation mitigating stress-driven GPP loss in edge vegetation. Our satellite driven, very high resolution modeling framework highlights the important influence of land use and fragmentation on urban carbon budgets, and provides a path forward for quantifying urban biospheric influences globally.
DOI: 10.48577/jpl.pxbhf4
title: Validation of SMAP Soil Moisture at Terrestrial National Ecological Observatory Network (NEON) Sites Show Potential for Soil Moisture Retrieval in Forested Areas

description: Soil moisture influences forest health, fire occurrence and extent, and insect and pathogen impacts, creating a need for regular, globally extensive soil moisture measurements that can only be achieved by satellite-based sensors, such as NASA’s Soil Moisture Active Passive (SMAP). However, SMAP data for forested regions, which account for ~20% of land cover globally, are flagged as unreliable due to interference from vegetation water content, and forests were underrepresented in previous validation efforts, preventing an assessment of measurement accuracy in these biomes. Here we compare over twelve thousand SMAP soil moisture measurements, representing 88 site-years, to in-situ soil moisture measurements from forty National Ecological Observatory Network (NEON) sites throughout the US, half of which are forested. At unforested NEON sites, agreement with SMAP soil moisture (unbiased RMSD: 0.046 m3 m-3) was similar to previous sparse network validations (which include inflation of the metric due to spatial representativeness errors). For the forested sites, SMAP achieved a reasonable level of accuracy (unbiased RMSD: 0.06 m3 m-3 or 0.053 m3 m-3 after accounting for random representativeness errors) indicating SMAP is sensitive to changes in soil moisture in forest ecosystems. Moreover, we identified that both an index of vegetation water content and canopy height were related to mean difference, which incorporates measurement bias and representativeness bias, and suggests a potential approach to improve SMAP algorithm parameterization for forested regions. In addition, expanding the number and extent of soil moisture measurements at forested validation sites would likely further reduce mean difference by minimizing representativeness errors.
DOI: 10.48577/jpl.6dewls
title: The Magellan-TESS Survey I: Survey Description and Mid-Survey Results

description: Kepler revealed that roughly one-third of Sun-like stars host planets orbiting within 100 days and between the size of Earth and Neptune. How do these planets form, what are they made of, and do they represent a continuous population or multiple populations? To help address these questions, we began the Magellan-TESS Survey (MTS), which uses Magellan II/PFS to obtain radial velocity (RV) masses of 30 TESS-detected exoplanets and develops an analysis framework that connects observed planet distributions to underlying populations. In the past, small planet RV measurements have been challenging to obtain due to host star faintness and low RV semi-amplitudes, and challenging to interpret due to the potential biases in target selection and observation planning decisions. The MTS attempts to minimize these biases by focusing on bright TESS targets and employing a quantitative selection function and observing strategy. In this paper, we (1) describe our motivation and survey strategy, (2) present our first catalog of planet density constraints for 27 TESS Objects of Interest (TOIs; 22 in our population analysis sample, 12 that are members of the same systems), and (3) employ a hierarchical Bayesian model to produce preliminary constraints on the mass-radius (M-R) relation. We find that the publication bias the biases causing previous M-R relations to predict fairly high masses at 1 R⊕ have been reduced. This work can inform more detailed studies of individual systems and offer a framework that can be applied to future RV surveys with the goal of population inferences.
DOI: 10.48577/jpl.6jpahr
title: Regularized Dual-Channel Algorithm for the Retrieval of Soil Moisture and Vegetation Optical Depth from SMAP Measurements

description: — In August 2020, SMAP released a new version of itssoil moisture (SM) and vegetation optical depth (VOD) retrievalproducts. In this work, we review the methodology followed by theSMAP regularized dual-channel (DCA) retrieval algorithm. Weshow that the new implementation generates SM retrievals thatnot only satisfy the SMAP accuracy requirements but also show aperformance comparable to the single-channel algorithm that usesthe V polarized brightness temperature (SCA-V). Due to a lack ofin situ measurements we cannot evaluate the accuracy of the VOD.In this work, we show analyses with the intention of providing anunderstanding of the VOD product. We compare the VOD resultswith those from SMOS. We also study the relation of the SMAPVOD with two vegetation parameters: tree height and biomass.
DOI: 10.48577/jpl.hcjbjs
title: Scale-dependent response of the Urban heat island to the European heatwave of 2018

description: Extreme heat continues to be a pressing challenge of the changing climate.The impacts of extreme heat manifest on two different spatio-temporal scales: (1) episodiccontinent-wide heatwaves (HW) and (2) the city-scale Urban Heat Island (UHI). As HWs arebecoming more frequent, longer, and severe, they pose serious implications of increased publichealth risks at a city scale, and have adverse impacts on agricultural and terrestrial/aquaticecosystems on the regional scale. Here we offer a fresh perspective of the HW as a forcing thatinvokes dynamic, heterogeneous, scale-dependent responses evident in inter and intra-urbanheat islets. A numerical simulation of the 2018 European heatwave including the surfaceand air temperature-based UHIs of six urban agglomerations, with a high-resolution focus onParis, serves as our case study. We find that the mean nighttime UHI intensities are reducedfor inland cities but increased for coastal cities. Our examination of the heat islets revealstwo major findings: (i) the HW homogenizes the intra-urban surface temperatures during thedaytime (reduces variance) (ii) the HW impacts are most significant on the scale of large,spatially discontiguous extreme heat islets during nighttime. These results underscore the needto move beyond the prevalent HW-mean UHI intensity characterization and toward intra-urbanheat islet analyses that aid targeted mitigation.
DOI: 10.48577/jpl.d2gmqq
title: Assessing Fish Habitat and the Effects of an Emergency Drought Barrier on Estuarine Turbidity Using Satellite Remote Sensing

description: : Estuaries worldwide are experiencing stress due to increased droughts, which often prompt intervention by environmental managers and government agencies. Effective management of water resources in estuarine systems can be enhanced by new technologies and methodologies to support decision-making processes. Here, we evaluate the use of high frequency, high resolution satellite remote sensing within two management-relevant case studies in the San Francisco Estuary and the Sacramento-San Joaquin River Delta. We used a remote sensing-derived time series of turbidity maps to 1) identify favorable turbidity conditions for the endangered fish species, delta smelt (Hypomesus transpacificus), during the height of the great California drought in the dry season of 2015, and 2) evaluate changes in turbidity following the installation of an emergency saltwater intrusion barrier. The mapping results indicate several persistent areas of turbidity refugia throughout the summer in the north and west Delta; however, there was infrequent connectivity. A comparison of images captured during ebb tides before and after barrier installation showed a mean increase of 6.6 Nephelometric Turbidity Units (NTU) in the San Joaquin River and 4 NTU in Fisherman’s Cut. Our assessment of the barrier supports previous findings, which used field samples to conclude barrier installation may have resulted in increased turbidity near the barrier and enhances these findings by providing spatial context.
DOI: 10.48577/jpl.izavqg
title: Evaluation of Surface Melt on the Greenland Ice Sheet using SMAP L-Band Microwave Radiometry

description: Monitoring melt extent and timing on the Greenland ice sheet is important for tracking the ice sheet’s mass and energy balance as well as the global and Arctic climate variability and change. In this study, we use L-band (1.4 GHz) brightness temperature observations collected by NASA’s Soil Moisture Active Passive (SMAP) mission to investigate the extent, duration and intensity of melt events on the Greenland Ice Sheet from 2015 to 2020. SMAP provides nearly all-weather surface monitoring over all of Greenland twice daily with morning and evening overpasses at approximately 40 km spatial resolution. We applied empirical threshold and geophysical model-based algorithms using horizontally and vertically polarized microwave brightness temperature differences to quantify both the intensity and extent of surface melting. Analysis of the melt seasons shows that Greenland experienced an unusually strong melt event at the end of July 2019, which extended the melt area across much of the dry snow zone over a period of two days. In-situ temperatures measured at Greenland’s Summit station confirm the above freezing temperatures during this event.
DOI: 10.48577/jpl.mxlwb9
title: Detection and Localization of Terrestrial L-Band RFI with GNSS Receivers

description: GNSS signals are critically important for a widerange of commercial, military, and science applications. Recentstudies have identified threats to the performance of GNSSfrom both intended and unintended sources of radio frequencyinterference (RFI). Understanding the distribution of the sourcesof RFI and the nature of the signals they are emitting is critical todetermine and mitigate their effects on the measurements madeby GNSS receivers. Terrestrial RFI can be substantially detrimental to the received GNSS signals, affecting the interpretationof related science measurements. NASA’s Blackjack/TriG GNSSreceivers are used for precise-orbit determination and radiooccultation measurements, providing a data record spanningmost of the Earth’s surface for nearly 20 years. We havedeveloped a highly sensitive detection algorithm which usesvariations in the measured signal to noise ratio (SNR), on theorder of 10-50 seconds, common to all satellites to identify timesand locations subject to RFI. Initial work has focused primarilyon detection of the presence of RFI and using the receiver’sorbital solution to record the location of detection events. Ourinter-mission analysis creates a unique record of global RFIwith the potential for a) rigorous detection of the presence ofinterfering signals during science measurements, b) geolocationof RFI sources, and c) characterization of the nature of thetransmitted signal to better identify intent. Preliminary analysishas shown the presence of RFI is well correlated with regionalconflicts and other geopolitical activity.
DOI: 10.48577/jpl.pnfofn
title: Visible-Light Photoionization of Aromatic Molecules in Water-Ice: Organic Chemistry across the Universe with Less Energy

description: Ionization of gas-phase organic molecules such as polycyclic aromatic hydrocarbons (PAHs) requires vacuum ultraviolet photons at wavelengths shorter than 200 nm (~6-9 eV). We present here for the first time that visible photons - accessible through sunlight - can cause photoionization of trapped PAHs in cryogenic water-ice, accounting for 4.4 eV less ionization energy than in the gas phase. This finding opens up new reaction pathways involving low-energy ionization in many environments where water and organic matter coexist. This include the interstellar medium, molecular clouds, protoplanetary disks, and planetary surfaces and atmospheres (including Earth).
DOI: 10.48577/jpl.yremow
title: A Quantitative Validation of Multi-Modal Image Fusion andSegmentation for Object Detection and Tracking

description: In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global coverage and granularity in order to test our models’ capabilities to represent structure at finer and broader scales, using many different kinds of instrumentation, that can be fused when applicable. In all cases tested, our models show a strong ability to segment the objects within input scenes, use multiple datasets fused together where appropriate to improve results, and, at times, outperform the pre-existing datasets. The success here will allow this methodology to be used within use concrete cases and become the basis for future dynamic object tracking across datasets from various remote sensing instruments.
DOI: 10.48577/jpl.elnxj0
title: Identifying ENSO-related interannual and decadal variability on terrestrial water storage

description: We apply two statistical techniques to satellite measurements to identify a relationship between terrestrial water storage (TWS) and El Niño-Southern Oscillation (ENSO). First, we modified and used the least-squares regression of a previous study using longer records. Second, we applied a cyclostationary empirical orthogonal function analysis (CSEOF). Although the CSEOF technique is distinct from the least-squares regression in that it does not consider proxies, each method produces two modes (decadal and interannual), showing consistency with each technique in spatial pattern and its evolution amplitudes. We also compared the results obtained by the two methods for thirty watersheds, of which five watersheds were compared with previous studies. The combination of the two modes explains the total variance in most watersheds showing the role that interannual and decadal ENSO-related signals in understanding terrestrial water storage variability. The results show that the decadal mode, along with the interannual mode, also plays an important role in describing the local TWS.
DOI: 10.48577/jpl.hxqdff
title: Seasonal Forecasting of Western US Precipitation: Interpretable Machine Learning Trained on Large Climate Model Simulations

description: A barrier to utilizing machine learning in seasonal forecasting applications is the limited sample size of observational data for model training. To circumvent this issue, we explore the feasibility of training various machine learning approaches on a large climate model ensemble, providing a long training set with physically consistent model realizations. After training on thousands of seasons of climate model simulations, the machine learning models are tested for producing seasonal forecasts across the historical observational period (1980-2020). For forecasting large scale spatial patterns of precipitation across the Western United States, the machine learning based models are capable of competing with or outperforming existing dynamical models from the North American Multi Model Ensemble. We further show that this approach need not be considered a ‘black box’ by utilizing machine learning interpretability methods to identify the relevant physical processes that lead to prediction skill.
DOI: 10.48577/jpl.t6nfdx
title: Satellite constraints on the latitudinal distribution and temperature sensitivity of wetland methane emissions

description: 38Wetland methane (CH4) emissions comprise about one-third of the global CH4 source. The 39 latitudinal distribution and climate sensitivity of wetland CH4 fluxes are the key determinants of 40 the global CH4-climate feedback. However, large differences exist between bottom-up estimates, 41 informed by ground-based flux measurements, and top-down estimates derived from spaceborne 42 total column CH4. Despite the extensive coverage of satellite CH4 concentration observations, 43 challenges remain with using top-down estimates to test bottom-up models, mainly because of the 44 uncertainties in the satellite retrievals, the model representation errors, the variable prior emissions, 45 and the confounding role of the posterior error covariance structures. Here, we use satellite-based 46 top-down CH4 flux estimates (2010-2012) to test and refine 42 bottom-up estimates of wetland 47 emissions that use a range of hypothesized wetland extents and process controls. Our comparison 48 between bottom-up models and satellite-based fluxes innovatively accounts for cross-correlations 49 and spatial uncertainties typically found in top-down inverse estimates, such that only the 50 information from satellite observations and the atmospheric transport model is kept as a constraint. 51 We present a satellite-constrained wetland CH4 ensemble product derived from assembling the 52 highest-performance bottom-up models, which estimates global wetland CH4 emissions of 148 53 (117-189, 5th-95th percentile) Tg CH4 yr-1. We find that tropical wetland emissions contribute 54 72% (63-85%) to the global wetland total. We also find that a lower-than-expected temperature 55 sensitivity agrees better with atmospheric CH4 measurements. Overall, our approach demonstrates 56 the potential for using satellites to quantitatively refine bottom-up wetland CH4 emission estimates, 57 their latitudinal distributions, and their sensitivity to climate. 58Keywords: wetland CH4, top-down constraints, terrestrial ecosystem models, climate sensitivity, 59 global budget, latitudinal distribution
DOI: 10.48577/jpl.wkvzmm
title: Starshade Rendezvous: Exoplanet Orbit Constraints from Multi-Epoch Direct Imaging

description: The addition of an external starshade to the Nancy Grace Roman Space Telescope will enable the direct imaging of Earth-radius planets orbiting at ~1 AU. Classification of any detected planets as Earth-like requires both spectroscopy to characterize their atmospheres and multi-epoch imaging to trace their orbits. We consider here the ability of the Starshade Rendezvous Probe to constrain the orbits of directly imaged Earth-like planets. The target list for this proposed mission consists of the 16 nearby stars best suited for direct imaging, around which ~10-15 planets are expected to be discovered. Of these planets, ~1-2 will be Earth-like in mass and temperature. The field of regard for the starshade mission is constrained by solar exclusion angles, resulting in four observing windows during a two-year mission. We find that for Earth-like planets that are detected at least three times during the four viewing opportunities, their semi-major axes are measured with a median precision of 7 mas, or a median fractional precision of 3%. Habitable-zone planets can be correctly identified as such 96.7% of the time, with a false positive rate of 2.8%. If a more conservative criteria is used for habitable-zone classification (95% probability), the false positive rate drops close to zero, but with only 81% of the truly Earth-like planets correctly classified as residing in the habitable zone.