Remote Sensing Commons^™

Evapotranspiration Everywhere, All The Time: Towards A Unified View From Earth Observation, Joshua B. Fisher, Martha C. Anderson, Diego G. Miralles, Kanishka Mallick, Paul C. Stoy, Youngryel Ryu, Wim G. M. Bastiaanssen

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Scientists want to know everything, everywhere, and all the time. This is particularly true in Earth science, where we seek to understand processes that span from the molecular to the planetary scale in how the world works, how it affects us, and how we impact it—especially the water cycle. Evapotranspiration (ET) was the last component to be measured in closing the water cycle: for decades, closing the water budget meant adding up all the measurable components, then inferring ET as the residual. Early measurements relied on water loss from pans and weighing lysimeters, followed by sensors inserted into plants to …

Changes In Land, Ocean, Atmospheric Parameters Associated With The 2025 Myanmar (Mw 7.7) Earthquake, Feng Jing, Akshansa Chauhan, Ashwani Raju, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Multiple parameters associated with the land, atmosphere, and ocean were analyzed to study short-term and immediate pre-earthquake changes associated with the 28 March 2025 Myanmar earthquake (Mw 7.7). Anomalous clear-sky outgoing longwave radiation (ClrOLR) and trace gases (CH₄, CO, and O₃) were detected within two months prior to the mainshock. Vertical changes at different pressure levels suggest a possible underground source. High-temporal-resolution observations of the infrared brightness temperature and surface air pressure revealed short-lived fluctuations shortly before the earthquake, which may reflect localized stress adjustments and surface latent heat flux release during the final stage of earthquake preparation. In the …

Long-Term Variability Of Air Quality And Greenhouse Gas Emissions From Rice Crop Burning In Punjab During 2012–2020, Harsimranjit Kaur Romana, Dericks Praise Shukla, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Punjab, India's primary rice and wheat production hub, has witnessed rapid expansion of paddy cultivation over the past two decades, driven by minimum support price incentives, changes in government policies, alignment of sowing with the monsoon season and the adoption of high-yielding varieties. This transition has intensified groundwater extraction and shortened the fallow period between rabi and kharif crop seasons, reducing the window between rice harvesting and wheat sowing, leading to widespread open-field burning of rice residue and recurrent post-monsoon air-quality deterioration across the Indo-Gangetic Plain. Despite numerous short-term or single-pollutant assessments, a spatially resolved, multi-pollutant and multi-decadal evaluation linking …

Automated Machine Learning For High-Resolution Daily And Hourly Methane Emission Mapping For Rice Paddies Over South Korea: Integrating Modis, Era5-Land, And Soil Data, Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong, Yangwon Lee

Institute for ECHO Articles and Research

Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine …

Deep Learning Style Transfer For Enhanced Smoke Plume Visibility: A Standardized False Color Composite (Sfcc) In Gems Satellite Imagery, Yemin Jeong, Seung Hee Kim, Menas Kafatos, Jeong-Ah Yu, Kyoung-Hee Sung, Seung-Yeon Kim, Goo Kim, Jae-Jin Kim, Yangwon Lee

Institute for ECHO Articles and Research

Wildfire smoke visualization using geostationary satellite imagery is essential for real-time monitoring and atmospheric analysis; however, inconsistencies in color tone across Geostationary Environment Monitoring Spectrometer (GEMS) images hinder reliable interpretation and model training. This study proposes a Standardized False Color Composite (SFCC) framework based on deep learning style transfer to enhance the visual consistency and interpretability of wildfire smoke scenes. Four tone-standardization methods were compared: the statistical Empirical Cumulative Distribution Function (ECDF) correction and three neural approaches—ReHistoGAN, StyTr2, and Style Injection Diffusion Model (SI-DM). Each model was evaluated visually and quantitatively using six metrics (SSIM, LPIPS, FID, histogram similarity, ArtFID, …

Multi-Satellite Image Matching And Deep Learning Segmentation For Detection Of Daytime Sea Fog Using Gk2a Ami And Gk2b Goci-Ii, Jonggu Kang, Hiroyuki Miyazaki, Seung Hee Kim, Menas Kafatos, Daesun Kim, Jinsoo Kim, Yangwon Lee

Institute for ECHO Articles and Research

Traditionally, sea fog detection technologies have relied primarily on in situ observations. However, point-based observations suffer from limitations in extensive monitoring in marine environments due to the scarcity of observation stations and the limited nature of measurement data. Satellites effectively address these issues by covering vast areas and operating across multiple spectral channels, enabling precise detection and monitoring of sea fog. Despite the increasing adoption of deep learning in this field, achieving further improvements in accuracy and reliability necessitates the simultaneous use of multiple satellite datasets rather than relying on a single source. Therefore, this study aims to achieve higher …

Rainfall-Runoff Modelling In An Indonesian Humid Tropical Area Using Satellite-Based Precipitation Products, Noordiah Helda

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation explores three rainfall-runoff models in the humid tropical regions of Indonesia using satellite-based precipitation products (SBPPs) and develops integrated machine-learning modeling frameworks. Several ground-based observations from BMKG (Badan Meteorologi, Klimatologi, dan Geofisika (also known as the Indonesian Agency for Meteorology, Climatology, and Geophysics)) stations across Indonesia (133−165 stations) are compared and evaluated against satellite products, indicating that GPM performs well, with R-squared values ranging from 0.54 to 0.76 and correlation coefficients ranging from 0.45 to 0.69, respectively.

In the Martapura Watershed, South Kalimantan, due to a lack of observational discharge data, streamflow was generated using the FJ Mock …

Unraveling Crop Nitrogen-Water Dynamics With Hyperspectral-Thermal Sensing In Northern Central Valley, California, Shahryar Fazli, Surendra Maharjan, Wenzhao Li, Joshua B. Fisher, Rejoice Thomas, Fernando Romero Galvan, Gabriela Shirkey, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Ensuring global food security in the face of climate change requires optimizing crop water use and nutrient management. This study investigates the relationship between canopy nitrogen (N) and evapotranspiration (ET) across sunflower, rice, walnut, alfalfa, and plum crops using advanced remote sensing technologies. High-resolution hyperspectral data from NASAs Earth Surface Mineral Dust Source Investigation (EMIT) and thermal multispectral data from the Landsat-based OpenET system were analyzed over 1,135 km² in California. Regression analysis revealed strong spatial association between canopy N and ET for sunflower (R² = 0.82), rice (R² = 0.71), and walnut (R² = 0.68), …

A Novel Approach To Increase Accuracy In Remotely Sensed Evapotranspiration Through Basin Water Balance And Flux Tower Constraints, Kul Khand, Gabriel B. Senay, Mackenzie Friedrichs, Koong Yi, Joshua B. Fisher, Lixin Wang, Kosana Suvočarev, Arman Ahmadi, Housen Chu, Stephen Good, Kanishka Mallick, Justine Missik, Jacob A. Nelson, David E. Reed, Tianxin Wang, Xiangming Xiao

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address …

Development And Application Of Self-Supervised Machine Learning For Smoke Plume And Active Fire Identification From The Fire Influence On Regional To Global Environments And Air Quality Datasets, Nicholas Lahaye, Anastasija Easley, Kyongsik Yun, Hugo Lee, Erik Linstead, Michael J. Garay, Olga V. Kalashnikova

Engineering Faculty Articles and Research

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. With as much as a 10% …

Atmospheric Chemistry Experiment (Ace) Winds, Matthew Wyatt, Peter F. Bernath, Chris Boone, Léo Lavy, Ryan Johnson

Chemistry & Biochemistry Faculty Publications

The Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) uses limb geometry to measure transmittance spectra of Earth's atmosphere by solar occultation. Line-of-sight wind speeds can be derived via Doppler shifts of molecular lines in infrared spectra. The wind look direction angles relative to geodetic North are derived from geometry. We validate the new ACE version 5.3 (v.5.3) line-of-sight winds with MIGHTI and meteor radar vector wind observations and find a ±15 m/s sunrise/sunset shift above 80 km. We also compare line-of-sight winds from ACE-FTS v.5.2 and v.5.3 with vector winds from the MERRA-2, HWM14, and WACCM-X models. A ±15 m/s …

Analysis Of Aerosols In The Asian Monsoon Anticyclone As Observed By The Atmospheric Chemistry Experiment, M. Lecours, R. Dodangodage, C. D. Boone, P. F. Bernath

Chemistry & Biochemistry Faculty Publications

During the Asian summer monsoon season, pollutants from the lower troposphere are transported through deep convection to the upper troposphere and lower stratosphere. Surface pollutants such as CO are transported upward and trapped in the anticyclone during this unique atmospheric phenomenon. Associated with the anticyclone is a layer of enhanced aerosols located near the tropopause often referred to as the Asian tropopause aerosol layer (ATAL). The chemical and physical properties of aerosols in the ATAL are not yet fully understood as direct observations of the aerosols are limited. The Atmospheric Chemistry Experiment (ACE) is a satellite mission that provides high-resolution …

Spatial Gap-Filling Of Himawari-8 Hourly Aod Products Using Machine Learning With Model-Based Aod And Meteorological Data: A Focus On The Korean Peninsula, Youjeong Youn, Seoyeon Kim, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps that limit comprehensive environmental analysis. This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. Developed and validated over South Korea from 1 January to 31 December 2019, the model effectively improved data coverage from 6% to 100%. The approach demonstrated high performance in blind tests, …

Climatological Trends And Effects Of Aerosols And Clouds On Large Solar Parks: Application Examples In Benban (Egypt) And Al Dhafrah (Uae), Harshal Dhake, Panagiotis Kosmopoulos, Antonis Mantakas, Yashwant Kashyap, Hesham El-Askary, Omar Elbadawy

Mathematics, Physics, and Computer Science Faculty Articles and Research

Solar energy production is vastly affected by climatological factors. This study examines the impact of two primary climatological factors, aerosols and clouds, on solar energy production at two of the world’s largest solar parks, Benban and Al Dhafrah Solar Parks, by using Earth observation data. Cloud microphysics were obtained from EUMETSAT, and aerosol data were obtained from the CAMS and assimilated with MODIS data for higher accuracy. The impact of both factors was analysed by computing their trends over the past 20 years. These climatological trends indicated the variations in the change in each of the factors and their resulting …

The Ecosystem As Super-Organ/Ism, Revisited: Scaling Hydraulics To Forests Under Climate Change, Jeffrey D. Wood, Matteo Detto, Marvin Browne, Nathan J.B. Kraft, Alexandra G. Konings, Joshua B. Fisher, Gregory R. Quetin, Anna T. Trugman, Troy S. Magney, Camila D. Medeiros, Nidhi Vinod, Thomas N. Buckley, Lawren Sack

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO₂) uptake and water vapor loss, important for predicting and managing the future of Earth's ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals, or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for …

Deterministic Global 3d Fractal Cloud Model For Synthetic Scene Generation, Aaron M. Schinder, Shannon R. Young, Bryan J. Steward, Michael L. Dexter, Andrew Kondrath, Stephen Hinton, Ricardo Davila

Faculty Publications

This paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal function. The fractal function is distorted by a displacement map, which is generated using horizontal wind data from a Global Forecast System (GFS) weather file. The vertical windspeed and relative humidity are used to mask the creation of clouds to match realistic large-scale weather patterns over the Earth. Small-scale detail is provided by the fractal functions which are tuned to …

Kinematic And Dynamic Structure Of The 18 May 2020 Squall Line Over South Korea, Wishnu Agum Swastiko, Chia-Lun Tsai, Seung Hee Kim, Gyuwon Lee

Institute for ECHO Articles and Research

The diagonal squall line that passed through the Korean Peninsula on the 18 May 2020 was examined using wind data retrieved from multiple Doppler radar synthesis focusing on its kinematic and dynamic aspects. The low-level jet, along with warm and moist air in the lower level, served as the primary source of moisture supply during the initiation and formation process. The presence of a cold pool accompanying the squall line played a role in retaining moisture at the surface. As the squall line approached the Korean Peninsula, the convective bands in the northern segment (NS) and southern segment (SS) of …

Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos

Institute for ECHO Articles and Research

The recent decades have seen an increasing academic interest in leveraging machine learning approaches to nowcast, or forecast in a highly short-term manner, precipitation at a high resolution, given the limitations of the traditional numerical weather prediction models on this task. To capture the spatiotemporal associations of data on input variables, a deep learning (DL) architecture with the combination of a convolutional neural network and a recurrent neural network can be an ideal design for nowcasting rainfall. In this study, a long short-term memory (LSTM) modeling structure is proposed with convolutional operations on input variables. To resolve the issue of …

Ground Electric Field, Atmospheric Weather And Electric Grid Variations In Northeast Greece Influenced By The March 2012 Solar Activity And The Moderate To Intense Geomagnetic Storms, Georgios Anagnostopoulos, Anastasios Karkanis, Athanasios Kampatagis, Panagiotis Marhavilas, Sofia-Anna Menesidou, Dimitrios Efthymiadis, Stefanos Keskinis, Dimitar Ouzounov, Nick Hatzigeorgiu, Michael Danakis

Mathematics, Physics, and Computer Science Faculty Articles and Research

In a recent paper, we extended a previous study on the solar solar influence to the generation of the March 2012 heatwave in the northeastern USA. In the present study we check the possible relationship of solar activity with the early March 2012 bad weather in northeast Thrace, Greece. To this end, we examined data from various remote sensing instrumentation monitoring the Sun (SDO satellite), Interplanetary space (ACE satellite), the Earth’s magnetosphere (Earth-based measurements, NOAA-19 satellite), the top of the clouds (Terra and Aqua satellites), and the near ground atmosphere. Our comparative data analysis suggests that: (i) the winter-like weather …

Spatial Analyses On Pre-Earthquake Ionospheric Anomalies And Magnetic Storms Observed By China Seismo-Electromagnetic Satellite In August 2018, Jann-Yeng Tiger Liu, Xuhui Shen, Fu-Yuan Chang, Yuh-Ing Chen, Yang-Yi Sun, Chieh‑Hung Chen, Sergey Pulinets, Katsumi Hattori, Dimitar Ouzounov, Valerio Tramutoli, Michel Parrot, Wei-Sheng Chen, Cheng-Yan Liu, Fei Zhang, Dapeng Liu, Xue-Min Zhang, Rui Yan, Qiao Wang

Mathematics, Physics, and Computer Science Faculty Articles and Research

The China Seismo-Electromagnetic Satellite (CSES), with a sun-synchronous orbit at 507 km altitude, was launched on 2 February 2018 to investigate pre-earthquake ionospheric anomalies (PEIAs) and ionospheric space weather. The CSES probes manifest longitudinal features of four-peak plasma density and three plasma depletions in the equatorial/low-latitudes as well as mid-latitude troughs. CSES plasma and the total electron content (TEC) of the global ionosphere map (GIM) are used to study PEIAs associated with a destructive M7.0 earthquake and its followed M6.5 and M6.3/M6.9 earthquakes in Lombok, Indonesia, on 5, 17, and 19 August 2018, respectively, as well as to examine ionospheric …

Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian

I-GUIDE Forum

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or …

A Comparative Study Of Vinti-Based Orbit Propagation And Estimation For Cubesats In Very Low Earth Orbits, Ethan Michael Senecal

Mechanical & Aerospace Engineering Theses & Dissertations

In recent years, there has been a growing interest in CubeSats and very low Earth orbit (VLEO) space missions. Mission SeaLion, a collaborative CubeSat mission between Old Dominion University, the U.S. Coast Guard Academy, and U.S. Air Force Institute of Technology, planned to launch a 3U CubeSat into VLEO. The VLEO mission is a particularly challenging environment for navigation and orbit propagation because drag introduces a significant perturbation for orbit models such as SGP4. Additionally, mission requirements left no capacity for attitude determination or control, further reducing knowledge of drag behavior of the satellite in flight. This deficiency is a …

Possible Overestimation Of Nitrogen Dioxide Outgassing During The Beirut 2020 Explosion, Ashraf Farahat, Nayla El-Kork, Ramesh P. Singh, Feng Jing

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

On 4 August 2020, a strong explosion occurred near the Beirut seaport, Lebanon and killed more than 200 people and damaged numerous buildings in the vicinity. As Amonium Nitrate (AN) caused the explosion, many studies claimed the release of large amounts of NO₂ in the atmosphere may have resulted in a health hazard in Beirut and the vicinity. In order to reasonably evaluate the significance of NO₂ amounts released in the atmosphere, it is important to investigate the spatio-temporal distribution of NO₂ during and after the blast and compare it to the average day-to-day background emissions from …

Meteor Radar Vertical Wind Observation Biases And Mathematical Debiasing Strategies Including The 3dvar+Div Algorithm, Alan Z. Liu, Zishun Qiao, Gunter Stober, Alexander Kozlovsky, Ales Kuchar, Christoph Jacobi, Chris Meek, Diego Janches, Guiping Liu, Masaki Tsutsumi, Njål Gulbrandsen, Satonori Nozawa, Mark Lester, Evgenia Belova, Johan Kero, Nicholas Mitchell

Publications

Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110 km altitude region. These systems have been proven to provide reliable and continuous measurements of horizontal winds in the mesosphere and lower thermosphere. Recently, there have been many attempts to utilize specular and/or transverse scatter meteor measurements to estimate vertical winds and vertical wind variability. In this study we investigate potential biases in vertical wind estimation that are intrinsic to the meteor radar observation geometry and scattering mechanism, and we introduce a mathematical debiasing process to mitigate them. This process makes use of …

Characterization Of Smoke Particles Toward Improved Remote Sensing Retrievals And Chemical Transport Modeling, Chenchong Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Wildfires increase in extent, intensity, and frequency across the globe over the recent decades. The uncontrolled fires trigger cascading effects on local ecosystems and the fire emissions pose a higher risk to air quality and climate. Wildfire emissions contain a variety of trace gases and particulate matters. The particle-phase emissions, especially those light-absorbing species including black carbon (BC) and brown carbon (BrC), significantly affect the regional and global climate by modulating the radiative transfer phenomena in the atmosphere. A great discrepancy still exists between model- and observation-based estimates of aerosol-radiation interactions (ARI). The discrepancy is partially attributed to the mischaracterizations …

Response Of Surface And Atmospheric Parameters Associated With The Iran M 7.3 Earthquake, Feng Jing, Ramesh P. Singh

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Multiparameter observed from satellite, including microwave brightness temperature, skin temperature, air temperature, and carbon monoxide, have been analyzed to identify the anomalous signals associated with the M 7.3 Iran earthquake of November 12, 2017. Besides removing the multiyear variability of parameters as background, the effect of surface and atmosphere of a dust storm event in Middle East region during October 29–November 1 is considered to distinguish the possible anomalies associated with the earthquake. The characteristic behaviors of surface and atmospheric parameters clearly show the signals associated with the M 7.3 earthquake and the dust storm event. The multiple parameters at …

Remote Sensing & Land Surface Temperature From Satellite Observations, Isatu Jollah

Publications and Research

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is designed and developed in 1995. • It is a critical instrument aboard Terra and Aqua satellites. Terra's orbit around the Earth is timed so that it crosses the equator from north to south in the morning, while Aqua crosses the equator from south to north in the afternoon. • Every 1 to 2 days, Terra MODIS and Aqua MODIS scan the entire Earth's surface, collecting data in groups of wavelengths. • In this research, a whole month of MODIS Land Surface Temperature data from both Aqua and Terra were explored and investigated.

Global Gnss-Ro Electron Density In The Lower Ionosphere, Dong L. Wu, Daniel J. Emmons Ii, Nimalan Swarnalingam

Faculty Publications

Lack of instrument sensitivity to low electron density (N_e) concentration makes it difficult to measure sharp N_e vertical gradients (four orders of magnitude over 30 km) in the D/E-region. A robust algorithm is developed to retrieve global D/E-region N_e from the high-rate GNSS radio occultation (RO) data, to improve spatiotemporal coverage using recent SmallSat/CubeSat constellations. The new algorithm removes F-region contributions in the RO excess phase profile by fitting a linear function to the data below the D-region. The new GNSS-RO observations reveal many interesting features in the diurnal, seasonal, solar-cycle, and magnetic-field-dependent variations in the …

A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons

Faculty Publications

Sporadic-E (E_s) occurrence rates from Global Position Satellite radio occultation (GPS-RO) measurements have shown to vary by a factor of five between studies, motivating the need for a comparison with ground-based measurements. In an attempt to find accurate GPS-RO techniques for detecting E_s formation, occurrence rates derived using five previously developed GPS-RO techniques are compared to ionosonde measurements over an eight-year period from 2010–2017. GPS-RO measurements within 170 km of a ionosonde site are used to calculate E_s occurrence rates and compared to the ground-truth ionosonde measurements. The techniques are compared individually for each ionosonde site …

Characterization Of Site-Specific Vegetation Activity In Alaskan Wet And Dry Tundra As Related To Climate And Soil State, Michael Gregory Brown, Kyle C. Mcdonald, Reiner Zimmermann, Nicholas Steiner, Stephanie Devries, Laura Bourgeau-Chavez

Advanced Science Research Center

We present discrete (2-h resolution) multi-year (2008–2017) in situ measurements of seasonal vegetation growth and soil biophysical properties from two sites on Alaska's North Slope, USA, representing dry and wet sedge tundra. We examine measurements of vertical active soil layer temperature and soil moisture profiles (freeze/thaw status), woody shrub vegetation physiological activity, and meteorological site data to assess interrelationships within (and between) these two study sites. Vegetation phenophases (cold de-hardening start, physiological function start, stem growth start, stem growth end, physiological function end, cold hardening completion) were found to have greater interannual day of year (DOY) occurrence variability at the …