Profiling Airborne Microwave Radiometer for Atmospheric and Cloud Research
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54 ENVIRONMENTAL SCIENCES↗
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Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?
Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m). Location Eastern USA. Time period 2017–2018. Taxa studied Trees. Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics. Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity. Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.
54 ENVIRONMENTAL SCIENCES↗
Remotely Sensed High‐Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society
This paper reviews the current state of high‐resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high‐resolution remote sensing using visible, near‐infrared, thermal‐infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio‐geo‐physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state‐of‐the‐science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open‐access, high‐resolution (3–30 m), sub‐daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data‐limited regions of the world, especially the Global South for socio‐economic development.
54 ENVIRONMENTAL SCIENCES↗
Impact of Seasonal Snow-Cover Change on the Observed and Simulated State of the Atmospheric Boundary Layer in a High-Altitude Mountain Valley
The structure and evolution of the atmospheric boundary layer (ABL) under clear-sky fair weather conditions over mountainous terrain is dominated by the diurnal cycle of the surface energy balance and thus strongly depends on surface snow cover. We use data from three passive ground-based infrared spectrometers deployed in the East River Valley in Colorado's Rocky Mountains to investigate the response of the thermal ABL structure to changes in surface energy balance during the seasonal transition from low to high snow cover. Temperature profiles were retrieved from the infrared radiances using the optimal estimation physical retrieval Tropospheric Remotely Observed Profiling via Optimal Estimation. A nocturnal surface inversion formed in the valley during clear-sky days, which was subsequently mixed out during daytime with the development of a convective boundary layer when snow cover was low. Over high snow cover, a very shallow convective boundary layer formed, above which the inversion persisted through the daytime hours. We compare these observations to NOAA's operational High-Resolution-Rapid-Refresh model and find large warm biases on clear-sky days resulting from the model's inability to form strong nocturnal inversions and to maintain the stable stratification in the valley during daytime when there was snow on the ground. We suggest several factors contributing to the large model errors. These are (a) the inability of the model to represent well-developed thermally driven flows likely due to the too coarse horizontal grid spacing (3 km), (b) too much convective mixing during daytime, and (c) too strong vertical coupling between the valley atmosphere and the free troposphere.
54 ENVIRONMENTAL SCIENCES↗
Ground-based temperature and humidity profiling: combining active and passive remote sensors
Abstract. Thermodynamic profiles in the planetary boundary layer (PBL) are important observations for a range of atmospheric research and operational needs. These profiles can be retrieved from passively sensed spectral infrared (IR) or microwave (MW) radiance observations or can be more directly measured by active remote sensors such as water vapor differential absorption lidars (DIALs). This paper explores the synergy of combining ground-based IR, MW, and DIAL observations using an optimal-estimation retrieval framework, quantifying the reduction in the uncertainty in the retrieved profiles and the increase in information content as additional observations are added to IR-only and MW-only retrievals. This study uses ground-based observations collected during the Perdigão field campaign in central Portugal in 2017 and during the DIAL demonstration campaign at the Atmospheric Radiation Measurement Southern Great Plains site in 2017. The results show that the information content in both temperature and water vapor is higher for the IR instrument relative to the MW instrument (thereby resulting in smaller uncertainties) and that the combined IR + MW retrieval is very similar to the IR-only retrieval below 1.5 km. However, including the partial profile of water vapor observed by the DIAL increases the information content in the combined IR + DIAL and MW + DIAL water vapor retrievals substantially, with the exact impact vertically depending on the characteristics of the DIAL instrument itself. Furthermore, there is a slight increase in the information content in the retrieved temperature profile using the IR + DIAL relative to the IR-only; this was not observed in the MW + DIAL retrieval.
54 ENVIRONMENTAL SCIENCES↗
Underwater thermomagnetic generator for remote marine thermal energy harvesting and sensing
Thermomagnetic generators offer a promising approach for sustainable power generation in remote marine environments. Here, this study presents the design, thermal modeling, and experimental validation of a passively driven underwater thermomagnetic generator developed for powering ocean observation and monitoring sensors. The generator was evaluated under varying working fluids, thermal boundary conditions, and extended operation to assess real-world applicability. Two fluids, deionized water and silicone-based Thermal C5, were tested under simulated shallow- and deep-ocean conditions. Deionized water outperformed Thermal C5, especially in colder environments (~5°C), achieving a peak output of 2.7 mW due to larger temperature gradients and enhanced convective-evaporative heat transfer. Long-duration tests revealed a transient evaporation-condensation cycle that temporarily reduced rotor immersion and performance before stabilizing. The generator powered commercial marine sensors for over 6 h without external batteries, demonstrating the viability of compact, passively cooled thermomagnetic systems for autonomous, off-grid marine sensing.
13 HYDRO ENERGY↗
Energetics of surface melt in West Antarctica
We use reanalysis data and satellite remote sensing of cloud properties to examine how meteorological conditions alter the surface energy balance to cause surface melt that is detectable in satellite passive microwave imagery over West Antarctica. This analysis can detect each of the three primary mechanisms for inducing surface melt at a specific location: thermal blanketing involving sensible heat flux and/or longwave heating by optically thick cloud cover, all-wave radiative enhancement by optically thin cloud cover, and föhn winds. We examine case studies over Pine Island and Thwaites glaciers, which are of interest for ice shelf and ice sheet stability, and over Siple Dome, which is more readily accessible for field work. During January 2015 over Siple Dome we identified a melt event whose origin is an all-wave radiative enhancement by optically thin clouds. During December 2011 over Pine Island and Thwaites glaciers, we identified a melt event caused mainly by thermal blanketing from optically thick clouds. Over Siple Dome, those same 2011 synoptic conditions yielded a thermal-blanketing-driven melt event that was initiated by an impulse of sensible heat flux and then prolonged by cloud longwave heating. The December 2011 synoptic conditions also generated föhn winds at a location on the Ross Ice Shelf adjacent to the Transantarctic Mountains, and we analyze this case with additional support from automatic weather station data. In contrast, a late-summer thermal blanketing period over Pine Island and Thwaites glaciers during February 2013 showed surface melt initiated by cloud longwave heating and then prolonged by enhanced sensible heat flux. One limitation thus far with this type of analysis involves uncertainties in the cloud optical properties. Nevertheless, with improvements this type of analysis can enable quantitative prediction of atmospheric stress on the vulnerable Antarctic ice shelves in a steadily warming climate.
54 ENVIRONMENTAL SCIENCES↗
Advanced Laboratory and Field Arrays: Evaluating Sampling Techniques for MHK Biological Monitoring (Task 6)
The overall goal of the task was to identify cost effective biological sampling techniques for MHK environmental monitoring. Protected, demersal, and pelagic fish and selected nektonic invertebrates were surveyed using capture and remote sensing techniques at the PacWave sites. The performance of capture and remote sensing monitoring techniques were evaluated and generic nekton monitoring indices were developed for MHK technologies and sites. In parallel to data collections at the PacWave site, the ability of regression models to characterize, detect, and predict change in acoustic data was evaluated using acoustic data collected in Admiralty Inlet, WA, during a biological monitoring study of the proposed SnoPud tidal turbine project site. Expected outcomes of these efforts included an evaluation of instrumentation and techniques used to monitor biological variability; identification of data streams that can be used to detect and quantify change; and sampling requirements to ensure detection of change in monitored variables.
13 HYDRO ENERGY↗
PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere
Forward models are a key tool to generate synthetic observations given knowledge of the atmospheric state. In this way, they are an integral part of inversion algorithms that aim to retrieve geophysical variables from observations or in data assimilation. Their application for the exploitation of the full information content of remote sensing observations becomes increasingly important when these are used to evaluate the performance of cloud-resolving models (CRMs). Herein, CRM profiles or fields provide the input to the forward model whose simulation results are subsequently compared to the observations. This paper introduces the freely available comprehensive microwave forward model PAMTRA (Passive and Active Microwave TRAnsfer), demonstrates its capabilities to simulate passive and active measurements across the microwave spectral region for upward- and downward-looking geometries, and illustrates how the forward simulations can be used to evaluate CRMs and to interpret measurements to improve our understanding of cloud processes. PAMTRA is unique as it treats passive and active radiative transfer (RT) in a consistent way with the passive forward model providing upwelling and downwelling polarized brightness temperatures and radiances for arbitrary observation angles. The active part is capable of simulating the full radar Doppler spectrum and its moments. PAMTRA is designed to be flexible with respect to instrument specifications and interfaces to many different formats of input and output, especially CRMs, spanning the range from bin-resolved microphysical output to one- and two-moment schemes, and to in situ measured hydrometeor properties. A specific highlight is the incorporation of the self-similar Rayleigh–Gans approximation (SSRGA) for both active and passive applications, which becomes especially important for the investigation of frozen hydrometeors.
54 ENVIRONMENTAL SCIENCES↗
Constraining the Multiscale Structure of Geophysical Fields in Machine Learning: The Case of Precipitation
The use of deep-learning algorithms for estimating the value of geophysical variables from remotely sensed information is rapidly expanding. The typical objective function minimized in such algorithms is the mean square error (MSE), which is known to lead to smooth estimates with compressed dynamical range as compared to the true distribution of the variable of interest. Here, we introduce and evaluate alternative objective functions, focusing on the retrieval of precipitation rates from satellite passive microwave radiometric measurements using a deep convolutional neural network. For this testbed application, the results show that explicitly imposing the preservation of the statistical distribution and spatial wavelet power spectrum of the target variable allows to accurately reproduce extreme values and sharp gradients across multiple scales in this study.
58 GEOSCIENCES↗
Using satellite data to assess Hurricane Helene’s impact on vegetation by land use in the CSRA
Many regions within Georgia, South Carolina, and North Carolina experienced record-breaking rainfall and catastrophic winds due to Hurricane Helene. Helene made landfall on Florida’s big bend on September 26 th , 2024, as a category 4 hurricane, and tracked northward through Georgia and the southern Appalachian Mountains before dissipating on September 29 th , 2024. One significant impact of the hurricane was severe damage to tree canopies across the southeastern United States. This study utilizes satellite-based remotely sensed data provided by the National Aeronautical and Space Administration (NASA) to examine the resilience of these tree canopies following the hurricane. Specifically, the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Cover Type, Soil Moisture Active Passive (SMAP), and the Global Precipitation Measurement (GPM) mission datasets were employed to study the tree canopy’s response to such events in the Central Savannah River Area (CSRA). A significant increase was observed in the LAI in November of 2024. This increase in LAI was likely influenced by warmer-than-average temperatures throughout October of 2024, along with a second record-breaking rainfall event in the CSRA on November 6th, 2024. The LAI increase was then broken down into land cover type to understand which areas contributed most. The complex nature and resilience of trees became evident, offering opportunities for further exploration and application in urban planning, emergency response, and environmental management.
54 ENVIRONMENTAL SCIENCES↗
Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)
Abstract. Arctic rain on snow (ROS) deposits liquid water onto existing snowpacks. Upon refreezing, this can form icy crusts at the surface or within the snowpack. By altering radar backscatter and microwave emissivity, ROS over sea ice can influence the accuracy of sea ice variables retrieved from satellite radar altimetry, scatterometers, and passive microwave radiometers. During the Arctic Ocean MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, there was an unprecedented opportunity to observe a ROS event using in situ active and passive microwave instruments similar to those deployed on satellite platforms. During liquid water accumulation in the snowpack from rain and increased melt, there was a 4-fold decrease in radar energy returned at Ku- and Ka-bands. After the snowpack refroze and ice layers formed, this decrease was followed by a 6-fold increase in returned energy. Besides altering the radar backscatter, analysis of the returned waveforms shows the waveform shape changed in response to rain and refreezing. Microwave emissivity at 19 and 89 GHz increased with increasing liquid water content and decreased as the snowpack refroze, yet subsequent ice layers altered the polarization difference. Corresponding analysis of the CryoSat-2 waveform shape and backscatter as well as AMSR2 brightness temperatures further shows that the rain and refreeze were significant enough to impact satellite returns. Our analysis provides the first detailed in situ analysis of the impacts of ROS and subsequent refreezing on both active and passive microwave observations, providing important baseline knowledge for detecting ROS over sea ice and assessing their impacts on satellite-derived sea ice variables.
54 ENVIRONMENTAL SCIENCES↗
Integration of Wireless Sensor Networks and Battery-free RFID for Advanced Reactors
To address an important need for Nuclear Power Plants (NPPs) to significantly reduce the amount of the required cables for sensor data communications, this Phase I SBIR effort successfully developed and demonstrated a novel low-cost proof-of-concept prototype of a secure wireless sensor network backbone communications system. This system combines commercially available low power, low cost XBEE wireless communications network and passive (battery-free) Radio Frequency Identification (RFID) systems to report individual sensor data and their location through the containment wall for rapid response to anomalies in nuclear facilities. This WIreless Sensing and Locating (WISLO) system network architecture allows non-intrusive wireless collection of sensor data with the sensor’s accurate location information from inside of the instrumentation or containment area with minimal need for power sources. The multi-node sensor data in the containment area is wirelessly transmitted to outside through the metal reinforced concrete walls without the need for any batteries. The WISLO system could be readily used in current reactor fleet and future advanced reactors, as well as in small modular reactors (SMR). The WISLO system is the first dual frequency battery-free trough the wall communications backbone system developed specifically for use in nuclear power plants. It not only can modernize the sensor monitoring practices in the existing reactor fleet, but also offers a secure, low-cost solution to advanced reactors and SMRs by removing cables and issues related to them such as cable integrity, reporting delays, and installation and maintenance costs. The intent is to improve process safety, reliability, efficiency, and cost effectiveness to the monitoring and maintenance process in current and future plants. As we address the next productization and manufacturing capabilities in the next phase, this system will serve the needs of many commercial and government applications in remote monitoring of sensor data that demand battery-free transmission of sensor data such as Internet-of-Things (IOT).
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Zero-Power Analog Optical Processing
The motivation behind this research is the growing challenge of handling the massive amounts of data generated by modern imaging systems. Conventional digital image processing techniques are struggling to keep pace with the demands of high-resolution and high-speed imaging systems for remote sensing due to their high-power consumption and data storage requirements. We present a novel approach based on analog photonics to address this challenge. The proposed system utilizes a silicon-photonics-based image encoder positioned after image formation and initial optical-to-electrical conversion. The photonic encoder compresses image data using a passive disordered photonic structure to perform kernel-type random projections of the raw data. The compressed data is then processed by a back-end neural network, which reconstructs the original image with high fidelity (structural similarity exceeding 90%). Our proposed approach has the potential to compress images with ~ 1000X lower power consumption compared to digital approaches with data rates exceeding 1 terapixel/second.
97 MATHEMATICS AND COMPUTING↗
Drivers of Phytoplankton Variability in and Near the Pearl River Estuary, South China Sea During Typhoon Hato (2017): A Numerical Study
The Pearl River estuary-coastal system in the Northern South China Sea is frequently affected by tropical cyclones (TCs) every year. Previous in-situ and remote sensing studies have found typhoons can enhance phytoplankton biomass and induce blooms in this region. However, the mechanistic links between phytoplankton blooms and typhoons have not been well elucidated due to the interplay of multiple processes along the land-ocean-atmosphere interfaces. Unravelling these interactions will have to rely on an integrated modelling system. In this work, we constructed a realistic, 3-dimensional, land-ocean-atmosphere modelling system with the marine ecosystem and sediment components for the China Great Bay Area. By using the integrated modelling system, we quantitatively investigated phytoplankton response to hydrological conditions variations under Typhoon Hato (2017), a strong typhoon case. Passive tracer experiments showed that with high river discharge induced by heavy rainfall, the residence time of Lingding Bay is as short as 15-day, less than half of that under the climatological discharge. The increase in freshwater pulse washed out the phytoplankton biomass within Lingding Bay. While for the offshore region, the source and sink terms analysis showed that the increase of phytoplankton biomass in the first week was because of the uplift of nutrient-rich subsurface water, while in the second week was because of the seaward propagated nearshore high phytoplankton biomass water. While riverine nutrients support phytoplankton growth in the third week, a large part of phytoplankton biomass was lost to zooplankton grazing, showing the system shifted from the bottom-up control to the top-down control.
54 ENVIRONMENTAL SCIENCES↗
Parameterization of arctic hydrometeor physics using new precipitation measurement technologies: Final Report
Predictions of precipitation are highly sensitive to the accuracy of parameterized growth and sedimentation processes, especially in remote regions such as the Arctic where observations are scarce. The goal of this study was to combine measurements of meteorological conditions and retrievals from passive and active remote sensors to provide refined parameterizations of precipitation properties and processes with a particular focus on the Arctic. To accomplish the project goals, new instrumentation was used from the Oliktok Point Mobile Facility and the Utqiagvik (Barrow) North Slope of Alaska ARM site focusing in particular on Multi-Angle Snowflake Camera (MASC) data. The MASC is the first device able to automatically photograph precipitation particles in free-fall from multiple angles while simultaneously measuring their fall speed. The MASC installed at the ARM Oliktok Point and Utqiagvik facilities was used to examine the nature of Arctic precipitation, taking particular advantage of the unique suite of precipitation, meteorological, and remote sensing instrumentation that is available at the high latitude sites. Combined with radiometer data we were able to explore the relationship between precipitation particle characteristics and the clouds where the snow is created. Ground-based wind measurements provided detailed data on turbulence. Further theoretical work explored the physical mechanisms controlling precipitation size distributions, the physics determining hydrometeor fallspeed, and the existence of general solutions to the Navier-Stokes equations for falling particles. Outreach work introduced hydrometeor classification to classrooms across the United States.
47 OTHER INSTRUMENTATION↗
Pan-tropical daily L-band microwave land surface emissivity retrieval from GNSS-R observations
The uncertainties in microwave land surface emissivity (MLSE) measurements have long limited the use of spaceborne microwave radiometer data. As an emerging observation method, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated great potential in several land and ocean applications. In this study, a method for obtaining daily MLSE dataset in the pan-tropical region from Cyclone GNSS (CYGNSS) observations is presented and evaluated. The CYGNSS observations are first aggregated into the Equal-Area-Scalable-Earth (EASE) 2.0 36 km grid by a combined weight function of distance, time, and signal-to-noise ratio variance. Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. Furthermore, on the test set from January 2019 to May 2019, the RMSE values amounted to 0.030 and 0.023 for horizontal and vertical polarization, respectively. Temperature records from the International Soil Moisture Network are employed to calculate the emissivity and for in-situ validation, which yield an RMSE of 0.034 and 0.026 for the two polarizations, respectively. The proposed algorithm provides an encouraging approach to obtain accurate daily MLSE dataset for microwave remote sensing. Compared to the SMAP MLSE, the CYGNSS MLSE has a remarkable improvement of 86% in temporal resolution, greatly complementing the existing microwave emissivity datasets.
54 ENVIRONMENTAL SCIENCES↗
Individually identifiable surface acoustic wave sensors, tags and systems
A surface-launched acoustic wave sensor tag system for remotely sensing and/or providing identification information using sets of surface acoustic wave (SAW) sensor tag devices is characterized by acoustic wave device embodiments that include coding and other diversity techniques to produce groups of sensors that interact minimally, reducing or alleviating code collision problems typical of prior art coded SAW sensors and tags, and specific device embodiments of said coded SAW sensor tags and systems. These sensor/tag devices operate in a system which consists of one or more uniquely identifiable sensor/tag devices and a wireless interrogator. The sensor device incorporates an antenna for receiving incident RF energy and re-radiating the tag identification information and the sensor measured parameter(s). Since there is no power source in or connected to the sensor, it is a passive sensor. The device is wirelessly interrogated by the interrogator.
Hines, Jacqueline H.↗