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DOE OSTI2023

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.

43 PARTICLE ACCELERATORS↗

NASA NTRS2017

Assessment of Satellite-Derived Surface Reflectances by NASA's CAR Airborne Radiometer over Railroad Valley, Nevada

CAR (Cloud Absorption Radiometer) is a multi-angular and multi-spectral airborne radiometer instrument, whose radiometric and geometric characteristics are well calibrated and adjusted before and after each flight campaign. CAR was built by NASA (National Aeronautics and Space Administration) in 1984. On 16 May 2008, a CAR flight campaign took place over the well-known calibration and validation site of Railroad Valley in Nevada (38.504 deg N, 115.692 deg W).The campaign coincided with the overpasses of several key EO (Earth Observation) satellites such as Landsat-7, Envisat and Terra. Thus, there are nearly simultaneous measurements from these satellites and the CAR airborne sensor over the same calibration site. The CAR spectral bands are close to those of most EO satellites. CAR has the ability to cover the whole range of azimuth view angles and a variety of zenith angles depending on altitude and, as a consequence, the biases seen between satellite and CAR measurements due to both unmatched spectral bands and unmatched angles can be significantly reduced. A comparison is presented here between CARs land surface reflectance (BRF or Bidirectional Reflectance Factor) with those derived from Terra/MODIS (MOD09 and MAIAC), Terra/MISR, Envisat/MERIS and Landsat-7. In this study, we utilized CAR data from low altitude flights (approx. 180 m above the surface) in order to minimize the effects of the atmosphere on these measurements and then obtain a valuable ground-truth data set of surface reflectance. Furthermore, this study shows that differences between measurements caused by surface heterogeneity can be tolerated, thanks to the high homogeneity of the study site on the one hand, and on the other hand, to the spatial sampling and the large number of CAR samples. These results demonstrate that satellite BRF measurements over this site are in good agreement with CAR with variable biases across different spectral bands. This is most likely due to residual aerosol effects in the EO derived reflectances.

calibration↗

DOE OSTI2015

Complete Fiber/Copper Cable Solution for Long-Term Temperature and Pressure Measurement in Supercritical Reservoirs and EGS Wells

High Temperature insulated wire and optical fiber cable is a key enabling technology for the Geothermal Technologies Program (GTP). Without insulated electrical wires and optical fiber, downhole temperature and pressure sensors, flow meters and gauges cannot communicate with the surface. Unfortunately, there are currently no insulated electrical wire or fiber cable constructions capable of surviving for extended periods of deployment in a geothermal well (240-325°C) or supercritical (374°C) reservoir. This has severely hindered engineered reservoir creation, management and utilization, as hot zones and cool water intrusions cannot be understood over time. The lack of a insulated electrical wire and fiber cable solution is a fundamental limitation to the viability of this energy source. The High Temperature Downhole Tools target specification is development of tools and sensors for logging and monitoring wellbore conditions at depths of up to 10,000 meters and temperatures up to 374oC. It well recognized in the industry that no current electronic or fiber cable can be successfully deployed in a well and function successfully for more a few days at temperatures over 240oC. The goal of this project was to raise this performance level significantly. Prysmian Group’s objective in this project was to develop a complete, multi-purpose cable solution for long-term deployment in geothermal wells/reservoirs that can be used with the widest variety of sensors. In particular, the overall project objective was to produce a manufacturable cable design that can perform without serious degradation: • At temperatures up to 374°C; • At pressures up to 220 bar; • In a hydrogen-rich environment; and • For the life of the well (> 5 years). This cable incorporates: • Specialty optical fibers, with specific glass chemistry and high temperature and pressure protective coatings for data communication and distributed temperature and pressure sensing, and • High-temperature insulated wire conductors Prysmian Group has developed a geothermal fiber optic cable (GFOC) solution which incorporates novel glass chemistry for optical fibers to operate at the required bandwidths in high temperature/high pressure hydrogen rich environments with fiber protection, high temperature insulated conductors and protective cladding for cable components. The cable solution has been tested in a geothermal installation for 10 months. The electrical insulation and optical fibers have been validated through laboratory testing to ensure successful operation for greater than 5 years at 300°C, with the possibility of higher temperatures depending on the particular well environment. With the 300°C optical fiber and electrical insulation developments completed and validated in laboratory tests the greatest challenge to a complete 300°C cable solution was protecting the optical fibers in the cable. Optical fibers are typically incased in a protective tube where the tube is filled with a gel. The gel serves as mechanical protection, prevent moisture ingress, and can include hydrogen scavenging materials. A suitable gel for use at 300°C could not be identified and an industrialized alternative was not fully attained. Despite the problems encountered and the lower long-term operating temperature of the cable solution, the project showed success in developing a complete cable solution for a large portion of the geothermal wells in operation today. Further work to obtain the higher long-term temperature goal of the project can be achieved based on the knowledge gained in the current project. This project is significant for many reasons including the new materials science, manufacturing technology, energy independence, and jobs created and will create.

15 GEOTHERMAL ENERGY↗

DOE OSTI2017

Deep Controlled Source Electro-Magnetic Sensing: A Cost Effective, Long-Term Tool for Sequestration Monitoring

The proposed system was designed to operate as a permanent, autonomous monitoring and data collection system that can provide much higher temporal data density than can be achieved economically with 3-Dimensional (3D) seismic surveys. It can operate over broad areas for long periods of time providing full 3D data sets on a monthly basis at a very low cost. By borrowing techniques commonly used in marine CSEM, structural information from background seismic surveys can be incorporated into the CSEM modeling to provide high resolution models of CO 2 progression within reservoirs. The system uses borehole-based vertical-electric-dipole sources placed at reservoir depths in the formation. The electric and magnetic fields induced by this source are received on the surface using an array of stations. The project was conducted in three phases. Phase I demonstrated the feasibility of the system to collect static/reference data at the Ketzin CO 2 storage pilot site in Germany. In Phase I, numerical modeling was used to determine the optimal configurations and requirements for sensor sensitivity and data accuracy. Based on the model results, existing hardware and software were modified. The CSEM system was then field tested at the Ketzin site. The data were imaged and the results were compared with independent studies of the reservoir and overburden geo-electrical characteristics. Phase II demonstrated the ability to provide sensitive, cost-effective measurement of changes in reservoir properties and changes in the overlying formations using a second round of measurements at the Ketzin site. A prototype autonomous recording system was developed and tested as a subset of the measurement points. Phase III of the project quantified the advantages (and disadvantages) of the fully autonomous data collection subsystems by comparing them with repeated measurements made with mobile stations. The Phase III also provided an additional time point in measuring post-closure changes in and above the Ketzin CO 2 injection site. Validation of the CSEM field survey results were completed using a method such as other alternative and available field site data.

58 GEOSCIENCES↗

DOE OSTI2019

Multi-Formalism Modeling for Disaster Resilience, Forecasting, and Response

Disaster response preparedness, risk mitigation, and effective post-event response are critical for national security, public health, and economic integrity. For areas impacted by extreme events, timely and accurate impact assessments of lifeline infrastructures are critical. Traditional disaster management and situational awareness platforms are not capable of handling the massive influx of available information from traditional and non-traditional sources. Next-generation analytic assessment approaches, where use of dynamically sourced, heterogeneous data with near real-time availability are integrated into a continually-updated analytics framework. The objective of this research was to develop a foundational multi-formalism modeling platform to integrate novel data sources for use in improving situational awareness after a disaster event. Specifically, to 1) provide an architecture for machine-learning based data fusion various multi-sensor datasets; 2) classify, geolocate, and temporally bin novel data sources for use in spatially-enabled machine-learning based data fusion; and 3) derive confidence-based observation data with machine-learning based prediction to fill spatial and temporal observation gaps. This project developed a reusable cloud-based pipeline architecture for ingesting, storing, and analyzing relevant data, executing a range of deep-learning models, and visualize the information geographically. The pipeline development was informed by previous PNNL investments (StreamKit) and built upon leading-edge technologies consisting of: 1) a raw datastore; 2) a data enrichment pipeline with custom and third-party analytics; 3) enriched data storage; and 4) a custom UI to display the enriched data. Machine learning/deep learning models were selected and tested to classify social media images for relevancy and presence of flood waters. Our custom trained relevance models were run on a validation dataset and the overall accuracy of the relevance classifiers ranged from 65% to 90%. We found that convolutional neural network (CNN) models were both faster and more accurate than more traditional machine learning models. We applied the relevance classifier to remove non-relevant images and then undertook a limited training of CNN models to detect flood on ~1,800 labeled images. The testing accuracy of the flood detection models were ~88%. To improve the geographic location of both social media and oblique aerial images we tested a multi-step workflow built on a pre-trained deep-learning model. To identify similarity features from test and reference scenes, we applied the Xception and Faiss models to a set of test images with inaccurate spatial locations, as well and precisely-located reference scenes from the general area. Matching reference scenes to the test scenes with the closest similarity index had limited success in improving the precision of test scene location and additional methods have been identified for testing. Data fusion between authoritative geographic data (satellite imaging) and non-authoritative crowdsourced data is an underlying theme throughout this research and methods are tested bring these data to a common geographic form through feature matching and transformations. The automated extraction of supporting information for disaster response from oblique aerial images or ground-level social media images is accomplished through semantic image segmentation and transformation. The non-authoritative data are used to validate the authoritative data collections and processing and optionally, t+0 flood models. In addition, the common form geographic data are combined with other static landscape features to develop spatially-explicit training vectors used in deep generative statistical inference models to develop complex non-linear relationships in static and dynamic data and ultimately derive a spatially continuous and probabilistic assessment of flood damage with the event domain.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1994

SeaWiFS technical report series. Volume 20: The SeaWiFS bio-optical archive and storage system (SeaBASS), part 1

This document provides an overview of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-Optical Archive and Storage System (SeaBASS), which will serve as a repository for numerous data sets of interest to the SeaWiFS Science Team and other approved investigators in the oceanographic community. The data collected will be those data sets suitable for the development and evaluation of bio-optical algorithms which include results from SeaWiFS Intercalibration Round-Robin Experiments (SIRREXs), prelaunch characterization of the SeaWiFS instrument by its manufacturer -- Hughes/Santa Barbara Research Center (SBRC), Marine Optical Characterization Experiment (MOCE) cruises, Marine Optical Buoy (MOBY) deployments and refurbishments, and field studies of other scientists outside of NASA. The primary goal of the data system is to provide a simple mechanism for querying the available archive and requesting specific items, while assuring that the data is made available only to authorized users. The design, construction, and maintenance of SeaBASS is the responsibility of the SeaWiFS Calibration and Validation Team (CVT). This report is concerned with documenting the execution of this task by the CVT and consists of a series of chapters detailing the various data sets involved. The topics presented are as follows: 1) overview of the SeaBASS file architecture, 2) the bio-optical data system, 3) the historical pigment database, 4) the SIRREX database, and 5) the SBRC database.

Hooker, Stanford B.↗

NASA NTRS2021

Single-Turnover Variable Chlorophyll Fluorescence as a Tool for Assessing Phytoplankton Photosynthesis and Primary Productivity: Opportunities, Caveats and Recommendations

Phytoplankton photosynthetic physiology can be investigated through single-turnover variable chlorophyll fluorescence (ST-ChlF) approaches, which carry unique potential to autonomously collect data at high spatial and temporal resolution. Over the past decades, significant progress has been made in the development and application of ST-ChlF methods in aquatic ecosystems, and in the interpretation of the resulting observations. At the same time, however, an increasing number of sensor types, sampling protocols, and data processing algorithms have created confusion and uncertainty among potential users, with a growing divergence of practice among different research groups. In this review, we assist the existing and upcoming user community by providing an overview of current approaches and consensus recommendations for the use of ST-ChlF measurements to examine in-situ phytoplankton productivity and photo-physiology. We argue that a consistency of practice and adherence to basic operational and quality control standards is critical to ensuring data inter-comparability. Large datasets of inter-comparable and globally coherent ST-ChlF observations hold the potential to reveal large-scale patterns and trends in phytoplankton photo-physiology, photosynthetic rates and bottom-up controls on primary productivity. As such, they hold great potential to provide invaluable physiological observations on the scales relevant for the development and validation of ecosystem models and remote sensing algorithms.

variable chlorophyll fluorescence↗

NASA NTRS1990

Aircraft/island/ship/satellite intercomparison: Preliminary results from July 16, 1987

The First ISCCP Regional Experiment (FIRE) objective of validating and improving satellite algorithms for inferring cloud properties from satellite radiances was one of the central motivating factors in the design of the specific field experimental strategies used in the July, 1987 marine stratocumulus intensive field observations (IFO). The in situ measuring platforms were deployed to take maximum advantage of redundant measurements (for intercomparison of the in situ sensors) and to provide optimal coverage within satellite images. One of the most ambitious of these strategies was the attempt to coordinate measurements from San Nicolas Island (SNI), the R/V Pt. Sur, the meteorological aircraft, and the satellites. For the most part, this attempt was frustrated by flight restrictions in the vicinity of SNI. The exception was the mission of July 16, 1987, which achieved remarkable success in the coordination of the platforms. This presentation concerns operations conducted by the National Center for Atmospheric Research (NCAR) Electra and how data from the Electra can be integrated with and compared to data from the Pt. Sur, SNI, and the satellites. The focus is on the large-scale, integrated picture of the conditions on July 16 from the perspective of the Electra's flight operations.

Hanson, Howard P.↗

NASA NTRS1995

SeaWiFS technical report series. Volume 27: Case studies for SeaWiFS calibration and validation, part 3

This document provides brief reports, or case studies, on a number of investigations sponsored by the Calibration and Validation Team (CVT) within the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project. Chapter I describes a comparison of the irradiance immersion coefficients determined for several different marine environmental radiometers (MERs). Chapter 2 presents an analysis of how light absorption by atmospheric oxygen will influence the radiance measurements in band 7 of the SeaWiFS instrument. Chapter 3 gives the results of the second ground-based solar calibration of the instrument, which was undertaken after the sensor was modified to reduce the effects of internal stray light. (The first ground-based solar calibration of SeaWiFS is described in Volume 19 in the SeaWiFS Technical Report Series.) Chapter 4 evaluates the effects of ship shadow on subsurface irradiance and radiance measurements deployed from the deck of the R/V Weatherbird 11 in the Atlantic Ocean near Bermuda. Chapter 5 illustrates the various ways in which a single data day of SeaWiFS observations can be defined, and why the spatial definition is superior to the temporal definition for operational usage.

Hooker, Stanford B.↗

NASA NTRS2014

Phonesat In-flight Experience Results

Consumer technology, over the last decade, has begun to encompass devices that enable us to figure out where we are, which way we are pointing, observe the world around us, and store and transmit this information to wherever we want. Once separate consumer products such as GPS units, digital cameras and mobile phones are now combined into the modern day Smartphone. Since these capabilities are remarkably similar to those required for the multi-million dollar satellites - so why not use a multihundred dollar Smartphone instead? The PhoneSat project of NASA Ames Research Center is developing technology demonstrations utilizing these extraordinary advances to show just how simple and cheap Space can be. The style of development revolves around the "release early, release often" Silicon Valley mentality. PhoneSat is a series of 1U CubeSat size spacecrafts that use an off-the-shelf Smartphone as their onboard computer. By doing so, PhoneSat takes advantage of the high computational capability, large memory as well as ultra-tiny sensors like high-resolution cameras and navigation devices that Smartphones offer. Along with a Smartphone, PhoneSat is equipped with other commercially available technology products, such as medical brushless motors that are used as reaction wheels. Over the four years that NASA Ames Research Center has been developing the PhoneSat project, different suborbital and orbital flight activities have proven the validity of this revolutionary approach. In early 2013, the PhoneSat project launched the first triage of PhoneSats into LEO. In the five day orbital life time, the nano-satellites flew the first functioning Smartphone based satellites (using the Nexus One and Nexus S phones), the cheapest satellite (a total parts cost below $3,500) and one of the fastest on-board processors (CPU speed of 1GHz). In late 2013, the PhoneSat project launched an improved version of its bus to a higher altitude orbit which provided data about the overall system's tolerance to the space environment. In this paper, an overview of the PhoneSat project as well as a summary of the in-flight experimental results is presented. NASA Ames Research Center is carrying on its effort to bring a paradigm shift in the way we conceive Space exploration, this new approach is certainly incarnated by PhoneSat. A set of eight PhoneSat-based CubeSats is manifested to launch in 2014 with the purpose of demonstrating new technical capabilities and being a pathfinder for future Spacecraft technology missions.

CubeSat↗

NASA NTRS2003

Assessment, Validation, and Refinement of the Atmospheric Correction Algorithm for the Ocean Color Sensors

The primary focus of this proposed research is for the atmospheric correction algorithm evaluation and development and satellite sensor calibration and characterization. It is well known that the atmospheric correction, which removes more than 90% of sensor-measured signals contributed from atmosphere in the visible, is the key procedure in the ocean color remote sensing (Gordon and Wang, 1994). The accuracy and effectiveness of the atmospheric correction directly affect the remotely retrieved ocean bio-optical products. On the other hand, for ocean color remote sensing, in order to obtain the required accuracy in the derived water-leaving signals from satellite measurements, an on-orbit vicarious calibration of the whole system, i.e., sensor and algorithms, is necessary. In addition, it is important to address issues of (i) cross-calibration of two or more sensors and (ii) in-orbit vicarious calibration of the sensor-atmosphere system. The goal of these researches is to develop methods for meaningful comparison and possible merging of data products from multiple ocean color missions. In the past year, much efforts have been on (a) understanding and correcting the artifacts appeared in the SeaWiFS-derived ocean and atmospheric produces; (b) developing an efficient method in generating the SeaWiFS aerosol lookup tables, (c) evaluating the effects of calibration error in the near-infrared (NIR) band to the atmospheric correction of the ocean color remote sensors, (d) comparing the aerosol correction algorithm using the singlescattering epsilon (the current SeaWiFS algorithm) vs. the multiple-scattering epsilon method, and (e) continuing on activities for the International Ocean-Color Coordinating Group (IOCCG) atmospheric correction working group. In this report, I will briefly present and discuss these and some other research activities.

Wang, Menghua↗

NASA NTRS2001

Fires and Smoke Observed from the Earth Observing System MODIS Instrument: Products, Validation, and Operational Use

The MODIS sensor, launched on NASA's Terra satellite at the end of 1999, was designed with 36 spectral channels for a wide array of land, ocean, and atmospheric investigations. MODIS has a unique ability to observe fires, smoke, and burn scars globally. Its main fire detection channels saturate at high brightness temperatures: 500 K at 4 microns and 400 K at 11 microns, which can only be attained in rare circumstances at the I kin fire detection spatial resolution. Thus, unlike other polar orbiting satellite sensors with similar thermal and spatial resolutions, but much lower saturation temperatures (e.g. AVHRR and ATSR), MODIS can distinguish between low intensity ground surface fires and high intensity crown forest fires. Smoke column concentration over land is for the first time being derived from the MOMS solar channels, extending from 0.41 microns to 2.1 microns. The smoke product has been provisionally validated both globally and regionally over southern Africa and central and south America. Burn scars are observed from MODIS even in the presence of smoke, using the 1.2 to 2.1 micron channels. MODIS burned area information is used to estimate pyrogenic emissions. A wide range of these fire and related products and validation are demonstrated for the wild fires that occurred in northwestern United States in the summer of 2000. The MODIS rapid response system and direct broadcast capability is being developed to enable users to obtain and generate data in near real time. It is expected that health and land management organizations will use these systems for monitoring the occurrence of fires and the dispersion of smoke within two to six hours after data acquisition.

Kaufman, Y. J.↗

NASA NTRS2018

Validation of MODIS 3 km Land Aerosol Optical Depth from NASA's EOS Terra and Aqua Missions

In addition to the standard resolution product (10km), the MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C006) data release included a higher resolution (3km). Other than accommodations for the two different resolutions, the 10 and 3km Dark Target (DT) algorithms are basically the same. In this study, we perform global validation of the higher-resolution aerosol optical depth (AOD) over global land by comparing against AErosol RObotic NETwork (AERONET) measurements. The MODIS-AERONET collocated data sets consist of 161410 high-confidence AOD pairs from 2000 to 2015 for Terra MODIS and 2003 to 2015 for Aqua MODIS. We find that 62.5 and 68.4% of AODs retrieved from Terra MODIS and Aqua MODIS, respectively, fall within previously published expected error bounds of +/-(0.05+0.2 × AOD), with a high correlation (R = 0.87). The scatter is not random, but exhibits a mean positive bias of ∼ 0.06 for Terra and ∼ 0.03 for Aqua. These biases for the 3km product are approximately 0.03 larger than the biases found in similar validations of the 10km product. The validation results for the 3km product did not have a relationship to aerosol loading (i.e., true AOD), but did exhibit dependence on quality flags, region, viewing geometry, and aerosol spatial variability. Time series of global MODIS-AERONET differences show that validation is not static, but has changed over the course of both sensors' lifetimes, with Terra MODIS showing more change over time. The likely cause of the change of validation over time is sensor degradation, but changes in the distribution of AERONET stations and differences in the global aerosol system itself could be contributing to the temporal variability of validation.

Gupta, Pawan↗

DOE OSTI2018

Natural Gas Fugitive Leak Detection Using an Unmanned Aerial Vehicle: Localization and Quantification of Emission Rate

We describe a set of methods for locating and quantifying natural gas leaks using a small unmanned aerial system equipped with a path-integrated methane sensor. The algorithms are developed as part of a system to enable the continuous monitoring of methane, supported by a series of over 200 methane release trials covering 51 release location and flow rate combinations. The system was found throughout the trials to reliably distinguish between cases with and without a methane release down to 2 standard cubic feet per hour (0.011 g/s). Among several methods evaluated for horizontal localization, the location corresponding to the maximum path-integrated methane reading performed best with a mean absolute error of 1.2 m if the results from several flights are spatially averaged. Additionally, a method of rotating the data around the estimated leak location according to the wind is developed, with the leak magnitude calculated from the average crosswind integrated flux in the region near the source location. The system is initially applied at the well pad scale (100–1000 m2 area). Validation of these methods is presented including tests with unknown leak locations. Sources of error, including GPS uncertainty, meteorological variables, data averaging, and flight pattern coverage, are discussed. The techniques described here are important for surveys of small facilities where the scales for dispersion-based approaches are not readily applicable.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

AEPF: Attention-Enabled Point Fusion for 3D Object Detection

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class’s easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

Chemistry↗

NASA NTRS2002

Calibration Methodology for the Lightweight Rainfall Radiometer STAR Aircraft Sensor

A proposed methodology for the in-flight calibration of a Synthetic Thinned Aperture Radiometer (STAR) airborne sensor with the potential application to a space flight version. The application of the spaceflight version of this instrument will address several pressing issues related to the Global Precipitation Measurement Mission (GPM). The X-Band Lightweight Rainfall Radiometer using STAR technology (LRR-X) is an aircraft sensor that is jointly developed by the NASA Goddard Space Flight Center and the University of Michigan. This paper will describe the theory of calibration as well as the hardware design specifications used by the method. The on-board hardware uses individual uncorrelated warm loads on each receiver as well as to a single noise diode providing a correlated noise source to each receiver. A procedure for maintaining onboard calibration with an optimum running average using correlated bursts of thermal noise interleaved with scene data will be exercised during the maiden flight of the LRR-X instrument during the spring of 2003. The final component of calibration of a synthetic aperture radiometer is the image reconstruction algorithm that uses the measured correlations to produce the temperature brightness (TB) images. An overview of system-level testing, both on the ground and in-flight, will be presented to validate the absolute accuracy of the image reconstruction algorithm.

Principe, Caleb↗

DOE OSTI2022

Estimation of Corn Latent Heat Flux from High Resolution Thermal Imagery

Crop evapotranspiration (ET), which is directly related to latent heat flux, is also a key indicator in determining the water status of crops. In order to estimate the latent heat flux, two-source energy balance (TSEB) models have been developed for thermal imagery from satellite platforms. However, because of the coarse resolution of thermal sensors on the satellite, distinguishing soil and vegetation is difficult which complicates the calculation process and introduces errors in latent heat estimates. In this research, high-resolution thermal datasets (0.05 m) and corresponding RGB datasets (0.03 m) were used for calculating crop latent heat flux using an adapted TSEB model. The RGB datasets were used for supervised classification of soil and vegetation, and the classification results were then used to filter the thermal mosaics to separate vegetation and soil temperatures. The vegetation temperature is used for calculating latent heat flux and the results are validated against the ground reference measurements of latent heat using a handheld porometer. The objective of this research is to introduce a workflow including an adapted TSEB model which is customized for high resolution thermal images from unmanned aircraft systems (UAS) to estimate the latent heat flux of row crops in agricultural fields. Nine dates of data collection in 2018 and 2020 have been evaluated and the root mean square error (RMSE) varies between 16 to 106 W/m2 depending on the days after planting (DAP) and the time of measurement for each day. The results indicate that the workflow introduced here is able to provide estimates of instantaneous latent heat flux (evapotranspiration) measurements for row crops in agricultural fields which will enable people to make reliable decisions related to irrigation scheduling.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Hurricane Imaging Radiometer Wind Speed and Rain Rate Retrievals during the 2010 GRIP Flight Experiment

Microwave remote sensing observations of hurricanes, from NOAA and USAF hurricane surveillance aircraft, provide vital data for hurricane research and operations, for forecasting the intensity and track of tropical storms. The current operational standard for hurricane wind speed and rain rate measurements is the Stepped Frequency Microwave Radiometer (SFMR), which is a nadir viewing passive microwave airborne remote sensor. The Hurricane Imaging Radiometer, HIRAD, will extend the nadir viewing SFMR capability to provide wide swath images of wind speed and rain rate, while flying on a high altitude aircraft. HIRAD was first flown in the Genesis and Rapid Intensification Processes, GRIP, NASA hurricane field experiment in 2010. This paper reports on geophysical retrieval results and provides hurricane images from GRIP flights. An overview of the HIRAD instrument and the radiative transfer theory based, wind speed/rain rate retrieval algorithm is included. Results are presented for hurricane wind speed and rain rate for Earl and Karl, with comparison to collocated SFMR retrievals and WP3D Fuselage Radar images for validation purposes.

Sahawneh, Saleem↗