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Microwave ellipsometry apparatus reflects circularly polarized train of microwaves from vegetation at predetermined angle of incidence to determine ratio of intensities of electric field components and their phase differences. Refractive index given by water content of vegetation and thickness of vegetation layer are computed from formula based on Maxwell's equations.
The STS-111 Expedition Five Crew begins with training on payload operations. Flight Engineer Peggy Whitson and Mission Specialist Sandy Magnus are shown in Shuttle Remote Manipulator System (SRMS) procedures. Flight Engineer Sergei Treschev gets suited for Neutral Neutral Buoyancy Lab (NBL) training. Virtual Reality lab training is shown with Peggy Whitson. Habitation Equipment and procedures are also presented.
Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.
Remote sensing measurements have been widely used to estimate the planetary boundary layer height (PBLHT). Each remote sensing approach offers unique strengths and faces different limitations. In this study, we use machine learning (ML) methods to produce a best-estimate PBLHT (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. Three ML models – random forest (RF) classifier, RF regressor, and light gradient-boosting machine (LightGBM) – were trained on a dataset from 2017 to 2023 that included radiosonde, various remote sensing PBLHT estimates, and atmospheric meteorological conditions. Evaluations indicated that PBLHT-BE-ML from all three models improved alignment with the PBLHT derived from radiosonde data (PBLHT-SONDE), with LightGBM demonstrating the highest accuracy under both stable and unstable boundary layer conditions. Feature analysis revealed that the most influential input features at the SGP site were the PBLHT estimates derived from (a) potential temperature profiles retrieved using Raman lidar (RL) and atmospheric emitted radiance interferometer (AERI) measurements (PBLHT-THERMO), (b) vertical velocity variance profiles from Doppler lidar (PBLHT-DL), and (c) aerosol backscatter profiles from micropulse lidar (PBLHT-MPL). The trained models were then used to predict PBLHT-BE-ML at a temporal resolution of 10 min, effectively capturing the diurnal evolution of PBLHT and its significant seasonal variations, with the largest diurnal variation observed over summer at the SGP site. We applied these trained models to data from the ARM Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field campaign (EPC), where the PBLHT-BE-ML, particularly with the LightGBM model, demonstrated improved accuracy against PBLHT-SONDE. Analyses of model performance at both the SGP and EPC sites suggest that expanding the training dataset to include various surface types, such as ocean and ice-covered areas, could further enhance ML model performance for PBLHT estimation across varied geographic regions.
NASA!s Earth Sciences Program supports a wide range of endeavors in basic Earth system scientific research, technology development to support that research, development of materials and training for educators and students based on that research and information, and increasingly practical applications. A brief overview of the scope of this scientific research and the key features of the necessary remote sensing instrumentation will be given. I will also describe available educational materials and training courses for a wide range of grade levels. Information will be provided on how to obtain educational materials or to participate in a training course. Finally, a few examples will be given to illustrate how Earth remote sensing effects our daily life.
Gigapixel images are prevalent in scientific domains ranging from remote sensing, and satellite imagery to microscopy, etc. However, training a deep learning model at the natural resolution of those images has been a challenge in terms of both, overcoming the resource limit (e.g. HBM memory constraints), as well as scaling up to a large number of GPUs. In this paper, we trained Residual neural Networks (ResNet) on 22,528 x 22,528-pixel size images using a distributed spatial decomposition method on 2,304 GPUs on the Summit Supercomputer. We applied our method on a Whole Slide Imaging (WSI) dataset from The Cancer Genome Atlas (TCGA) database. WSI images can be in the size of 100,000 x 100,000 pixels or even larger, and in this work we studied the effect of image resolution on a classification task, while achieving state-of-the-art AUC scores. Moreover, our approach doesn't need pixel-level labels, since we're avoiding patching from the WSI images completely, while adding the capability of training arbitrary large-size images. This is achieved through a distributed spatial decomposition method, by leveraging the non-block fat-tree interconnect network of the Summit architecture, which enabled GPU-to-GPU direct communication. Finally, detailed performance analysis results are shown, as well as a comparison with a data-parallel approach when possible.
Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.
Abstract. The planetary boundary layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, which can estimate PBLH by integrating the morning temperature profiles and surface meteorological observations. The DNN model is developed by leveraging a rich dataset of PBLH derived from long-standing radiosonde records augmented with high-resolution micro-pulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden-layer structures, which collectively yield a robust 27-year PBLH dataset over the southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micro-pulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (Green Ocean Amazon; tropical rainforest) and CACTI (Cloud, Aerosol, and Complex Terrain Interactions; middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary layer processes with implications for improving the representation of PBL in weather forecasting and climate modeling.
The planetary boundary-layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, designed to estimate PBLH by integrating morning temperature profiles with surface meteorological observations. The DNN model is developed by leveraging a rich data set of PBLH derived from long-standing radiosonde records and augmented with high-resolution micropulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden layer structures, which collectively yield a robust 27-year PBLH data set over the Southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote-sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micropulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote-sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (tropical rainforest) and CACTI (middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary-layer dynamics with implications for enhancing the representation of PBL in weather forecasting and climate modeling.
The inversion of snow parameters from passive microwave remote sensing measurements is performed with a neural network trained with a dense media multiple scattering model. A constrained iterative inversion scheme is used. Inversion of four parameters is performed from five brightness temperatures. The four parameters are: mean grain size of ice particles in snow, snow density, snow temperature, and snow depth. The five brightness temperatures are that of 19-GHz vertical polarization, 19-GHz horizontal polarization, 22-GHz vertical polarization, 37-GHz vertical polarization, and 37-GHz horizontal polarization. Based on the neural network constrained iterative inversion algorithm, synthetic mapping of the terrain is performed. The retrieval of synthetic mapping has been achieved. The incorporation of ground truth information is considered.
A background and highlights of a 10 year remote sensing industry analysis are provided.Included are the following:Training, educational analysis, staff levels, and end-users analysis, market drivers, market segments,application areas, spatial resolution needs, use of image types.
This viewgraph presentation reviews the use of Astronaut Photography (AP) as taken from the International Space Station (ISS) in Earth System Science (ESS). Included are slides showing basic remote sensing theory, data characteristics of astronaut photography, astronaut training and operations, crew Earth observations group, targeting sites and acquisition, cataloging and database, analysis and applications for ESS, image analysis of particular interest urban areas, megafans, deltas, coral reefs. There are examples of the photographs and the analysis.
The Earth Science and Remote Sensing (ESRS) Data Portal, managed by the ESRS Unit at NASA's Johnson Space Center, is a web GIS environment built on Esri's ArcGIS Enterprise platform. Its current intent is to visualize, analyze, and distribute geospatial data to our internal organization and the communities of Houston and Galveston. Applications on the ESRS Data Portal include a Regional Remote Sensing web app, story maps developed for astronaut training, and a web scene for visualizing LiDAR.
The purpose of this project was to establish a new hyperspectral remote sensing laboratory at the Mid-America Remote sensing Center (MARC), dedicated to in situ and laboratory measurements of environmental samples and to the manipulation, analysis, and storage of remotely sensed data for environmental monitoring and research in ecological modeling using hyperspectral remote sensing at MARC, one of three research facilities of the Center of Reservoir Research at Murray State University (MSU), a Kentucky Commonwealth Center of Excellence. The equipment purchased, a FieldSpec FR portable spectroradiometer and peripherals, and ENVI hyperspectral data processing software, allowed MARC to provide hands-on experience, education, and training for the students of the Department of Geosciences in quantitative remote sensing using hyperspectral data, Geographic Information System (GIS), digital image processing (DIP), computer, geological and geophysical mapping; to provide field support to the researchers and students collecting in situ and laboratory measurements of environmental data; to create a spectral library of the cover types and to establish a World Wide Web server to provide the spectral library to other academic, state and Federal institutions. Much of the research will soon be published in scientific journals. A World Wide Web page has been created at the web site of MARC. Results of this project are grouped in two categories, education and research accomplishments. The Principal Investigator (PI) modified remote sensing and DIP courses to introduce students to ii situ field spectra and laboratory remote sensing studies for environmental monitoring in the region by using the new equipment in the courses. The PI collected in situ measurements using the spectroradiometer for the ER-2 mission to Puerto Rico project for the Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS). Currently MARC is mapping water quality in Kentucky Lake and vegetation in the Land-Between-the Lakes (LBL) using Landsat-TM data. A Landsat-TM scene of the same day was obtained to relate ground measurements to the satellite data. A spectral library has been created for overstory species in LBL. Some of the methods, such as NPDF and IDFD techniques for spectral unmixing and reduction of effects of shadows in classifications- comparison of hyperspectral classification techniques, and spectral nonlinear and linear unmixing techniques, are being tested using the laboratory.
The Seventh Alumni Conference of the International Space University, coordinated by the ISU U.S. Alumni Organization (IUSAO), was held at Cleveland State University in Cleveland, Ohio on Friday, July 24, 1998. These proceedings are a record of the presentations. The following topics are included: Remote sensing education in developing countries; Integrated global observing strategy; NASA's current earth science program; Europe's lunar initiative; Lunarsat: Searching for the South Polar cold traps; Asteroid hazards; ESA exobiological activities; Space testbed for photovoltaics; Teledesic Space infrastructure; Space instrument's concurrent design; NASA advanced fuel program; Mission preparation and training for the European Robotic Arm (ERA); and Global access to remote sensing systems.
An Earth Resources Data Processing System was developed to evaluate the system for training, technology transfer, and data processing. In addition to the five sites included in this project two other sites were connected to the system under separate agreements. The experience of these two sites is discussed. The results of the remote terminal project are documented in seven reports: one from each of the five project sites, Purdue University, and an overview report summarizing the other six reports.
Certain technical problems arising in the recent past (1975) in the field of the processing of remote sensing data are reviewed including approaches to the analysis of Landsat MSS data and technical difficulties which must be overcome to achieve operational data processing. The current status of remote sensing data processing is then examined with emphasis on such current technical issues as training selection and labeling, sampling schemes and classification and mensuration. Hardware projections are made for the near future (1979) relative to the development of remote sensing data processing.
While Ocular Coherence Tomography (OCT) is not a first-line modality to evaluate anterior eye structures terrestrially, it is a resource already available on the International Space Station (ISS) that can be used in medical contingencies that involve the anterior eye. With remote guidance and subject matter expert (SME) support from the ground, a minimally trained crewmember can now use OCT to evaluate anterior eye pathologies on orbit. OCT utilizes low-coherence interferometry to produce detailed cross-sectional and 3D images of the eye in real time. Terrestrially, it has been used to evaluate macular pathologies and glaucoma. Since 2013, OCT has been used onboard the ISS as one part of a suite of hardware to evaluate the Visual Impairment/Intracranial Pressure risk faced by astronauts, specifically assessing changes in the retina and choroid during space flight. The Anterior Segment Module (ASM), an add-on lens, was also flown for research studies, providing an opportunity to evaluate the anterior eye in real time if clinically indicated. Anterior eye pathologies that could be evaluated using OCT were identified. These included corneal abrasions and ulcers, scleritis, and acute angle closure glaucoma. A remote guider script was written to provide ground specialists with step-by-step instructions to guide ISS crewmembers, who do not get trained on the ASM, to evaluate the anterior eye. The instructions were tested on novice subjects and/or operators, whose feedback was incorporated iteratively. The final remote guider script was reviewed by SME optometrists and NASA flight surgeons. The novel application of OCT technology to space flight allows for the acquisition of objective data to diagnose anterior eye pathologies when other modalities are not available. This demonstrates the versatility of OCT and highlights the advantages of using existing hardware and remote guidance skills to expand clinical capabilities in space flight.