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This paper discusses the Team Raptor passive perception system hardware and software design, implementation, and performance, and describes a road map to faster and improved passive perception.
Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the land surface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth"s water cycle and climate variability. NASA"s Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type).
The Greenland Ice Sheet has been the focus of much attention recently because of increasing melt in response to regional climate warming. To improve our ability to measure surface melt, we use remote-sensing data products to study surface and near-surface melt characteristics of the Greenland Ice Sheet for the 2007 melt season when record melt extent and runoff occurred. Moderate Resolution Imaging Spectroradiometer (MODIS) daily land-surface temperature (LST), MODIS daily snow albedo, and a special diurnal melt product derived from QuikSCAT (QS) scatterometer data, are all effective in measuring the evolution of melt on the ice sheet. These daily products, produced from different parts of the electromagnetic spectrum, are sensitive to different geophysical features, though QS- and MODIS-derived melt generally show excellent correspondence when surface melt is present on the ice sheet. Values derived from the daily MODIS snow albedo product drop in response to melt, and change with apparent grain-size changes. For the 2007 melt season, the QS and MODIS LST products detect 862,769 square kilometers and 766,184 square kilometers of melt, respectively. The QS product detects about 11% greater melt extent than is detected by the MODIS LST product probably because QS is more sensitive to surface melt, and can detect subsurface melt. The consistency of the response of the different products demonstrates unequivocally that physically-meaningful melt/freeze boundaries can be detected. We have demonstrated that these products, used together, can improve the precision in mapping surface and near-surface melt extent on the Greenland Ice Sheet.
Robotic mining could prove to be an efficient method of mining resources for extended missions on the Moon or Mars. One component of robotic mining is scouting an area for resources to be mined by other robotic systems. Writing controllers for scouting can be difficult due to the need for fault tolerance, inter-agent cooperation, and agent problem solving. Reinforcement learning could solve these problems by enabling the scouts to learn to improve their performance over time. This work is divided into two sections, with each section addressing the use of machine learning in this domain. The first contribution of this work focuses on the application of reinforcement learning to mining mission analysis. Various mission parameters were modified and control policies were learned. Then agent performance was used to assess the effect of the mission parameters on the performance of the mission. The second contribution of this work explores the potential use of reinforcement learning to learn a controller for the scouts. Through learning, these scouts would improve their ability to map their surroundings over time.
Determining the distribution of redshifts for galaxies in wide-field photometric surveys is essential for robust cosmological studies of weak gravitational lensing. We present the methodology, calibrated redshift distributions, and uncertainties of the final Dark Energy Survey Year 6 (Y6) weak lensing galaxy data, divided into four redshift bins centered at $\langle z \rangle = [0.414, 0.538, 0.846, 1.157]$. We combine independent information from two methods on the full shape of redshift distributions: optical and near-infrared photometry within an improved Self-Organizing Map $p(z)$ (SOMPZ) framework, and cross-correlations with spectroscopic galaxy clustering measurements (WZ), which we demonstrate to be consistent both in terms of the redshift calibration itself and in terms of resulting cosmological constraints within 0.1$σ$. We describe the process used to produce an ensemble of redshift distributions that account for several known sources of uncertainty. Among these, imperfection in the calibration sample due to the lack of faint, representative spectra is the dominant factor. The final uncertainty on mean redshift in each bin is $σ_{\langle z\rangle} = [0.012, 0.008,0.009, 0.024]$. We ensure the robustness of the redshift distributions by leveraging new image simulations and a cross-check with galaxy shape information via the shear ratio (SR) method.
The Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) on board the Suomi National Polar-orbiting Partnership (SNPP) satellite was launched on Oct. 28, 2011. Limb profilers measures the radiance scattered from the Earth's atmospheric in limb viewing mode from 290 to 1000 nm and infer ozone profiles from tropopause to 60 km. The recently released OMPS-LP Version 2 data product contains the first publicly released ozone profiles retrievals, and these are now available for the entire OMPS mission, which extends from April, 2012. The Version 2 data product retrievals incorporate several important improvements to the algorithm. One of the primary changes is to turn off the aerosol retrieval module. The aerosol profiles retrieved inside the ozone code was not helping the ozone retrieval and was adding noise and other artifacts. Aerosols including polar stratospheric cloud (PSC) and polar mesospheric clouds (PMC) have a detectable effect on OMPS-LP data. Our results show that ignoring the aerosol contribution would produce an ozone density bias of up to 10 percent in the region of maximum aerosol extinction. Therefore, aerosol correction is needed to improve the quality of the retrieved ozone concentration profile. We provide Aerosol Scattering Index (ASI) for detecting aerosols-PMC-PSC, defined as ln(Im-Ic) normalized at 45km, where Im is the measured radiance and Ic is the calculated radiance assuming no aerosols. Since ASI varies with wavelengths, latitude and altitude, we can start by assuming no aerosol profiles in calculating the ASIs and then use the aerosol profile to see if it significantly reduces the residuals. We also discuss the effect of aerosol size distribution on the ozone profile retrieval process. Finally, we present an aerosol-PMC-PSC correction scheme.
The author has identified the following significant results. Land use data derived from high altitude photography and satellite imagery were studied for 49 basins in Delaware, and eastern Maryland and Virginia. Applying multiple regression techniques to a network of gaging stations monitoring runoff from 39 of the basins, demonstrated that land use data from high altitude photography provided an effective means of significantly improving estimates of stream flow. Forty stream flow characteristic equations for incorporating remotely sensed land use information, were compared with a control set of equations using map derived land cover. Significant improvement was detected in six equations where level 1 data was added and in five equations where level 2 information was utilized. Only four equations were improved significantly using land use data derived from LANDSAT imagery. Significant losses in accuracy due to the use of remotely sensed land use information were detected only in estimates of flood peaks. Losses in accuracy for flood peaks were probably due to land cover changes associated with temporal differences among the primary land use data sources.
Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In this study, we used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). We then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, we found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution.
During the MAP/MAC period, considerable improvements in instrumentation and experimental technique have occurred, and many hitherto unavailable parameters relating to gravity waves have become available. Studies of individual wave events and simultaneous observations made with a variety of techniques have provided insight into wave saturation mechanisms. In addition, long data sets of upper middle atmosphere winds were collected at a number of widely spaced sites, allowing climatological investigations of gravity wave amplitudes, wave number spectra, polarization, mean flow acceleration, and other saturation effects to be undertaken. Observations of gravity wave scales, momentum fluxes, saturation and saturation effects obtained during MAP/MAC, made on both a statistical and case study basis are reviewed.
As an astronomer, I use light to travel through the universe, and to look back in time to when the universe was young. So do you! All of us see things as they were when the light was emitted, not as they are now. The farthest thing you can easily see without a telescope is the Andromeda Nebula, which is a galaxy like the Milky Way, about 2.5 million light years away. You see it as it was 2.5 million years ago, and we really don't know what it looks like today; the disk will have rotated a bit, new stars will have been born, there could have been all kinds of exploding stars, and the black hole in the middle could be lighting up. People may be skeptical of the Big Bang theory, even though we have a TV show named for it, but we (I should say Penzias and Wilson) measured its heat radiation 51 years ago at Bell Telephone Labs in New Jersey. Their discovery marks the beginning of the era of cosmology as a measurement science rather than speculation. Penzias and Wilson received the Nobel Prize in 1978 for their finding, which had been predicted in 1948 by Alpher and Herman. By the way, heat radiation is just another form of light - we call it radiation because we can't see it, but it's exactly the same phenomenon of electromagnetic waves, and the only difference is the wavelength. In the old days of analog television, if you tuned your TV in between channels, about 1% of the snow that you could see came from the Big Bang. So when we look at the heat radiation of the early universe, we really are gazing right at what seems to us a cosmic fireball, which surrounds us completely. It's a bit of an illusion; if you can imagine what astronomers in other galaxies would see, they would also feel surrounded by the fireball, and they would also think they were in the middle. So from a mathematical version of imagination, we conclude that there is no observable center and no edge of our universe, and that the heat of the fireball fills the entire universe uniformly. Astronomers are also using light to find out whether we are alone in the universe. The Kepler observatory showed that thousands of stars blink a little when their orbiting planets pass between us and them, and other observatories use light to measure the wobble of stars as their planets pull on them. Eventually, we will find out whether planets like Earth have atmospheres like Earth's too - with water, carbon dioxide, oxygen, methane, and other gases that would be evidence of photosynthetic life. I think in a few decades we will have evidence that some planets do have life, and it will be done using light for remote chemical analysis. Also, astronomers at the SETI project are using light (long wavelength light we can pick up with radio telescopes) to look for signals from intelligent civilizations. That's a harder project because we don't know what to look for. But if we wanted to send signals all the way across the Milky Way, we could do it with laser beams, and if somebody over there knew what to look for, he or she could decode the message. On with the search! Dr. John C. Mather is a Senior Astrophysicist and is the Senior Project Scientist for the James Webb Space Telescope at NASA's Goddard Space Flight Center in Greenbelt, MD. His research centers on infrared astronomy and cosmology. With the Cosmic Background Explorer (COBE) team, he showed that the cosmic microwave background radiation has a blackbody spectrum within 50 parts per million, confirming the expanding universe model (aka the Big Bang Theory) to extraordinary accuracy, and initiating the study of cosmology as a precision science. The COBE team also made the first map of the hot and cold spots in the background radiation. The COBE maps have been confirmed and improved by two succeeding space missions, the Wilkinson Microwave Anisotropy Probe (WMAP, built by GSFC with Princeton University), and the Planck mission built by ESA. Based on these maps, astronomers have now developed a "standard model" of cosmology and have built detailed numerical simulations that begin to match Hubble observations, and require the existence of both "dark matter" and "dark energy", neither of which has been detected or deduced in laboratory experiments. Dr. Mather is the recipient of numerous awards, including the Nobel Prize in Physics (2006) with George Smoot, for the COBE work, and the NASA Distinguished Service Medal (2007). He is a member of many professional societies including the National Academy of Sciences and the American Academy of Arts and Sciences. Dr. Mather is now working with teams and committees to develop plans for a future great telescope capable of observing signs of life on planets orbiting other stars.
Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. Our results show WRF-Hydro soil moisture exhibits increased correlation coefficients ( r ), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well-established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-adjusted WRF-Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO-informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre-calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.
This report describes an improved instrument characterization used for the Version 7 processing of the Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) data record. An improved internal calibration technique referred to as spectral discrimination is used to provide long-term calibration precision of +/- 1%/decade in total column ozone amount. A revised wavelength scale results in a day one calibration that agrees with other satellite and ground-based measurements of total ozone, while a wavelength independent adjustment of the initial radiometric calibration constants provides good agreement with surface reflectivity measured by other satellite-borne ultraviolet measurements. The impact of other aspects of the Nimbus-7 TOMS instrument performance are also discussed. The Version 7 data should be used in all future studies involving the Nimbus-7 TOMS measurements of ozone. The data are available through the NASA Goddard Space Flight Center's Distributive Active Archive Center (DAAC).
Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering
The results of a preliminary assessment of both raw and NASA processed Thematic Mapper (TM) data are discussed. Geometric correction of NASA processed TM data was carried out. Correction was possible to within 3 pixels in the along track direction and 2 pixels in the across track direction. A preliminary evaluation of TM imagery provided by the NASA LANDSAT Assessmemt System for geometric accuracy and map information content was performed on samples of imagery. The initial indications were that bands 3, 5 and 7 contain the most useful cartographic information. The resolution of rural and urban detail as well as the fit to plotted map detail was found to be improved over LANDSAT MSS, and such images may provide adequate revision information for 1:250,000 maps in areas where it is not currently profitable to use LANDSAT MSS. The relative gains and offsets for each detector in each band of raw data were calculated in a study of the radiometric correction of TM data. This was done for different subscenes as well as a full scene and the variation of the results with direction of scan and position of subscene were studied.
This viewgraph presentation regards one element of a larger project on the integration of NASA science models and data into the Hazards U.S. Multi-Hazard (HAZUS-MH) Hurricane module for hurricane damage and loss risk assessment. HAZUS-MH is a decision support tool being developed by the National Institute of Building Sciences for the Federal Emergency Management Agency (FEMA). It includes the Hurricane Module, which employs surface roughness maps made from National Land Cover Data (NLCD) maps to estimate coastal hurricane wind damage and loss. NLCD maps are produced and distributed by the U.S. Geological Survey. This presentation discusses an effort to improve upon current HAZUS surface roughness maps by employing ASTER multispectral classifications with QuickBird "ground reference" imagery.
The author has identified the following significant results. ERTS-1 data is ideally suited for small-scale geologic mapping and structural analysis of remote, inaccessible areas such as the Andes of South America. The synoptic view of large areas, low sun-angle and multispectral nature of the images provide the right ingredients for improving existing geologic and other maps of the regions. In most areas it has been possible to compile geologic, drainage, and cultural interpretive overlays to individual scenes mainly using MSS bands 4, 5, and 7. A test image mosaic using MSS band 6 is being compiled for Test Area 7 (La Paz, Bolivia). It will be at a scale of 1:1,000,000 and cover 4 x 6 degrees of latitude and longitude and will serve as a compilation base on which to join the overlays. Repetitive data shows changes in river channels and sedimentation plumes, changes in lake shorelines, and surface moisture distribution. Vegetation and snow line changes in the Andes have been recognized. A year of seasonal data, however, has not yet been acquired due to tape recorder failure.
Light sheet microscopy has made possible the high temporal and spatial 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual process. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through the application of refinements such as transfer learning.
Land cover information for the Clinton River Basin (Michigan) derived from LANDSAT multispectral scanner (MSS) data was compared with that from airborne thematic mapper simulator (TMS) to investigate the probable capabilities of the thematic mapper (TM) launched aboard LANDSAT-4 in July 1982. The preliminary findings for one 7.5 minute topographic map, Mt. Clemens West, are reported. Significant improvements in land cover classification accuracy were obtained using TMS data as compared with MSS data. Overall mapping accuracy increased from 49 to 61 percent with an improvement from 71 to 84 percent in the residential category. A combination of four bands with one band in each major region of the spectrum (visible, near IR, middle IR and thermal IR) provided as good a discrimination of land cover as all seven TM bands. Based on the improved land cover classification accuracy of TM, TM data has the potential to provide more useful and effective input to US Army Corps of Engineers flood forecasting and flood damage prediction/assessment models.