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DOE OSTI2022

Application of Manufacturing Quality Management Principles to PV System Installations

To help SETO/DOE achieve its goals, the IBTS team proposed a project addressing system reliability by improving installation standards and quality management. The proposed approach was designed to help achieve measurable reductions in installation defect density and improvements in the performance of PV systems by optimizing design and installation of residential and commercial PV systems. This approach addressed the soft costs associated with installations and quality management. The project demonstrated improved system reliability and reduced PV system installation costs. The software developed improved operations, decreased risk, and increased the overall value of PV systems across their lifecycle. The project used several data collection methods, including extensive industry surveys, face-to-face high-level interviews at industry conferences, stakeholder teleconferences, and in-depth interviews conducted by IBTS staff. Results from the research found the industry needs a uniform assessment method for national providers to be more efficient; the software should support both code officials and installers; most industry stakeholders would find value in a centralized software system that allows them to collect, report, and review information on in-process and completed solar installations; and mobile solutions that bridge existing knowledge gaps with inspectors and integrate with existing methodologies (such as permitting software) are of great value. The software solution developed is web-based, allowing for national access, and is built on a Google Firebase platform that can handle significant users and data. It can be used onsite or remotely, allowing for code compliance to continue despite ongoing pandemic related delays or shutdowns for local economies. The information provided by the software tool allows users to uniformly assess a system for compliance and use that aggregated data to identify training topics or create internal process designed to improving issues and reducing occurrence. This solution has multiple benefits in managing quality at time of use and promoting an increase in future safety and quality through education. Perhaps most importantly, this software increases public safety by ensuring compliance of installed systems and allows for local AHJs to remotely engage specialized and qualified solar specific expertise for oversite of the installation in their jurisdictions. Data analysis provides the quality feedback loop identifying the root cause of failure and drives installation practices to improve through training and education, resulting in systems with higher performance, greater reliability, and reduced operations and maintenance costs. With the successful completion of this project, the industry can expect reduced soft costs and increased performance and safety and will ultimately benefit from longer performing systems that cost less to operate.

14 SOLAR ENERGY↗

NASA NTRS1987

TAVERNS and the space station software support environment

The Space Station Information System (SSIS) provides the data processing capability for the Space Station Program (SSP). The Software Support Environment (SSE) System for the SSP is the collection of software, procedures, standards, hardware specification, documentation, policy, and training materials. The Ada programming language was baselined by the Space Station Program Office as the language for development and maintenance of all space station software including the software of the SSE itself. The Test And Validation Environment for Remote Networked Systems (TAVERNS) is a distributed philosophy for development and validation of Ada applications software for the space station and as such is closely related to the SSE. An overview of the system is provided.

Howes, Norman R.↗

NASA NTRS2005

Diffuse Attenuation Coefficient of Downwelling Irradiance: An Evaluation of Remote Sensing Methods

The propagation of downwelling irradiance at wavelength lambda from surface to a depth (z) in the ocean is governed by the diffuse attenuation coefficient, K(sup -)(sub d)(lambda). There are two standard methods for the derivation of K(sup -)(sub d)(lambda) in remote sensing, which both are based on empirical relationships involving the blue-to-green ratio of ocean color. Recently, a semianalytical method to derive K(sup -)(sub d)(lambda) from reflectance has also been developed. In this study, using K(sup -)(sub d)(490) and K(sup -)(sub d)(443) as examples, we compare the K(sup -)(sub d)(lambda) values derived from the three methods using data collected in three different regions that cover oceanic and coastal waters, with K(sup -)(sub d)(490) ranging from approximately 0.04 to 4.0 per meter. The derived values are compared with the data calculated from in situ measurements of the vertical profiles of downwelling irradiance. The comparisons show that the two standard methods produced satisfactory estimates of K(sup -)(sub d)(lambda) in oceanic waters where attenuation is relatively low but resulted in significant errors in coastal waters. The newly developed semianalytical method appears to have no such limitation as it performed well for both oceanic and coastal waters. For all data in this study the average of absolute percentage difference between the in situ measured and the semianalytically derived K(sup -)(sub d) is approximately 14% for lambda = 490 nm and approximately 11% for lambda = 443 nm.

Lee, Zhong-Ping↗

NASA NTRS2014

Algorithm Development and Validation of CDOM Properties for Estuarine and Continental Shelf Waters Along the Northeastern U.S. Coast

An extensive set of field measurements have been collected throughout the continental margin of the northeastern U.S. from 2004 to 2011 to develop and validate ocean color satellite algorithms for the retrieval of the absorption coefficient of chromophoric dissolved organic matter (aCDOM) and CDOM spectral slopes for the 275:295 nm and 300:600 nm spectral range (S275:295 and S300:600). Remote sensing reflectance (Rrs) measurements computed from in-water radiometry profiles along with aCDOM() data are applied to develop several types of algorithms for the SeaWiFS and MODIS-Aqua ocean color satellite sensors, which involve least squares linear regression of aCDOM() with (1) Rrs band ratios, (2) quasi-analytical algorithm-based (QAA based) products of total absorption coefficients, (3) multiple Rrs bands within a multiple linear regression (MLR) analysis, and (4) diffuse attenuation coefficient (Kd). The relative error (mean absolute percent difference; MAPD) for the MLR retrievals of aCDOM(275), aCDOM(355), aCDOM(380), aCDOM(412) and aCDOM(443) for our study region range from 20.4-23.9 for MODIS-Aqua and 27.3-30 for SeaWiFS. Because of the narrower range of CDOM spectral slope values, the MAPD for the MLR S275:295 and QAA-based S300:600 algorithms are much lower ranging from 9.9 and 8.3 for SeaWiFS, respectively, and 8.7 and 6.3 for MODIS, respectively. Seasonal and spatial MODIS-Aqua and SeaWiFS distributions of aCDOM, S275:295 and S300:600 processed with these algorithms are consistent with field measurements and the processes that impact CDOM levels along the continental shelf of the northeastern U.S. Several satellite data processing factors correlate with higher uncertainty in satellite retrievals of aCDOM, S275:295 and S300:600 within the coastal ocean, including solar zenith angle, sensor viewing angle, and atmospheric products applied for atmospheric corrections. Algorithms that include ultraviolet Rrs bands provide a better fit to field measurements than algorithms without the ultraviolet Rrs bands. This suggests that satellite sensors with ultraviolet capability could provide better retrievals of CDOM. Because of the strong correlations between CDOM parameters and DOM constituents in the coastal ocean, satellite observations of CDOM parameters can be applied to study the distributions, sources and sinks of DOM, which are relevant for understanding the carbon cycle, modeling the Earth system, and to discern how the Earth is changing.

CDOM Properties↗

NASA NTRS2021

Three-dimensional estimation of deciduous forest canopy structure and leaf area using multi-directional, leaf-on and leaf-off airborne lidar data

Airborne laser scanning (ALS) has been widely used to map gap probability and leaf area index (LAI) distribution at plot and landscape scales. As an indirect measurement, most ALS methods to estimate LAI combine waveform or point density information with supporting field measurements such as the leaf angle distribution, gap probability, or direct LAI measures. The development of a more independent estimation approach would facilitate more widespread use of existing ALS data to investigate patterns of forest structure and build realistic 3-D vegetation scenes to simulate remote sensing imagery and energy balance. Here, we develop a data processing workflow (named PVlad) using ALS point cloud apparent reflectance to estimate LAI and voxel-based leaf area density (LAD), aiming to reduce the need for associated field measurements such as the gap probability. The adaptation of the path volume (PV) concept derived from apparent reflectance integrates information from multi-directional ALS pulses, and quantifies the percentage exploration of each voxel for classification and occlusion correction, such that rigorous volumetric sampling approaches can be developed to derive LAI and LAD. The PVlad workflow was applied to discrete-return lidar data (Riegl VQ480i) acquired by NASA Goddard's LiDAR, Hyperspectral and Thermal Imager (G-LiHT) Airborne Imager during leaf-on (summer) and leaf-off (spring) conditions at the Smithsonian Environmental Research Center (SERC). The estimates of LAI and LAD captured structural differences between mature, logged, and intermediate-aged stands over eight deciduous forest plots. The derived LAI values were compared to field litter collection measurements, and the derived LAD vertical distribution was compared to the output of the VoxLAD model using terrestrial laser scan (TLS) field survey data. Using voxel sizes ranging from 0.5 m to 5 m, overall LAI estimation showed linear fitting coefficient bias and for 1 and 2 m voxel sizes, and vertical LAD distribution showed strong correlation with and for 0.5 and 1m voxel sizes. For every forest stand, upper-canopy LAD had a low variance for voxel sizes of ≤ . Application of PVlad to the G-LiHT and other similar ALS data archives enables the development of fine-resolution LAI map products, including voxelization of LAD for ecosystem science and radiative transfer simulations of remote sensing imagery or surface energy balance.

Tiangang Yin↗

NASA NTRS1971

The Role of Remotely Sensed and Relayed Data in the Delaware River Basin

The planned integration of the existing water quality monitoring and data processing systems in the Delaware River Basin with a data relay experiment proposed for the ERTS-1 is discussed. The experiment is designed to use ERTS-1 as a data relay link for a maximum of 20 hydrologic stations in the basin, including stream gaging, reservoir level, ground water level, and water quality monitoring stations. This experiment has the potential for reducing the time lag between data collection and dissemination to less than 12 hours. The experiment will also provide impetus to develop an operational system of real time data processing and dissemination to handle the large quantity of data that will be obtained from the stations in the basin. The results of this experiment will demonstrate the relative merits of satellite relay of data versus conventional means of data telemetry and will provide a basis for the development of operational satellite relay of hydrologic data.

Paulson, R. W.↗

NASA NTRS1982

Mineral identification from orbit - Initial results from the Shuttle multispectral infrared radiometer

The Shuttle multispectral IR radiometer (SMIRR) was designed to obtain surface reflectance data in ten spectral bands in order to evaluate the usefulness of a future imaging system for remote mineral identification. Attention was given to the 2.0-2.4 micron region, which has a wealth of spectral absorption features and appeared to have potential for the identification of CO3- and OH-bearing minerals such as the kaolinite and montmorillonite clays. SMIRR radiances were normalized by using a spectrum for dune sand collected in the Kharga Depression in Egypt. Direct identifications have been made of kaolinite-containing and carbonate material, indicating an exceptional potential for future orbital platform narrowband spectral imaging systems for mineralogical mapping.

Goetz, A. F. H.↗

NASA NTRS1991

Microphysical and radiative characteristics of convective clouds during COHMEX

This paper has two purposes: first, to document the structure and evolution of two strong thunderstorms in Alabama using radar multiparameter data; and second, to relate the inferred microphysics to the resulting upwelling microwave radiance observed concurrently by high altitude aircraft. These measurements were collected during the COHMEX field program in the summer of 1986. The radar analysis includes a description of the parameters reflectivity Z, differential reflectivity Z(DR), linear depolarization ratio LDR, and hail signal HS for two thunderstorm cases on July 11, 1986. The simultaneous aircraft data include passive microwave brightness temperature, T(B), measurements at four frequencies ranging from 18 to 183 GHz, as well as visible and infrared data. The remote radar observations reveal the existence of large ice particles within the storms, which is likely to have caused the observed low microwave brightness temperatures. By relating the evolution of the radar measureables to the microwave T(B)s it has been found that knowledge of the storm microphysics and its evolution is important to adequately understand the microwave T(B)s.

Fulton, Richard↗

NASA NTRS1995

Evaluating the Type and State of Alaska Taiga Forests with Imaging Radar for Use in Ecosystem Models

Changes in the seasonal CO2 flux of the boreal forests may result from increased atmospheric CO2 concentrations and associated global warming patterns. To monitor this potential change, a combination of information derived from remote sensing data, including forest type and growing season length, and ecophysiological models which predict the CO2 flux and its seasonal amplitude based on meteorological data, are required. In this paper we address the use of synthetic aperture radar (SAR) to map fores type and monitor canopy and soil freeze/thaw, which define the growing season for conifers, and leaf on/off, which defines the growing season for deciduous species. Aircraft SAR (AIRSAR) data collected in March 1988 during a freeze/thaw event are used to generate species maps and to determine the sensitivity of SAR to canopy freeze/thaw transitions. These data are also used to validate a microwave scattering model which is then used to determine the sensitivity of SAR to leaf on/off transitions and soil freeze/thaw. Finally, a CO2 flux algorithm is presented which utilizes SAR data and an ecophysiological model to estimate CO2 flux. CO2 flux maps are generated, from which areal estimates of CO2 flux are derived.

Alaska↗

NASA NTRS2022

Validating Salinity from SMAP and HYCOM Data with Saildrone Data during EUREC4A-OA/ATOMIC

The 2020 ‘Elucidating the role of clouds-circulation coupling in climate-Ocean-Atmosphere’ (EUREC4A-OA) and the ‘Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign’ (ATOMIC) campaigns focused on improving our understanding of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign utilized many data collection technologies, some of which are relatively new. In this study, we used saildrone uncrewed surface vehicles, one of the newer cutting edge technologies available for marine data collection, to validate Level 2 and Level 3 Soil Moisture Active Passive (SMAP) satellite and Hybrid Coordinate Ocean Model (HYCOM) sea surface salinity (SSS) products in the Western Tropical Atlantic. The saildrones observed fine-scale salinity variability not present in the lower-spatial resolution satellite and model products. In regions that lacked significant small-scale salinity variability, the satellite and model salinities performed well. However, SMAP Remote Sensing Systems (RSS) 70 km generally outperformed its counterparts outside of areas with submesoscale SSS variation, whereas RSS 40 km performed better within freshening events such as a fresh tongue. HYCOM failed to detect the fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product and its drawbacks for their applications in this region and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models.

Kashawn Hall↗

NASA NTRS1989

Remotely sensed soil moisture input to a hydrologic model

The possibility of using detailed spatial soil moisture maps as input to a runoff model was investigated. The water balance of a small drainage basin was simulated using a simple storage model. Aircraft microwave measurements of soil moisture were used to construct two-dimensional maps of the spatial distribution of the soil moisture. Data from overflights on different dates provided the temporal changes resulting from soil drainage and evapotranspiration. The study site and data collection are described, and the soil measurement data are given. The model selection is discussed, and the simulation results are summarized. It is concluded that a time series of soil moisture is a valuable new type of data for verifying model performance and for updating and correcting simulated streamflow.

Engman, E. T.↗

DOE OSTI2025

BLOC Site - Radar Wind Profiler / Derived Data Reformatted

A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities are remotely sensed along all beam directions and combined to derive the horizontal wind field over the radar. These data are typically sampled and averaged hourly or sub-hourly (15-min) and usually have 60-m and/or 100-m vertical resolutions up to 4 km for the 915 MHz system. Both a high-resolution, lower height coverage mode and a low-resolution, higher height coverage mode are used to collect the data.

17 WIND ENERGY↗

DOE OSTI2025

NANT Site - Radar Wind Profiler / Derived Data Reformatted

17 WIND ENERGY↗

NASA NTRS2020

Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature using Aquarius Observations over Non-Frozen Land across North America

L-band brightness temperature (T(sub b)) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in T(sub b) observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, T(sub b) estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily T(sub b) observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed T(sub b) were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed T(sub b) with a relatively low, domain-average bias (0.79 K (ascending) and -2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled T(sub b) due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.

L-band radiometry↗

NASA NTRS2003

Syringe filtration methods for examining dissolved and colloidal trace element distributions in remote field locations

It is well-established that sampling and sample processing can easily introduce contamination into dissolved trace element samples if precautions are not taken. However, work in remote locations sometimes precludes bringing bulky clean lab equipment into the field and likewise may make timely transport of samples to the lab for processing impossible. Straightforward syringe filtration methods are described here for collecting small quantities (15 mL) of 0.45- and 0.02-microm filtered river water in an uncontaminated manner. These filtration methods take advantage of recent advances in analytical capabilities that require only small amounts of waterfor analysis of a suite of dissolved trace elements. Filter clogging and solute rejection artifacts appear to be minimal, although some adsorption of metals and organics does affect the first approximately 10 mL of water passing through the filters. Overall the methods are clean, easy to use, and provide reproducible representations of the dissolved and colloidal fractions of trace elements in river waters. Furthermore, sample processing materials can be prepared well in advance in a clean lab and transported cleanly and compactly to the field. Application of these methods is illustrated with data from remote locations in the Rocky Mountains and along the Yukon River. Evidence from field flow fractionation suggests that the 0.02-microm filters may provide a practical cutoff to distinguish metals associated with small inorganic and organic complexes from those associated with silicate and oxide colloids.

Colloids/analysis↗

NASA NTRS2023

Evaluating MODIS Snow Products Using an Extensive Wildlife Camera Network

Snow covers a maximum of 47 million km 2 of Earth's northern hemisphere each winter and is an important component of the planet's energy balance, hydrology cycles, and ecosystems. Monitoring regional and global snow cover has increased in urgency in recent years due to warming temperatures and declines in snow cover extent. Optical satellite instruments provide large-scale observations of snow cover, but cloud cover and dense forest canopy can reduce accuracy in mapping snow cover. Remote camera networks deployed for wildlife monitoring operate below cloud cover and in forests, representing a virtually untapped source of snow cover observations to supplement satellite observations. Using images from 1,181 wildlife cameras deployed by the Norwegian Institute for Nature Research (NINA), we compared snow cover extracted from camera images to Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products during winter months of 2018–2020. Ordinal snow classifications (scale = 0–4) from cameras were closely related to normalized difference snow index (NDSI) values from the MODIS Terra Snow Cover Daily L3 Global 500m (MOD10A1) Collection 6 product (R 2 = 0.70). Tree canopy cover, the normalized difference vegetation index (NDVI), and image color mode influenced agreement between camera images and MOD10A1 NDSI values. For MOD10A1F, MOD10A1's corresponding cloud-gap filled product, agreement with cloud-gap filled values decreased from 78.5% to 56.4% in the first three days of cloudy periods and stabilized thereafter. Using our camera data as validation, we derived a threshold to create daily binary maps of snow cover from the MOD10A1 product. The threshold corresponding to snow presence was an NDSI value of 40.50, which closely matched a previously defined global binary threshold of 40 using the MOD10A2 8-day product. These analyses demonstrate the utility of camera trap networks for validation of snow cover products from satellite remote sensing, as well as their potential to identify sources of inaccuracy.

Validation↗

NASA NTRS1981

Application of digital analysis of MSS data to agro-environmental studies

Progress in the application of digital analysis of multispectral scanner data to agro-environmental studies is described. Simulation of LANDSAT D thematic mapper (TM) observations from aircraft multispectral scanner data and field spectrometer data collected over a corn-soybean agricultural region in Webster County, Iowa during the 1979 growing season in support of the NASA/AgRISTARS program is described. The simulations were analyzed to evaluate the potential utility of the TM (1.55-1.75 micron) mid-infrared observations in corn-soybean discrimination. Current LANDSAT data was analyzed to study snow cover in northern New England and wetlands in Nebraska and Vermont. The application of satellite remote sensor data in additional environmental research areas is described.

Lewis, R. A.↗

NASA NTRS1986

Remote sensing of coastal wetlands

Various aircraft and satellite sensors for detecting and mapping wetlands properties are examined. The uses of color IR photography to map coastal vegetation, and of Landsat MSS and TM and SPOT data to quantify biomass and productivity for large wetland areas are discussed. For spectral estimation of biomass and productivity, the relation between radiance and biomass needs to be studied; the quantity and orientation of dead biomass and the amount of soil reflectance in comparison with vegetation reflectance in a given target area affect the spectral estimation of biomass. The radiometric evaluation of brackish wetland, and remote sensing in mangroves are described. The collection of images in narrow, contiguous spectral band using imaging spectrometry is considered.

Hardisky, M. A.↗