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NASA NTRS1977

The Shuttle era - A challenge to the earth scientist

Satellite observations of large-scale earth features and phenomena, with either instruments or astronauts, are discussed on the basis of earlier experience (mainly Skylab). Off-nadir views and photographs by astronauts have provided valuable supplements to instrument nadir views, providing cross-checks through remote sensing at different angles, different altitudes, and in different seasons. New information on plate tectonics, global cooling/drying trends, global oceanographic data (changing positions of major ocean current patterns, evolution of warm and cold eddies and their relation to sea temperatures and concentrations of marine fauna, location of internal sea waves, interactions between ocean currents and atmosphere, plankton blooms), storm development, snow cover patterns, lake and sea ice growth, sand-dune patterns, desert storms blown out to sea, effects of grazing and swidden agriculture, and other earth features and phenomena are surveyed.

Muehlberger, W. R.↗

NASA NTRS1999

Predicting Decade-to-Century Climate Change: Prospects for Improving Models

Recent research has led to a greatly increased understanding of the uncertainties in today's climate models. In attempting to predict the climate of the 21st century, we must confront not only computer limitations on the affordable resolution of global models, but also a lack of physical realism in attempting to model key processes. Until we are able to incorporate adequate treatments of critical elements of the entire biogeophysical climate system, our models will remain subject to these uncertainties, and our scenarios of future climate change, both anthropogenic and natural, will not fully meet the requirements of either policymakers or the public. The areas of most-needed model improvements are thought to include air-sea exchanges, land surface processes, ice and snow physics, hydrologic cycle elements, and especially the role of aerosols and cloud-radiation interactions. Of these areas, cloud-radiation interactions are known to be responsible for much of the inter-model differences in sensitivity to greenhouse gases. Recently, we have diagnostically evaluated several current and proposed model cloud-radiation treatments against extensive field observations. Satellite remote sensing provides an indispensable component of the observational resources. Cloud-radiation parameterizations display a strong sensitivity to vertical resolution, and we find that vertical resolutions typically used in global models are far from convergence. We also find that newly developed advanced parameterization schemes with explicit cloud water budgets and interactive cloud radiative properties are potentially capable of matching observational data closely. However, it is difficult to evaluate the realism of model-produced fields of cloud extinction, cloud emittance, cloud liquid water content and effective cloud droplet radius until high-quality measurements of these quantities become more widely available. Thus, further progress will require a combination of theoretical and modeling research, together with intensified emphasis on both in situ and space-based remote sensing observations.

Somerville, Richard C. J.↗

NASA NTRS1975

Application of satellite remote-sensing data to land selection and management

A pilot project conducted to demonstrate the utility and economy of satellite data in preparing thematic maps of a wilderness area emphasizing those resources of greatest interest to the potential owner is described. Vegetation maps delineating potential commercial timber and maps of suggested mineral prospecting areas of seven scattered regions were prepared by interpretation of LANDSAT images, coupled with a limited amount of ground truth. Images acquired both in winter and summer seasons were registered to township maps and used in making interpretations of the areal extent of commercial timber potentials. The amount of snow cover visible through the forest canopies was found to be a useful indicator of timber potentials. Identification was made of characteristic topographic features which are typical of flood plain deposits or of the well developed trellis drainage patterns which can indicate the strike of structural grain of underlying Cretaceous sedimentary rocks. The presence of igneous and mixed igneous and metamorphic rocks were indicated by combinations of spectral differences and anomalous interruptions of local radial drainage patterns.

Stringer, W. J.↗

NASA NTRS2018

NASA Observations and Modeling During ICE-POP

Recap: NASA-Specific Objectives for ICE-POP: Provide real-time observational and NWP data in support of ICE-POP, participate in significant international science effort; GPM (Global Precipitation Measurement) Ground Validation and NASA Weather Program -Direct/physical validation of active/passive satellite-based snowfall retrieval algorithms over coastline and mountains; melting layer interaction with terrain -Physics of snow, coupling to snow water equivalent rate and satellite remote sensor retrieval algorithm assumptions - -Size distributions, types/habit, water equivalent, profiles -NU-WRF (NASA-Unified Weather Research and Forecasting) Model plus Observational analyses: Movement toward “level IV products” leverage intensive and multi-faceted NWP (Numerical Weather Prediction) component -Model precipitation processes (liquid, mixed phase and frozen); Build model testing database for further active/passive remote sensing algorithm development (e.g., satellite data simulators) -"Integrated" validation of products in operational context.

Precipitation Science↗

NASA NTRS1981

Satellite hydrology; Proceedings of the Fifth Annual William T. Pecora Memorial Symposium on Remote Sensing, Sioux Falls, SD, June 10-15, 1979

Philosophical and technical backgrounds for the application of remote sensing by earth scientists are presented. Interests and activities of participating agencies of the United States and Canadian governments, universities, and the private sector in implementing satellite technology in a diverse array of water-related programs are described. Consideration is then given to applications of satellite data to the various aspects of the hydrologic cycle and man's impact on it: meteorology, snow and ice, surface water, soil moisture, ground water, wetlands, coastal zone, water quality and environment, and water use and management.

Deutsch, M.↗

NASA NTRS2020

Ensemble-Based Deep Learning for Estimating PM2.5 over California with Multisource Big Data Including Wildfire Smoke

Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g. wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.

air quality↗

NASA NTRS1979

Adaptive remote sensing technology for feature recognition and tracking

A technology development plan designed to reduce the data load and data-management problems associated with global study and monitoring missions is described with a heavy emphasis placed on developing mission capabilities to eliminate the collection of unnecessary data. Improved data selectivity can be achieved through sensor automation correlated with the real-time needs of data users. The first phase of the plan includes the Feature Identification and Location Experiment (FILE) which is scheduled for the 1980 Shuttle flight. The FILE experiment is described with attention given to technology needs, development plan, feature recognition and classification, and cloud-snow detection/discrimination. Pointing, tracking and navigation received particular consideration, and it is concluded that this technology plan is viewed as an alternative to approaches to real-time acquisition that are based on extensive onboard format and inventory processing and reliance upon global-satellite-system navigation data.

Wilson, R. G.↗

NASA NTRS2012

Characterization Approaches to Place Invariant Sites on SI-Traceable Scales

The effort to understand the Earth's climate system requires a complete integration of remote sensing imager data across time and multiple countries. Such an integration necessarily requires ensuring inter-consistency between multiple sensors to create the data sets needed to understand the climate system. Past efforts at inter-consistency have forced agreement between two sensors using sources that are viewed by both sensors at nearly the same time, and thus tend to be near polar regions over snow and ice. The current work describes a method that would provide an absolute radiometric calibration of a sensor rather than an inter-consistency of a sensor relative to another. The approach also relies on defensible error budgets that eventually provides a cross comparison of sensors without systematic errors. The basis of the technique is a model-based, SI-traceable prediction of at-sensor radiance over selected sites. The predicted radiance would be valid for arbitrary view and illumination angles and for any date of interest that is dominated by clear-sky conditions. The effort effectively works to characterize the sites as sources with known top-of-atmosphere radiance allowing accurate intercomparison of sensor data that without the need for coincident views. Data from the Advanced Spaceborne Thermal Emission and Reflection and Radiometer (ASTER), Enhanced Thematic Mapper Plus (ETM+), and Moderate Resolution Imaging Spectroradiometer (MODIS) are used to demonstrate the difficulties of cross calibration as applied to current sensors. Special attention is given to the differences caused in the cross-comparison of sensors in radiance space as opposed to reflectance space. The radiance comparisons lead to significant differences created by the specific solar model used for each sensor. The paper also proposes methods to mitigate the largest error sources in future systems. The results from these historical intercomparisons provide the basis for a set of recommendations to ensure future SI-traceable cross calibration using future missions such as CLARREO and TRUTHS. The paper describes a proposed approach that relies on model-based, SI-traceable predictions of at-sensor radiance over selected sites. The predicted radiance would be valid for arbitrary view and illumination angles and for any date of interest that is dominated by clear-sky conditions. The basis of the method is highly accurate measurements of at-sensor radiance of sufficient quality to understand the spectral and BRDF characteristics of the site and sufficient historical data to develop an understanding of temporal effects from changing surface and atmospheric conditions.

Thome, Kurtis↗

NASA NTRS2017

The Infrared Sensor Suite for SnowEx 2017

SnowEx is a winter airborne and field campaign designed to measure snow-water equivalent in forested landscapes. A major focus of Year 1 (2016-17) of NASA's SnowEx campaign will be an extensive field program involving dozens of participants from U.S. government agencies and from many universities and institutions, both domestic and foreign. Along with other instruments, two infrared (IR) sensors will be flown on a Naval Research Laboratory P-3 aircraft. Surface temperature is a critical input to hydrologic models and will be measured during the SnowEx mission. A Quantum Well Infrared Photodetector (QWIP) IR imaging camera system will be flown along with a KT-15 remote thermometer to aid in the calibration of the IR image data. Together, these instruments will measure surface temperature of snow and ice targets to an expected accuracy of less than 1C.

SnowEx↗

NASA NTRSDate not supplied

Improving the Representation of Land Surface Processes using the Data Assimilation Research Testbed (DART)

The land surface is a critical part of the earth system as processes related to water, carbon, energy and nitrogen cycling have important implications for climate forcing, air quality, water availability and seasonal atmospheric forecasting. Despite advances in land surface modeling, land surface model performance is often limited because of errors related to initial and boundary conditions, model structure, and parameters. Data assimilation (DA) techniques combined with an expanding network of earth system observations present an opportunity to reduce these errors and improve simulations. Here we apply an Ensemble Kalman Filter DA system as part of the Data Assimilation Research Testbed to a variety of land surface simulations. First, we describe the use of remotely sensed biomass observations to provide improved simulations of plant phenology, carbon and water cycling for regions highly sensitive to climate change (Western US, China, and Arctic). We discuss approaches to account for systemic biases between models and observations, including the use of spatially-varying adaptive ensemble inflation as an alternative approach to re-scaling soil moisture observations. Finally, we discuss a strategy to incorporate complementary observations (snow water equivalent, solar-induced fluorescence) to better constrain the representation of carbon and water cycling across complex terrain.

DART↗

NASA NTRS2021

Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval

In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that reduces early snow accumulation, leading to shallower snowpacks at the start of the freeze-up season. These differences persist through spring despite overall more winter snow accumulation in the reanalysis-based products than in the climatologies. Among the products evaluated, the University of Washington (UW) snow depth product produces the deepest spring (March–April) snowpacks, while the snow product from the Danish Meteorological Institute (DMI) provides the shallowest spring snow depths. Most snow products show significant correlation with snow depths retrieved from Operational IceBridge (OIB) while correlations are quite low against buoy measurements, with no correlation and very low variability from University of Bremen and DMI products. Inconsistencies in reconstructed snow depth among the products, as well as differences between these products and in situ and airborne observations, can be partially attributed to differences in effective footprint and spatial–temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in situ and remote sensing observations.

Lu Zhou↗

NASA NTRS1993

Optical properties of CO2 ice and CO2 snow in the ultraviolet, visible, and infrared

The project was to measure the optical constants of CO2 ice in the spectral regions of weak absorption. Many previous measurements had been made by others in the strong absorption bands, but the weak regions had been poorly measured or not measured at all. In these regions the emissivity of CO2 frost and CO2 clouds is quite sensitive to particle sizes and to the value of the absorption coefficient, so the new measurements will have applications to energy budget and remote sensing of the Martian surface and atmosphere. During the time period covered by this grant, a method was developed for growing clear crystals of CO2, 4 cm in length. An apparatus was constructed for accurate measurement of spectral transmission through these crystals, for wavelengths 0.18-3.8 micrometers, covering the near-ultraviolet, visible, and near-infrared regions, bounded by the strong ultraviolet absorption and the 4.3-micrometer band. Our new best estimate of the spectral absorption coefficient (expressed as imaginary refractive index) is shown. It is a composite result of many measurements on dozens of crystals. Now that we have a good dataset for the shortwave absorption spectrum, the focus of the research has moved to the thermal infrared, for which a Fourier Transform interferometer has been adapted for use with C02-ice crystals.

Warren, Stephen↗

NASA NTRS1986

Predicting earth's dynamic changes

Given a suitable strategy for conducting measurements, satellite-based remote sensing of the earth can furnish valuable information on the dynamic changes of such planetary characteristics as ocean surface temperatures and atmospheric CO2. Observations must be global and synoptic, quantitatively validated, and consistent over the long term. A program spanning 20 years will study such critical variables as solar flux, stratospheric temperature, aerosols and ozone, cloud cover, tropospheric gases and aerosols, radiation balance, surface temperature, albedo, precipitation, vegetation cover, moisture, snow and ice, as well as oceanic color, topography, and wind stress.

Rasool, S. I.↗

NASA NTRS1978

Remote sensing of the atmosphere from environmental satellites

The paper outlines the basis of remote sensing in satellite meteorology, the evolution of remote sensors, examples of practical applications, and a prospectus of future developments. The meteorological satellites use instrumentation sensitive to different regions of the electromagnetic spectrum to observe and measure atmospheric and surface properties. In the ultraviolet, solar variability is measured and total atmospheric ozone determined. In the visible, imagery of cloud systems provides better location of storms in data-sparse areas, observes ice and snow boundaries, and monitors floods. Capabilities of microwave imagery are discussed along with successful uses of geosynchronous satellites. Future environmental satellite programs are discussed in some detail, including GARP, TIROS-N and CLIMSAT.

Allison, L. J.↗

NASA NTRS1991

Global lightning studies

The global lightning signatures were analyzed from the DMSP Optical Linescan System (OLS) imagery archived at the National Snow and Ice Data Center. Transition to analysis of the digital archive becomes available and compare annual, interannual, and seasonal variations with other global data sets. An initial survey of the quality of the existing film archive was completed and lightning signatures were digitized for the summer months of 1986 to 1987. The relationship is studied between: (1) global and regional lightning activity and rainfall, and (2) storm electrical development and environment. Remote sensing data sets obtained from field programs are used in conjunction with satellite/radar/lightning data to develop and improve precipitation estimation algorithms, and to provide a better understanding of the co-evolving electrical, microphysical, and dynamical structure of storms.

Goodman, Steven J.↗

NASA NTRS2004

Science Data Processing for the Advanced Microwave Scanning Radiometer: Earth Observing System

The National Aeronautics and Space Administration established the framework for the Science Investigator-led Processing Systems (SIPS) to enable the Earth science data products to be produced by personnel directly associated with the instrument science team and knowledgeable of the science algorithms. One of the first instantiations implemented for NASA was the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) SIPS. The AMSR-E SIPS is a decentralized, geographically distributed ground data processing system composed of two primary components located in California and Alabama. Initial science data processing is conducted at Remote Sensing Systems (RSS) in Santa Rosa, California. RSS ingests antenna temperature orbit data sets from JAXA and converts them to calibrated, resampled, geolocated brightness temperatures. The brightness temperatures are sent to the Global Hydrology and Climate Center in Huntsville, Alabama, which generates the geophysical science data products (e.g., water vapor, sea surface temperature, sea ice extent, etc.) suitable for climate research and applications usage. These science products are subsequently sent to the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado for archival and dissemination to the at-large science community. This paper describes the organization, coordination, and production techniques employed by the AMSR-E SIPS in implementing, automating and operating the distributed data processing system.

Goodman, H. Michael↗

NASA NTRS2016

National Climate Assessment - Land Data Assimilation System (NCA-LDAS) Data and Services at NASA GES DISC

The National Climate Assessment-Land Data Assimilation System (NCA-LDAS) is an Integrated Terrestrial Water Analysis, and is one of NASAs contributions to the NCA of the United States. The NCA-LDAS has undergone extensive development, including multi-variate assimilation of remotely-sensed water states and anomalies as well as evaluation and verification studies, led by the Goddard Space Flight Centers Hydrological Sciences Laboratory (HSL). The resulting NCA-LDAS data have recently been released to the general public and include those from the Noah land-surface model (LSM) version 3.3 (Noah-3.3) and the Catchment LSM version Fortuna-2.5 (CLSM-F2.5). Standard LSM output variables including soil moistures temperatures, surface fluxes, snow cover depth, groundwater, and runoff are provided, as well as streamflow using a river routing system. The NCA-LDAS data are archived at and distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The data can be accessed via HTTP, OPeNDAP, Mirador search and download, and NASA Earth data Search. To further facilitate access and use, the NCA-LDAS data are integrated into the NASA Giovanni, for quick visualization and analysis, and into the Data Rods system, for retrieval of time series of long time periods. The temporal and spatial resolutions of the NCA-LDAS data are, respectively, daily-averages and 0.125x0.125 degree, covering North America (25N 53N; 125W 67W) and the period January 1979 to December 2015. The data files are in self-describing, machine-independent, CF-compliant netCDF-4 format.

data assimiliation↗

NASA NTRS1982

Combining remotely sensed and other measurements for hydrologic areal averages

A method is described for combining measurements of hydrologic variables of various sampling geometries and measurement accuracies to produce an estimated mean areal value over a watershed and a measure of the accuracy of the mean areal value. The method provides a means to integrate measurements from conventional hydrological networks and remote sensing. The resulting areal averages can be used to enhance a wide variety of hydrological applications including basin modeling. The correlation area method assigns weights to each available measurement (point, line, or areal) based on the area of the basin most accurately represented by the measurement. The statistical characteristics of the accuracy of the various measurement technologies and of the random fields of the hydrologic variables used in the study (water equivalent of the snow cover and soil moisture) required to implement the method are discussed.

Johnson, E. R.↗