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NASA NTRS2012

Measuring the Surface Temperature of the Cryosphere using Remote Sensing

A general description of the remote sensing of cryosphere surface temperatures from satellites will be provided. This will give historical information on surface-temperature measurements from space. There will also be a detailed description of measuring the surface temperature of the Greenland Ice Sheet using Moderate-Resolution Imaging Spectroradiometer (MODIS) data which will be the focus of the presentation. Enhanced melting of the Greenland Ice Sheet has been documented in recent literature along with surface-temperature increases measured using infrared satellite data since 1981. Using a recently-developed climate data record, trends in the clear-sky ice-surface temperature (IST) of the Greenland Ice Sheet have been studied using the MODIS IST product. Daily and monthly MODIS ISTs of the Greenland Ice Sheet beginning on 1 March 2000 and continuing through 31 December 2010 are now freely available to download at 6.25-km spatial resolution on a polar stereographic grid. Maps showing the maximum extent of melt for the entire ice sheet and for the six major drainage basins have been developed from the MODIS IST dataset. Twelve-year trends of the duration of the melt season on the ice sheet vary in different drainage basins with some basins melting progressively earlier over the course of the study period. Some (but not all) of the basins also show a progressively-longer duration of melt. The consistency of this IST record, with temperature and melt records from other sources will be discussed.

Hall, Dorothy K.↗

NASA NTRS2012

Terra MODIS Band 27 Electronic Crosstalk Effect and Its Removal

The MODerate-resolution Imaging Spectroradiometer (MODIS) is one of the primary instruments in the NASA Earth Observing System (EOS). The first MODIS instrument was launched in December, 1999 on-board the Terra spacecraft. MODIS has 36 bands, covering a wavelength range from 0.4 micron to 14.4 micron. MODIS band 27 (6.72 micron) is a water vapor band, which is designed to be insensitive to Earth surface features. In recent Earth View (EV) images of Terra band 27, surface feature contamination is clearly seen and striping has become very pronounced. In this paper, it is shown that band 27 is impacted by electronic crosstalk from bands 28-30. An algorithm using a linear approximation is developed to correct the crosstalk effect. The crosstalk coefficients are derived from Terra MODIS lunar observations. They show that the crosstalk is strongly detector dependent and the crosstalk pattern has changed dramatically since launch. The crosstalk contributions are positive to the instrument response of band 27 early in the mission but became negative and much larger in magnitude at later stages of the mission for most detectors of the band. The algorithm is applied to both Black Body (BB) calibration and MODIS L1B products. With the crosstalk effect removed, the calibration coefficients of Terra MODIS band 27 derived from the BB show that the detector differences become smaller. With the algorithm applied to MODIS L1B products, the Earth surface features are significantly removed and the striping is substantially reduced in the images of the band. The approach developed in this report for removal of the electronic crosstalk effect can be applied to other MODIS bands if similar crosstalk behaviors occur.

Sun, Junqiang↗

NASA NTRS2012

Short-Term Prediction Research and Transition (SPoRT) Center: Transitioning Satellite Data to Operations

The Short-term Prediction Research and Transition (SPoRT) Center located at NASA Marshall Space Flight Center has been conducting testbed activities aimed at transitioning satellite products to National Weather Service operational end users for the last 10 years. SPoRT is a NASA/NOAA funded project that has set the bar for transition of products to operational end users through a paradigm of understanding forecast challenges and forecaster needs, displaying products in end users decision support systems, actively assessing the operational impact of these products, and improving products based on forecaster feedback. Aiming for quality partnerships rather than a large quantity of data users, SPoRT has become a community leader in training operational forecasters on the use of up-and-coming satellite data through the use of legacy instruments and proxy data. Traditionally, SPoRT has supplied satellite imagery and products from NASA instruments such as the Moderate-resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS). However, recently, SPoRT has been funded by the GOES-R and Joint Polar Satellite System (JPSS) Proving Grounds to accelerate the transition of selected imagery and products to help improve forecaster awareness of upcoming operational data from the Visible Infrared Imager Radiometer Suite (VIIRS), Cross-track Infrared Sounder (CrIS), Advanced Baseline Imager (ABI), and Geostationary Lightning Mapper (GLM). This presentation provides background on the SPoRT Center, the SPoRT paradigm, and some example products that SPoRT is excited to work with forecasters to evaluate.

Zavodsky, Bradley↗

NASA NTRS2012

Using NASA Environmental Data to Enhance Public Health Decision Making

The Universities Space Research Association at the NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) to address issues of environmental health and enhance public health decision making by utilizing NASA remotely sensed data and products. The objectives of this collaboration are to develop high-quality spatial data sets of environmental variables, and deliver the data sets and associated analyses to local, state and federal end-user groups. These data can be linked spatially and temporally to public health data, such as mortality and disease morbidity, for further analysis and decision making. Three daily environmental data sets have been developed for the conterminous U.S. on different spatial resolutions for the time period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid utilizing the US Environmental Protection Agency (EPA) ground observations and NASA s MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Solar Insolation (SI) and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets will be linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline and other health outcomes. These environmental datasets and public health linkage analyses will be made available to public health professionals, researchers and the general public through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system and through peer reviewed publications. To date, two of the data sets have been released to the public in CDC WONDER, Daily Air Temperature and Heat Index for years 1979-2010, and Daily Fine Particulate Matter (PM2.5) air quality measures for years 2003-2008. These data in CDC WONDER can be aggregated to the county-level, state-level, or regional-level as per users need and downloaded in tabular, graphical, and map formats. The summary statistical output are available to web and app developers via the WONDER Application Programming Interface (API). The linkage of these data with the CDC WONDER system provides a significant addition to CDC WONDER, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER online system. It also substantially expands public access to NASA environmental data, making their use by a wide range of decision makers feasible.

Al-Hamdan, Mohammad↗

NASA NTRS2012

Middle East Health and Air Quality Utilizing NASA EOS in the Saharan and Arabian Deserts to Examine Dust Particle Size and Mineralogy of Aerosols

Ground-based studies conducted in Iraq have revealed the presence of potential human pathogens in airborne dust. According to the Environmental Protection Agency (EPA), airborne particulate matter below 2.5micron (PM2.5) can cause long-term damage to the human respiratory system. NASA fs Earth Observing System (EOS) can be used to determine spectral characteristics of dust particles and dust particle sizes. Comparing dust particle size from the Sahara and Arabian Deserts gives insight into the composition and atmospheric transport characteristics of dust from each desert. With the use of NASA SeaWiFS DeepBlue Aerosol, dust particle sizes were estimated using Angstrom Exponent. Brightness Temperature Difference (BTD) equation was used to determine the area of the dust storm. The Moderate-resolution Imaging Spectroradiometer (MODIS) on Terra satellite was utilized in calculating BTD. Mineral composition of a dust storm that occurred 17 April 2008 near Baghdad was determined using imaging spectrometer data from the JPL Spectral Library and EO-1 Hyperion data. Mineralogy of this dust storm was subsequently compared to that of a dust storm that occurred over the Bodele Depression in the Sahara Desert on 7 June 2003.

Keeton, Tiffany↗

NASA NTRS2012

Evaluating the Contribution of NASA Remotely-Sensed Data Sets on a Convection-Allowing Forecast Model

The Short-term Prediction Research and Transition (SPoRT) Center is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service forecast offices. SPoRT provides real-time NASA products and capabilities to help its partners address specific operational forecast challenges. One challenge that forecasters face is using guidance from local and regional deterministic numerical models configured at convection-allowing resolution to help assess a variety of mesoscale/convective-scale phenomena such as sea-breezes, local wind circulations, and mesoscale convective weather potential on a given day. While guidance from convection-allowing models has proven valuable in many circumstances, the potential exists for model improvements by incorporating more representative land-water surface datasets, and by assimilating retrieved temperature and moisture profiles from hyper-spectral sounders. In order to help increase the accuracy of deterministic convection-allowing models, SPoRT produces real-time, 4-km CONUS forecasts using a configuration of the Weather Research and Forecasting (WRF) model (hereafter SPoRT-WRF) that includes unique NASA products and capabilities including 4-km resolution soil initialization data from the Land Information System (LIS), 2-km resolution SPoRT SST composites over oceans and large water bodies, high-resolution real-time Green Vegetation Fraction (GVF) composites derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument, and retrieved temperature and moisture profiles from the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). NCAR's Model Evaluation Tools (MET) verification package is used to generate statistics of model performance compared to in situ observations and rainfall analyses for three months during the summer of 2012 (June-August). Detailed analyses of specific severe weather outbreaks during the summer will be presented to assess the potential added-value of the SPoRT datasets and data assimilation methodology compared to a WRF configuration without the unique datasets and data assimilation.

Zavodsky, Bradley T.↗

NASA NTRS2012

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

The overall goal of this study is to address issues of environmental health and enhance public health decision making by using NASA remotely sensed data and products. This study is a collaboration between NASA Marshall Space Flight Center, Universities Space Research Association (USRA), the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) Office of Surveillance, Epidemiology and Laboratory Services. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the environmental data sets and associated public health analyses to local, state and federal end ]user groups. Three daily environmental data sets were developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) on a 10-km grid using US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of MODIS Land Surface Temperature (LST); and (3) a 12-km grid of daily incoming solar radiation and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) data. These environmental datasets were linked with public health data from the UAB REasons for Geographic and Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline, stroke and other health outcomes. These environmental national datasets will also be made available to public health professionals, researchers and the general public via the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system, where they can be aggregated to the county-level, state-level, or regional-level as per users f need and downloaded in tabular, graphical, and map formats. This provides a significant addition to the CDC WONDER online system, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER. It also substantially expands public access to NASA data, making their use by a wide range of decisionmakers feasible.

Al-Hamdan, Mohammad↗

NASA NTRS2013

Public Health Applications of Remotely-sensed Environmental Datasets for the Conterminous United States

NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) National Center for Public Health Informatics to address issues of environmental health and enhance public health decision-making using NASA remotely-sensed data and products. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the linked data sets and associated analyses to local, state and federal end-user groups. Three daily environmental data sets were developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid using the US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Incoming Solar Radiation (Insolation) and heat-related products using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets were linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline, stroke and other health outcomes. These environmental datasets and the results of the public health linkage analyses will be disseminated to end-users for decision-making through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system and through peer-reviewed publications respectively. The linkage of these data with the CDC WONDER system substantially expands public access to NASA data, making their use by a wide range of decision makers feasible. By successful completion of this research, decision-making activities, including policy-making and clinical decision-making, can be positively affected through utilization of the data products and analyses provided on the CDC WONDER system.

Al-Hamdan, Mohammad↗

NASA NTRS2013

Downscaling MODIS Land Surface Temperature for Urban Public Health Applications

This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for downscaling the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.

Al-Hamdan, Mohammad↗

NASA NTRS2013

Downscaling MODIS Land Surface Temperature for Urban Public Health Applications

Al-Hamdan, Mohammad↗

NASA NTRS2014

Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA's Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Previously, it has been shown that cloud top designation associated with quality control procedures within the Gridpoint Statistical Interpolation (GSI) system used operationally by a number of Joint Center for Satellite Data Assimilation (JCSDA) partners may not provide the best representation of cloud top pressure (CTP). Because this designated CTP determines which channels are cloud-free and, thus, available for assimilation, ensuring the most accurate representation of this value is imperative to obtaining the greatest impact from satellite radiances. This paper examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing analysis increments and numerical forecasts generated using operational techniques with a research technique that swaps CTP from the Moderate-resolution Imaging Spectroradiometer (MODIS) for the value of CTP calculated from the radiances within GSI.

Zavodsky, Bradley↗

NASA NTRS2014

The Seasonal Cycle of Satellite Chlorophyll Fluorescence Observations and its Relationship to Vegetation Phenology and Ecosystem Atmosphere Carbon Exchange

Mapping of terrestrial chlorophyll uorescence from space has shown potentialfor providing global measurements related to gross primary productivity(GPP). In particular, space-based fluorescence may provide information onthe length of the carbon uptake period that can be of use for global carboncycle modeling. Here, we examine the seasonal cycle of photosynthesis asestimated from satellite fluorescence retrievals at wavelengths surroundingthe 740nm emission feature. These retrievals are from the Global OzoneMonitoring Experiment 2 (GOME-2) flying on the MetOp A satellite. Wecompare the fluorescence seasonal cycle with that of GPP as estimated froma diverse set of North American tower gas exchange measurements. Because the GOME-2 has a large ground footprint (40 x 80km2) as compared with that of the flux towers and requires averaging to reduce random errors, we additionally compare with seasonal cycles of upscaled GPP in the satellite averaging area surrounding the tower locations estimated from the Max Planck Institute for Biogeochemistry (MPI-BGC) machine learning algorithm. We also examine the seasonality of absorbed photosynthetically-active radiation(APAR) derived with reflectances from the MODerate-resolution Imaging Spectroradiometer (MODIS). Finally, we examine seasonal cycles of GPP as produced from an ensemble of vegetation models. Several of the data-driven models rely on satellite reflectance-based vegetation parameters to derive estimates of APAR that are used to compute GPP. For forested sites(particularly deciduous broadleaf and mixed forests), the GOME-2 fluorescence captures the spring onset and autumn shutoff of photosynthesis as delineated by the tower-based GPP estimates. In contrast, the reflectance-based indicators and many of the models tend to overestimate the length of the photosynthetically-active period for these and other biomes as has been noted previously in the literature. Satellite fluorescence measurements therefore show potential for improving model GPP estimates.

Joiner, J.↗

NASA NTRS2014

Early On-Orbit Performance of the Visible Infrared Imaging Radiometer Suite Onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key environmental remote-sensing instruments onboard the Suomi National Polar-Orbiting Partnership spacecraft, which was successfully launched on October 28, 2011 from the Vandenberg Air Force Base, California. Following a series of spacecraft and sensor activation operations, the VIIRS nadir door was opened on November 21, 2011. The first VIIRS image acquired signifies a new generation of operational moderate resolution-imaging capabilities following the legacy of the advanced very high-resolution radiometer series on NOAA satellites and Terra and Aqua Moderate-Resolution Imaging Spectroradiometer for NASA's Earth Observing system. VIIRS provides significant enhancements to the operational environmental monitoring and numerical weather forecasting, with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns, providing the sensor data records for 23 environmental data records including aerosol, cloud properties, fire, albedo, snow and ice, vegetation, sea surface temperature, ocean color, and nigh-time visible-light-related applications. Preliminary results from the on-orbit verification in the postlaunch check-out and intensive calibration and validation have shown that VIIRS is performing well and producing high-quality images. This paper provides an overview of the onorbit performance of VIIRS, the calibration/validation (cal/val) activities and methodologies used. It presents an assessment of the sensor initial on-orbit calibration and performance based on the efforts from the VIIRS-SDR team. Known anomalies, issues, and future calibration efforts, including the long-term monitoring, and intercalibration are also discussed.

viirs↗

NASA NTRS2012

Filling-in of Near-infrared Solar Lines by Terrestrial Fluorescence and Other Geophysical Effects: Simulations and Space-based Observations from SCIAMACHY and GOSAT

Global mapping of terrestrial vegetation fluorescence from space has recently been accomplished with high spectral resolution (nu/nu greater than 35 000) measurements from the Japanese Greenhouse gases Observing SAellite (GOSAT). These data are of interest because they can potentially provide global information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling. Quantifying the impact of fluorescence on the O2-A band is important as this band is used for photon pathlength characterization in cloud- and aerosol-contaminated pixels for trace-gas retrievals including CO2. Here, we examine whether fluorescence information can be derived from space using potentially lower-cost hyperspectral instrumentation, i.e., more than an order of magnitude less spectral resolution (nu/nu approximately 1600) than GOSAT, with a relatively simple algorithm. We discuss laboratory measurements of fluorescence near one of the few wide and deep solar Fraunhofer lines in the long-wave tail of the fluorescence emission region, the calcium (Ca) II line at 866 nm that is observable with a spectral resolution of approximately 0.5 nm. The filling-in of the Ca II line due to additive signals from various atmospheric and terrestrial effects, including fluorescence, is simulated. We then examine filling-in of this line using the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) satellite instrument. In order to interpret the satellite measurements, we developed a general approach to correct for various instrumental artifacts that produce false filling-in of solar lines in satellite measurements. The approach is applied to SCIAMACHY at the 866 nm Ca II line and to GOSAT at 758 and 770 nm on the shoulders of the O2-A feature where there are several strong solar Fraunhofer lines that are filled in primarily by vegetation fluorescence. Finally, we compare temporal and spatial variations of SCIAMACHY additive signals with those of GOSAT and the Enhanced Vegetation Index (EVI) from the MODerate-resolution Imaging Spectroradiometer (MODIS). Although the derived additive signals from SCIAMACHY are extremely weak at 866 nm, their spatial and temporal variations are consistent with chlorophyll a fluorescence or another vegetation-related source. We also show that fillingin occurs at 866 nm over some barren areas, possibly originating from luminescent minerals in rock and soil.

vegetation↗

NASA NTRS2014

Global Top-Down Smoke-Aerosol Emissions Estimation Using Satellite Fire Radiative Power Measurements

Fire emissions estimates have long been based on bottom-up approaches that are not only complex, but also fraught with compounding uncertainties. We present the development of a global gridded (1 deg ×1 deg) emission coefficients (Ce) product for smoke total particulate matter (TPM) based on a top-down approach using coincident measurements of fire radiative power (FRP) and aerosol optical thickness (AOT) from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra and Aqua satellites. This new Fire Energetics and Emissions Research version 1.0 (FEER.v1) Ce product has now been released to the community and can be obtained from http://feer.gsfc. nasa.gov/, along with the corresponding 1-to-1 mapping of their quality assurance (QA) flags that will enable the Ce values to be filtered by quality for use in various applications. The regional averages of Ce values for different ecosystem types were found to be in the ranges of 16-21/gMJ−1 for savanna and grasslands, 15-32/gMJ−1 for tropical forest, 9-12/gMJ−1 for North American boreal forest, and 18- 26/MJ−1 for Russian boreal forest, croplands and natural vegetation. The FEER.v1 Ce product was multiplied by time-integrated FRP data to calculate regional smoke TPM emissions, which were compared with equivalent emissions products from three existing inventories. FEER.v1 showed higher and more reasonable smoke TPM estimates than two other emissions inventories that are based on bottom-up approaches and already reported in the literature to be too low, but portrayed an overall reasonable agreement with another top-down approach. This suggests that top-down approaches may hold better promise and need to be further developed to accelerate the reduction of uncertainty associated with fire emissions estimation in air-quality and climate research and applications. Results of the analysis of FEER.v1 data for 2004-2011 show that 65-85 Tg yr−1 of TPM is emitted globally from open biomass burning, with a generally decreasing trend over this short time period. The FEER.v1 Ce product is the first global gridded product in the family of "emission factors", that is based essentially on satellite measurements, and requires only direct satellite FRP measurements of an actively burning fire anywhere to evaluate its emission rate in near-real time, which is essential for operational activities, such as the monitoring and forecasting of smoke emission impacts on air quality.

biomass burning↗

NASA NTRS2014

Greenland Ice Sheet Melt from MODIS and Associated Atmospheric Variability

Daily June-July melt fraction variations over the Greenland Ice Sheet (GIS) derived from the MODerate-resolution Imaging Spectroradiometer (MODIS) (2000-2013) are associated with atmospheric blocking forming an omega-shape ridge over the GIS at 500hPa height (from NCEPNCAR). Blocking activity with a range of time scales, from synoptic waves breaking poleward ( 5 days) to full-fledged blocks (5 days), brings warm subtropical air masses over the GIS controlling daily surface temperatures and melt. The temperature anomaly of these subtropical air mass intrusions is also important for melting. Based on the largest MODIS melt years (2002 and 2012), the area-average temperature anomaly of 2 standard deviations above the 14-year June-July mean, results in a melt fraction of 40 or more. Summer 2007 had the most blocking days, however atmospheric temperature anomalies were too small to instigate extreme melting.

Ice Sheet Melt↗

NASA NTRS2011

Snow Cover, Snowmelt Timing and Stream Power in the Wind River Range, Wyoming

Earlier onset of springtime weather, including earlier snowmelt, has been documented in the western United States over at least the last 50 years. Because the majority (is greater than 70%) of the water supply in the western U.S. comes from snowmelt, analysis of the declining spring snowpack (and shrinking glaciers) has important implications for the management of streamflow. The amount of water in a snowpack influences stream discharge which can also influence erosion and sediment transport by changing stream power, or the rate at which a stream can do work, such as move sediment and erode the stream bed. The focus of this work is the Wind River Range (WRR) in west-central Wyoming. Ten years of Moderate-Resolution Imaging Spectroradiometer (MODIS) snow-cover, cloud-gap-filled (CGF) map products and 30 years of discharge and meteorological station data are studied. Streamflow data from streams in WRR drainage basins show lower annual discharge and earlier snowmelt in the decade of the 2000s than in the previous three decades, though no trend of either lower streamflow or earlier snowmelt was observed within the decade of the 2000s. Results show a statistically-significant trend at the 95% confidence level (or higher) of increasing weekly maximum air temperature (for three out of the five meteorological stations studied) in the decade of the 1970s, and also for the 40-year study period as a whole. The extent of snow-cover (percent of basin covered) derived from the lowest elevation zone (2500-3000 m) of the WRR, using MODIS CGF snow-cover maps, is strongly correlated with maximum monthly discharge on 30 April, where Spearman's Rank correlation, rs,=0.89 for the decade of the 2000s. We also investigated stream power for Bull Lake Creek above Bull Lake; and found a trend (significant at the 90% confidence level) toward reduced stream power from 1970 to 2009. Observed changes in streamflow and stream power may be related to increasing weekly maximum air temperature measured during the 40-year study period, possibly contributing to a reduction in snow cover. In addition, the strong relationship between percent of basin that was snow covered, and maximum monthly streamflow indicates that MODIS snow-cover maps are useful for predicting streamflow, and can be used to improve management of water resources in the drought-prone western United States.

MODIS↗

NASA NTRS2013

Evaluation of MODIS Albedo Product (MCD43A) over Grassland, Agriculture and Forest Surface Types During Dormant and Snow-Covered Periods

This study assesses the Moderate-resolution Imaging Spectroradiometer (MODIS) BRDF/albedo 8 day standard product and products from the daily Direct Broadcast BRDF/albedo algorithm, and shows that these products agree well with ground-based albedo measurements during the more difficult periods of vegetation dormancy and snow cover. Cropland, grassland, deciduous and coniferous forests are considered. Using an integrated validation strategy, analyses of the representativeness of the surface heterogeneity under both dormant and snow-covered situations are performed to decide whether direct comparisons between ground measurements and 500-m satellite observations can be made or whether finer spatial resolution airborne or spaceborne data are required to scale the results at each location. Landsat Enhanced Thematic Mapper Plus (ETM +) data are used to generate finer scale representations of albedo at each location to fully link ground data with satellite data. In general, results indicate the root mean square errors (RMSEs) are less than 0.030 over spatially representative sites of agriculture/grassland during the dormant periods and less than 0.050 during the snow-covered periods for MCD43A albedo products. For forest, the RMSEs are less than 0.020 during the dormant period and 0.025 during the snow-covered periods. However, a daily retrieval strategy is necessary to capture ephemeral snow events or rapidly changing situations such as the spring snow melt.

MODIS↗