Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines

NASA Astrophysics Data System (ADS)

Çiftçi, B. B.; Kuter, S.; Akyürek, Z.; Weber, G.-W.

2017-11-01

Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models with different model building settings are trained by using Moderate Resolution Imaging Spectroradiometer surface reflectance values of bands 1-7, normalized difference snow index and normalized difference vegetation index as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat ETM+ binary snow cover maps. Results on the independent test data set indicate that the developed ANN model with hyperbolic tangent transfer function in the output layer and the SVM model with radial basis function kernel produce high FSC mapping accuracies with the corresponding values of R = 0.93 and R = 0.92, respectively.

Enhancement of the MODIS Daily Snow Albedo Product

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Schaaf, Crystal B.; Wang, Zhuosen; Riggs, George A.

2009-01-01

The MODIS daily snow albedo product is a data layer in the MOD10A1 snow-cover product that includes snow-covered area and fractional snow cover as well as quality information and other metadata. It was developed to augment the MODIS BRDF/Albedo algorithm (MCD43) that provides 16-day maps of albedo globally at 500-m resolution. But many modelers require daily snow albedo, especially during the snowmelt season when the snow albedo is changing rapidly. Many models have an unrealistic snow albedo feedback in both estimated albedo and change in albedo over the seasonal cycle context, Rapid changes in snow cover extent or brightness challenge the MCD43 algorithm; over a 16-day period, MCD43 determines whether the majority of clear observations was snow-covered or snow-free then only calculates albedo for the majority condition. Thus changes in snow albedo and snow cover are not portrayed accurately during times of rapid change, therefore the current MCD43 product is not ideal for snow work. The MODIS daily snow albedo from the MOD10 product provides more frequent, though less robust maps for pixels defined as "snow" by the MODIS snow-cover algorithm. Though useful, the daily snow albedo product can be improved using a daily version of the MCD43 product as described in this paper. There are important limitations to the MOD10A1 daily snow albedo product, some of which can be mitigated. Utilizing the appropriate per-pixel Bidirectional Reflectance Distribution Functions (BRDFs) can be problematic, and correction for anisotropic scattering must be included. The BRDF describes how the reflectance varies with view and illumination geometry. Also, narrow-to-broadband conversion specific for snow on different surfaces must be calculated and this can be difficult. In consideration of these limitations of MOD10A1, we are planning to improve the daily snow albedo algorithm by coupling the periodic per-pixel snow albedo from MCD43, with daily surface ref|outanoom, In this paper, we compare a daily version of MCD43B3 with the daily albedo from MOD10A1. and MCD43B3 with a 16-day average of MOD10A1, over Greenland. We also discuss some near-future planned enhancements to MOD10A1.

Merging the MODIS and NESDIS Monthly Snow-Cover Records to Study Decade-Scale Changes in Northern Hemisphere Snow Cover

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Robinson, David A.; Riggs, George A.

2004-01-01

A decade-scale record of Northern Hemisphere snow cover has been available from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite Data and Information Service (NESDIS) and has been reconstructed and validated by Rutgers University following adjustments for inconsistencies that were discovered in the early years of the data set. This record provides weekly, monthly (and, in recent years, daily) snow cover from 1966 to the present for the Northern Hemisphere. With the December 1999 launch of NASA's Earth observing System (EOS) Terra satellite, snow maps are being produced globally, using automated algorithms, on a daily, weekly and monthly basis from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument. The resolution of the MODIS monthly snow maps (0.05deg or about 5 km) is an improvement over that of the NESDIS-derived monthly snow maps (>approx.10 km) the maps, it is necessary to study the datasets carefully to determine if it is possible to merge the datasets into a continuous record. The months in which data are available for both the NESDIS and MODIS maps (March 2000 to the present) will be compared quantitatively to analyze differences in North American and Eurasian snow cover. Results from the NESDIS monthly maps show that for North America (including all 12 months), there is a trend toward slightly less snow cover in each succeeding decade. Interannual snow-cover extent has varied significantly since 2000 as seen in both the NESDIS and MODIS maps. As the length of the satellite record increases through the MODIS era, and into the National Polar-orbiting Environmental Satellite System (NPOESS) era, it should become easier to identify trends in areal extent of snow cover, if present, that may have climatic significance. Thus it is necessary to analyze the validity of merging the NESDIS and MODIS, and, in the future, the NPOESS datasets for determination of long-term continuity in measurement of Northern Hemisphere snow cover.

Development and Evaluation of a Cloud-Gap-Filled MODIS Daily Snow-Cover Product

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Foster, James L.; Kumar, Sujay V.

2010-01-01

The utility of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover products is limited by cloud cover which causes gaps in the daily snow-cover map products. We describe a cloud-gap-filled (CGF) daily snowcover map using a simple algorithm to track cloud persistence, to account for the uncertainty created by the age of the snow observation. Developed from the 0.050 resolution climate-modeling grid daily snow-cover product, MOD10C1, each grid cell of the CGF map provides a cloud-persistence count (CPC) that tells whether the current or a prior day was used to make the snow decision. Percentage of grid cells "observable" is shown to increase dramatically when prior days are considered. The effectiveness of the CGF product is evaluated by conducting a suite of data assimilation experiments using the community Noah land surface model in the NASA Land Information System (LIS) framework. The Noah model forecasts of snow conditions, such as snow-water equivalent (SWE), are updated based on the observations of snow cover which are obtained either from the MOD1 OC1 standard product or the new CGF product. The assimilation integrations using the CGF maps provide domain averaged bias improvement of -11 %, whereas such improvement using the standard MOD1 OC1 maps is -3%. These improvements suggest that the Noah model underestimates SWE and snow depth fields, and that the assimilation integrations contribute to correcting this systematic error. We conclude that the gap-filling strategy is an effective approach for increasing cloud-free observations of snow cover.

Twenty-four year record of Northern Hemisphere snow cover derived from passive microwave remote sensing

NASA Astrophysics Data System (ADS)

Armstrong, Richard L.; Brodzik, Mary Jo

2003-04-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. It is now possible to monitor the global fluctuation of snow cover over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a smiliar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible statellite data and the visible data typically show higher monthly variability. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as on into the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the negative spectral gradient driving the passive microwave algorithm is enhanced. Trends in annual averages are similar, decreasing at rates of approximately 2% per decade. The only region where the passive microwave data consistently indicate snow and the visible data do not is over the Tibetan Plateau and surrounding mountain areas. In the effort to determine the accuracy of the microwave algorithm over this region we are acquiring surface snow observations through a collaborative study with CAREERI/Lanzhou. In order to provide an optimal snow cover product in the future, we are developing a procedure that blends snow extent maps derived from MODIS data with snow water equivalent maps derived from both SSM/I and AMSR.

An automated approach for mapping persistent ice and snow cover over high latitude regions

USGS Publications Warehouse

Selkowitz, David J.; Forster, Richard R.

2016-01-01

We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above ~65°N). Our approach exploits all available Landsat scenes acquired during the late summer (1 August–15 September) over a multi-year period and employs an automated cloud masking algorithm optimized for snow and ice covered mountainous environments. Pixels from individual Landsat scenes were classified as snow/ice covered or snow/ice free based on the Normalized Difference Snow Index (NDSI), and pixels consistently identified as snow/ice covered over a five-year period were classified as persistent ice and snow cover. The same NDSI and ratio of snow/ice-covered days to total days thresholds applied consistently across eight study regions resulted in persistent ice and snow cover maps that agreed closely in most areas with glacier area mapped for the Randolph Glacier Inventory (RGI), with a mean accuracy (agreement with the RGI) of 0.96, a mean precision (user’s accuracy of the snow/ice cover class) of 0.92, a mean recall (producer’s accuracy of the snow/ice cover class) of 0.86, and a mean F-score (a measure that considers both precision and recall) of 0.88. We also compared results from our approach to glacier area mapped from high spatial resolution imagery at four study regions and found similar results. Accuracy was lowest in regions with substantial areas of debris-covered glacier ice, suggesting that manual editing would still be required in these regions to achieve reasonable results. The similarity of our results to those from the RGI as well as glacier area mapped from high spatial resolution imagery suggests it should be possible to apply this approach across large regions to produce updated 30-m resolution maps of persistent ice and snow cover. In the short term, automated PISC maps can be used to rapidly identify areas where substantial changes in glacier area have occurred since the most recent conventional glacier inventories, highlighting areas where updated inventories are most urgently needed. From a longer term perspective, the automated production of PISC maps represents an important step toward fully automated glacier extent monitoring using Landsat or similar sensors.

A long-term Northern Hemisphere snow cover extent product (JASMES) deriving from satellite-borne optical sensors using consistent objective criteria

NASA Astrophysics Data System (ADS)

Hori, M.; Sugiura, K.; Kobayashi, K.; Aoki, T.; Tanikawa, T.; Niwano, M.; Enomoto, H.

2017-12-01

A long-term Northern Hemisphere (NH) snow cover extent (SCE) product (JASMES SCE) was developed from the application of a consistent objective snow cover mapping algorithm to satellite-borne optical sensors (NOAA/AVHRR and NASA's optical sensor MODIS) from 1982 to the present. We estimated NH SCE from weekly composited snow cover maps and evaluated the accuracies of snow cover detection using in-situ snow data. As benchmark SCE product, we also evaluated the accuracy of SCE maps from the National Oceanic and Atmospheric Administration Climate Data Record (NOAA-CDR) product. The evaluation showed that JASMES SCE has more temporally stable accuracies. Seasonally averaged SCE derived from JASMES exhibited negative slopes in all seasons which is opposite to those of NOAA-CDR SCE in the fall and winter seasons. The spatial pattern of annual snow cover duration (SCD) trends exhibited noticeable asymmetric pattern between continents with the largest negative trends seen over western Eurasia. The NH SCE product will be connected to the data of the Japanese Earth Observing satellite named "Global Change Observation Mission for Climate (GCOM-C)" to be launched in late 2017.

MODIS Snow-Cover Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vinvent V.; DiGirolamo, Nicolo; Bayr, Klaus J.; Houser, Paul (Technical Monitor)

2001-01-01

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. These products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to the currently available operation products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set is generated for long-term climates studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the product. The snow product suite starts with a 500-m resolution swath snow-cover map which is gridded to the Integerized Sinusoidal Grid to produce daily and eight-day composite tile products. The sequence then proceeds to a climate-modeling grid product at 5-km spatial resolution, with both daily and eight-day composite products. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented. Near-term enhancements include daily snow albedo and fractional snow cover.

A Full Snow Season in Yellowstone: A Database of Restored Aqua Band 6

NASA Technical Reports Server (NTRS)

Gladkova, Irina; Grossberg, Michael; Bonev, George; Romanov, Peter; Riggs, George; Hall, Dorothy

2013-01-01

The algorithms for estimating snow extent for the Moderate Resolution Imaging Spectroradiometer (MODIS) optimally use the 1.6- m channel which is unavailable for MODIS on Aqua due to detector damage. As a test bed to demonstrate that Aqua band 6 can be restored, we chose the area surrounding Yellowstone and Grand Teton national parks. In such rugged and difficult-to-access terrain, satellite images are particularly important for providing an estimation of snow-cover extent. For the full 2010-2011 snow season covering the Yellowstone region, we have used quantitative image restoration to create a database of restored Aqua band 6. The database includes restored radiances, normalized vegetation index, normalized snow index, thermal data, and band-6-based snow-map products. The restored Aqua-band-6 data have also been regridded and combined with Terra data to produce a snow-cover map that utilizes both Terra and Aqua snow maps. Using this database, we show that the restored Aqua-band-6-based snow-cover extent has a comparable performance with respect to ground stations to the one based on Terra. The result of a restored band 6 from Aqua is that we have an additional band-6 image of the Yellowstone region each day. This image can be used to mitigate cloud occlusion, using the same algorithms used for band 6 on Terra. We show an application of this database of restored band-6 images to illustrate the value of creating a cloud gap filling using the National Aeronautics and Space Administration s operational cloud masks and data from both Aqua and Terra.

The assessment of EUMETSAT HSAF Snow Products for mountainuos areas in the eastern part of Turkey

NASA Astrophysics Data System (ADS)

Akyurek, Z.; Surer, S.; Beser, O.; Bolat, K.; Erturk, A. G.

2012-04-01

Monitoring the snow parameters (e.g. snow cover area, snow water equivalent) is a challenging work. Because of its natural physical properties, snow highly affects the evolution of weather from daily basis to climate on a longer time scale. The derivation of snow products over mountainous regions has been considered very challenging. This can be done by periodic and precise mapping of the snow cover. However inaccessibility and scarcity of the ground observations limit the snow cover mapping in the mountainous areas. Today, it is carried out operationally by means of optical satellite imagery and microwave radiometry. In retrieving the snow cover area from satellite images bring the problem of topographical variations within the footprint of satellite sensors and spatial and temporal variation of snow characteristics in the mountainous areas. Most of the global and regional operational snow products use generic algorithms for flat and mountainous areas. However the non-uniformity of the snow characteristics can only be modeled with different algorithms for mountain and flat areas. In this study the early findings of Satellite Application Facilities on Hydrology (H-SAF) project, which is financially supported by EUMETSAT, will be presented. Turkey is a part of the H-SAF project, both in product generation (eg. snow recognition, fractional snow cover and snow water equivalent) for mountainous regions for whole Europe, cal/val of satellite-derived snow products with ground observations and cal/val studies with hydrological modeling in the mountainous terrain of Europe. All the snow products are operational on a daily basis. For the snow recognition product (H10) for mountainous areas, spectral thresholding methods were applied on sub pixel scale of MSG-SEVIRI images. The different spectral characteristics of cloud, snow and land determined the structure of the algorithm and these characteristics were obtained from subjective classification of known snow cover features in the MSG/SEVIRI images. The fractional snow cover area (H12) algorithm is based on a sub-pixel reflectance model applied on METOP-AVHRR data. Knowing the effects of topography on satellite-measured radiances for rough terrain, the sun zenith and azimuth angles, as well as direction of observation relative to these are taken into account in estimating the target reflectances from the satellite images. The values of SWE products (H13) were obtained using an assimilation process based on the Helsinki University of Technology model using Advanced Microwave Scanning Radiometer for EOS (AMSR-E) daily brightness-temperature values. The validation studies for three products have been performed for the water years 2010 and 2011. Average values of 70% of probability of detection for snow recognition product, 60% of overall accuracy for the fractional snow cover product and 45 mm RMSE for the snow water equivalent product have been obtained from the validation studies. Final versions of these three products will be presented and discussed. Key words: snow, satellite images, mountain, HSAF, snow cover, snow water equivalent

Next Generation Snow Cover Mapping: Can Future Hyperspectral Satellite Spectrometer Systems Improve Subpixel Snow-covered Area and Grain Size in the Sierra Nevada?

NASA Astrophysics Data System (ADS)

Hill, R.; Calvin, W. M.; Harpold, A.

2017-12-01

Mountain snow storage is the dominant source of water for humans and ecosystems in western North America. Consequently, the spatial distribution of snow-covered area is fundamental to both hydrological, ecological, and climate models. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were collected along the entire Sierra Nevada mountain range extending from north of Lake Tahoe to south of Mt. Whitney during the 2015 and 2016 snow-covered season. The AVIRIS dataset used in this experiment consists of 224 contiguous spectral channels with wavelengths ranging 400-2500 nanometers at a 15-meter spatial pixel size. Data from the Sierras were acquired on four days: 2/24/15 during a very low snow year, 3/24/16 near maximum snow accumulation, and 5/12/16 and 5/18/16 during snow ablation and snow loss. Building on previous retrieval of subpixel snow-covered area algorithms that take into account varying grain size we present a model that analyzes multiple endmembers of varying snow grain size, vegetation, rock, and soil in segmented regions along the Sierra Nevada to determine snow-cover spatial extent, snow sub-pixel fraction, and approximate grain size. In addition, varying simulated models of the data will compare and contrast the retrieval of current snow products such as MODIS Snow-Covered Area and Grain Size (MODSCAG) and the Airborne Space Observatory (ASO). Specifically, does lower spatial resolution (MODIS), broader resolution bandwidth (MODIS), and limited spectral resolution (ASO) affect snow-cover area and grain size approximations? The implications of our findings will help refine snow mapping products for planned hyperspectral satellite spectrometer systems such as EnMAP (slated to launch in 2019), HISUI (planned for inclusion on the International Space Station in 2018), and HyspIRI (currently under consideration).

A Blended Global Snow Product using Visible, Passive Microwave and Scatterometer Satellite Data

NASA Technical Reports Server (NTRS)

Foster, James L.; Hall, Dorothy K.; Eylander, John B.; Riggs, George A.; Nghiem, Son V.; Tedesco, Marco; Kim, Edward; Montesano, Paul M.; Kelly, Richard E. J.; Casey, Kimberly A.;

Trends in annual minimum exposed snow and ice cover in High Mountain Asia from MODIS

NASA Astrophysics Data System (ADS)

Rittger, Karl; Brodzik, Mary J.; Painter, Thomas H.; Racoviteanu, Adina; Armstrong, Richard; Dozier, Jeff

2016-04-01

Though a relatively short record on climatological scales, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000-2014 can be used to evaluate changes in the cryosphere and provide a robust baseline for future observations from space. We use the MODIS Snow Covered Area and Grain size (MODSCAG) algorithm, based on spectral mixture analysis, to estimate daily fractional snow and ice cover and the MODICE Persistent Ice (MODICE) algorithm to estimate the annual minimum snow and ice fraction (fSCA) for each year from 2000 to 2014 in High Mountain Asia. We have found that MODSCAG performs better than other algorithms, such as the Normalized Difference Index (NDSI), at detecting snow. We use MODICE because it minimizes false positives (compared to maximum extents), for example, when bright soils or clouds are incorrectly classified as snow, a common problem with optical satellite snow mapping. We analyze changes in area using the annual MODICE maps of minimum snow and ice cover for over 15,000 individual glaciers as defined by the Randolph Glacier Inventory (RGI) Version 5, focusing on the Amu Darya, Syr Darya, Upper Indus, Ganges, and Brahmaputra River basins. For each glacier with an area of at least 1 km2 as defined by RGI, we sum the total minimum snow and ice covered area for each year from 2000 to 2014 and estimate the trends in area loss or gain. We find the largest loss in annual minimum snow and ice extent for 2000-2014 in the Brahmaputra and Ganges with 57% and 40%, respectively, of analyzed glaciers with significant losses (p-value<0.05). In the Upper Indus River basin, we see both gains and losses in minimum snow and ice extent, but more glaciers with losses than gains. Our analysis shows that a smaller proportion of glaciers in the Amu Darya and Syr Darya are experiencing significant changes in minimum snow and ice extent (3.5% and 12.2%), possibly because more of the glaciers in this region are smaller than 1 km2 than in the Indus, Ganges, and Brahmaputra making analysis from MODIS (pixel area ~0.25 km2) difficult. Overall, we see 23% of the glaciers in the 5 river basins with significant trends (in either direction). We relate these changes in area to topography and climate to understand the driving processes related to these changes. In addition to annual minimum snow and ice cover, the MODICE algorithm also provides the date of minimum fSCA for each pixel. To determine whether the surface was snow or ice we use the date of minimum fSCA from MODICE to index daily maps of snow on ice (SOI), or exposed glacier ice (EGI) and systematically derive an equilibrium line altitude (ELA) for each year from 2000-2014. We test this new algorithm in the Upper Indus basin and produce annual estimates of ELA. For the Upper Indus basin we are deriving annual ELAs that range from 5350 m to 5450 m which is slightly higher than published values of 5200 m for this region.

Assessment of Consistencies and Uncertainties between the NASA MODIS and VIIRS Snow-Cover Maps

NASA Astrophysics Data System (ADS)

Hall, D. K.; Riggs, G. A., Jr.; DiGirolamo, N. E.; Roman, M. O.

2017-12-01

Snow cover has great climatic and economic importance in part due to its high albedo and low thermal conductivity and large areal extent in the Northern Hemisphere winter, and its role as a freshwater source for about one-sixth of the world's population. The Rutgers University Global Snow Lab's 50-year climate-data record (CDR) of Northern Hemisphere snow cover is invaluable for climate studies, but, at 25-km resolution, the spatial resolution is too coarse to provide accurate snow information at the basin scale. Since 2000, global snow-cover maps have been produced from the MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites at 500-m resolution, and from the Suomi-National Polar Program (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) since 2011 at 375-m resolution. Development of a moderate-resolution (375 - 500 m) earth system data record (ESDR) that utilizes both MODIS and VIIRS snow maps is underway. There is a 6-year overlap between the data records. In late 2017 the second in a series of VIIRS sensors will be launched on the Joint Polar Satellite System-1 (JPSS-1), with the JPSS-2 satellite scheduled for launch in 2021, providing the potential to extend NASA's snow-cover ESDR for decades into the future and to create a CDR. Therefore it is important to investigate the continuity between the MODIS and VIIRS NASA snow-cover data products and evaluate whether there are any inconsistencies and biases that would affect their value as CDR. Time series of daily normalized-difference snow index (NDSI) Terra and Aqua MODIS Collection 6 (C6) and NASA VIIRS Collection 1 (C1) snow-cover tile maps (MOD10A1 and VNP10A1) are studied for North America to identify NDSI differences and possible biases between the datasets. Developing a CDR using the MODIS and VIIRS records is challenging. Though the instruments and orbits are similar, differences in bands, viewing geometry, spatial resolution, and cloud- and snow-mapping algorithms affect snow detection.

Normalized-Difference Snow Index (NDSI)

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.

2010-01-01

The Normalized-Difference Snow Index (NDSI) has a long history. 'The use of ratioing visible (VIS) and near-infrared (NIR) or short-wave infrared (SWIR) channels to separate snow and clouds was documented in the literature beginning in the mid-1970s. A considerable amount of work on this subject was conducted at, and published by, the Air Force Geophysics Laboratory (AFGL). The objective of the AFGL work was to discriminate snow cover from cloud cover using an automated algorithm to improve global cloud analyses. Later, automated methods that relied on the VIS/NIR ratio were refined substantially using satellite data In this section we provide a brief history of the use of the NDSI for mapping snow cover.

MODIS Snow-Cover Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.; DiGirolamo, Nicole E.; Bayr, Klaus J.; Houser, Paul R. (Technical Monitor)

2002-01-01

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. MODIS snow and ice products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to or enhancement of the currently-available operational products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set may be generated for long-term climate studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the products. The MODIS snow product suite begins with a 500-m resolution, 2330-km swath snow-cover map which is then gridded to an integerized sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to a climate-modeling grid (CMG) product at about 5.6-km spatial resolution, with both daily and 8-day composite products. Each pixel of the CMG contains fraction of snow cover from 40 - 100%. Measured errors of commission in the CMG are low, for example, on the continent of Australia in the spring, they vary from 0.02 - 0.10%. Near-term enhancements include daily snow albedo and fractional snow cover. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented to show some early validation work.

Finland Validation of the New Blended Snow Product

NASA Technical Reports Server (NTRS)

Kim, E. J.; Casey, K. A.; Hallikainen, M. T.; Foster, J. L.; Hall, D. K.; Riggs, G. A.

2008-01-01

As part of an ongoing effort to validate satellite remote sensing snow products for the recentlydeveloped U.S. Air Force Weather Agency (AFWA) - NASA blended snow product, Satellite and in-situ data for snow extent and snow water equivalent (SWE) are evaluated in Finland for the 2006-2007 snow season Finnish Meteorological Institute (FMI) daily weather station data and Finnish Environment Institute (SYKE) bi-monthly snow course data are used as ground truth. Initial comparison results display positive agreement between the AFWA NASA Snow Algorithm (ANSA) snow extent and SWE maps and in situ data, with discrepancies in accordance with known AMSR-E and MODIS snow mapping limitations. Future ANSA product improvement plans include additional validation and inclusion of fractional snow cover in the ANSA data product. Furthermore, the AMSR-E 19 GHz (horizontal channel) with the difference between ascending and descending satellite passes (Diurnal Amplitude Variations, DAV) will be used to detect the onset of melt, and QuikSCAT scatterometer data (14 GHz) will be used to map areas of actively melting snow.

Remote Sensing of Snow Cover. Section; Snow Extent

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Frei, Allan; Drey, Stephen J.

2012-01-01

Snow was easily identified in the first image obtained from the Television Infrared Operational Satellite-1 (TIROS-1) weather satellite in 1960 because the high albedo of snow presents a good contrast with most other natural surfaces. Subsequently, the National Oceanic and Atmospheric Administration (NOAA) began to map snow using satellite-borne instruments in 1966. Snow plays an important role in the Earth s energy balance, causing more solar radiation to be reflected back into space as compared to most snow-free surfaces. Seasonal snow cover also provides a critical water resource through meltwater emanating from rivers that originate from high-mountain areas such as the Tibetan Plateau. Meltwater from mountain snow packs flows to some of the world s most densely-populated areas such as Southeast Asia, benefiting over 1 billion people (Immerzeel et al., 2010). In this section, we provide a brief overview of the remote sensing of snow cover using visible and near-infrared (VNIR) and passive-microwave (PM) data. Snow can be mapped using the microwave part of the electromagnetic spectrum, even in darkness and through cloud cover, but at a coarser spatial resolution than when using VNIR data. Fusing VNIR and PM algorithms to produce a blended product offers synergistic benefits. Snow-water equivalent (SWE), snow extent, and melt onset are important parameters for climate models and for the initialization of atmospheric forecasts at daily and seasonal time scales. Snowmelt data are also needed as input to hydrological models to improve flood control and irrigation management.

Automated Snow Extent Mapping Based on Orthophoto Images from Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Spallek, Waldemar; Witek-Kasprzak, Matylda

2018-04-01

The paper presents the application of the k-means clustering in the process of automated snow extent mapping using orthophoto images generated using the Structure-from-Motion (SfM) algorithm from oblique aerial photographs taken by unmanned aerial vehicle (UAV). A simple classification approach has been implemented to discriminate between snow-free and snow-covered terrain. The procedure uses the k-means clustering and classifies orthophoto images based on the three-dimensional space of red-green-blue (RGB) or near-infrared-red-green (NIRRG) or near-infrared-green-blue (NIRGB) bands. To test the method, several field experiments have been carried out, both in situations when snow cover was continuous and when it was patchy. The experiments have been conducted using three fixed-wing UAVs (swinglet CAM by senseFly, eBee by senseFly, and Birdie by FlyTech UAV) on 10/04/2015, 23/03/2016, and 16/03/2017 within three test sites in the Izerskie Mountains in southwestern Poland. The resulting snow extent maps, produced automatically using the classification method, have been validated against real snow extents delineated through a visual analysis and interpretation offered by human analysts. For the simplest classification setup, which assumes two classes in the k-means clustering, the extent of snow patches was estimated accurately, with areal underestimation of 4.6% (RGB) and overestimation of 5.5% (NIRGB). For continuous snow cover with sparse discontinuities at places where trees or bushes protruded from snow, the agreement between automatically produced snow extent maps and observations was better, i.e. 1.5% (underestimation with RGB) and 0.7-0.9% (overestimation, either with RGB or with NIRRG). Shadows on snow were found to be mainly responsible for the misclassification.

Comparison of MODIS and VIIRS Snow Cover Products for the 2016 Hydrological Year

NASA Astrophysics Data System (ADS)

Klein, A. G.; Thapa, S.

2017-12-01

The VIIRS (Visible Infrared Imaging Radiometer Suite) instrument on board the Suomi-NPP satellite aims to provide long-term continuity of several environmental data series including snow cover initiated with MODIS. While it is speculated that MODIS and VIIRS snow cover products may differ because of their differing spatial resolutions and spectral coverage quantitative comparisons between their snow products are currently limited. Therefore this study intercompares MODIS and VIIRS snow products for the 2016 Hydrological Year over the Midwestern United States and southern Canada. Two hundred and forty-four swath snow products from MODIS/Aqua (MYD10L2) and the VIIRS EDR (VSCMO/binary) were intercompared using confusion matrices, comparison maps and false color imagery. Thresholding the MODIS NDSI Snow Cover product at a snow cover fraction of 30% generated binary snow maps most comparable to the NOAA VIIRS binary snow product. Overall agreement between MODIS and VIIRS was found to be approximately 98%. This exceeds the VIIRS accuracy requirements of 90% probability of correct typing. Agreement was highest during the winter but lower during late fall and spring. Comparability was lowest over forest. MODIS and VIIRS often mapped snow/no-snow transition zones as cloud. The assessment of total snow and cloud pixels and comparison snow maps of MODIS and VIIRS indicates that VIIRS is mapping more snow cover and less cloud cover compared to MODIS. This is evidenced by the average area of snow in MYD10L2 and VSCMO being 5.72% and 11.43%, no-snow 26.65% and 28.67%, and cloud 65.02% and 59.91%, respectively. Visual comparisons depict good qualitative agreement between snow cover area visible in MODIS and VIIRS false color imagery and mapped in their respective snow cover products. While VIIRS and MODIS have similar capacity to map snow cover, VIIRS has the potential to more accurately map snow cover area for the successive development of climate data records.

Improving snow fraction spatio-temporal continuity using a combination of MODIS and Fengyun-2 satellites over China

NASA Astrophysics Data System (ADS)

Jiang, L.; Wang, G.

2017-12-01

Snow cover is one of key elements in the investigations of weather, climatic change, water resource, and snow hazard. Satellites observations from on-board optical sensors provides the ability to snow cover mapping through the discrimination of snow from other surface features and cloud. MODIS provides maximum of snow cover data using 8-day composition data in order to reduce the cloud obscuration impacts. However, snow cover mapping is often required to obtain at the temporal scale of less than one day, especially in the case of disasters. Geostationary satellites provide much higher temporal resolution measurements (typically at 15 min or half or one hour), which has a great potential to reduce cloud cover problem and observe ground surface for identifying snow. The proposed method in this work is that how to take the advantages of polar-orbiting and geostationary optical sensors to accurately map snow cover without data gaps due to cloud. FY-2 geostationary satellites have high temporal resolution observations, however, they are lacking enough spectral bands essential for snow cover monitoring, such as the 1.6 μm band. Based on our recent work (Wang et al., 2017), we improved FY-2/VISSR fractional snow cover estimation with a linear spectral unmixing analysis method. The linear approach is applied then using the reflectance observed at the certain hourly image of FY-2 to calculate pixel-wise snow cover fraction. The composition of daily factional snow cover employs the sun zenith angle, where the snow fraction under lowest sun zenith angle is considered as the most confident result. FY-2/VISSR fractional snow cover map has less cloud due to the composition of multi-temporal snow maps in a single day. In order to get an accurate and cloud-reduced fractional snow cover map, both of MODIS and FY-2/VISSR daily snow fraction maps are blended together. With the combination of FY-2E/VISSR and MODIS, there are still some cloud existing in the daily snow fraction map. Then the combination snow fraction map is temporally reconstructed using MATLAB Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) function to derive a completely daily cloud-free snow cover map under all the sky conditions.

High-resolution LIDAR and ground observations of snow cover in a complex forested terrain in the Sierra Nevada - implications for optical remote sensing of seasonal snow.

NASA Astrophysics Data System (ADS)

Kostadinov, T. S.; Harpold, A.; Hill, R.; McGwire, K.

2017-12-01

Seasonal snow cover is a key component of the hydrologic regime in many regions of the world, especially those in temperate latitudes with mountainous terrain and dry summers. Such regions support large human populations which depend on the mountain snowpack for their water supplies. It is thus important to quantify snow cover accurately and continuously in these regions. Optical remote-sensing methods are able to detect snow and leverage space-borne spectroradiometers with global coverage such as MODIS to produce global snow cover maps. However, snow is harder to detect accurately in mountainous forested terrain, where topography influences retrieval algorithms, and importantly - forest canopies complicate radiative transfer and obfuscate the snow. Current satellite snow cover algorithms assume that fractional snow-covered area (fSCA) under the canopy is the same as the fSCA in the visible portion of the pixel. In-situ observations and first principles considerations indicate otherwise, therefore there is a need for improvement of the under-canopy correction of snow cover. Here, we leverage multiple LIDAR overflights and in-situ observations with a distributed fiber-optic temperature sensor (DTS) to quantify snow cover under canopy as opposed to gap areas at the Sagehen Experimental Forest in the Northern Sierra Nevada, California, USA. Snow-off LIDAR overflights from 2014 are used to create a baseline high-resolution digital elevation model and classify pixels at 1 m resolution as canopy-covered or gap. Low canopy pixels are excluded from the analysis. Snow-on LIDAR overflights conducted by the Airborne Snow Observatory in 2016 are then used to classify all pixels as snow-covered or not and quantify fSCA under canopies vs. in gap areas over the Sagehen watershed. DTS observations are classified as snow-covered or not based on diel temperature fluctuations and used as validation for the LIDAR observations. LIDAR- and DTS-derived fSCA is also compared with retrievals from hyperspectral imaging spectroradiometer (AVIRIS) data. Initial evidence suggest fSCA was generally lower under canopy and that overall snow cover estimates were overestimated as a result. Implications for a canopy correction applicable to coarser-resolution sensors like MODIS are discussed, as are topography and view angle effects.

Theoretical Accuracy of Global Snow-Cover Mapping Using Satellite Data in the Earth Observing System (EOS) Era

NASA Technical Reports Server (NTRS)

Hall, D. K.; Foster, J. L.; Salomonson, V. V.; Klein, A. G.; Chien, J. Y. L.

1998-01-01

Following the launch of the Earth Observing System first morning (EOS-AM1) satellite, daily, global snow-cover mapping will be performed automatically at a spatial resolution of 500 m, cloud-cover permitting, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. A technique to calculate theoretical accuracy of the MODIS-derived snow maps is presented. Field studies demonstrate that under cloud-free conditions when snow cover is complete, snow-mapping errors are small (less than 1%) in all land covers studied except forests where errors are greater and more variable. The theoretical accuracy of MODIS snow-cover maps is largely determined by percent forest cover north of the snowline. Using the 17-class International Geosphere-Biosphere Program (IGBP) land-cover maps of North America and Eurasia, the Northern Hemisphere is classified into seven land-cover classes and water. Snow-mapping errors estimated for each of the seven land-cover classes are extrapolated to the entire Northern Hemisphere for areas north of the average continental snowline for each month. Average monthly errors for the Northern Hemisphere are expected to range from 5 - 10%, and the theoretical accuracy of the future global snow-cover maps is 92% or higher. Error estimates will be refined after the first full year that MODIS data are available.

A novel approach for automatic snow depth estimation using UAV-taken images without ground control points

NASA Astrophysics Data System (ADS)

Mizinski, Bartlomiej; Niedzielski, Tomasz

2017-04-01

Recent developments in snow depth reconstruction based on remote sensing techniques include the use of photographs of snow-covered terrain taken by unmanned aerial vehicles (UAVs). There are several approaches that utilize visible-light photos (RGB) or near infrared images (NIR). The majority of the methods in question are based on reconstructing the digital surface model (DSM) of the snow-covered area with the use of the Structure-from-Motion (SfM) algorithm and the stereo-vision software. Having reconstructed the above-mentioned DSM it is straightforward to calculate the snow depth map which may be produced as a difference between the DSM of snow-covered terrain and the snow-free DSM, known as the reference surface. In order to use the aforementioned procedure, the high spatial accuracy of the two DSMs must be ensured. Traditionally, this is done using the ground control points (GCPs), either artificial or natural terrain features that are visible on aerial images, the coordinates of which are measured in the field using the Global Navigation Satellite System (GNSS) receiver by qualified personnel. The field measurements may be time-taking (GCPs must be well distributed in the study area, therefore the field experts should travel over long distances) and dangerous (the field experts may be exposed to avalanche risk or cold). Thus, there is a need to elaborate methods that enable the above-mentioned automatic snow depth map production without the use of GCPs. One of such attempts is shown in this paper which aims to present the novel method which is based on real-time processing of snow-covered and snow-free dense point clouds produced by SfM. The two stage georeferencing is proposed. The initial (low accuracy) one assigns true geographic, and subsequently projected, coordinates to the two dense point clouds, while the said initially-registered dense point clouds are matched using the iterative closest point (ICP) algorithm in the final (high accuracy) stage. The stable reference is offered by specially-selected trees which are located in the vicinity of the terrain of interest. The method has already been implemented and along with the presentation of its concept, a few case studies from the Izerskie Mountains (southwestern Poland) are discussed. Although the method reveals several constraints, it may serve the purpose of generating the snow depth maps with reasonable accuracy, in particular in the absence of GCPs. The snow depth estimation algorithm has been elaborated in frame of the research grant no. LIDER/012/223/L-5/13/NCBR/2014 financed by the National Centre for Research and Development of Poland.

Mapping forest canopy disturbance in the Upper Great Lakes, USA

Treesearch

James D. Garner; Mark D. Nelson; Brian G. Tavernia; Charles H. (Hobie) Perry; Ian W. Housman

2015-01-01

A map of forest canopy disturbance was generated for Michigan, Wisconsin, and most of Minnesota using 42 Landsat time series stacks (LTSS) and a vegetation change tracker (VCTw) algorithm. Corresponding winter imagery was used to reduce commission errors of forest disturbance by identifying areas of persistent snow cover. The resulting disturbance age map was classed...

Enhanced hemispheric-scale snow mapping through the blending of optical and microwave satellite data

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M. J.; Savoie, M.; Knowles, K.

2003-04-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. Global snow cover fluctuation can now be monitored over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere weekly snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Decadal trends and their significance are compared for the two data types. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as throughout the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the negative spectral gradient driving the passive microwave algorithm is enhanced. Because the current generation of microwave snow algorithms is unable to consistently detect shallow and intermittent snow, we combine visible satellite data with the microwave data in a single blended product to overcome this problem. For the period 1978 to 2002 we combine data from the NOAA weekly snow charts with passive microwave data from the SMMR and SSM/I brightness temperature record. For the current and future time period we blend MODIS and AMSR-E data sets, both of which have greatly enhanced spatial resolution compared to the earlier data sources. Because it is not possible to determine snow depth or snow water equivalent from visible data, the regions where only the NOAA or MODIS data indicate snow are defined as "shallow snow". However, because our current blended product is being developed in the 25 km EASE-Grid and the MODIS data being used are in the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) the blended product also includes percent snow cover over the larger grid cell. A prototype version of the blended MODIS/AMSR-E product will be available in near real-time from NSIDC during the 2002-2003 winter season.

Natural resources inventory and land evaluation in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1976-01-01

The author has identified the following significant results. Using MSS channels 5 and 7 and a supervised classification system with a PPD classification algorithm, it was possible to map the exact areal extent of the snow cover and of the transition zone with melting snow patches and snow free parts of various sizes over a large area under different aspects such as relief, exposure, shadows etc. A correlation of the data from ground control, areal underflights and earth resources satellites provided a very accurate interpretation of the melting procedure of snow in high mountains.

MODIS Snow Cover Recovery Using Variational Interpolation

NASA Astrophysics Data System (ADS)

Tran, H.; Nguyen, P.; Hsu, K. L.; Sorooshian, S.

2017-12-01

Cloud obscuration is one of the major problems that limit the usages of satellite images in general and in NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) global Snow-Covered Area (SCA) products in particular. Among the approaches to resolve the problem, the Variational Interpolation (VI) algorithm method, proposed by Xia et al., 2012, obtains cloud-free dynamic SCA images from MODIS. This method is automatic and robust. However, computational deficiency is a main drawback that degrades applying the method for larger scales (i.e., spatial and temporal scales). To overcome this difficulty, this study introduces an improved version of the original VI. The modified VI algorithm integrates the MINimum RESidual (MINRES) iteration (Paige and Saunders., 1975) to prevent the system from breaking up when applied to much broader scales. An experiment was done to demonstrate the crash-proof ability of the new algorithm in comparison with the original VI method, an ability that is obtained when maintaining the distribution of the weights set after solving the linear system. After that, the new VI algorithm was applied to the whole Contiguous United States (CONUS) over four winter months of 2016 and 2017, and validated using the snow station network (SNOTEL). The resulting cloud free images have high accuracy in capturing the dynamical changes of snow in contrast with the MODIS snow cover maps. Lastly, the algorithm was applied to create a Cloud free images dataset from March 10, 2000 to February 28, 2017, which is able to provide an overview of snow trends over CONUS for nearly two decades. ACKNOWLEDGMENTSWe would like to acknowledge NASA, NOAA Office of Hydrologic Development (OHD) National Weather Service (NWS), Cooperative Institute for Climate and Satellites (CICS), Army Research Office (ARO), ICIWaRM, and UNESCO for supporting this research.

Enhancement of snow cover change detection with sparse representation and dictionary learning

NASA Astrophysics Data System (ADS)

Varade, D.; Dikshit, O.

2014-11-01

Sparse representation and decoding is often used for denoising images and compression of images with respect to inherent features. In this paper, we adopt a methodology incorporating sparse representation of a snow cover change map using the K-SVD trained dictionary and sparse decoding to enhance the change map. The pixels often falsely characterized as "changes" are eliminated using this approach. The preliminary change map was generated using differenced NDSI or S3 maps in case of Resourcesat-2 and Landsat 8 OLI imagery respectively. These maps are extracted into patches for compressed sensing using Discrete Cosine Transform (DCT) to generate an initial dictionary which is trained by the K-SVD approach. The trained dictionary is used for sparse coding of the change map using the Orthogonal Matching Pursuit (OMP) algorithm. The reconstructed change map incorporates a greater degree of smoothing and represents the features (snow cover changes) with better accuracy. The enhanced change map is segmented using kmeans to discriminate between the changed and non-changed pixels. The segmented enhanced change map is compared, firstly with the difference of Support Vector Machine (SVM) classified NDSI maps and secondly with a reference data generated as a mask by visual interpretation of the two input images. The methodology is evaluated using multi-spectral datasets from Resourcesat-2 and Landsat-8. The k-hat statistic is computed to determine the accuracy of the proposed approach.

Unusually Low Snow Cover in the U.S.

NASA Technical Reports Server (NTRS)

2002-01-01

New maps of snow cover produced by NASA's Terra satellite show that this year's snow line stayed farther north than normal. When combined with land surface temperature measurements, the observations confirm earlier National Oceanic and Atmospheric Administration reports that the United States was unusually warm and dry this past winter. The above map shows snow cover over the continental United States from February 2002 and is based on data acquired by the Moderate-Resolution Imaging Spectroradiometer (MODIS). The amount of land covered by snow during this period was much lower than usual. With the exception of the western mountain ranges and the Great Lakes region, the country was mostly snow free. The solid red line marks the average location of the monthly snow extent; white areas are snow-covered ground. Snow was mapped at approximately 5 kilometer pixel resolution on a daily basis and then combined, or composited, every eight days. If a pixel was at least 50 percent snow covered during all of the eight-day periods that month, it was mapped as snow covered for the whole month. For more information, images, and animations, read: Terra Satellite Data Confirm Unusually Warm, Dry U.S. Winter Image by Robert Simmon, based on data from the MODIS Snow/Ice Global Mapping Project

Satellite Based Probabilistic Snow Cover Extent Mapping (SCE) at Hydro-Québec

NASA Astrophysics Data System (ADS)

Teasdale, Mylène; De Sève, Danielle; Angers, Jean-François; Perreault, Luc

2016-04-01

Over 40% of Canada's water resources are in Quebec and Hydro-Quebec has developed potential to become one of the largest producers of hydroelectricity in the world, with a total installed capacity of 36,643 MW. The Hydro-Québec fleet park includes 27 large reservoirs with a combined storage capacity of 176 TWh, and 668 dams and 98 controls. Thus, over 98% of all electricity used to supply the domestic market comes from water resources and the excess output is sold on the wholesale markets. In this perspective the efficient management of water resources is needed and it is based primarily on a good river flow estimation including appropriate hydrological data. Snow on ground is one of the significant variables representing 30% to 40% of its annual energy reserve. More specifically, information on snow cover extent (SCE) and snow water equivalent (SWE) is crucial for hydrological forecasting, particularly in northern regions since the snowmelt provides the water that fills the reservoirs and is subsequently used for hydropower generation. For several years Hydro Quebec's research institute ( IREQ) developed several algorithms to map SCE and SWE. So far all the methods were deterministic. However, given the need to maximize the efficient use of all resources while ensuring reliability, the electrical systems must now be managed taking into account all risks. Since snow cover estimation is based on limited spatial information, it is important to quantify and handle its uncertainty in the hydrological forecasting system. This paper presents the first results of a probabilistic algorithm for mapping SCE by combining Bayesian mixture of probability distributions and multiple logistic regression models applied to passive microwave data. This approach allows assigning for each grid point, probabilities to the set of the mutually exclusive discrete outcomes: "snow" and "no snow". Its performance was evaluated using the Brier score since it is particularly appropriate to measure the accuracy of probabilistic discrete predictions. The scores were measured by comparing the snow probabilities produced by our models with the Hydro-Québec's snow ground data.

LANDSAT-D investigations in snow hydrology

NASA Technical Reports Server (NTRS)

Dozier, J.

1983-01-01

Progress on the registration of TM data to digital topographic data; on comparison of TM, MSS and NOAA meteorological satellite data for snowcover mapping; and on radiative transfer models for atmospheric correction is reported. Some methods for analyzing spatial contiguity of snow within the snow covered area were selected. The methods are based on a two-channel version of the grey level co-occurence matrix, combined with edge detection derived from an algorithm for computing slopes and exposures from digital terrain data.

Machine Learning Algorithms for Automated Satellite Snow and Sea Ice Detection

NASA Astrophysics Data System (ADS)

Bonev, George

The continuous mapping of snow and ice cover, particularly in the arctic and poles, are critical to understanding the earth and atmospheric science. Much of the world's sea ice and snow covers the most inhospitable places, making measurements from satellite-based remote sensors essential. Despite the wealth of data from these instruments many challenges remain. For instance, remote sensing instruments reside on-board different satellites and observe the earth at different portions of the electromagnetic spectrum with different spatial footprints. Integrating and fusing this information to make estimates of the surface is a subject of active research. In response to these challenges, this dissertation will present two algorithms that utilize methods from statistics and machine learning, with the goal of improving on the quality and accuracy of current snow and sea ice detection products. The first algorithm aims at implementing snow detection using optical/infrared instrument data. The novelty in this approach is that the classifier is trained using ground station measurements of snow depth that are collocated with the reflectance observed at the satellite. Several classification methods are compared using this training data to identify the one yielding the highest accuracy and optimal space/time complexity. The algorithm is then evaluated against the current operational NASA snow product and it is found that it produces comparable and in some cases superior accuracy results. The second algorithm presents a fully automated approach to sea ice detection that integrates data obtained from passive microwave and optical/infrared satellite instruments. For a particular region of interest the algorithm generates sea ice maps of each individual satellite overpass and then aggregates them to a daily composite level, maximizing the amount of high resolution information available. The algorithm is evaluated at both, the individual satellite overpass level, and at the daily composite level. Results show that at the single overpass level for clear-sky regions, the developed multi-sensor algorithm performs with accuracy similar to that of the optical/infrared products, with the advantage of being able to also classify partially cloud-obscured regions with the help of passive microwave data. At the daily composite level, results show that the algorithm's performance with respect to total ice extent is in line with other daily products, with the novelty of being fully automated and having higher resolution.

Multi Satellites Monitoring of Land Use/Cover Change and Its Driving Forces in Kashgar Region, China

NASA Astrophysics Data System (ADS)

Maimaitiaili, Ayisulitan; Aji, xiaokaiti; Kondoh, Akihiko

2016-04-01

Multi Satellites Monitoring of Land Use/Cover Change and Its Driving Forces in Kashgar Region, China Ayisulitan Maimaitiaili1, Xiaokaiti Aji2 Akihiko Kondoh2 1Graduate School of Science, Chiba University, Japan 2Center for Environmental Remote Sensing, Chiba University The spatio-temporal changes of Land Use/Cover (LUCC) and its driving forces in Kashgar region, Xinjiang Province, China, are investigated by using satellite remote sensing and a geographical information system (GIS). Main goal of this paper is to quantify the drivers of LUCC. First, considering lack of the Land Cover (LC) map in whole study area, we produced LC map by using Landsat images. Land use information from Landsat data was collected using maximum likelihood classification method. Land use change was studied based on the change detection method of land use types. Second, because the snow provides a key water resources for stream flow, agricultural production and drinking water for sustaining large population in Kashgar region, snow cover are estimated by Spot Vegetation data. Normalized Difference Snow Index (NDSI) algorithm are applied to make snow cover map, which is used to screen the LUCC and climate change. The best agreement is found with threshold value of NDSI≥0.2 to generate multi-temporal snow cover and snowmelt maps. Third, driving forces are systematically identified by LC maps and statistical data such as climate and socio-economic data, regarding to i) the climate changes and ii) socioeconomic development that the spatial correlation among LUCC, snow cover change, climate and socioeconomic changes are quantified by using liner regression model and negative / positive trend analysis. Our results showed that water bodies, bare land and grass land have decreasing notably. By contrast, crop land and urban area have continually increasing significantly, which are dominated in study area. The area of snow/ice have fluctuated and has strong seasonal trends, total annual snow cover has two peaks in 2005 and 2009. With increasing population from 2,324,375 in 1984 to 4,228,200 in 2014 and crop land reclamation from 6031.4 km2 in 1972 to 16549km2 in 2014 at the study area. Water resources consumption increased with support to large population and irrigate whole crop land area, caused the water shortages that the surface water bodies decreased from 2531.43km2 in the 1972s to 1067.05km2 in the 2014. The grass land with an acreage larger than 6749km2 in 1972 decreased to 922.6 km2 in 2014. The transformations between water bodies, garss land and bare land are remarkbale. The results also suggested high linearity between the LUCC and socioeconomic changes that specific land cover change be cause of the fact that socioeconomic development. In the recent 42 years, average annual temperature have been increasing significantly, although, precipitation have increased but partly weaken effect of the rising temperature, in addition snow cover more sensitive to precipitation than temperature. Results the change of climate showed a nagitive relationship between the NDSI with decrased of the snow cover and climate with increasing of the tempreature. Morover, the relationship between the LUCC and snow cover recorded higher linearity, because the temperature have increased, consequence influence on snow cover that provides melt water for study area which expanding crop land.

Potential and limitations of webcam images for snow cover monitoring in the Swiss Alps

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2017-04-01

In Switzerland, several thousands of outdoor webcams are currently connected to the Internet. They deliver freely available images that can be used to analyze snow cover variability on a high spatio-temporal resolution. To make use of this big data source, we have implemented a webcam-based snow cover mapping procedure, which allows to almost automatically derive snow cover maps from such webcam images. As there is mostly no information about the webcams and its parameters available, our registration approach automatically resolves these parameters (camera orientation, principal point, field of view) by using an estimate of the webcams position, the mountain silhouette, and a high-resolution digital elevation model (DEM). Combined with an automatic snow classification and an image alignment using SIFT features, our procedure can be applied to arbitrary images to generate snow cover maps with a minimum of effort. Resulting snow cover maps have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or hidden from webcams' positions. Up to now, we processed images of about 290 webcams from our archive, and evaluated images of 20 webcams using manually selected ground control points (GCPs) to evaluate the mapping accuracy of our procedure. We present methodological limitations and ongoing improvements, show some applications of our snow cover maps, and demonstrate that webcams not only offer a great opportunity to complement satellite-derived snow retrieval under cloudy conditions, but also serve as a reference for improved validation of satellite-based approaches.

TOMS UV Algorithm: Problems and Enhancements. 2

NASA Technical Reports Server (NTRS)

Krotkov, Nickolay; Herman, Jay; Bhartia, P. K.; Seftor, Colin; Arola, Antti; Kaurola, Jussi; Kroskinen, Lasse; Kalliskota, S.; Taalas, Petteri; Geogdzhaev, I.

2002-01-01

Satellite instruments provide global maps of surface ultraviolet (UV) irradiance by combining backscattered radiance measurements with radiative transfer models. The models are limited by uncertainties in input parameters of the atmosphere and the surface. We evaluate the effects of possible enhancements of the current Total Ozone Mapping Spectrometer (TOMS) surface UV irradiance algorithm focusing on effects of diurnal variation of cloudiness and improved treatment of snow/ice. The emphasis is on comparison between the results of the current (version 1) TOMS UV algorithm and each of the changes proposed. We evaluate different approaches for improved treatment of pixel average cloud attenuation, with and without snow/ice on the ground. In addition to treating clouds based only on the measurements at the local time of the TOMS observations, the results from other satellites and weather assimilation models can be used to estimate attenuation of the incident UV irradiance throughout the day. A new method is proposed to obtain a more realistic treatment of snow covered terrain. The method is based on a statistical relation between UV reflectivity and snow depth. The new method reduced the bias between the TOMS UV estimations and ground-based UV measurements for snow periods. The improved (version 2) algorithm will be applied to re-process the existing TOMS UV data record (since 1978) and to the future satellite sensors (e.g., Quik/TOMS, GOME, OMI on EOS/Aura and Triana/EPIC).

Methods for mapping and monitoring global glaciovolcanism

NASA Astrophysics Data System (ADS)

Curtis, Aaron; Kyle, Philip

2017-03-01

The most deadly (Nevado del Ruiz, 1985) and the most costly (Eyjafjallajökull, 2010) eruptions of the last 100 years were both glaciovolcanic. Considering its great importance to studies of volcanic hazards, global climate, and even astrobiology, the global distribution of glaciovolcanism is insufficiently understood. We present and assess three algorithms for mapping, monitoring, and predicting likely centers of glaciovolcanic activity worldwide. Each algorithm intersects buffer zones representing known Holocene-active volcanic centers with existing datasets of snow, ice, and permafrost. Two detection algorithms, RGGA and PZGA, are simple spatial join operations computed from the Randolph Glacier Inventory and the Permafrost Zonation Index, respectively. The third, MDGA, is an algorithm run on all 15 available years of the MOD10A2 weekly snow cover product from the Terra MODIS satellite radiometer. Shortcomings and advantages of the three methods are discussed, including previously unreported blunders in the MOD10A2 dataset. Comparison of the results leads to an effective approach for integrating the three methods. We show that 20.4% of known Holocene volcanic centers host glaciers or areas of permanent snow. A further 10.9% potentially interact with permafrost. MDGA and PZGA do not rely on any human input, rendering them useful for investigations of change over time. An intermediate step in MDGA involves estimating the snow-covered area at every Holocene volcanic center. These estimations can be updated weekly with no human intervention. To investigate the feasibility of an automatic ice-loss alert system, we consider three examples of glaciovolcanism in the MDGA weekly dataset. We also discuss the potential use of PZGA to model past and future glaciovolcanism based on global circulation model outputs. Combined, the three algorithms provide an automated system for understanding the geographic and temporal patterns of global glaciovolcanism which should be of use for hazard assessment, the search for extreme microbiomes, climate models, and implementation of ice-cover-based volcano monitoring systems.

Snow Cover Mapping and Ice Avalanche Monitoring from the Satellite Data of the Sentinels

NASA Astrophysics Data System (ADS)

Wang, S.; Yang, B.; Zhou, Y.; Wang, F.; Zhang, R.; Zhao, Q.

2018-04-01

In order to monitor ice avalanches efficiently under disaster emergency conditions, a snow cover mapping method based on the satellite data of the Sentinels is proposed, in which the coherence and backscattering coefficient image of Synthetic Aperture Radar (SAR) data (Sentinel-1) is combined with the atmospheric correction result of multispectral data (Sentinel-2). The coherence image of the Sentinel-1 data could be segmented by a certain threshold to map snow cover, with the water bodies extracted from the backscattering coefficient image and removed from the coherence segment result. A snow confidence map from Sentinel-2 was used to map the snow cover, in which the confidence values of the snow cover were relatively high. The method can make full use of the acquired SAR image and multispectral image under emergency conditions, and the application potential of Sentinel data in the field of snow cover mapping is exploited. The monitoring frequency can be ensured because the areas obscured by thick clouds are remedied in the monitoring results. The Kappa coefficient of the monitoring results is 0.946, and the data processing time is less than 2 h, which meet the requirements of disaster emergency monitoring.

MODSNOW-Tool: an operational tool for daily snow cover monitoring using MODIS data

NASA Astrophysics Data System (ADS)

Gafurov, Abror; Lüdtke, Stefan; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Schöne, Tilo; Schmidt, Sebastian; Kalashnikova, Olga; Merz, Bruno

2017-04-01

Spatially distributed snow cover information in mountain areas is extremely important for water storage estimations, seasonal water availability forecasting, or the assessment of snow-related hazards (e.g. enhanced snow-melt following intensive rains, or avalanche events). Moreover, spatially distributed snow cover information can be used to calibrate and/or validate hydrological models. We present the MODSNOW-Tool - an operational monitoring tool offers a user-friendly application which can be used for catchment-based operational snow cover monitoring. The application automatically downloads and processes freely available daily Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data. The MODSNOW-Tool uses a step-wise approach for cloud removal and delivers cloud-free snow cover maps for the selected river basins including basin specific snow cover extent statistics. The accuracy of cloud-eliminated MODSNOW snow cover maps was validated for 84 almost cloud-free days in the Karadarya river basin in Central Asia, and an average accuracy of 94 % was achieved. The MODSNOW-Tool can be used in operational and non-operational mode. In the operational mode, the tool is set up as a scheduled task on a local computer allowing automatic execution without user interaction and delivers snow cover maps on a daily basis. In the non-operational mode, the tool can be used to process historical time series of snow cover maps. The MODSNOW-Tool is currently implemented and in use at the national hydrometeorological services of four Central Asian states - Kazakhstan, Kyrgyzstan, Uzbekistan and Turkmenistan and used for seasonal water availability forecast.

Applications systems verification and transfer project. Volume 1: Operational applications of satellite snow cover observations: Executive summary. [usefulness of satellite snow-cover data for water yield prediction

NASA Technical Reports Server (NTRS)

Rango, A.

1981-01-01

Both LANDSAT and NOAA satellite data were used in improving snowmelt runoff forecasts. When the satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident. A cost benefit analysis run in conjunction with the snow mapping indicated a $36.5 million annual benefit accruing from a one percent improvement in forecast accuracy using the snow cover data for the western United States. The annual cost of employing the system would be $505,000. The snow mapping has proven that satellite snow cover data can be used to reduce snowmelt runoff forecast error in a cost effective manner once all operational satellite data are available within 72 hours after acquisition. Executive summaries of the individual snow mapping projects are presented.

Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches

USGS Publications Warehouse

Selkowitz, David J.; Forster, Richard; Caldwell, Megan K.

2014-01-01

Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed) pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days and averaged 37 days. Our results suggest that mixed snow-covered snow-free pixels are common at the spatial resolutions imaged by both the Landsat and MODIS sensors. This highlights the additional information available from fractional SCA products and suggests fractional SCA can provide a major advantage for hydrological and climatological monitoring and modeling, particularly when accurate representation of the spatial distribution of snow cover is critical.

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J.; Martin, K. (Principal Investigator); Amato, R. V.; Leshendok, T.

1973-01-01

The author has identified the following significant results. Comparative analysis of snow-free and snow-covered imagery of the New England Test Area has resulted in a larger number of lineaments mapped from snow-covered imagery in three out of four sets of comparative imagery. Analysts unfamiliar with the New England Test Area were utilized; the quality of imagery was independently judged to be uniform. In all image sets, a greater total length of lineaments was mapped with the snow-covered imagery. The value of this technique for fracture mapping in areas with thick soil cover is suggested. A number of potentially useful environmental applications of snow enhancement related to such areas as mining, land use, and hydrology have been identified.

Design, Development and Testing of Web Services for Multi-Sensor Snow Cover Mapping

NASA Astrophysics Data System (ADS)

Kadlec, Jiri

This dissertation presents the design, development and validation of new data integration methods for mapping the extent of snow cover based on open access ground station measurements, remote sensing images, volunteer observer snow reports, and cross country ski track recordings from location-enabled mobile devices. The first step of the data integration procedure includes data discovery, data retrieval, and data quality control of snow observations at ground stations. The WaterML R package developed in this work enables hydrologists to retrieve and analyze data from multiple organizations that are listed in the Consortium of Universities for the Advancement of Hydrologic Sciences Inc (CUAHSI) Water Data Center catalog directly within the R statistical software environment. Using the WaterML R package is demonstrated by running an energy balance snowpack model in R with data inputs from CUAHSI, and by automating uploads of real time sensor observations to CUAHSI HydroServer. The second step of the procedure requires efficient access to multi-temporal remote sensing snow images. The Snow Inspector web application developed in this research enables the users to retrieve a time series of fractional snow cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) for any point on Earth. The time series retrieval method is based on automated data extraction from tile images provided by a Web Map Tile Service (WMTS). The average required time for retrieving 100 days of data using this technique is 5.4 seconds, which is significantly faster than other methods that require the download of large satellite image files. The presented data extraction technique and space-time visualization user interface can be used as a model for working with other multi-temporal hydrologic or climate data WMTS services. The third, final step of the data integration procedure is generating continuous daily snow cover maps. A custom inverse distance weighting method has been developed to combine volunteer snow reports, cross-country ski track reports and station measurements to fill cloud gaps in the MODIS snow cover product. The method is demonstrated by producing a continuous daily time step snow presence probability map dataset for the Czech Republic region. The ability of the presented methodology to reconstruct MODIS snow cover under cloud is validated by simulating cloud cover datasets and comparing estimated snow cover to actual MODIS snow cover. The percent correctly classified indicator showed accuracy between 80 and 90% using this method. Using crowdsourcing data (volunteer snow reports and ski tracks) improves the map accuracy by 0.7--1.2%. The output snow probability map data sets are published online using web applications and web services. Keywords: crowdsourcing, image analysis, interpolation, MODIS, R statistical software, snow cover, snowpack probability, Tethys platform, time series, WaterML, web services, winter sports.

Multi-Sensor Approach to Mapping Snow Cover Using Data From NASA's EOS Aqua and Terra Spacecraft

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M. J.

2003-12-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Over the past several decades both optical and passive microwave satellite data have been utilized for snow mapping at the regional to global scale. For the period 1978 to 2002, we have shown earlier that both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are, depending on season, less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Snow mapping using optical data is based on the magnitude of the surface reflectance while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Our previous work has defined the respective advantages and disadvantages of these two types of satellite data for snow cover mapping and it is clear that a blended product is optimal. We present a multi-sensor approach to snow mapping based both on historical data as well as data from current NASA EOS sensors. For the period 1978 to 2002 we combine data from the NOAA weekly snow charts with passive microwave data from the SMMR and SSM/I brightness temperature record. For the current and future time period we blend MODIS and AMSR-E data sets. An example of validation at the brightness temperature level is provided through the comparison of AMSR-E with data from the well-calibrated heritage SSM/I sensor over a large homogeneous snow-covered surface (Dome C, Antarctica). Prototype snow cover maps from AMSR-E compare well with maps derived from SSM/I. Our current blended product is being developed in the 25 km EASE-Grid while the MODIS data being used are in the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) allowing the blended product to indicate percent snow cover over the larger grid cell. Relationships between the percent area covered by snow as indicated by the MODIS data and the threshold for the appearance of snow as indicated by the passive microwave data are presented. Both MODIS and AMSR-E data have enhanced spatial resolution compared to the earlier data sources and examples of how this increased spatial resolution results in more accurate snow cover maps are presented. A wide range of validation data sets are being employed in this study including the NASA Cold Lands Processes Field Experiment undertaken in Colorado during 2002 and 2003.

Computational provenance in hydrologic science: a snow mapping example.

PubMed

Dozier, Jeff; Frew, James

2009-03-13

Computational provenance--a record of the antecedents and processing history of digital information--is key to properly documenting computer-based scientific research. To support investigations in hydrologic science, we produce the daily fractional snow-covered area from NASA's moderate-resolution imaging spectroradiometer (MODIS). From the MODIS reflectance data in seven wavelengths, we estimate the fraction of each 500 m pixel that snow covers. The daily products have data gaps and errors because of cloud cover and sensor viewing geometry, so we interpolate and smooth to produce our best estimate of the daily snow cover. To manage the data, we have developed the Earth System Science Server (ES3), a software environment for data-intensive Earth science, with unique capabilities for automatically and transparently capturing and managing the provenance of arbitrary computations. Transparent acquisition avoids the scientists having to express their computations in specific languages or schemas in order for provenance to be acquired and maintained. ES3 models provenance as relationships between processes and their input and output files. It is particularly suited to capturing the provenance of an evolving algorithm whose components span multiple languages and execution environments.

Improving alpine-region spectral unmixing with optimal-fit snow endmembers

NASA Technical Reports Server (NTRS)

Painter, Thomas H.; Roberts, Dar A.; Green, Robert O.; Dozier, Jeff

1995-01-01

Surface albedo and snow-covered-area (SCA) are crucial inputs to the hydrologic and climatologic modeling of alpine and seasonally snow-covered areas. Because the spectral albedo and thermal regime of pure snow depend on grain size, areal distribution of snow grain size is required. Remote sensing has been shown to be an effective (and necessary) means of deriving maps of grain size distribution and snow-covered-area. Developed here is a technique whereby maps of grain size distribution improve estimates of SCA from spectral mixture analysis with AVIRIS data.

Classification of surface types using SIR-C/X-SAR, Mount Everest Area, Tibet

USGS Publications Warehouse

Albright, Thomas P.; Painter, Thomas H.; Roberts, Dar A.; Shi, Jiancheng; Dozier, Jeff; Fielding, Eric

1998-01-01

Imaging radar is a promising tool for mapping snow and ice cover in alpine regions. It combines a high-resolution, day or night, all-weather imaging capability with sensitivity to hydrologic and climatic snow and ice parameters. We use the spaceborne imaging radar-C/X-band synthetic aperture radar (SIR-C/X-SAR) to map snow and glacial ice on the rugged north slope of Mount Everest. From interferometrically derived digital elevation data, we compute the terrain calibration factor and cosine of the local illumination angle. We then process and terrain-correct radar data sets acquired on April 16, 1994. In addition to the spectral data, we include surface slope to improve discrimination among several surface types. These data sets are then used in a decision tree to generate an image classification. This method is successful in identifying and mapping scree/talus, dry snow, dry snow-covered glacier, wet snow-covered glacier, and rock-covered glacier, as corroborated by comparison with existing surface cover maps and other ancillary information. Application of the classification scheme to data acquired on October 7 of the same year yields accurate results for most surface types but underreports the extent of dry snow cover.

Improving automated disturbance maps using snow-covered landsat time series stacks

Treesearch

Kirk M. Stueve; Ian W. Housman; Patrick L. Zimmerman; Mark D. Nelson; Jeremy Webb; Charles H. Perry; Robert A. Chastain; Dale D. Gormanson; Chengquan Huang; Sean P. Healey; Warren B. Cohen

2012-01-01

Snow-covered winter Landsat time series stacks are used to develop a nonforest mask to enhance automated disturbance maps produced by the Vegetation Change Tracker (VCT). This method exploits the enhanced spectral separability between forested and nonforested areas that occurs with sufficient snow cover. This method resulted in significant improvements in Vegetation...

Snow Cover Mapping at the Continental to Global Scale Using Combined Visible and Passive Microwave Satellite Data

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M.; Savoie, M. H.

2007-12-01

Over the past several decades both visible and passive microwave satellite data have been utilized for snow mapping at the continental to global scale. Snow mapping using visible data has been based primarily on the magnitude of the surface reflectance, and in more recent cases on specific spectral signatures, while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible satellite data and the visible data typically show higher monthly variability. We describe the respective problems as well as the advantages and disadvantages of these two types of satellite data for snow cover mapping and demonstrate how a multi-sensor approach is optimal. For the period 1978 to present we combine data from the NOAA weekly snow charts with snow cover derived from the SMMR and SSM/I brightness temperature data. For the period since 2002 we blend NASA EOS MODIS and AMSR-E data sets. Our current product incorporates MODIS data from the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) with microwave-derived snow water equivalent (SWE) at 25 km, resulting in a blended product that includes percent snow cover in the larger grid cell whenever the microwave SWE signal is absent. Validation of AMSR-E at the brightness temperature level is provided through the comparison with data from the well-calibrated heritage SSM/I sensor over large homogeneous snow-covered surfaces (e.g. Dome C region, Antarctica). We also describe how the application of the higher frequency microwave channels (85 and 89 GHz)enhances accurate mapping of shallow and intermittent snow cover.

MODIS Snow and Ice Products from the NSIDC DAAC

NASA Technical Reports Server (NTRS)

Scharfen, Greg R.; Hall, Dorothy K.; Riggs, George A.

1997-01-01

The National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) provides data and information on snow and ice processes, especially pertaining to interactions among snow, ice, atmosphere and ocean, in support of research on global change detection and model validation, and provides general data and information services to cryospheric and polar processes research community. The NSIDC DAAC is an integral part of the multi-agency-funded support for snow and ice data management services at NSIDC. The Moderate Resolution Imaging Spectroradiometer (MODIS) will be flown on the first Earth Observation System (EOS) platform (AM-1) in 1998. The MODIS Instrument Science Team is developing geophysical products from data collected by the MODIS instrument, including snow and ice products which will be archived and distributed by NSIDC DAAC. The MODIS snow and ice mapping algorithms will generate global snow, lake ice, and sea ice cover products on a daily basis. These products will augment the existing record of satellite-derived snow cover and sea ice products that began about 30 years ago. The characteristics of these products, their utility, and comparisons to other data set are discussed. Current developments and issues are summarized.

Design of a High Resolution Open Access Global Snow Cover Web Map Service Using Ground and Satellite Observations

NASA Astrophysics Data System (ADS)

Kadlec, J.; Ames, D. P.

2014-12-01

The aim of the presented work is creating a freely accessible, dynamic and re-usable snow cover map of the world by combining snow extent and snow depth datasets from multiple sources. The examined data sources are: remote sensing datasets (MODIS, CryoLand), weather forecasting model outputs (OpenWeatherMap, forecast.io), ground observation networks (CUAHSI HIS, GSOD, GHCN, and selected national networks), and user-contributed snow reports on social networks (cross-country and backcountry skiing trip reports). For adding each type of dataset, an interface and an adapter is created. Each adapter supports queries by area, time range, or combination of area and time range. The combined dataset is published as an online snow cover mapping service. This web service lowers the learning curve that is required to view, access, and analyze snow depth maps and snow time-series. All data published by this service are licensed as open data; encouraging the re-use of the data in customized applications in climatology, hydrology, sports and other disciplines. The initial version of the interactive snow map is on the website snow.hydrodata.org. This website supports the view by time and view by site. In view by time, the spatial distribution of snow for a selected area and time period is shown. In view by site, the time-series charts of snow depth at a selected location is displayed. All snow extent and snow depth map layers and time series are accessible and discoverable through internationally approved protocols including WMS, WFS, WCS, WaterOneFlow and WaterML. Therefore they can also be easily added to GIS software or 3rd-party web map applications. The central hypothesis driving this research is that the integration of user contributed data and/or social-network derived snow data together with other open access data sources will result in more accurate and higher resolution - and hence more useful snow cover maps than satellite data or government agency produced data by itself.

Fractional Snowcover Estimates from Earth Observing System (EOS) Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS)

NASA Technical Reports Server (NTRS)

Salomonson, Vincent V.

2002-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System (EOS) Terra and Aqua missions has shown considerable capability for mapping snowcover. The typical approach that has used, along with other criteria, the Normalized Snow Difference Index (NDSI) that takes the difference between 500 meter observations at 1.64 micrometers (MODIS band 6) and 0.555 micrometers (MODIS band 4) over the sum of these observations to determine whether MODIS pixels are snowcovered or not in mapping the extent of snowcover. For many hydrological and climate studies using remote sensing of snowcover, it is desirable to assess if the MODIS snowcover observations could not be enhanced by providing the fraction of snowcover in each MODIS observation (pixel). Pursuant to this objective studies have been conducted to assess whether there is sufficient "signal%o in the NDSI parameter to provide useful estimates of fractional snowcover in each MODIS 500 meter pixel. To accomplish this objective high spatial resolution (30 meter) Landsat snowcover observations were used and co-registered with MODIS 500 meter pixels. The NDSI approach was used to assess whether a Landsat pixel was or was not snowcovered. Then the number of snowcovered Landsat pixels within a MODIS pixel was used to determine the fraction of snowcover within each MODIS pixel. The e results were then used to develop statistical relationships between the NDSI value for each 500 meter MODIS pixel and the fraction of snowcover in the MODIS pixel. Such studies were conducted for three widely different areas covered by Landsat scenes in Alaska, Russia, and the Quebec Province in Canada. The statistical relationships indicate that a 10 percent accuracy can be attained. The variability in the statistical relationship for the three areas was found to be remarkably similar (-0.02 for mean error and less than 0.01 for mean absolute error and standard deviation). Independent tests of the relationships were accomplished by taking the relationship of fractional snow-cover to NDSI from one area (e.g., Alaska) and testing it on the other two areas (e.g. Russia and Quebec). Again the results showed that fractional snow-cover can be estimated to 10 percent. The results have been shown to have advantages over other published fractional snowcover algorithms applied to MODIS data. Most recently the fractional snow-cover algorithm has been applied using 500-meter observations over the state of Colorado for a period spanning 25 days. The results exhibit good behavior in mapping the spatial and temporal variability in snowcover over that 25-day period. Overall these studies indicate that robust estimates of fractional snow-cover can be attained using the NDSI parameter over areas extending in size from watersheds relatively large compared to MODIS pixels to global land cover. Other refinements to this approach as well as different approaches are being examined for mapping fractional snow-cover using MODIS observations.

Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Model

NASA Technical Reports Server (NTRS)

Zaitchik, Benjamin F.; Rodell, Matthew

2008-01-01

Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation SCA indicates only the presence or absence of snow, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes both during the snow season and, in some regions, on into the following spring.

Snow survey and vegetation growth in high mountains (Swiss Alps)

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1973-01-01

The author has identified the following significant results. A method for mapping snow over large areas was developed combining the possibilities of a Quantimet (QTM 72) to evaluate the exact density level of the snow cover for each individual image (or a selected section of the photo) with the higher resolution of photographic techniques. The density level established on the monitor by visual control is used as reference for the exposure time of a lithographic film, producing a clear tonal separation of all snow- and ice-covered areas from uncovered land in black and white. The data is projected onto special maps 1:500,000 or 1:100,000 showing the contour lines and the hydrographic features only. The areal extent of the snow cover may be calculated directly with the QTM 720 or on the map. Bands 4 and 5 provide the most accurate results for mapping snow. Using all four bands a separation of an old melting snow cover from a new one is possible. Regional meteorological studies combining ERTS-1 imagery and conventional sources describe synoptical evolution of meteorological systems over the Alps.

Georectification and snow classification of webcam images: potential for complementing satellite-derrived snow maps over Switzerland

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2016-04-01

The spatial and temporal variability of snow cover has a significant impact on climate and environment and is of great socio-economic importance for the European Alps. Satellite remote sensing data is widely used to study snow cover variability and can provide spatially comprehensive information on snow cover extent. However, cloud cover strongly impedes the surface view and hence limits the number of useful snow observations. Outdoor webcam images not only offer unique potential for complementing satellite-derived snow retrieval under cloudy conditions but could also serve as a reference for improved validation of satellite-based approaches. Thousands of webcams are currently connected to the Internet and deliver freely available images with high temporal and spatial resolutions. To exploit the untapped potential of these webcams, a semi-automatic procedure was developed to generate snow cover maps based on webcam images. We used daily webcam images of the Swiss alpine region to apply, improve, and extend existing approaches dealing with the positioning of photographs within a terrain model, appropriate georectification, and the automatic snow classification of such photographs. In this presentation, we provide an overview of the implemented procedure and demonstrate how our registration approach automatically resolves the orientation of a webcam by using a high-resolution digital elevation model and the webcam's position. This allows snow-classified pixels of webcam images to be related to their real-world coordinates. We present several examples of resulting snow cover maps, which have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or not visible from webcams' positions. The procedure is expected to work under almost any weather condition and demonstrates the feasibility of using webcams for the retrieval of high-resolution snow cover information.

Utilizing Multiple Datasets for Snow Cover Mapping

NASA Technical Reports Server (NTRS)

Tait, Andrew B.; Hall, Dorothy K.; Foster, James L.; Armstrong, Richard L.

1999-01-01

Snow-cover maps generated from surface data are based on direct measurements, however they are prone to interpolation errors where climate stations are sparsely distributed. Snow cover is clearly discernable using satellite-attained optical data because of the high albedo of snow, yet the surface is often obscured by cloud cover. Passive microwave (PM) data is unaffected by clouds, however, the snow-cover signature is significantly affected by melting snow and the microwaves may be transparent to thin snow (less than 3cm). Both optical and microwave sensors have problems discerning snow beneath forest canopies. This paper describes a method that combines ground and satellite data to produce a Multiple-Dataset Snow-Cover Product (MDSCP). Comparisons with current snow-cover products show that the MDSCP draws together the advantages of each of its component products while minimizing their potential errors. Improved estimates of the snow-covered area are derived through the addition of two snow-cover classes ("thin or patchy" and "high elevation" snow cover) and from the analysis of the climate station data within each class. The compatibility of this method for use with Moderate Resolution Imaging Spectroradiometer (MODIS) data, which will be available in 2000, is also discussed. With the assimilation of these data, the resolution of the MDSCP would be improved both spatially and temporally and the analysis would become completely automated.

Snow water equivalent mapping in Norway

NASA Astrophysics Data System (ADS)

Tveito, O. E.; Udnæs, H.-C.; Engeset, R.; Førland, E. J.; Isaksen, K.; Mengistu, Z.

2003-04-01

In high latitude area snow covers the ground large parts of the year. Information about the water volume as snow is of major importance in many respects. Flood forecasters at NVE need it in order to assess possible flood risks. Hydropower producers need it to plan the most efficient production of the water in their reservoirs, traders to estimate the potential energy available for the market. Meteorologists on their side use the information as boundary conditions in weather forecasting models. The Norwegian meteorological institute has provided snow accumulation maps for Norway for more than 50 years. These maps are now produced twice a month in the winter season. They show the accumulated precipitation in the winter season from the day the permanent snow cover is established. They do however not take melting into account, and do therefore not give a good description of the actual snow amounts during and after periods with snowmelt. Due to an increased need for a direct measure of water volumes as snow cover, met.no and NVE initialized a joint project in order to establish maps of the actual snow cover expressed in water equivalents. The project utilizes recent developments in the use of GIS in spatial modeling. Daily precipitation and temperature are distributed in space by using objective spatial interpolation methods. The interpolation considers topographical and other geographical parameters as well as weather type information. A degree-day model is used at each modeling point to calculate snow-accumulation and snowmelt. The maps represent a spatial scale of 1x1 km2. The modeled snow reservoir is validated by snow pillow values as well traditional snow depth observations. Preliminary results show that the new snow modeling approach reproduces the snow water equivalent well. The spatial approach also opens for a wide use in the terms of areal analysis.

Improved Passive Microwave Algorithms for North America and Eurasia

NASA Technical Reports Server (NTRS)

Foster, James; Chang, Alfred; Hall, Dorothy

1997-01-01

Microwave algorithms simplify complex physical processes in order to estimate geophysical parameters such as snow cover and snow depth. The microwave radiances received at the satellite sensor and expressed as brightness temperatures are a composite of contributions from the Earth's surface, the Earth's atmosphere and from space. Owing to the coarse resolution inherent to passive microwave sensors, each pixel value represents a mixture of contributions from different surface types including deep snow, shallow snow, forests and open areas. Algorithms are generated in order to resolve these mixtures. The accuracy of the retrieved information is affected by uncertainties in the assumptions used in the radiative transfer equation (Steffen et al., 1992). One such uncertainty in the Chang et al., (1987) snow algorithm is that the snow grain radius is 0.3 mm for all layers of the snowpack and for all physiographic regions. However, this is not usually the case. The influence of larger grain sizes appears to be of more importance for deeper snowpacks in the interior of Eurasia. Based on this consideration and the effects of forests, a revised SMMR snow algorithm produces more realistic snow mass values. The purpose of this study is to present results of the revised algorithm (referred to for the remainder of this paper as the GSFC 94 snow algorithm) which incorporates differences in both fractional forest cover and snow grain size. Results from the GSFC 94 algorithm will be compared to the original Chang et al. (1987) algorithm and to climatological snow depth data as well.

Multitemporal Snow Cover Mapping in Mountainous Terrain for Landsat Climate Data Record Development

NASA Technical Reports Server (NTRS)

Crawford, Christopher J.; Manson, Steven M.; Bauer, Marvin E.; Hall, Dorothy K.

2013-01-01

A multitemporal method to map snow cover in mountainous terrain is proposed to guide Landsat climate data record (CDR) development. The Landsat image archive including MSS, TM, and ETM+ imagery was used to construct a prototype Landsat snow cover CDR for the interior northwestern United States. Landsat snow cover CDRs are designed to capture snow-covered area (SCA) variability at discrete bi-monthly intervals that correspond to ground-based snow telemetry (SNOTEL) snow-water-equivalent (SWE) measurements. The June 1 bi-monthly interval was selected for initial CDR development, and was based on peak snowmelt timing for this mountainous region. Fifty-four Landsat images from 1975 to 2011 were preprocessed that included image registration, top-of-the-atmosphere (TOA) reflectance conversion, cloud and shadow masking, and topographic normalization. Snow covered pixels were retrieved using the normalized difference snow index (NDSI) and unsupervised classification, and pixels having greater (less) than 50% snow cover were classified presence (absence). A normalized SCA equation was derived to independently estimate SCA given missing image coverage and cloud-shadow contamination. Relative frequency maps of missing pixels were assembled to assess whether systematic biases were embedded within this Landsat CDR. Our results suggest that it is possible to confidently estimate historical bi-monthly SCA from partially cloudy Landsat images. This multitemporal method is intended to guide Landsat CDR development for freshwaterscarce regions of the western US to monitor climate-driven changes in mountain snowpack extent.

Sentinels for snow science

NASA Astrophysics Data System (ADS)

Gascoin, S.; Grizonnet, M.; Baba, W. M.; Hagolle, O.; Fayad, A.; Mermoz, S.; Kinnard, C.; Fatima, K.; Jarlan, L.; Hanich, L.

2017-12-01

Current spaceborne sensors do not allow retrieving the snow water equivalent in mountain regions, "the most important unsolved problem in snow hydrology" (Dozier, 2016). While the NASA is operating an airborne mission to survey the SWE in the western USA, elsewhere, however, snow scientists and water managers do not have access to routine SWE measurements at the scale of a mountain range. In this presentation we suggest that the advent of the Copernicus Earth Observation programme opens new perspectives to address this issue in mountain regions worldwide. The Sentinel-2 mission will provide global-scale multispectral observations at 20 m resolution every 5-days (cloud permitting). The Sentinel-1 mission is already imaging the global land surface with a C-band radar at 10 m resolution every 6 days. These observations are unprecedented in terms of spatial and temporal resolution. However, the nature of the observation (radiometry, wavelength) is in the continuity of previous and ongoing missions. As a result, it is relatively straightforward to re-use algorithms that were developed by the remote sensing community over the last decades. For instance, Sentinel-2 data can be used to derive maps of the snow cover extent from the normalized difference snow index, which was initially proposed for Landsat. In addition, the 5-days repeat cycle allows the application of gap-filling algorithms, which were developed for MODIS based on the temporal dimension. The Sentinel-1 data can be used to detect the wet snow cover and track melting areas as proposed for ERS in the early 1990's. Eventually, we show an example where Sentinel-2-like data improved the simulation of the SWE in the data-scarce region of the High Atlas in Morocco through assimilation in a distributed snowpack model. We encourage snow scientists to embrace Sentinel-1 and Sentinel-2 data to enhance our knowledge on the snow cover dynamics in mountain regions.

A snow cover climatology for the Pyrenees from MODIS snow products

NASA Astrophysics Data System (ADS)

Gascoin, S.; Hagolle, O.; Huc, M.; Jarlan, L.; Dejoux, J.-F.; Szczypta, C.; Marti, R.; Sánchez, R.

2014-11-01

The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (we) and 105 mm respectively, for both MOD10A1 and MYD10A1. Kappa coefficients are within 0.74 and 0.92 depending on the product and the variable. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both datasets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decreases over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gapfilling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band. We finally analyze the snow patterns for the atypical winter 2011-2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

Mapping the spatial distribution and time evolution of snow water equivalent with passive microwave measurements

USGS Publications Warehouse

Guo, J.; Tsang, L.; Josberger, E.G.; Wood, A.W.; Hwang, J.-N.; Lettenmaier, D.P.

2003-01-01

This paper presents an algorithm that estimates the spatial distribution and temporal evolution of snow water equivalent and snow depth based on passive remote sensing measurements. It combines the inversion of passive microwave remote sensing measurements via dense media radiative transfer modeling results with snow accumulation and melt model predictions to yield improved estimates of snow depth and snow water equivalent, at a pixel resolution of 5 arc-min. In the inversion, snow grain size evolution is constrained based on pattern matching by using the local snow temperature history. This algorithm is applied to produce spatial snow maps of Upper Rio Grande River basin in Colorado. The simulation results are compared with that of the snow accumulation and melt model and a linear regression method. The quantitative comparison with the ground truth measurements from four Snowpack Telemetry (SNOTEL) sites in the basin shows that this algorithm is able to improve the estimation of snow parameters.

MODIS Snow and Ice Production

NASA Technical Reports Server (NTRS)

Hall, Dorthoy K.; Hoser, Paul (Technical Monitor)

2002-01-01

Daily, global snow cover maps, and sea ice cover and sea ice surface temperature (IST) maps are derived from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS), are available at no cost through the National Snow and Ice Data Center (NSIDC). Included on this CD-ROM are samples of the MODIS snow and ice products. In addition, an animation, done by the Scientific Visualization studio at Goddard Space Flight Center, is also included.

Temporal trend of the snow-related variables in Sierra Nevada in the last years: An analysis combining Earth Observation and hydrological modelling

NASA Astrophysics Data System (ADS)

Pérez-Luque, Antonio J.; Herrero, Javier; Bonet, Francisco J.; Pérez-Pérez, Ramón

2016-04-01

Climate change is causing declines in snow-cover extent and duration in European mountain ranges. This is especially important in Mediterranean mountain ranges where the observed trends towards precipitation and higher temperatures can provoke problems of water scarcity. In this work, we analyzed temporal trends (2000 to 2014) of snow-related variables obtained from satellite and modelling data in Sierra Nevada, a Mediterranean high-mountain range located in Southern Spain, at 37°N. Snow cover indicators (snow-cover duration, snow-cover onset dates and snow-cover melting dates) were obtained by processing images of MOD10A2 MODIS product using an automated workflow. Precipitation data were obtained using WiMMed, a complete and fully distributed hydrological model that is used to map the annual rainfall and snowfall with a resolution of 30x30 m over the whole study area. It uses expert algorithms to interpolate precipitation and temperature at an hourly scale, and simulates partition of precipitation into snowfall with several methods. For each snow-related indicator (snow-covers and snowfall), a trend analysis was applied at the MODIS pixel scale during the study period (2000-2014). We applied Mann-Kendall test and Theil-Sen slope estimation in each of the pixels comprising Sierra Nevada. The trend analysis assesses the intensity, magnitude and degree of statistical significance during the period analysed. The spatial pattern of these trends was explored according to elevation ranges. Finally, we explored the relationship between trends of snow-cover related indicators and precipitation trends. Our results show that snow-cover has undergone significant changes in the last 14 years. 80 % of the pixels covering Sierra Nevada showed a negative trend in the duration of snow-cover. We also observed a delay in the snow-cover onset date (68.03 % pixels showing a positive trend in the snow-cover onset date) and an advance in the melt date (80.72 % of pixels followed a negative trend for the snow-cover melting date). Precipitation does not show a significant trend for these years, even though its inter-annual variability has been outstanding. The maximum mean annual precipitation of 906 mm/year doubles the mean precipitation, which somehow compensates for the occurrence of a sequence of dry years with a minimum of 250 mm/year. The assessment of the spatial pattern of snow cover duration shows that both the trend and the slope of the trend becomes more pronounced with elevation. At higher elevations the snow-cover duration decreased an average of 3 days from 2000-2014. This research has been funded by ECOPOTENTIAL (Improving future ecosystem benefits through Earth Observations) Horizon 2020 EU project, and Sierra Nevada Global Change Observatory (LTER-site)

MODIS-based Snow Cover Variability of the Upper River Grande Basin

NASA Astrophysics Data System (ADS)

Yu, B.; Wang, X.; Xie, H.

2007-12-01

Snow cover and its spring melting in the Upper Rio Grande Basin provides a major water source for the Upper to Middle Rio Grande valley and Elephant Butte Reservoir. Thus understanding the snowpack and its variability in the context of global climate change is crucial to the sustainable water resources for the region. MODIS instruments (on Terra and Aqua) have provided time series of snow cover products since 2000, but suffering with cloud contaminations. In this study, we evaluated four newly developed cloudless snow cover products (less than 10%) and four standard products: daily (MOD10A1, MYD10A1) and 8-day (MOD10A2, MYD10A2), in comparison with in situ Snowpack Telemetry (SNOTEL) measurements for the hydrological year 2003-2004. The four new products are daily composite of Terra and Aqua (MODMYD10DC), multi-day composites of Terra (MOD10MC), Aqua (MYD10MC), and Terra and Aqua (MODMYD10MC). The standard daily and 8-day products can classify land correctly, but had fairly low accuracy in snow classification due to cloud contamination (a average of 39.4% for Terra and 45% for Aqua in the year 2003-2004). All the new multi-day composite products tended to have high accuracy in classifying both snow and land (over 90%), as the cloud cover has been reduced to less than 10% (~5% for the year) under the new algorithm . This result is consistent with a previous study in the Xinjiang area, China (Wang and Xie, 2007). Therefore, MOD10MC (before the Aqua data available) and MODMYD10MC products are used to get the mean snow cover of the Upper Rio Grande Basin from 2000 to 2007. The snow depletion curve derived from the new cloud-free snow cover map will be used to examine its effect on stream discharge.

Using Commercial Digital Cameras and Structure-for-Motion Software to Map Snow Cover Depth from Small Aircraft

NASA Astrophysics Data System (ADS)

Sturm, M.; Nolan, M.; Larsen, C. F.

2014-12-01

A long-standing goal in snow hydrology has been to map snow cover in detail, either mapping snow depth or snow water equivalent (SWE) with sub-meter resolution. Airborne LiDAR and air photogrammetry have been used successfully for this purpose, but both require significant investments in equipment and substantial processing effort. Here we detail a relatively inexpensive and simple airborne photogrammetric technique that can be used to measure snow depth. The main airborne hardware consists of a consumer-grade digital camera attached to a survey-quality, dual-frequency GPS. Photogrammetric processing is done using commercially available Structure from Motion (SfM) software that does not require ground control points. Digital elevation models (DEMs) are made from snow-free acquisitions in the summer and snow-covered acquisitions in winter, and the maps are then differenced to arrive at snow thickness. We tested the accuracy and precision of snow depths measured using this system through 1) a comparison with airborne scanning LiDAR, 2) a comparison of results from two independent and slightly different photogrameteric systems, and 3) comparison to extensive on-the-ground measured snow depths. Vertical accuracy and precision are on the order of +/-30 cm and +/- 8 cm, respectively. The accuracy can be made to approach that of the precision if suitable snow-free ground control points exists and are used to co-register summer to winter DEM maps. Final snow depth accuracy from our series of tests was on the order of ±15 cm. This photogrammetric method substantially lowers the economic and expertise barriers to entry for mapping snow.

Comparison of Snow Mass Estimates from a Prototype Passive Microwave Snow Algorithm, a Revised Algorithm and a Snow Depth Climatology

NASA Technical Reports Server (NTRS)

Foster, J. L.; Chang, A. T. C.; Hall, D. K.

1997-01-01

While it is recognized that no single snow algorithm is capable of producing accurate global estimates of snow depth, for research purposes it is useful to test an algorithm's performance in different climatic areas in order to see how it responds to a variety of snow conditions. This study is one of the first to develop separate passive microwave snow algorithms for North America and Eurasia by including parameters that consider the effects of variations in forest cover and crystal size on microwave brightness temperature. A new algorithm (GSFC 1996) is compared to a prototype algorithm (Chang et al., 1987) and to a snow depth climatology (SDC), which for this study is considered to be a standard reference or baseline. It is shown that the GSFC 1996 algorithm compares much more favorably to the SDC than does the Chang et al. (1987) algorithm. For example, in North America in February there is a 15% difference between the GSFC 198-96 Algorithm and the SDC, but with the Chang et al. (1987) algorithm the difference is greater than 50%. In Eurasia, also in February, there is only a 1.3% difference between the GSFC 1996 algorithm and the SDC, whereas with the Chang et al. (1987) algorithm the difference is about 20%. As expected, differences tend to be less when the snow cover extent is greater, particularly for Eurasia. The GSFC 1996 algorithm performs better in North America in each month than dose the Chang et al. (1987) algorithm. This is also the case in Eurasia, except in April and May when the Chang et al.(1987) algorithms is in closer accord to the SDC than is GSFC 1996 algorithm.

Evaluation and time series analysis of mountain snow from MODIS and VIIRS fractional snow cover products

NASA Astrophysics Data System (ADS)

Bormann, K.; Rittger, K.; Painter, T. H.

2016-12-01

The continuation of large-scale snow cover records into the future is crucial for monitoring the impacts of global pressures such as climate change and weather variability on the cryosphere. With daily MODIS records since 2000 from a now ageing MODIS constellation (Terra & Aqua) and daily VIIRS records since 2012 from the Suomi-NPP platform, the consistency of information between the two optical sensors must be understood. First, we evaluated snow cover maps derived from both MODIS and VIIRS retrievals with coincident cloud-free Landsat 8 OLI maps across a range of locations. We found that both MODIS and VIIRS snow cover maps show similar errors when evaluated with Landsat OLI retrievals. Preliminary results also show a general agreement in regional snowline between the two sensors that is maintained during the spring snowline retreat where the proportion of mixed pixels is increased. The agreement between sensors supports the future use of VIIRS snow cover maps to continue the long-term record beyond the lifetime of MODIS. Second, we use snowline elevation to quantify large scale snow cover variability and to monitor potential changes in the rain/snow transition zone where climate change pressures may be enhanced. Despite the large inter-annual variability that is often observed in snow metrics, we expect that over the 16-year time series we will see a rise in seasonal elevation of the snowline and consequently an increasing rain/snow transition boundary in mountain environments. These results form the basis for global snowline elevation monitoring using optical remote sensing data and highlight regional differences in snowline elevation dynamics. The long-term variability in observed snowline elevation provides a recent climatology of mountain snowpack across several regions that will likely to be of interest to those interested in climate change impacts in mountain environments. This work will also be of interest to existing users of MODSCAG and VIIRSCAG snow cover products and those working in remote sensing of the mountain snowpack.

Formation, distribution and variability in snow cover on the Asian territory of the USSR

NASA Technical Reports Server (NTRS)

Pupkov, V. N.

1985-01-01

A description is given of maps compiled for annual and average multiple-year water reserves. The annual and average multiple-year maximum snow cover height for winter, extreme values of maximum snow reserves, and the average height and snow reserves at the end of each decade are shown. These maps were made for the entire Asian territory of the USSR, excluding Central Asia, Kamchatka Peninsula, and the Sakhalin Islands.

Estimation of Subpixel Snow-Covered Area by Nonparametric Regression Splines

NASA Astrophysics Data System (ADS)

Kuter, S.; Akyürek, Z.; Weber, G.-W.

2016-10-01

Measurement of the areal extent of snow cover with high accuracy plays an important role in hydrological and climate modeling. Remotely-sensed data acquired by earth-observing satellites offer great advantages for timely monitoring of snow cover. However, the main obstacle is the tradeoff between temporal and spatial resolution of satellite imageries. Soft or subpixel classification of low or moderate resolution satellite images is a preferred technique to overcome this problem. The most frequently employed snow cover fraction methods applied on Moderate Resolution Imaging Spectroradiometer (MODIS) data have evolved from spectral unmixing and empirical Normalized Difference Snow Index (NDSI) methods to latest machine learning-based artificial neural networks (ANNs). This study demonstrates the implementation of subpixel snow-covered area estimation based on the state-of-the-art nonparametric spline regression method, namely, Multivariate Adaptive Regression Splines (MARS). MARS models were trained by using MODIS top of atmospheric reflectance values of bands 1-7 as predictor variables. Reference percentage snow cover maps were generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also employed to estimate the percentage snow-covered area on the same data set. The results indicated that the developed MARS model performed better than th

The performance of the new enhanced-resolution satellite passive microwave dataset applied for snow water equivalent estimation

NASA Astrophysics Data System (ADS)

Pan, J.; Durand, M. T.; Jiang, L.; Liu, D.

2017-12-01

The newly-processed NASA MEaSures Calibrated Enhanced-Resolution Brightness Temperature (CETB) reconstructed using antenna measurement response function (MRF) is considered to have significantly improved fine-resolution measurements with better georegistration for time-series observations and equivalent field of view (FOV) for frequencies with the same monomial spatial resolution. We are looking forward to its potential for the global snow observing purposes, and therefore aim to test its performance for characterizing snow properties, especially the snow water equivalent (SWE) in large areas. In this research, two candidate SWE algorithms will be tested in China for the years between 2005 to 2010 using the reprocessed TB from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), with the results to be evaluated using the daily snow depth measurements at over 700 national synoptic stations. One of the algorithms is the SWE retrieval algorithm used for the FengYun (FY) - 3 Microwave Radiation Imager. This algorithm uses the multi-channel TB to calculate SWE for three major snow regions in China, with the coefficients adapted for different land cover types. The second algorithm is the newly-established Bayesian Algorithm for SWE Estimation with Passive Microwave measurements (BASE-PM). This algorithm uses the physically-based snow radiative transfer model to find the histogram of most-likely snow property that matches the multi-frequency TB from 10.65 to 90 GHz. It provides a rough estimation of snow depth and grain size at the same time and showed a 30 mm SWE RMS error using the ground radiometer measurements at Sodankyla. This study will be the first attempt to test it spatially for satellite. The use of this algorithm benefits from the high resolution and the spatial consistency between frequencies embedded in the new dataset. This research will answer three questions. First, to what extent can CETB increase the heterogeneity in the mapped SWE? Second, will the SWE estimation error statistics be improved using this high-resolution dataset? Third, how will the SWE retrieval accuracy be improved using CETB and the new SWE retrieval techniques?

Polar View Snow Service- Operational Snow Cover Mapping for Downstream Runoff Modeling and Hydropower Predictions

NASA Astrophysics Data System (ADS)

Bach, Heike; Appel, Florian; Rust, Felix; Mauser, Wolfram

2010-12-01

Information on snow cover and snow properties are important for hydrology and runoff modelling. Frequent updates of snow cover observation, especially for areas characterized by short-term snow dynamics, can help to improve water balance and discharge calculations. Within the GMES service element Polar View, VISTA offers a snow mapping service for Central Europe since several years [1, 2]. We outline the use of this near-real- time product for hydrological applications in Alpine environment. In particular we discuss the integration of the Polar View product into a physically based hydrological model (PROMET). This allows not only the provision of snow equivalent values, but also enhances river runoff modelling and its use in hydropower energy yield prediction. The GMES snow products of Polar View are thus used in a downstream service for water resources management, providing information services for renewable energy suppliers and energy traders.

Satellite and Surface Perspectives of Snow Extent in the Southern Appalachian Mountains

NASA Technical Reports Server (NTRS)

Sugg, Johnathan W.; Perry, Baker L.; Hall, Dorothy K.

2012-01-01

Assessing snow cover patterns in mountain regions remains a challenge for a variety of reasons. Topography (e.g., elevation, exposure, aspect, and slope) strongly influences snowfall accumulation and subsequent ablation processes, leading to pronounced spatial variability of snow cover. In-situ observations are typically limited to open areas at lower elevations (<1000 m). In this paper, we use several products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess snow cover extent in the Southern Appalachian Mountains (SAM). MODIS daily snow cover maps and true color imagery are analyzed after selected snow events (e.g., Gulf/Atlantic Lows, Alberta Clippers, and Northwest Upslope Flow) from 2006 to 2012 to assess the spatial patterns of snowfall across the SAM. For each event, we calculate snow cover area across the SAM using MODIS data and compare with the Interactive Multi-sensor Snow and ice mapping system (IMS) and available in-situ observations. Results indicate that Gulf/Atlantic Lows are typically responsible for greater snow extent across the entire SAM region due to intensified cyclogenesis associated with these events. Northwest Upslope Flow events result in snow cover extent that is limited to higher elevations (>1000 m) across the SAM, but also more pronounced along NW aspects. Despite some limitations related to the presence of ephemeral snow or cloud cover immediately after each event, we conclude that MODIS products are useful for assessing the spatial variability of snow cover in heavily forested mountain regions such as the SAM.

An operational application of satellite snow cover observations, northwest United States. [using LANDSAT 1

NASA Technical Reports Server (NTRS)

Dillard, J. P.

1975-01-01

LANDSAT-1 imagery showing extent of snow cover was collected and is examined for the 1973 and 1974 snowmelt seasons for three Columbia River Basins. Snowlines were mapped and the aerial snow cover was determined using satellite data. Satellite snow mapping products were compared products from conventional information sources (computer programming and aerial photography was used). Available satellite data were successfully analyzed by radiance thresholding to determine snowlines and the attendant snow-covered area. Basin outline masks, contour elevation masks, and grid overlays were utilized as satellite data interpretation aids. Verification of the LANDSAT-1 data was generally good although there were exceptions. A major problem was lack of adequate cloud-free satellite imagery of high resolution and determining snowlines in forested areas.

Contribution of Lake-Effect Snow to the Catskill Mountains Snowpack

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Digirolamo, Nicolo E.; Frei, Allan

2017-01-01

Meltwater from snow that falls in the Catskill Mountains in southern New York contributes to reservoirs that supply drinking water to approximately nine million people in New York City. Using the NOAA National Ice Centers Interactive Multisensor Snow and Ice Mapping System (IMS) 4km snow maps, we have identified at least 32 lake-effect (LE) storms emanating from Lake Erie andor Lake Ontario that deposited snow in the CatskillDelaware Watershed in the Catskill Mountains of southern New York State between 2004 and 2017. This represents a large underestimate of the contribution of LE snow to the Catskills snowpack because many of the LE snowstorms are not visible in the IMS snow maps when they travel over snow-covered terrain. Most of the LE snowstorms that we identified originate from Lake Ontario but quite a few originate from both Erie and Ontario, and a few from Lake Erie alone. Using satellite, meteorological and reanalysis data we identify conditions that contributed to LE snowfall in the Catskills. Clear skies following some of the storms permitted measurement of the extent of snow cover in the watershed using multiple satellite sensors. IMS maps tend to overestimate the extent of snow compared to MODerate resolution Imaging Spectroradiometer (MODIS) and Landsat-derived snow-cover extent maps. Using this combination of satellite and meteorological data, we can begin to quantify the important contribution of LE snow to the Catskills Mountain snowpack. Changes that are predicted in LE snowfall from the Great Lakes could impact the distribution of rain vs snow in the Catskills which may affect future reservoir operations in the NYC Water Supply System.

Application of satellite data for snow mapping in Norway

NASA Technical Reports Server (NTRS)

Odegaard, H. A.; Andersen, T.; Ostrem, G. (Principal Investigator)

1980-01-01

The author has identified the following significant results. A close quantitative relationship was found between snow covered areas and subsequent runoff for different parts of the country despite climate differences. Digital LANDSAT data can be used for areas down to approximately 10 sq km to 20 sq km for accurate measurement of snow cover extent. On large watersheds (more than 500 sq km), digital NOAA/TIROS imagery can be used for snow mapping if the area/runoff relationship is determined by using observations from previous years.

An Ultra-Wideband, Microwave Radar for Measuring Snow Thickness on Sea Ice and Mapping Near-Surface Internal Layers in Polar Firn

NASA Technical Reports Server (NTRS)

Panzer, Ben; Gomez-Garcia, Daniel; Leuschen, Carl; Paden, John; Rodriguez-Morales, Fernando; Patel, Azsa; Markus, Thorsten; Holt, Benjamin; Gogineni, Prasad

2013-01-01

Sea ice is generally covered with snow, which can vary in thickness from a few centimeters to >1 m. Snow cover acts as a thermal insulator modulating the heat exchange between the ocean and the atmosphere, and it impacts sea-ice growth rates and overall thickness, a key indicator of climate change in polar regions. Snow depth is required to estimate sea-ice thickness using freeboard measurements made with satellite altimeters. The snow cover also acts as a mechanical load that depresses ice freeboard (snow and ice above sea level). Freeboard depression can result in flooding of the snow/ice interface and the formation of a thick slush layer, particularly in the Antarctic sea-ice cover. The Center for Remote Sensing of Ice Sheets (CReSIS) has developed an ultra-wideband, microwave radar capable of operation on long-endurance aircraft to characterize the thickness of snow over sea ice. The low-power, 100mW signal is swept from 2 to 8GHz allowing the air/snow and snow/ ice interfaces to be mapped with 5 c range resolution in snow; this is an improvement over the original system that worked from 2 to 6.5 GHz. From 2009 to 2012, CReSIS successfully operated the radar on the NASA P-3B and DC-8 aircraft to collect data on snow-covered sea ice in the Arctic and Antarctic for NASA Operation IceBridge. The radar was found capable of snow depth retrievals ranging from 10cm to >1 m. We also demonstrated that this radar can be used to map near-surface internal layers in polar firn with fine range resolution. Here we describe the instrument design, characteristics and performance of the radar.

Application of LANDSAT imagery for snow mapping in Norway

NASA Technical Reports Server (NTRS)

Odegaard, H. (Principal Investigator); Ostrem, G.

1977-01-01

The author has identified the following significant results. It was shown that if the snow cover extent was determined from all four LANDSAT bands, there were significant differences in results. The MSS 4 gave the largest snow cover, but only slightly more than MSS 5, whereas MSS 6 and 7 gave the smallest snow area. A study was made to show that there was a relationship between the last date of snow fall and the area covered with snow, as determined from different bands. Imagery obtained shortly after a snow fall showed no significant difference in the snow-covered area when the four bans were compared, whereas, pronounced differences in the snow-covered area were found in images taken after a long period without precipitation.

Application of LANDSAT imagery for snow mapping in Norway

NASA Technical Reports Server (NTRS)

Odegaard, H.; Skorve, J. E. (Principal Investigator)

1977-01-01

The author has identified the following significant results. During the summer seasons of 1975 and 1976, the snow cover was successfully monitored and measured in the four basins. By using elevation distributions for these basins combined with the measured snow cover percentages, the equivalent snow line altitude was calculated. Equivalent snow line altitude was used in accordance with Mark Meier's definition. Cumulative runoff data were collected for the basins. Tables showing percentage snow cover versus cumulative runoff were worked out for 1975.

Towards better understanding of high-mountain cryosphere changes using GPM data: A Joint Snowfall and Snow-cover Passive Microwave Retrieval Algorithm

NASA Astrophysics Data System (ADS)

Ebtehaj, A.; Foufoula-Georgiou, E.

2016-12-01

Scientific evidence suggests that the duration and frequency of snowfall and the extent of snow cover are rapidly declining under global warming. Both precipitation and snow cover scatter the upwelling surface microwave emission and decrease the observed high-frequency brightness temperatures. The mixture of these two scattering signals is amongst the largest sources of ambiguities and errors in passive microwave retrievals of both precipitation and snow-cover. The dual frequency radar and the high-frequency radiometer on board the GPM satellite provide a unique opportunity to improve passive retrievals of precipitation and snow-cover physical properties and fill the gaps in our understating of their variability in view of climate change. Recently, a new Bayesian rainfall retrieval algorithm (called ShARP) was developed using modern approximation methods and shown to yield improvements against other algorithms in retrieval of rainfall over radiometrically complex land surfaces. However, ShARP uses a large database of input rainfall and output brightness temperatures, which might be undersampled. Furthermore, it is not capable to discriminate between solid and liquid phase of precipitation and specifically discriminate the background snow-cover emission and its contamination effects on the retrievals. We address these problems by extending it to a new Bayesian land-atmosphere retrieval framework (ShARP-L) that allows joint retrievals of atmospheric constituents and land surface physical properties. Using modern sparse approximation techniques, the database is reduced to atomic microwave signatures in a family of compact class consistent dictionaries. These dictionaries can efficiently represent the entire database and allow us to discriminate between different land-atmosphere states. First the algorithm makes use of the dictionaries to detect the phase of the precipitation and type of the land-cover and then it estimates the physical properties of precipitation and snow cover using an extended version of the Dantzig Selector, which is robust to non-Gaussian and correlated geophysical noise. Promising results are presented in retrievals of snowfall and snow-cover over coastal orographic features of North America's Coast Range and South America's Andes.

Snow cover data records from satellite and conventional measurements

NASA Astrophysics Data System (ADS)

Derksen, C.; Brown, R.; Wang, L.

2008-12-01

A major goal of snow-related research in the Climate Research Division of Environment Canada is the development of consistent snow cover information from satellite and in situ data sources for climate monitoring and model evaluation. This work involves new satellite algorithm development for reliable mapping of snow water equivalent (SWE), snow cover extent (SCE) and snow cover onset and melt dates, evaluation of existing snow cover products such as the NOAA weekly data set with in situ and satellite data, and the reconstruction and reanalysis of snow cover information from the application of physical snow models, geostatistics and data assimilation methods. In the context of the International Polar Year, a major effort is being made to develop and evaluate snow cover information over the Arctic region with a particular focus on the dynamic spring melt period where positive feedbacks to the climate system are more pronounced. Assessment of the NOAA daily and weekly SCE products with MODIS and QuikSCAT derived datasets identified a systematic late bias of 2-3 weeks in snow-off dates over northern Canada. This bias was not observed over northern Eurasia which suggests that regional differences in variables such as lake fraction and cloud cover are systematically influencing the accuracy of the NOAA product over northern Canada. Considerable progress has been made in deriving passive microwave derived SWE information over sub- Arctic regions of North America where pre-existing algorithms were unable to account for the influence of forest cover and lake ice. Previous uncertainties in retrieving SWE across the boreal forest have been resolved with the combination of 18.7 and 10.7 GHz measurements from the Advanced Microwave Scanning Radiometer (AMSR-E; 2002-present). Full time series development (1978-onwards) remains problematic, however, because 10.7 GHz measurements are not available from the Special Sensor Microwave/Imager (1987-present). Satellite measurements coupled with lake ice model simulations have illustrated frequency dependent, seasonally evolving relationships between brightness temperature and lake fraction across tundra regions. A potential solution based on the temporal evolution of 37 GHz AMSR-E measurements shows some promise as this was found to be significantly correlated with field measurements of tundra SWE, and to be relatively insensitive to lake fraction. New pan-Arctic (N 60°N) snowmelt onset and end date records (2000-2006) were produced from enhanced resolution (4.45 km) QuikSCAT (QSCAT) Ku-band backscatter measurements. The goal is to merge this with melt onset information from other components of the cryosphere (e.g. glaciers, ice caps, ice sheets, lake ice, sea ice) to provide an integrated circumpolar melt onset and duration dataset for climate monitoring and research on cryosphere-climate links and feedbacks. A major challenge is expanding the relatively short time period of Ku-band satellite measurements with historical C-band data (i.e. from ERS-1). Geostatistical methods and snow cover modeling were used to develop a 10-km gridded SWE dataset over Quebec from 1970-2005 for climate studies and evaluation of the performance of the Canadian Regional Climate Model.

Monitoring Areal Snow Cover Using NASA Satellite Imagery

NASA Technical Reports Server (NTRS)

Harshburger, Brian J.; Blandford, Troy; Moore, Brandon

2011-01-01

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest.

A webgis supported snow information system with long time satellite data for Turkey

NASA Astrophysics Data System (ADS)

Surer, S.; Bolat, K.; Akyurek, Z.

2012-04-01

KARBILSIS is an online platform which is developed in order to provide end-users with daily remote sensing snow products for Turkey (www.karbilsis.com). The project has been started as a research activity after an award by Ministry of Science and Technology has been granted to our company. At the first stage of our project MODIS atmospherically corrected reflectance data has been downloaded covering the period of 2000-2011 which makes more than ten years of satellite imagery for Turkey. The archived MODIS data that have been obtained from National Snow and Ice Data Center (NSIDC) is mainly MOD09GA product that includes seven spectral bands. Only the tiles which are covering Turkey have been archived namely 19&20 horizontal and 4&5 vertical ones. In order to provide scientists with a website giving the availability of analysis of snow covered area for long terms based on their area of interests, a fractional snow extent (FSE) product has been generated. For FSE product a normalized difference snow index (NDSI) based algorithm has been developed using daily land surface reflectance values (MOD09GA). In addition to MODIS data, four different Landsat images belonging to different days of snowy period (January, March, and May) have been used during algorithm development taking into account a better representation of different reflectance values of snow which highly varies depending on the accumulation and melting periods. Landsat images were used as reference images. First the Landsat images were orthorectified and mapped to a cartographic projection. Then image segmentation was applied to obtain homogeneous tiles, where the homogeneity is defined as similarity in pixel values. The mean-shift segmentation approach, where each pixel was associated with a significant mode of the joint domain density located in its neighborhood, was applied. After segmentation, the image was classified into snow and no-snow classes with Maximum Likelihood Classification Method. FSE products have been produced for around 12 years from 2000 to 2012 and it is being produced daily as the data is available. 72% overall accuracy was obtained from the validation analysis. Our website will be available to give service to our users to make analysis on snow extent with a long time series database for free. By the help of WEBGIS interface it is going to be possible to produce time series of snow cover areas, and produce graphs and summary statistics for a better management of information on snow cover in various fields from flood forecast integration, energy production planning of hydropower plants which are fed from snow melting, and producing input for climate models.

Preliminary Validation of the AFWA-NASA Blended Snowcover Product Over the Lower Great Lakes region

NASA Technical Reports Server (NTRS)

Hall, D. K.; Montesano, P. M.; Foster, J. L.; Riggs, G. A.; Kelly, R. E. J.; Czajkowski, K.

2007-01-01

A new snow product created using the standard Moderate-Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) snow cover and snow-water equivalent products has been evaluated for the Lower Great Lakes region during the winter of 2002- 03. National Weather Service Co-Operative Observing Network stations and student-acquired snow data were used as ground truth. An interpolation scheme was used to map snow cover on the ground from the station measurements for each day of the study period. It is concluded that this technique does not represent the actual ground conditions adequately to permit evaluation of the new snow product in an absolute sense. However, use of the new product was found to improve the mapping of snow cover as compared to using either the MODIS or AMSR-E product, alone. Plans for further analysis are discussed.

BOREAS HYD-4 Standard Snow Course Data

NASA Technical Reports Server (NTRS)

Metcalfe, John R.; Goodison, Barry E.; Walker, Anne; Hall, Forrest G. (Editor); Knapp, David E. (Editor); Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-4 work was focused on collecting data during the winter focused field campaign (FFC-W) to improve the understanding of winter processes within the boreal forest. Knowledge of snow cover and its variability in the boreal forest is fundamental if BOREAS is to achieve its goals of understanding the processes and states involved in the exchange of energy and water. The development and validation of remote sensing algorithms will provide the means to extend the knowledge of these processes and states from the local to the regional scale. A specific thrust of the research is the development and validation of snow cover algorithms from airborne passive microwave measurements. Snow surveys were conducted at special snow courses throughout the 1993/94, 1994/95, 1995/96, and 1996/97 winter seasons. These snow courses were located in different boreal forest land cover types (i.e., old aspen, old black spruce, young jack pine, forest clearing, etc.) to document snow cover variations throughout the season as a function of different land cover. Measurements of snow depth, density, and water equivalent were acquired on or near the first and fifteenth of each month during the snow cover season. The data are provided in tabular ASCII files. The HYD-4 standard snow course data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers

NASA Technical Reports Server (NTRS)

Meier, M. F. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The area of snow cover on land was determined from ERTS-1 imagery. Snow cover in specific drainage basins was measured with the Stanford Research Institute console by electronically superimposing basin outlines on imagery, with video density slicing to measure areas. Snow covered area and snowline altitudes were also determined by enlarging ERTS-1 imagery 1:250,000 and using a transparent map overlay. Under very favorable conditions, snowline altitude was determined to an accuracy of about 60 m. Ability to map snow cover or to determine snowline altitude depends primarily on cloud cover and vegetation and secondarily on slope, terrain roughness, sun angle, radiometric fidelity, and amount of spectral information available. Glacier accumulation area ratios were determined from ERTS-1 imagery. Also, subtle flow structures, undetected on aerial photographs, were visible. Surging glaciers were identified, and the changes resulting from the surge of a large glacier were measured as were changes in tidal glacier termini.

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J. (Principal Investigator); Martin, K. R.; Sheffield, C.; Russell, O.; Amato, R. V.

1972-01-01

The author has identified the following significant results. Analysis of all available (Gemini, Apollo, Nimbus, NASA aircraft) small scale snow covered imagery has been conducted to develop and refine snow enhancement techniques. A detailed photographic interpretation of ERTS-simulation imagery covering the Feather River/Lake Tahoe area was completed and the 580-680nm. band was determined to be the optimum band for fracture detection. ERTS-1 MSS bands 5 and 7 are best suited for detailed fracture mapping. The two bands should provide more complete fracture detail when utilized in combination. Analysis of early ERTS-1 data along with U-2 ERTS simulation imagery indicates that snow enhancement is a viable technique for geological fracture mapping. A wealth of fracture detail on snow-free terrain was noted during preliminary analysis of ERTS-1 images 1077-15005-6 and 7, 1077-15011-5 and 7, and 1079-15124-5 and 7. A direct comparison of data yield on snow-free versus snow-covered terrain will be conducted within these areas following receipt of snow-covered ERTS-1 imagery.

Satellite snowcover and runoff monitoring in central Arizona. [Salt River Project: Salt-Verde Watershed

NASA Technical Reports Server (NTRS)

Schumann, H. H.; Kirdar, E.; Warskow, W. L. (Principal Investigator)

1980-01-01

The author has identified the following significant results. Although the very high resolution experimental LANDSAT imagery permits rapid snow cover mapping at low cost, only one observation is available very 9 days. In contrast, low resolution operational imagery acquired by the ITOS and SMS/GOES satellites provide the daily synoptic observations necessary to monitor the rapid changes in snow covered areas in the entire Salt-Verde watershed. Geometric distortions in meteorological satellite imagery require specialized optical equipment or digital image processing for snow cover mapping.

Snow and Ice Products from the Moderate Resolution Imaging Spectroradiometer

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Salomonson, Vincent V.; Riggs, George A.; Klein, Andrew G.

2003-01-01

Snow and sea ice products, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, flown on the Terra and Aqua satellites, are or will be available through the National Snow and Ice Data Center Distributed Active Archive Center (DAAC). The algorithms that produce the products are automated, thus providing a consistent global data set that is suitable for climate studies. The suite of MODIS snow products begins with a 500-m resolution, 2330-km swath snow-cover map that is then projected onto a sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to daily and 8-day composite climate-modeling grid (CMG) products at 0.05 resolution. A daily snow albedo product will be available in early 2003 as a beta test product. The sequence of sea ice products begins with a swath product at 1-km resolution that provides sea ice extent and ice-surface temperature (IST). The sea ice swath products are then mapped onto the Lambert azimuthal equal area or EASE-Grid projection to create a daily and 8-day composite sea ice tile product, also at 1 -km resolution. Climate-Modeling Grid (CMG) sea ice products in the EASE-Grid projection at 4-km resolution are planned for early 2003.

Automated mapping of persistent ice and snow cover across the western U.S. with Landsat

NASA Astrophysics Data System (ADS)

Selkowitz, David J.; Forster, Richard R.

2016-07-01

We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s-1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010-2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987-1988 and 2008-2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25-50 years.

Automated mapping of persistent ice and snow cover across the western U.S. with Landsat

USGS Publications Warehouse

Selkowitz, David J.; Forster, Richard R.

2016-01-01

We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s–1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010–2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987–1988 and 2008–2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25–50 years.

Subpixel Snow Cover Mapping from MODIS Data by Nonparametric Regression Splines

NASA Astrophysics Data System (ADS)

Akyurek, Z.; Kuter, S.; Weber, G. W.

2016-12-01

Spatial extent of snow cover is often considered as one of the key parameters in climatological, hydrological and ecological modeling due to its energy storage, high reflectance in the visible and NIR regions of the electromagnetic spectrum, significant heat capacity and insulating properties. A significant challenge in snow mapping by remote sensing (RS) is the trade-off between the temporal and spatial resolution of satellite imageries. In order to tackle this issue, machine learning-based subpixel snow mapping methods, like Artificial Neural Networks (ANNs), from low or moderate resolution images have been proposed. Multivariate Adaptive Regression Splines (MARS) is a nonparametric regression tool that can build flexible models for high dimensional and complex nonlinear data. Although MARS is not often employed in RS, it has various successful implementations such as estimation of vertical total electron content in ionosphere, atmospheric correction and classification of satellite images. This study is the first attempt in RS to evaluate the applicability of MARS for subpixel snow cover mapping from MODIS data. Total 16 MODIS-Landsat ETM+ image pairs taken over European Alps between March 2000 and April 2003 were used in the study. MODIS top-of-atmospheric reflectance, NDSI, NDVI and land cover classes were used as predictor variables. Cloud-covered, cloud shadow, water and bad-quality pixels were excluded from further analysis by a spatial mask. MARS models were trained and validated by using reference fractional snow cover (FSC) maps generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also developed. The mutual comparison of obtained MARS and ANN models was accomplished on independent test areas. The MARS model performed better than the ANN model with an average RMSE of 0.1288 over the independent test areas; whereas the average RMSE of the ANN model was 0.1500. MARS estimates for low FSC values (i.e., FSC<0.3) were better than that of ANN. Both ANN and MARS tended to overestimate medium FSC values (i.e., 0.30.7).

The Contribution to High Asia Runoff from Ice and Snow (CHARIS): Understanding the source and trends of cryospheric contributions to the water balance

NASA Astrophysics Data System (ADS)

Rittger, K.; Armstrong, R. L.; Bair, N.; Racoviteanu, A.; Brodzik, M. J.; Hill, A. F.; Wilson, A. M.; Khan, A. L.; Ramage, J. M.; Khalsa, S. J. S.; Barrett, A. P.; Raup, B. H.; Painter, T. H.

2017-12-01

The Contribution to High Asia Runoff from Ice and Snow, or CHARIS, project is systematically assessing the role that glaciers and seasonal snow play in the freshwater resources of Central and South Asia. The study area encompasses roughly 3 million square kilometers of the Himalaya, Karakoram, Hindu Kush, Pamir and Tien Shan mountain ranges that drain to five major rivers: the Ganges, Brahmaputra, Indus, Amu Darya and Syr Darya. We estimate daily snow and glacier ice contributions to the water balance. Our automated partitioning method generates daily maps of 1) snow over ice (SOI), 2) exposed glacier ice (EGI), 3) debris covered glacier ice (DGI) and 4) snow over land (SOL) using fractional snow cover, snow grain size, and annual minimum ice and snow from the 500 m MODIS-derived MODSCAG and MODICE products. Maps of snow and ice cover are validated using high-resolution (30 m) maps of snow, ice, and debris cover from Landsat. The probability of detection is 0.91 and precision is 0.85 for MODICE. We examine trends in annual and monthly snow and ice maps and use daily maps as inputs to a calibrated temperature-index model and an uncalibrated energy balance model, ParBal. Melt model results and measurements of isotopes and specific ions used as an independent validation of melt modeling indicate a sharp geographic contrast in the role of snow and ice melt to downstream water supplies between the arid Tien Shan and Pamir ranges of Central Asia, where melt water dominates dry season flows, and the monsoon influenced central and eastern Himalaya where rain controls runoff. We also compare melt onset and duration from the melt models to the Calibrated, Enhanced Resolution Passive Microwave Brightness Temperature Earth Science Data Record. Trend analysis of annual and monthly area of permanent snow and ice (the union of SOI and EGI) for 2000 to 2016 shows statistically significant negative trends in the Ganges and Brahmaputra basins. There are no statistically significant trends in permanent snow and ice in the other basins and no statistically significant trends in SOL, the renewable and seasonal component of snow and ice cover, in any of the five basins. This work gives a better understanding of the current hydrologic regime to guide realistic estimates of the future availability and vulnerability of water resources in these regions.

The seasonal cycle of snow cover, sea ice and surface albedo

NASA Technical Reports Server (NTRS)

Robock, A.

1980-01-01

The paper examines satellite data used to construct mean snow cover caps for the Northern Hemisphere. The zonally averaged snow cover from these maps is used to calculate the seasonal cycle of zonally averaged surface albedo. The effects of meltwater on the surface, solar zenith angle, and cloudiness are parameterized and included in the calculations of snow and ice albedo. The data allows a calculation of surface albedo for any land or ocean 10 deg latitude band as a function of surface temperature ice and snow cover; the correct determination of the ice boundary is more important than the snow boundary for accurately simulating the ice and snow albedo feedback.

Evaluating MODIS snow products for modelling snowmelt runoff: Case study of the Rio Grande headwaters

NASA Astrophysics Data System (ADS)

Steele, Caitriana; Dialesandro, John; James, Darren; Elias, Emile; Rango, Albert; Bleiweiss, Max

2017-12-01

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM) and in other models for simulating discharge from snowmelt. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM +) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and may not always provide sufficient cloud-free dates. The coarser spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) offers better temporal resolution and in cloudy years, MODIS data offer the best alternative for mapping snow cover when finer spatial resolution data are unavailable. However, MODIS' coarse spatial resolution (500 m) can obscure fine spatial patterning in snow cover and some MODIS products are not sensitive to end-of-season snow cover. In this study, we aimed to test MODIS snow products for use in simulating snowmelt runoff from smaller headwater basins by a) comparing maps of TM and MODIS-based SCA and b) determining how SRM streamflow simulations are changed by the different estimates of seasonal snow depletion. We compared gridded MODIS snow products (Collection 5 MOD10A1 fractional and binary SCA; SCA derived from Collection 6 MOD10A1 Normalised Difference Snow Index (NDSI) Snow Cover), and the MODIS Snow Covered-Area and Grain size retrieval (MODSCAG) canopy-corrected fractional SCA (SCAMG), with reference SCA maps (SCAREF) generated from binary classification of TM imagery. SCAMG showed strong agreement with SCAREF; excluding true negatives (where both methods agreed no snow was present) the median percent difference between SCAREF and SCAMG ranged between -2.4% and 4.7%. We simulated runoff for each of the four study years using SRM populated with and calibrated for snow depletion curves derived from SCAREF. We then substituted in each of the MODIS-derived depletion curves. With efficiency coefficients ranging between 0.73 and 0.93, SRM simulation results from the SCAMG runs yielded the best results of all the MODIS products and only slightly underestimated discharge volume (between 7 and 11% of measured annual discharge). SRM simulations that used SCA derived from Collection 6 NDSI Snow Cover also yielded promising results, with efficiency coefficients ranging between 0.73 and 0.91. In conclusion, we recommend that when simulating snowmelt runoff from small basins (<4000 km2) with SRM, we recommend that users select either canopy-corrected MODSCAG or create their own site-specific products from the Collection 6 MOD10A1 NDSI.

A snow cover climatology for the Pyrenees from MODIS snow products

NASA Astrophysics Data System (ADS)

Gascoin, S.; Hagolle, O.; Huc, M.; Jarlan, L.; Dejoux, J.-F.; Szczypta, C.; Marti, R.; Sanchez, R.

2015-05-01

The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (w.e.) and 150 mm, respectively, for both MOD10A1 and MYD10A1. κ coefficients are within 0.74 and 0.92 depending on the product and the variable for these thresholds. However, we also find a seasonal trend in the optimal SWE and SD thresholds, reflecting the hysteresis in the relationship between the depth of the snowpack (or SWE) and its extent within a MODIS pixel. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both data sets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decrease over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gap-filling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band and aspect classes. There is snow on the ground at least 50% of the time above 1600 m between December and April. We finally analyze the snow patterns for the atypical winter 2011-2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

Snow cover detection algorithm using dynamic time warping method and reflectances of MODIS solar spectrum channels

NASA Astrophysics Data System (ADS)

Lee, Kyeong-sang; Choi, Sungwon; Seo, Minji; Lee, Chang suk; Seong, Noh-hun; Han, Kyung-Soo

2016-10-01

Snow cover is biggest single component of cryosphere. The Snow is covering the ground in the Northern Hemisphere approximately 50% in winter season and is one of climate factors that affects Earth's energy budget because it has higher reflectance than other land types. Also, snow cover has an important role about hydrological modeling and water resource management. For this reason, accurate detection of snow cover acts as an essential element for regional water resource management. Snow cover detection using satellite-based data have some advantages such as obtaining wide spatial range data and time-series observations periodically. In the case of snow cover detection using satellite data, the discrimination of snow and cloud is very important. Typically, Misclassified cloud and snow pixel can lead directly to error factor for retrieval of satellite-based surface products. However, classification of snow and cloud is difficult because cloud and snow have similar optical characteristics and are composed of water or ice. But cloud and snow has different reflectance in 1.5 1.7 μm wavelength because cloud has lower grain size and moisture content than snow. So, cloud and snow shows difference reflectance patterns change according to wavelength. Therefore, in this study, we perform algorithm for classifying snow cover and cloud with satellite-based data using Dynamic Time Warping (DTW) method which is one of commonly used pattern analysis such as speech and fingerprint recognitions and reflectance spectral library of snow and cloud. Reflectance spectral library is constructed in advance using MOD21km (MODIS Level1 swath 1km) data that their reflectance is six channels including 3 (0.466μm), 4 (0.554μm), 1 (0.647μm), 2 (0.857μm), 26 (1.382μm) and 6 (1.629μm). We validate our result using MODIS RGB image and MOD10 L2 swath (MODIS swath snow cover product). And we use PA (Producer's Accuracy), UA (User's Accuracy) and CI (Comparison Index) as validation criteria. The result of our study detect as snow cover in the several regions which are did not detected as snow in MOD10 L2 and detected as snow cover in MODIS RGB image. The result of our study can improve accuracy of other surface product such as land surface reflectance and land surface emissivity. Also it can use input data of hydrological modeling.

Spatial Patterns of Snow Cover in North Carolina: Surface and Satellite Perspectives

NASA Technical Reports Server (NTRS)

Fuhrmann, Christopher M.; Hall, Dorothy K.; Perry, L. Baker; Riggs, George A.

2010-01-01

Snow mapping is a common practice in regions that receive large amounts of snowfall annually, have seasonally-continuous snow cover, and where snowmelt contributes significantly to the hydrologic cycle. Although higher elevations in the southern Appalachian Mountains average upwards of 100 inches of snow annually, much of the remainder of the Southeast U.S. receives comparatively little snowfall (< 10 inches). Recent snowy winters in the region have provided an opportunity to assess the fine-grained spatial distribution of snow cover and the physical processes that act to limit or improve its detection across the Southeast. In the present work, both in situ and remote sensing data are utilized to assess the spatial distribution of snow cover for a sample of recent snowfall events in North Carolina. Specifically, this work seeks to determine how well ground measurements characterize the fine-grained patterns of snow cover in relation to Moderate- Resolution Imaging Spectroradiometer (MODIS) snow cover products (in this case, the MODIS Fractional Snow Cover product).

Mapping snow depth in complex alpine terrain with close range aerial imagery - estimating the spatial uncertainties of repeat autonomous aerial surveys over an active rock glacier

NASA Astrophysics Data System (ADS)

Goetz, Jason; Marcer, Marco; Bodin, Xavier; Brenning, Alexander

2017-04-01

Snow depth mapping in open areas using close range aerial imagery is just one of the many cases where developments in structure-from-motion and multi-view-stereo (SfM-MVS) 3D reconstruction techniques have been applied for geosciences - and with good reason. Our ability to increase the spatial resolution and frequency of observations may allow us to improve our understanding of how snow depth distribution varies through space and time. However, to ensure accurate snow depth observations from close range sensing we must adequately characterize the uncertainty related to our measurement techniques. In this study, we explore the spatial uncertainties of snow elevation models for estimation of snow depth in a complex alpine terrain from close range aerial imagery. We accomplish this by conducting repeat autonomous aerial surveys over a snow-covered active-rock glacier located in the French Alps. The imagery obtained from each flight of an unmanned aerial vehicle (UAV) is used to create an individual digital elevation model (DEM) of the snow surface. As result, we obtain multiple DEMs of the snow surface for the same site. These DEMs are obtained from processing the imagery with the photogrammetry software Agisoft Photoscan. The elevation models are also georeferenced within Photoscan using the geotagged imagery from an onboard GNSS in combination with ground targets placed around the rock glacier, which have been surveyed with highly accurate RTK-GNSS equipment. The random error associated with multi-temporal DEMs of the snow surface is estimated from the repeat aerial survey data. The multiple flights are designed to follow the same flight path and altitude above the ground to simulate the optimal conditions of repeat survey of the site, and thus try to estimate the maximum precision associated with our snow-elevation measurement technique. The bias of the DEMs is assessed with RTK-GNSS survey observations of the snow surface elevation of the area on and surrounding the rock glacier. Additionally, one of the challenges with processing snow cover imagery with SfM-MVS is dealing with the general homogeneity of the surface, which makes is difficult for automated-feature detection algorithms to identify key features for point matching. This challenge depends on the snow cover surface conditions, such as scale, lighting conditions (high vs. low contrast), and availability of snow-free features within a scene, among others. We attempt to explore this aspect by spatial modelling the factors influencing the precision and bias of the DEMs from image, flight, and terrain attributes.

Permafrost, Seasonally Frozen Ground, Snow Cover and Vegetation in the USSR

DTIC Science & Technology

1984-12-01

Snow Cover in Physical Geographic Processes (1948). He covered aspects of the dynamics of the snow cover, its properties and the connection between...Bigl, Research Physical Scientist, of the Geotechnical Research Branch, Experimental Engineering Division, un- der the general supervision of Dr...generalized from a detailed vegetation map in the volume Physical Geographic Atlas of the World (Gerasimov 1964), The tundra zone consists mostly of

National Operational Hydrologic Remote Sensing Center - The ultimate source

Science.gov Websites

Analysis Satellite Obs Forecasts Data Archive SHEF Products Observations near City, ST Go Science Database Airborne Snow Surveys Satellite Snow Cover Mapping Snow Modeling and Data Assimilation Analyses polar-orbiting and geostationary satellite imagery. Maps are provided for the U.S. and the northern

Facilitating the exploitation of ERTS-1 imagery utilizing snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J. (Principal Investigator); Martin, K. R.; Amato, R. V.

1973-01-01

The author has identified the following significant results. Snow cover in combination with low angle solar illumination has been found to provide increased tonal contrast of surface feature and is useful in the detection of bedrock fractures. Identical fracture systems were not as readily detectable in the fall due to the lack of a contrasting surface medium (snow) and a relatively high sun angle. Low angle solar illumination emphasizes topographic expressions not as apparent on imagery acquired with a higher sun angle. A strong correlation exists between the major fracture-lineament directions interpreted from multi-sensor imagery (including snow-free and snow cover ERTS) and the strike of bedrock joints recorded in the field indicating the structural origin of interpreted fracture-lineaments. A fracture-annotated ERTS-1 photo base map (1:250,000 scale) is being prepared for western Massachusetts. The map will document the utilization of ERTS-1 imagery for geological analysis in comparative snow-free and snow-covered terrain.

Snow cover correlation between Mt. Villarrica and Mt. Lliama in Chile

NASA Astrophysics Data System (ADS)

Kim, Jeong-Cheol; Park, Sung-Hwan; Jung, Hyung-Sup

2014-11-01

The Southern Volcanic Zone (SVZ) of Chile consists of many volcanoes, and all of the volcanoes are covered with snow at the top of mountain. Monitoring snow cover variations in these regions can give us a key parameter in order to understand the mechanisms of volcanic activity. In this study, we investigate on the volcanic activity and snow cover interaction from snow cover area mapping, snow-line extraction. The study areas cover Mt. Villarrica and Mt. Llaima, Chile. Both of them are most active volcanos in SVZ. Sixty Landsat TM and Landsat ETM+ images are used for observing snow cover variations of Mt. Villarrica and Mt. Llaima, spanning the 25 years from September 1986 to February 2011. Results show that snow cover area between volcanic activity and non-activity are largely changed from 42.84 km2 to 13.41 km2, temporarily decreased 79% at the Mt. Villarrica and from 28.98 km2 to 3.82 km2, temporarily decreased 87% at the Mt. Villarrica. The snow line elevation of snow cover retreated by approximately 260 m from 1,606m to 1,871 m at the Mt. Villarrica, approximately 266m from 1,741m to 2,007m at the Mt. Llaima. The results show that there are definitely correlations between snow cover and volcanic activity.

Snow cover retrieval over Rhone and Po river basins from MODIS optical satellite data (2000-2009).

NASA Astrophysics Data System (ADS)

Dedieu, Jean-Pierre, ,, Dr.; Boos, Alain; Kiage, Wiliam; Pellegrini, Matteo

2010-05-01

Estimation of the Snow Covered Area (SCA) is an important issue for meteorological application and hydrological modeling of runoff. With spectral bands in the visible, near and middle infrared, the MODIS optical satellite sensor can be used to detect snow cover because of large differences between reflectance from snow covered and snow free surfaces. At the same time, it allows separation between snow and clouds. Moreover, the sensor provides a daily coverage of large areas (2,500 km range). However, as the pixel size is 500m x 500m, a MODIS pixel may be partially covered by snow, particularly in Alpine areas, where snow may not be present in valleys lying at lower altitudes. Also, variation of reflectance due to differential sunlit effects as a function of slope and aspect, as well as bidirectional effects may be present in images. Nevertheless, it is possible to estimate snow cover at the Sub-Pixel level with a relatively good accuracy and with very good results if the sub-pixel estimations are integrated for a few pixels relative to an entire watershed. Integrated into the EU-FP7 ACQWA Project (www.acqwa.ch), this approach was first applied over Alpine area of Rhone river basin upper Geneva Lake: Canton du Valais, Switzerland (5 375 km²). In a second step over Alps, rolling hills and plain areas in Po catchment for Val d'Aosta and Piemonte regions, Italy (37 190 km²). Watershed boundaries were provided respectively by GRID (Ch) and ARPA (It) partners. The complete satellite images database was extracted from the U.S. MODIS/NASA website (http://modis.gsfc.nasa.gov/) for MOD09_B1 Reflectance images, and from the MODIS/NSIDC website (http://nsidc.org/index.html) for MOD10_A2 snow cover images. Only the Terra platform was used because images are acquired in the morning and are therefore better correlated with dry snow surface, avoiding cloud coverage of the afternoon (Aqua Platform). The MOD9 Image reflectance and MOD10_A2 products were respectively analyzed to retrieve (i) Fractional Snow cover at sub-pixel scale, and (ii) maximum snow cover. All products were retrieved at 8-days over a complete time period of 10 years (2000-2009), giving 500 images for each river basin. Digital Model Elevation was given by NASA/SRTM database at 90-m resolution and used (i) for illumination versus topography correction on snow cover, (ii) geometric rectification of images. Geographic projection is WGS84, UTM 32. Fractional Snow cover mapping was derived from the NDSI linear regression method (Salomonson et al., 2004). Cloud mask was given by MODIS-NASA library (radiometric threshold) and completed by inverse slope regression to avoid lowlands fog confusing with thin snow cover (Po river basin). Maximum Snow Cover mapping was retrieved from the NSIDC database classification (Hall et al., 2001). Validation step was processed using comparison between MODIS Snow maps outputs and meteorological data provided by network of 87 meteorological stations: temperature, precipitation, snow depth measurement. A 0.92 correlation was observed for snow/non snow cover and can be considered as quite satisfactory, given the radiometric problems encountered in mountainous areas, particularly in snowmelt season. The 10-years time period results indicates a main difference between (i) regular snow accumulation and depletion in Rhone and (ii) the high temporal and spatial variability of snow cover for Po. Then, a high sensitivity to low variation of air temperature, often close to 1° C was observed. This is the case in particular for the beginning and the end of the winter season. The regional snow cover depletion is both influenced by thermal positives anomalies (e.g. 2000 and 2006), and the general trend of rising atmospheric temperatures since the late 1980s, particularly for Po river basin. Results will be combined with two hydrological models: Topkapi and Fest.

Snow survey and vegetation growth in high mountains (Swiss Alps) and additional ERTS investigations in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Two different methods, an analog and a digital one, have been developed for rapid and accurate mapping of the areal extent and changes in snow cover in high mountains. The quick-look method is based on individual visual control of each image using a photo quantizer which provides exact references for density slicing with high resolution lith-film. The digital snow classification system is based on discriminant analysis with the data of the four multispectral bands as variables and contains all preprocessing, feature extraction, and mapping steps for an operational application. Two different sets of sampling groups were established which apply to different conditions of snow cover. The first one serves for the normal situation with a uniform dry and new cover. The second one serves for situations with partly thawing and/or frozen snow.

No evidence of widespread decline of snow cover on the Tibetan Plateau over 2000-2015.

PubMed

Wang, Xiaoyue; Wu, Chaoyang; Wang, Huanjiong; Gonsamo, Alemu; Liu, Zhengjia

2017-11-07

Understanding the changes in snow cover is essential for biological and hydrological processes in the Tibetan Plateau (TP) and its surrounding areas. However, the changes in snow cover phenology over the TP have not been well documented. Using Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products and the Interactive Multi-sensor Snow and Ice Mapping System (IMS) data, we reported daily cloud-free snow cover product over the Tibetan Plateau (TP) for 2000-2015. Snow cover start (SCS), melt (SCM) and duration (SCD) dates were calculated for each hydrological year, and their spatial and temporal variations were analyzed with elevation variations. Our results show no widespread decline in snow cover over the past fifteen years and the trends of snow cover phenology over the TP has high spatial heterogeneity. Later SCS, earlier SCM, and thus decreased SCD mainly occurred in the areas with elevation below 3500 m a.s.l., while regions in central and southwestern edges of the TP showed advanced SCS, delayed SCM and consequently longer SCD. The roles of temperature and precipitation on snow cover penology varied in different elevation zones, and the impact of both temperature and precipitation strengthened as elevation increases.

Estimation of global snow cover using passive microwave data

NASA Astrophysics Data System (ADS)

Chang, Alfred T. C.; Kelly, Richard E.; Foster, James L.; Hall, Dorothy K.

2003-04-01

This paper describes an approach to estimate global snow cover using satellite passive microwave data. Snow cover is detected using the high frequency scattering signal from natural microwave radiation, which is observed by passive microwave instruments. Developed for the retrieval of global snow depth and snow water equivalent using Advanced Microwave Scanning Radiometer EOS (AMSR-E), the algorithm uses passive microwave radiation along with a microwave emission model and a snow grain growth model to estimate snow depth. The microwave emission model is based on the Dense Media Radiative Transfer (DMRT) model that uses the quasi-crystalline approach and sticky particle theory to predict the brightness temperature from a single layered snowpack. The grain growth model is a generic single layer model based on an empirical approach to predict snow grain size evolution with time. Gridding to the 25 km EASE-grid projection, a daily record of Special Sensor Microwave Imager (SSM/I) snow depth estimates was generated for December 2000 to March 2001. The estimates are tested using ground measurements from two continental-scale river catchments (Nelson River and the Ob River in Russia). This regional-scale testing of the algorithm shows that for passive microwave estimates, the average daily snow depth retrieval standard error between estimated and measured snow depths ranges from 0 cm to 40 cm of point observations. Bias characteristics are different for each basin. A fraction of the error is related to uncertainties about the grain growth initialization states and uncertainties about grain size changes through the winter season that directly affect the parameterization of the snow depth estimation in the DMRT model. Also, the algorithm does not include a correction for forest cover and this effect is clearly observed in the retrieval. Finally, error is also related to scale differences between in situ ground measurements and area-integrated satellite estimates. With AMSR-E data, improvements to snow depth and water equivalent estimates are expected since AMSR-E will have twice the spatial resolution of the SSM/I and will be able to characterize better the subnivean snow environment from an expanded range of microwave frequencies.

Spatiotemporal changes of snow cover over the Tibetan plateau based on cloud-removed moderate resolution imaging spectroradiometer fractional snow cover product from 2001 to 2011

NASA Astrophysics Data System (ADS)

Tang, Zhiguang; Wang, Jian; Li, Hongyi; Yan, Lili

2013-01-01

Snow cover changes over the Tibetan plateau (TP) are examined using moderate resolution imaging spectroradiometer (MODIS) daily fractional snow cover (FSC) data from 2001 to 2011 as well as in situ temperature data. First, the accuracy of the MODIS FSC data under clear sky conditions is evaluated by comparing with Landsat 30-m observations. Then we describe a cloud-gap-filled (CGF) method using cubic spline interpolation algorithm to fill in data gaps caused by clouds. Finally, the spatial and temporal changes of snow cover are analyzed on the basis of the MODIS-derived snow-covered area and snow-covered days (SCD) data. Results show that the mean absolute error of MODIS FSC data under clear sky condition is about 0.098 over the TP. The CGF method is efficient in cloud reduction (overall mean absolute error of the retrieved FSC data is 0.092). There is a very high inter-annual and intra-seasonal variability of snow cover in the 11 years. The higher snow cover corresponds well with the huge mountains. The accumulation and melt periods of snow cover vary in different elevation zones. About 34.14% (5.56% with a significant decline) and 24.75% (3.9% with a significant increase) of the study area presents declining and increasing trend in SCD, respectively. The inter-annual fluctuation of snow cover can be explained by the high negative correlations observed between the snow cover and the in situ temperature, especially in some elevations of February, April, May, August, and September.

Applications systems verification and transfer project. Volume 2: Operational applications of satellite snow-cover observations and data-collection systems in the Arizona test site

NASA Technical Reports Server (NTRS)

Schumann, H. H.

1981-01-01

Ground surveys and aerial observations were used to monitor rapidly changing moisture conditions in the Salt-Verde watershed. Repetitive satellite snow cover observations greatly reduce the necessity for routine aerial snow reconnaissance flights over the mountains. High resolution, multispectral imagery provided by LANDSAT satellite series enabled rapid and accurate mapping of snow-cover distributions for small- to medium-sized subwatersheds; however, the imagery provided only one observation every 9 days of about a third of the watershed. Low resolution imagery acquired by the ITOSa dn SMS/GOES meteorological satellite series provides the daily synoptic observation necessary to monitor the rapid changes in snow-covered area in the entire watershed. Short term runoff volumes can be predicted from daily sequential snow cover observations.

Integration of MODIS Snow, Cloud and Land Area Coverage Data with SNOTEL to Generate Inter-Annual and Within-Season Snow Depletion Curves and Maps

NASA Astrophysics Data System (ADS)

Qualls, R. J.; Woodruff, C.

2017-12-01

The behavior of inter-annual trends in mountain snow cover would represent extremely useful information for drought and climate change assessment; however, individual data sources exhibit specific limitations for characterizing this behavior. For example, SNOTEL data provide time series point values of Snow Water Equivalent (SWE), but lack spatial content apart from that contained in a sparse network of point values. Satellite observations in the visible spectrum can provide snow covered area, but not SWE at present, and are limited by cloud cover which often obscures visibility of the ground, especially during the winter and spring in mountainous areas. Cloud cover, therefore, often limits both temporal and spatial coverage of satellite remote sensing of snow. Among the platforms providing the best combination of temporal and spatial coverage to overcome the cloud obscuration problem by providing frequent overflights, the Aqua and Terra satellites carrying the MODIS instrument package provide 500 m, daily resolution observations of snow cover. These were only launched in 1999 and the early 2000's, thus limiting the historical period over which these data are available. A hybrid method incorporating SNOTEL and MODIS data has been developed which accomplishes cloud removal, and enables determination of the time series of watershed spatial snow cover when either SNOTEL or MODIS data are available. This allows one to generate spatial snow cover information for watersheds with SNOTEL stations for periods both before and after the launch of the Aqua and Terra satellites, extending the spatial information about snow cover over the period of record of the SNOTEL stations present in a watershed. This method is used to quantify the spatial time series of snow over the 9000 km2 Upper Snake River watershed and to evaluate inter-annual trends in the timing, rate, and duration of melt over the nearly 40 year period from the early 1980's to the present, and shows promise for generating snow cover depletion maps for drought and climate change scenarios.

The application of ERTS imagery to mapping snow cover in the western United States. [Salt Verde in Arizona and Sierra Nevada California

NASA Technical Reports Server (NTRS)

Barnes, J. C. (Principal Investigator); Bowley, C. J.; Simmes, D. A.

1974-01-01

The author has identified the following significant results. In much of the western United States a large part of the utilized water comes from accumulated mountain snowpacks; thus, accurate measurements of snow distributions are required for input to streamflow prediction models. The application of ERTS-1 imagery for mapping snow has been evaluated for two geographic areas, the Salt-Verde watershed in central Arizona and the southern Sierra Nevada in California. Techniques have been developed to identify snow and to differentiate between snow and cloud. The snow extent for these two drainage areas has been mapped from the MSS-5 (0.6 - 0.7 microns) imagery and compared with aerial survey snow charts, aircraft photography, and ground-based snow measurements. The results indicate that ERTS imagery has substantial practical applications for snow mapping. Snow extent can be mapped from ERTS-1 imagery in more detail than is depicted on aerial survey snow charts. Moreover, in Arizona and southern California cloud obscuration does not appear to be a serious deterrent to the use of satellite data for snow survey. The costs involved in deriving snow maps from ERTS-1 imagery appear to be very reasonable in comparison with existing data collection methods.

On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales

NASA Astrophysics Data System (ADS)

Härer, Stefan; Bernhardt, Matthias; Siebers, Matthias; Schulz, Karsten

2018-05-01

Knowledge of current snow cover extent is essential for characterizing energy and moisture fluxes at the Earth's surface. The snow-covered area (SCA) is often estimated by using optical satellite information in combination with the normalized-difference snow index (NDSI). The NDSI thereby uses a threshold for the definition if a satellite pixel is assumed to be snow covered or snow free. The spatiotemporal representativeness of the standard threshold of 0.4 is however questionable at the local scale. Here, we use local snow cover maps derived from ground-based photography to continuously calibrate the NDSI threshold values (NDSIthr) of Landsat satellite images at two European mountain sites of the period from 2010 to 2015. The Research Catchment Zugspitzplatt (RCZ, Germany) and Vernagtferner area (VF, Austria) are both located within a single Landsat scene. Nevertheless, the long-term analysis of the NDSIthr demonstrated that the NDSIthr at these sites are not correlated (r = 0.17) and different than the standard threshold of 0.4. For further comparison, a dynamic and locally optimized NDSI threshold was used as well as another locally optimized literature threshold value (0.7). It was shown that large uncertainties in the prediction of the SCA of up to 24.1 % exist in satellite snow cover maps in cases where the standard threshold of 0.4 is used, but a newly developed calibrated quadratic polynomial model which accounts for seasonal threshold dynamics can reduce this error. The model minimizes the SCA uncertainties at the calibration site VF by 50 % in the evaluation period and was also able to improve the results at RCZ in a significant way. Additionally, a scaling experiment shows that the positive effect of a locally adapted threshold diminishes using a pixel size of 500 m or larger, underlining the general applicability of the standard threshold at larger scales.

Snow depth and snow cover retrieval from FengYun3B microwave radiation imagery based on a snow passive microwave unmixing method in Northeast China

NASA Astrophysics Data System (ADS)

Gu, Lingjia; Ren, Ruizhi; Zhao, Kai; Li, Xiaofeng

2014-01-01

The precision of snow parameter retrieval is unsatisfactory for current practical demands. The primary reason is because of the problem of mixed pixels that are caused by low spatial resolution of satellite passive microwave data. A snow passive microwave unmixing method is proposed in this paper, based on land cover type data and the antenna gain function of passive microwaves. The land cover type of Northeast China is partitioned into grass, farmland, bare soil, forest, and water body types. The component brightness temperatures (CBT), namely unmixed data, with 1 km data resolution are obtained using the proposed unmixing method. The snow depth determined by the CBT and three snow depth retrieval algorithms are validated through field measurements taken in forest and farmland areas of Northeast China in January 2012 and 2013. The results show that the overall of the retrieval precision of the snow depth is improved by 17% in farmland areas and 10% in forest areas when using the CBT in comparison with the mixed pixels. The snow cover results based on the CBT are compared with existing MODIS snow cover products. The results demonstrate that more snow cover information can be obtained with up to 86% accuracy.

Development of an Algorithm for Satellite Remote Sensing of Sea and Lake Ice

NASA Astrophysics Data System (ADS)

Dorofy, Peter T.

Satellite remote sensing of snow and ice has a long history. The traditional method for many snow and ice detection algorithms has been the use of the Normalized Difference Snow Index (NDSI). This manuscript is composed of two parts. Chapter 1, Development of a Mid-Infrared Sea and Lake Ice Index (MISI) using the GOES Imager, discusses the desirability, development, and implementation of alternative index for an ice detection algorithm, application of the algorithm to the detection of lake ice, and qualitative validation against other ice mapping products; such as, the Ice Mapping System (IMS). Chapter 2, Application of Dynamic Threshold in a Lake Ice Detection Algorithm, continues with a discussion of the development of a method that considers the variable viewing and illumination geometry of observations throughout the day. The method is an alternative to Bidirectional Reflectance Distribution Function (BRDF) models. Evaluation of the performance of the algorithm is introduced by aggregating classified pixels within geometrical boundaries designated by IMS and obtaining sensitivity and specificity statistical measures.

MODIS Snow and Sea Ice Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.

2004-01-01

In this chapter, we describe the suite of Earth Observing System (EOS) Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow and sea ice products. Global, daily products, developed at Goddard Space Flight Center, are archived and distributed through the National Snow and Ice Data Center at various resolutions and on different grids useful for different communities Snow products include binary snow cover, snow albedo, and in the near future, fraction of snow in a 5OO-m pixel. Sea ice products include ice extent determined with two different algorithms, and sea ice surface temperature. The algorithms used to develop these products are described. Both the snow and sea ice products, available since February 24,2000, are useful for modelers. Validation of the products is also discussed.

Snow Depth Mapping at a Basin-Wide Scale in the Western Arctic Using UAS Technology

NASA Astrophysics Data System (ADS)

de Jong, T.; Marsh, P.; Mann, P.; Walker, B.

2015-12-01

Assessing snow depths across the Arctic has proven to be extremely difficult due to the variability of snow depths at scales from metres to 100's of metres. New Unmanned Aerial Systems (UAS) technology provides the possibility to obtain centimeter level resolution imagery (~3cm), and to create Digital Surface Models (DSM) based on the Structure from Motion method. However, there is an ongoing need to quantify the accuracy of this method over different terrain and vegetation types across the Arctic. In this study, we used a small UAS equipped with a high resolution RGB camera to create DSMs over a 1 km2 watershed in the western Canadian Arctic during snow (end of winter) and snow-free periods. To improve the image georeferencing, 15 Ground Control Points were marked across the watershed and incorporated into the DSM processing. The summer DSM was subtracted from the snowcovered DSM to deliver snow depth measurements across the entire watershed. These snow depth measurements were validated by over 2000 snow depth measurements. This technique has the potential to improve larger scale snow depth mapping across watersheds by providing snow depth measurements at a ~3 cm . The ability of mapping both shallow snow (less than 75cm) covering much of the basin and snow patches (up to 5 m in depth) that cover less than 10% of the basin, but contain a significant portion of total basin snowcover, is important for both water resource applications, as well as for testing snow models.

Antarctic sea ice thickness data archival and recovery at the Australian Antarctic Data Centre

NASA Astrophysics Data System (ADS)

Worby, A. P.; Treverrow, A.; Raymond, B.; Jordan, M.

2007-12-01

A new effort is underway to establish a portal for Antarctic sea ice thickness data at the Australian Antarctic Data Centre (http://aadc-maps.aad.gov.au/aadc/sitd/). The intention is to provide a central online access point for a wide range of sea ice data sets, including sea ice and snow thickness data collected using a range of techniques, and sea ice core data. The recommendation to establish this facility came from the SCAR/CliC- sponsored International Workshop on Antarctic Sea Ice Thickness, held in Hobart in July 2006. It was recognised, in particular, that satellite altimetry retrievals of sea ice and snow cover thickness rely on large-scale assumptions of the sea ice and snow cover properties such as density, freeboard height, and snow stratigraphy. The synthesis of historical data is therefore particularly important for algorithm development. This will be closely coordinated with similar efforts in the Arctic. A small working group was formed to identify suitable data sets for inclusion in the archive. A series of standard proformas have been designed for converting old data, and to help standardize the collection of new data sets. These proformas are being trialled on two Antarctic sea ice research cruises in September - October 2007. The web-based portal allows data custodians to remotely upload and manage their data, and for all users to search the holdings and extract data relevant to their needs. This presentation will report on the establishment of the data portal, recent progress in identifying appropriate data sets and making them available online. maps.aad.gov.au/aadc/sitd/

A Look at Seasonal Snow Cover and Snow Mass in the Southern Hemisphere from 1979-2006 Using SMMR and SSM/I Passive Microwave Data

NASA Technical Reports Server (NTRS)

Foster, James

2009-01-01

Seasonal snow cover in extra-tropical areas of South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite and from the Special Sensor Microwave Imagers (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2006, both snow cover extent and snow mass were estimated for the months of May-September. Most of the seasonal snow in South America occurs in the Patagonia region of Argentina. The average snow cover extent for July, the month with the greatest average extent during the 28-year period of record, is 321,674 sq km. The seasonal (May-September) 2 average snow cover extent was greatest in 1984 (464,250 sq km) and least in 1990 (69,875 sq km). In terms of snow mass, 1984 was also the biggest year (1.19 x 10(exp 13) kg) and 1990 was the smallest year (0.12 X 10(exp 13) kg). A strong relationship exists between the snow cover area and snow mass, correlated at 0.95, though no significant trend was found over the 28 year record for either snow cover extent or snow mass. For this long term climatology, snow mass and snow cover extent are shown to vary considerably from month to month and season to season. This analysis presents a consistent approach to mapping and measuring snow in South America utilizing an appropriate and readily available long term snow satellite dataset. This is the optimal dataset available, thus far, for deriving seasonal snow cover and snow mass in this region. Nonetheless, shallow snow, wet snow, snow beneath forests, as well as snow along coastal areas all may confound interpretation using passive microwave approaches. More work needs to be done to reduce the uncertainties in the data and hence, increase the confidence of the interpretation

Monitoring the spatio-temporal evolution of the snow cover in the eastern Alps from MODIS data

NASA Astrophysics Data System (ADS)

Cianfarra, P.; Salvini, F.; Valt, M.

2009-04-01

Estimating the snow cover extent in mountain ranges is important for a wide variety purposes including of scientific studies, environmental and meteo-climatic applications, as well as predicting water availability for energy resource and agriculture. Moreover, the monitoring of the spatio-temporal variation of the snow cover thickness, coupled with ground data from weather stations, allows to identify avalanche risk areas after heavy snowfall. The aim of this study is to test an automatic procedure to identify and map the snow coverage for different altitude interval in the eastern part of the Alpine range. There has been much progress since 1966 when the first operational snow mapping was done by NOAA with spaceborne sensors that provide daily, global observations to monitor the variability in space and time in the extent of snow cover. MODIS sensors offer increased improvements relative to the AVHRR that has been operational for many years on the NOAA Polar Operational Environmental Satellite System. In this context the MODIS provides observations at a nominal spatial resolution of 500 m versus the 1.1 km spatial resolution of the AVHRR and continuously available (spatially and temporally), spectral band observation that span the visible and short-wave infrared wavelengths, including those useful for recognize snow cover. The other advantage of using MODIS data is its availability and cost by the NASA's server. In this work we used MOD02 (L1B) data providing calibrated radiance values at the sensor (without atmospheric correction). Snow cover map production included the following steps: selection of the images with clear sky conditions, geometric correction and georeferencing to UTM zone 32 ,WSG 84 ellipsoid, to eliminate the distortion of and the typical bow-tie effect that produces the observed not alignment of the scan lines in the row image; spatial sub setting to produce an image covering an area of about 200 x 120 km; identification of the snow cover was done by computing the Normalised Difference Snow Index (NDSI) knowing that snow reflectance is higher in the visible (0.5-0.7 mm) wavelengths and has lower reflectance in the short wave infrared (1-4 mm) wavelengths. This allowed to separate snow from clouds and other non-snow-covered pixels. The NDSI for MODIS images is defined as the difference of reflectances observed in the visible band 4 (0.555 mm) and the short wave infrared band 6 (1.640 mm) divided by the sum of the two reflectances: NDSI=(B4 - B6)/ (B4 + B6) This approach allowed to reduce (yet not totally eliminate) the influence of the atmospheric effects and lighting conditions. A series of thresholds were tested to the ratio image to establish the best value for snow cover identification. Eventually, the snow cover extent was computed for 6 altitude intervals. Results from the different processed images were compared and statistically analysed. A complete set of ground truth of these preliminary results is still missing; yet we are confident that once the tuning of the processing will be completed, the automated processing of MODIS data will provide low cost, near real-time estimates of the snow cover distribution over the eastern Alps. This product would be a valuable tool for public administrations and authorities for environmental protection, control and risk management.

Mapping snow depth in open alpine terrain from stereo satellite imagery

NASA Astrophysics Data System (ADS)

Marti, R.; Gascoin, S.; Berthier, E.; de Pinel, M.; Houet, T.; Laffly, D.

2016-07-01

To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km2) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a -0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is -0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km2). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of -0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available.

Evaluation of snow cover and snow depth on the Qinghai-Tibetan Plateau derived from passive microwave remote sensing

NASA Astrophysics Data System (ADS)

Dai, Liyun; Che, Tao; Ding, Yongjian; Hao, Xiaohua

2017-08-01

Snow cover on the Qinghai-Tibetan Plateau (QTP) plays a significant role in the global climate system and is an important water resource for rivers in the high-elevation region of Asia. At present, passive microwave (PMW) remote sensing data are the only efficient way to monitor temporal and spatial variations in snow depth at large scale. However, existing snow depth products show the largest uncertainties across the QTP. In this study, MODIS fractional snow cover product, point, line and intense sampling data are synthesized to evaluate the accuracy of snow cover and snow depth derived from PMW remote sensing data and to analyze the possible causes of uncertainties. The results show that the accuracy of snow cover extents varies spatially and depends on the fraction of snow cover. Based on the assumption that grids with MODIS snow cover fraction > 10 % are regarded as snow cover, the overall accuracy in snow cover is 66.7 %, overestimation error is 56.1 %, underestimation error is 21.1 %, commission error is 27.6 % and omission error is 47.4 %. The commission and overestimation errors of snow cover primarily occur in the northwest and southeast areas with low ground temperature. Omission error primarily occurs in cold desert areas with shallow snow, and underestimation error mainly occurs in glacier and lake areas. With the increase of snow cover fraction, the overestimation error decreases and the omission error increases. A comparison between snow depths measured in field experiments, measured at meteorological stations and estimated across the QTP shows that agreement between observation and retrieval improves with an increasing number of observation points in a PMW grid. The misclassification and errors between observed and retrieved snow depth are associated with the relatively coarse resolution of PMW remote sensing, ground temperature, snow characteristics and topography. To accurately understand the variation in snow depth across the QTP, new algorithms should be developed to retrieve snow depth with higher spatial resolution and should consider the variation in brightness temperatures at different frequencies emitted from ground with changing ground features.

Summer snowmelt patterns in the South Shetlands using TerraSAR-X imagery

NASA Astrophysics Data System (ADS)

Mora, C.; Jimenez, J. J.; Catalao Fernades, J.; Ferreira, A.; David, A.; Ramos, M.; Vieira, G.

2014-12-01

Snow plays an important role in controlling ground thermal regime and thus influencing permafrost distribution in the lower areas of the South Shetlands archipelago, where late lying snowpatches protect the soil from summer warming. However, summer snow distribution is complex in the mountainous environments of the Maritime Antarctica and it is very difficult to obtain accurate mapping products of snow cover extent and also to monitor snowmelt. Field observations of snow cover in the region are currently based on: i) thickness data from a very scarce network of meteorological stations, ii) temperature poles allowing to estimate snow thickness, iii) and time-lapse cameras allowing for assessing snow distribution over relatively small areas. The high cloudiness of the Maritime Antarctic environment limits good mapping results from the analysis of optical remote sensing imagery such as Landsat, QuickBird or GeoEye. Therefore, microwave sensors provide the best imagery, since they are not influenced by cloudiness and are sensitive to wet-snow, typical of the melting season. We have acquired TerraSAR-X scenes for Deception and Livingston Islands for January-March 2014 in spotlight (HH, VV and HH/VV) and stripmap modes (HH) and analyse the radar backscattering for determining the differences between wet-snow, dry-snow and bare soil aiming at developing snow melt pattern maps. For ground truthing, snowpits were dug in order to characterize snow stratigraphy, grain size, grain type and snow density and to evaluate its effects on radar backscattering. Time-lapse cameras allow to identify snow patch boundaries in the field and ground surface temperatures obtained with minloggers, together with air temperatures, allow to identify the presence of snow cover in the ground. The current research is conducted in the framework of the project PERMANTAR-3 (Permafrost monitoring and modelling in Antarctic Peninsula - PTDC/AAG-GLO/3908/2012 of the FCT and PROPOLAR).

Cloud removing method for daily snow mapping over Central Asia and Xinjiang, China

NASA Astrophysics Data System (ADS)

Yu, Xiaoqi; Qiu, Yubao; Guo, Huadong; Chen, Lijuan

2017-02-01

Central Asia and Xinjiang, China are conjunct areas, located in the hinterland of the Eurasian continent, where the snowfall is an important water resource supplement form. The induced seasonal snow cover is vita factors to the regional energy and water balance, remote sensing plays a key role in the snow mapping filed, while the daily remote sensing products are normally contaminated by the occurrence of cloud, that obviously obstacles the utility of snow cover parameters. In this paper, based on the daily snow product from Moderate Resolution Imaging Spectroradiometer (MODIS A1), a cloud removing method was developed by considering the regional snow distribution characteristics with latitude and altitude dependence respectively. In the end, the daily cloud free products was compared with the same period of eight days MODIS standard product, revealing that the cloud free snow products are reasonable, while could provide higher temporal resolution, and more details over Center Asia and Xinjiang Province.

Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers

NASA Technical Reports Server (NTRS)

Meier, M. F. (Principal Investigator)

1972-01-01

The author has identified the following significant results. Preliminary results on the feasibility of mapping snow cover extent have been obtained from a limited number of ERTS-1 images of mountains in Alaska, British Columbia, and Washington. The snowline on land can be readily distinguished, except in heavy forest where such distinction appears to be virtually impossible. The snowline on very large glaciers can also be distinguished remarkably easily, leading to a convenient way to measure glacier accumulation area ratios or equilibrium line altitude. Monitoring of large surging glaciers appears to be possible, but only through observation of a change in area and/or medial moraine extent. Under certain conditions, ERTS-1 imagery appears to have high potential for mapping snow cover in mountainous areas. Distinction between snow and clouds appears to require use of the human eye, but in a cloud-free scene the snow cover is sufficiently distinct to allow use of automated techniques. This technique may prove very useful as an aid in the monitoring of the snowpack water resource and the prediction of summer snowmelt runoff volume.

Response of Alpine Grassland Vegetation Phenology to Snow Accumulation and Melt in Namco Basin

NASA Astrophysics Data System (ADS)

Chen, S.; Cui, X.; Liang, T.

2018-04-01

Snow/ice accumulation and melt, as a vital part of hydrological processes, is close related with vegetation activities. Taking Namco basin for example, based on multisource remote sensing data and the ground observation data of temperature and precipitation, phenological information was extracted by S-G filtering and dynamic threshold method. Daily snow cover fraction was calculated with daily cloud-free snow cover maps. Evolution characteristics of grassland vegetation greening, growth length and daily snow cover fraction and their relationship were analyzed from 2001 to 2013. The results showed that most of grassland vegetation had advanced greening and prolong growth length trend in Namco basin. There were negative correlations between snow cover fraction and vegetation greening or growth length. The response of vegetation phenology to snow cover fraction is more sensitive than that to temperature in spring. Meanwhile, vegetation growth condition turned worse with advanced greening and prolong growth length. To a certain extent, our research reveals the relationship between grassland vegetation growth cycle and snow in alpine ecosystem. It has provided reference to research the response mechanism of alpine grassland ecosystem to climate changes.

The Airborne Snow Observatory: fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo

USDA-ARS?s Scientific Manuscript database

Snow cover and its melt dominate regional climate and water resources in many of the world’s mountainous regions. Snowmelt timing and magnitude in mountains tend to be controlled by absorption of solar radiation and snow water equivalent, respectively, and yet both of these are very poorly known ev...

Validation of the Daily Passive Microwave Snow Depth Products Over Northern China

NASA Astrophysics Data System (ADS)

Qiao, D.; Li, Z.; Wang, N.; Zhou, J.; Zhang, P.; Gao, S.

2018-04-01

Passive microwave sensors have the capability to provide information on snow depth (SD), which is critically important for hydrological modeling and water resource management. However, the different algorithms used to produce SD products lead to discrepancies in the data. To determine which products might be most suitable for Northern China, this paper assesses the accuracy of the existing snow depth products in the period of 2002-2011. By comparing three daily snow depth products, including NSIDC, WESTDC and ESA Globsnow, with snow cover product and meteorological stations data, the accuracies of the different SD products are analyzed for different snow class and forest cover fraction. The results show that comparison between snow cover derived from snow depth of NSIDC, ESA GlobSnow and WESTDC with snow cover product shows that accuracy of WESTDC and ESA GlobSnow in snow cover detecting can reach 0.70. Compared to meteorological stations data below 20 cm, NSIDC consistently overestimate, WESTDC and ESA Globsnow underestimate, furthermore the product from WESTDC is superior to the others. The three products have the same tendency of significant undervaluation over 20 cm. The WESTDC is superior to the ESA Globsnow and NSIDC in non-forest regions, whereas the ESA GlobSnow estimate is superior to the WESTDC and NSIDC in forest regions. As for the prairie and alpine snow, WESTDC has smaller bias and RMSE, meanwhile Globsnow has advantages in the snow depth retrieval in tundra and taiga snow. Therefore, we should choose the more suitable snow depth products according to different needs.

Mapping snow depth from stereo satellite imagery

NASA Astrophysics Data System (ADS)

Gascoin, S.; Marti, R.; Berthier, E.; Houet, T.; de Pinel, M.; Laffly, D.

2016-12-01

To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km²) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a -0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is -0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km²). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of -0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available. Based on this method we have initiated a multi-year survey of the peak snow depth in the Bassiès catchment.

Survey of the seasonal snow cover in Alaska

NASA Technical Reports Server (NTRS)

Weller, G. E. (Principal Investigator)

1973-01-01

The author has identified the following significant results. ERTS-1 data are used together with synoptic-climatological data to describe the buildup of the seasonal snow and ice covers in a north-south transect of a total length of about 1250 km across Alaska. It has been demonstrated that the ERTS-1 data may, under favorable conditions, be used for accurate mapping of snow lines in high mountain regions. The analysis shows that especially in the Brooks Range and on the Arctic Slope where snow covers generally are relatively thin, the ERTS-1 scenes can be useful for qualitative descriptions of the snow and ice covers over wide expanses. The onset and retreat of the seasonal snow cover are sensitive indicators of climatic fluctuations and the ERTS-1 data offers a possibility to record variations of the snow and ice buildup from year to year in a practical and informative way, which should be especially useful for studies of climatic trends. This is particularly true in Alaska where the density of the station network is too low to permit interpolations between the stations.

Large area robust identification of snow cover from multitemporal COSMO-SkyMed images

NASA Astrophysics Data System (ADS)

Pettinato, S.; Santi, E.; Paloscia, S.; Aiazzi, B.; Baronti, S.; Palchetti, E.; Garzelli, A.

2015-10-01

This paper investigates the ability of the Information Theoretic Snow Detection Algorithm (ITSDA) in detecting changes due to snow cover between summer and winter seasons on large area images acquired by COSMO-SkyMed constellation. ITSDA is a method for change detection in multitemporal SAR images, which has been recently applied by the authors to a subset of Cosmo-SkyMed data. The proposed technique is based on a nonparametric approach in the framework of Shannon's information theory, and in particular it features the conditional probability of the local means between the two images taken at different times. Such an unsupervised approach does not require any preliminary despeckling procedure to be performed before the calculation of the change map. In the case of a low quantity of anomalous changes in relatively small-size images, a mean shift procedure can be utilized for refining the map. However, in the present investigation, the changes to be identified are pervasive in large size images. Consequently, for computational issues, the mean shift refinement has been omitted in the present work. However, a simplified implementation of mean shift procedure to save time will be possibly considered in future submissions. In any case, the present version of ITSDA method preserve its characteristics of flexibility and sensibility to backscattering changes, thanks to the possibility of setting up the number of quantization levels in the estimation of the conditional probability between the amplitude values at the two acquisition dates.

Spatial analysis and statistical modelling of snow cover dynamics in the Central Himalayas, Nepal

NASA Astrophysics Data System (ADS)

Weidinger, Johannes; Gerlitz, Lars; Böhner, Jürgen

2017-04-01

General circulation models are able to predict large scale climate variations in global dimensions, however small scale dynamic characteristics, such as snow cover and its temporal variations in high mountain regions, are not represented sufficiently. Detailed knowledge about shifts in seasonal ablation times and spatial distribution of snow cover are crucial for various research interests. Since high mountain areas, for instance the Central Himalayas in Nepal, are generally remote, it is difficult to obtain data in high spatio-temporal resolutions. Regional climate models and downscaling techniques are implemented to compensate coarse resolution. Furthermore earth observation systems, such as MODIS, also permit bridging this gap to a certain extent. They offer snow (cover) data in daily temporal and medium spatial resolution of around 500 m, which can be applied as evaluation and training data for dynamical hydrological and statistical analyses. Within this approach two snow distribution models (binary snow cover and fractional snow cover) as well as one snow recession model were implemented for a research domain in the Rolwaling Himal in Nepal, employing the random forest technique, which represents a state of the art machine learning algorithm. Both bottom-up strategies provide inductive reasoning to derive rules for snow related processes out of climate (temperature, precipitation and irradiance) and climate-related topographic data sets (elevation, aspect and convergence index) obtained by meteorological network stations, remote sensing products (snow cover - MOD10-A1 and land surface temperatures - MOD11-A1) along with GIS. Snow distribution is predicted reliably on a daily basis in the research area, whereas further effort is necessary for predicting daily snow cover recession processes adequately. Swift changes induced by clear sky conditions with high insolation rates are well represented, whereas steady snow loss still needs continuing effort. All approaches underline the technical difficulties of snow cover modelling during the monsoon season, in accordance with previous studies. The developed methods in combination with continuous in situ measurements provide a basis for further downscaling approaches.

Multi-decadal evolution of ice/snow covers in the Mont-Blanc massif (France)

NASA Astrophysics Data System (ADS)

Guillet, Grégoire; Ravanel, Ludovic

2017-04-01

Dynamics and evolution of the major glaciers of the Mont-Blanc massif have been vastly studied since the XXth century. Ice/snow covers on steep rock faces as part of the cryosphere however remain poorly studied with only qualitative descriptions existing. The study of ice/snow covers is primordial to further understand permafrost degradation throughout the Mont-Blanc massif and to improve safety and prevention for mountain sports practitioners. This study focuses on quantifying the evolution of ice/snow covers surface during the past century using a specially developed monoplotting tool using Bayesian statistics and Markov Chain Monte Carlo algorithms. Combining digital elevation models and photographs covering a time-span of 110 years, we calculated the ice/snow cover surface for 3 study sites — North faces of the Tour Ronde (3792 m a.s.l.) and the Grandes Jorasses (4208 m a.s.l.) and Triangle du Tacul (3970 m a.s.l.) — and deduced the evolution of their area throughout the XXth century. First results are showing several increase/decrease periods. The first decrease in ice/snow cover surface occurs between the 1940's and the 1950's. It is followed by an increase up to the 1980's. Since then, ice/snow covers show a general decrease in surface which is faster since the 2010's. Furthermore, the gain/loss during the increase/decrease periods varies with the considered ice/snow cover, making it an interesting cryospheric entity of its own.

A study to develop improved spacecraft show survey methods using Skylab/EREP data: Demonstration of the utility of the S190 and S192 data. [Sierra Nevadas in California, Cascades in Washington and Oregon, Upper Columbia Basin in Idaho and Montana, and Salt-Verde Watershed in Arizona

NASA Technical Reports Server (NTRS)

Barnes, J. C. (Principal Investigator); Bowley, C. J.; Smallwood, M. D.

1974-01-01

The author has identified the following significant results. This interim report provides a demonstration of the utility of spacecraft acquired Skylab S190A and S190B photography and S192 imagery for mapping areal extent of snow cover in western United States test site areas. The data sample is from the SL-2 mission flown in June 1973. Results of the investigation indicate that areal snow cover extent can be mapped more accurately from the S190A and S190B photography than from any other spacecraft system, including ERTS. The results of a qualitative analysis of the S192 imagery indicate considerable potential for the utility of multispectral snow cover analysis; the potential for distinguishing snow from clouds automatically is particularly significant.

Snow cover volumes dynamic monitoring during melting season using high topographic accuracy approach for a Lebanese high plateau witness sinkhole

NASA Astrophysics Data System (ADS)

Abou Chakra, Charbel; Somma, Janine; Elali, Taha; Drapeau, Laurent

2017-04-01

Climate change and its negative impact on water resource is well described. For countries like Lebanon, undergoing major population's rise and already decreasing precipitations issues, effective water resources management is crucial. Their continuous and systematic monitoring overs long period of time is therefore an important activity to investigate drought risk scenarios for the Lebanese territory. Snow cover on Lebanese mountains is the most important water resources reserve. Consequently, systematic observation of snow cover dynamic plays a major role in order to support hydrologic research with accurate data on snow cover volumes over the melting season. For the last 20 years few studies have been conducted for Lebanese snow cover. They were focusing on estimating the snow cover surface using remote sensing and terrestrial measurement without obtaining accurate maps for the sampled locations. Indeed, estimations of both snow cover area and volumes are difficult due to snow accumulation very high variability and Lebanese mountains chains slopes topographic heterogeneity. Therefore, the snow cover relief measurement in its three-dimensional aspect and its Digital Elevation Model computation is essential to estimate snow cover volume. Despite the need to cover the all lebanese territory, we favored experimental terrestrial topographic site approaches due to high resolution satellite imagery cost, its limited accessibility and its acquisition restrictions. It is also most challenging to modelise snow cover at national scale. We therefore, selected a representative witness sinkhole located at Ouyoun el Siman to undertake systematic and continuous observations based on topographic approach using a total station. After four years of continuous observations, we acknowledged the relation between snow melt rate, date of total melting and neighboring springs discharges. Consequently, we are able to forecast, early in the season, dates of total snowmelt and springs low water flows which are essentially feeded by snowmelt water. Simulations were ran, predicting the snow level between two sampled dates, they provided promising result for national scale extrapolation.

UAS applications in high alpine, snow-covered terrain

NASA Astrophysics Data System (ADS)

Bühler, Y.; Stoffel, A.; Ginzler, C.

2017-12-01

Access to snow-covered, alpine terrain is often difficult and dangerous. Hence parameters such as snow depth or snow avalanche release and deposition zones are hard to map in situ with adequate spatial and temporal resolution and with spatial continuous coverage. These parameters are currently operationally measured at automated weather stations and by observer networks. However such isolated point measurements are not able to capture the information spatial continuous and to describe the high spatial variability present in complex mountain topography. Unmanned Aerial Systems (UAS) have the potential to fill this gap by frequently covering selected high alpine areas with high spatial resolution down to ground resolutions of even few millimeters. At the WSL Institute for Snow and Avalanche Research SLF we test different photogrammetric UAS with visual and near infrared bands. During the last three years we were able to gather experience in more than 100 flight missions in extreme terrain. By processing the imagery applying state-of-the-art structure from motion (SfM) software, we were able to accurately document several avalanche events and to photogrammetrically map snow depth with accuracies from 1 to 20 cm (dependent on the flight height above ground) compare to manual snow probe measurements. This was even possible on homogenous snow surfaces with very little texture. A key issue in alpine terrain is flight planning. We need to cover regions at high elevations with large altitude differences (up to 1 km) with high wind speeds (up to 20 m/s) and cold temperatures (down to - 25°C). Only a few UAS are able to cope with these environmental conditions. We will give an overview on our applications of UAS in high alpine terrain that demonstrate the big potential of such systems to acquire frequent, accurate and high spatial resolution geodata in high alpine, snow covered terrain that could be essential to answer longstanding questions in avalanche and snow hydrology research.

BOREAS RSS-8 Snow Maps Derived from Landsat TM Imagery

NASA Technical Reports Server (NTRS)

Hall, Dorothy; Chang, Alfred T. C.; Foster, James L.; Chien, Janeet Y. L.; Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Remote Sensing Science (RSS)-8 team utilized Landsat Thematic Mapper (TM) images to perform mapping of snow extent over the Southern Study Area (SSA). This data set consists of two Landsat TM images that were used to determine the snow-covered pixels over the BOREAS SSA on 18 Jan 1993 and on 06 Feb 1994. The data are stored in binary image format files. The RSS-08 snow map data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers

NASA Technical Reports Server (NTRS)

Meier, M. F. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The percentage of snow cover area on specific drainage basins was measured from ERTS-1 imagery by video density slicing with a repeatability of 4 percent of the snow covered area. Data from ERTS-1 images of the melt season snow cover in the Thunder Creek drainage basin in the North Cascades were combined with existing hydrologic and meteorologic observations to enable calculations of the time distribution of the water stored in this mountain snowpack. Similar data could be used for frequent updating of expected inflow to reservoirs. Equivalent snowline altitudes were determined from area measurements. Snowline altitudes were also determined by combining enlarged ERTS-1 images with maps. ERTS-1 imagery was also successfully used to measure glacier accumulation area ratios for a small test basin.

Demonstrating the Uneven Importance of Fine-Scale Forest Structure on Snow Distributions using High Resolution Modeling

NASA Astrophysics Data System (ADS)

Broxton, P. D.; Harpold, A. A.; van Leeuwen, W.; Biederman, J. A.

2016-12-01

Quantifying the amount of snow in forested mountainous environments, as well as how it may change due to warming and forest disturbance, is critical given its importance for water supply and ecosystem health. Forest canopies affect snow accumulation and ablation in ways that are difficult to observe and model. Furthermore, fine-scale forest structure can accentuate or diminish the effects of forest-snow interactions. Despite decades of research demonstrating the importance of fine-scale forest structure (e.g. canopy edges and gaps) on snow, we still lack a comprehensive understanding of where and when forest structure has the largest impact on snowpack mass and energy budgets. Here, we use a hyper-resolution (1 meter spatial resolution) mass and energy balance snow model called the Snow Physics and Laser Mapping (SnowPALM) model along with LIDAR-derived forest structure to determine where spatial variability of fine-scale forest structure has the largest influence on large scale mass and energy budgets. SnowPALM was set up and calibrated at sites representing diverse climates in New Mexico, Arizona, and California. Then, we compared simulations at different model resolutions (i.e. 1, 10, and 100 m) to elucidate the effects of including versus not including information about fine scale canopy structure. These experiments were repeated for different prescribed topographies (i.e. flat, 30% slope north, and south-facing) at each site. Higher resolution simulations had more snow at lower canopy cover, with the opposite being true at high canopy cover. Furthermore, there is considerable scatter, indicating that different canopy arrangements can lead to different amounts of snow, even when the overall canopy coverage is the same. This modeling is contributing to the development of a high resolution machine learning algorithm called the Snow Water Artificial Network (SWANN) model to generate predictions of snow distributions over much larger domains, which has implications for improving land surface models that do not currently resolve or parameterize fine-scale canopy structure. In addition, these findings have implications for understanding the potential of different forest management strategies (i.e. thinning) based on local topography and climate to maximize the amount and retention of snow.

Use of MODIS Snow-Cover Maps for Detecting Snowmelt Trends in North America

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Riggs, George A.; Robinson, David A.; Hoon-Starr, Jody A.

2012-01-01

Research has shown that the snow season in the Northern Hemisphere has been getting shorter in recent decades, consistent with documented global temperature increases. Specifically, the snow is melting earlier in the spring allowing for a longer growing season and associated land-cover changes. Here we focus on North America. Using the Moderate-Resolution Imaging Radiometer (MODIS) cloud-gap-filled standard snow-cover data product we can detect a trend toward earlier spring snowmelt in the approx 12 years since the MODIS launch. However, not all areas in North America show earlier spring snowmelt over the study period. We show examples of springtime snowmelt over North America, beginning in March 2000 and extending through the winter of 2012 for all of North America, and for various specific areas such as the Wind River Range in Wyoming and in the Catskill Mountains in New York. We also compare our approx 12-year trends with trends derived from the Rutgers Global Snow Lab snow cover climate-data record.

Mapping the Snow Line Altitude for Large Glacier Samples from Multitemporal Landsat Imagery

NASA Astrophysics Data System (ADS)

Rastner, P.; Nicholson, L. I.; Notarnicola, C.; Prinz, R.; Sailer, R.

2015-12-01

The cryosphere of mountain regions is fastly changing in response to climate change. This is particularly evident in global-scale glacier retreat. Trends in snow cover, however, are more difficult to determine, as annual fluctuations can be very large. Snow is an important parameter in the energy and mass balance of glaciers and the snow line altitude (SLA) at the end of the melting period can be considered as a proxy for the equilibrium line altitude (ELA). By frequently observing the SLA from satellite, region-wide monitoring of glaciers and improved calibration and validation of transient glacier (mass balance) models is possible. In the near future, frequent mapping of the SLA will be strongly facilitated by satellite missions like Sentinel 2A/B, where the same region will be covered every 5 days with 10 m spatial resolution. For this study we have developed an automated tool to derive the SLA for large glacier samples from remote sensing data. The method is first applied in the Ötztal Alps (Austria) where reliable in-situ data of mass balance and ELA are available for several glaciers over a 30-years period. The algorithm currently works with multi-temporal Landsat imagery (1972-2015), digital glacier outlines and a high-quality national DEM. All input datasets are atmospherically and topographically pre-processed before the SLA is automatically retrieved for each glacier. The remote-sensing derived SLA is generally about 200 m lower than the ELA, however, a clear trend in the altitude of the end of summer snow line is detectable (~ 200 m), which is in agreement with the ELA trend observed in the field. After bias correction and conversion to mass balance, the variability in observed mass balance can be well reproduced from the satellite-derived SLA time series. This is promising for application of the approach in other regions.

Using NASA Earth Observations to Assist the National Park Service in Assessing Snow Cover Distribution and Persistence Changes in the Sky Islands

NASA Astrophysics Data System (ADS)

Bayat, F.; Barrow, C., III; Gonsoroski, E.; Dutta, S.; Lynn, T.; Harville, K.; Spruce, J.

2017-12-01

Saguaro National Park in southeastern Arizona occupies one of several unique mountain ranges known collectively as the Sky Islands or the Madrean Archipelago. The Sky Islands are biodiversity hotspots and host different ecosystems, ranging from arid deserts to temperate forests. Snowmelt provides a source of water during the dry season for various flora and fauna inhabiting the region. Climate change and its effect on snow cover is of growing concern by resource managers in this location. Currently, the National Park Service (NPS) monitors water presence via stream gauges, but a synoptic record of snow presence does not exist due to the remote and rugged topography of the region. As a result, it is difficult to study how climate change has affected water resources in the Sky Islands and what effect this has on wildlife and vegetation. This project used NASA Earth observations (e.g., Landsat data) and GIS technology to help the NPS in understanding the role of snow cover in the Sky Islands. Historical snow cover maps were compiled using a combination of snow detection indices to provide spatio-temporal information on snow presence and phenology. With a more complete understanding of snow cover trends in the park, the NPS can further analyze snow cover impacts to improve future land management decisions.

High fidelity remote sensing of snow properties from MODIS and the Airborne Snow Observatory: Snowflakes to Terabytes

NASA Astrophysics Data System (ADS)

Painter, T.; Mattmann, C. A.; Brodzik, M.; Bryant, A. C.; Goodale, C. E.; Hart, A. F.; Ramirez, P.; Rittger, K. E.; Seidel, F. C.; Zimdars, P. A.

2012-12-01

The response of the cryosphere to climate forcings largely determines Earth's climate sensitivity. However, our understanding of the strength of the simulated snow albedo feedback varies by a factor of three in the GCMs used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, mainly caused by uncertainties in snow extent and the albedo of snow-covered areas from imprecise remote sensing retrievals. Additionally, the Western US and other regions of the globe depend predominantly on snowmelt for their water supply to agriculture, industry and cities, hydroelectric power, and recreation, against rising demand from increasing population. In the mountains of the Upper Colorado River Basin, dust radiative forcing in snow shortens snow cover duration by 3-7 weeks. Extended to the entire upper basin, the 5-fold increase in dust load since the late-1800s results in a 3-week earlier peak runoff and a 5% annual loss of total runoff. The remotely sensed dynamics of snow cover duration and melt however have not been factored into hydrological modeling, operational forecasting, and policymaking. To address these deficiencies in our understanding of snow properties, we have developed and validated a suite of MODIS snow products that provide accurate fractional snow covered area and radiative forcing of dust and carbonaceous aerosols in snow. The MODIS Snow Covered Area and Grain size (MODSCAG) and MODIS Dust Radiative Forcing in Snow (MODDRFS) algorithms, developed and transferred from imaging spectroscopy techniques, leverage the complete MODIS surface reflectance spectrum. The two most critical properties for understanding snowmelt runoff and timing are the spatial and temporal distributions of snow water equivalent (SWE) and snow albedo. We have created the Airborne Snow Observatory (ASO), an imaging spectrometer and scanning LiDAR system, to quantify SWE and snow albedo, generate unprecedented knowledge of snow properties, and provide complete, robust inputs to water management models and systems of the future. In the push to better understand the physical and ecological processes of snowmelt and how they influence regional to global hydrologic and climatic cycles, these technologies and retrievals provide markedly improved detail. We have implemented a science computing facility anchored upon the open source Apache OODT data processing framework. Apache OODT provides adaptable, rapid, and effective workflow technologies that we leverage to execute 10s of thousands of MOD-DRFS and MODSCAG jobs in the Western US, Alaska, and High Asia, critical regions where snowmelt and runoff must be more accurately and precisely identified. Apache OODT also provides us data dissemination capabilities built upon the popular, open source WebDAV protocol that allow our system to disseminate over 20 TB of MOD-DRFS and MODSCAG to the decision making community. Our latest endeavor involves building out Apache OODT to support Geospatial exploration of our data, including providing a Leaflet.js based Map, Geoserver backed protocols, and seamless integration with our Apache OODT system. This framework provides the foundation for the ASO data system.

Snow fraction products evaluation with Landsat-8/OLI data and its spatial scale effects over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Jiang, L.

2016-12-01

Snow cover is one of important elements in the water supply of large populations, especially in those downstream from mountainous watershed. The cryosphere process in the Tibetan Plateau is paid much attention due to rapid change of snow amount and cover extent. Snow mapping from MODIS has been increased attention in the study of climate change and hydrology. But the lack of intensive validation of different snow mapping methods especially at Tibetan Plateau hinders its application. In this work, we examined three MODIS snow products, including standard MODIS fractional snow product (MOD10A1) (Kaufman et al., 2002; Salomonson & Appel, 2004, 2006), two other fractional snow product, MODSCAG (Painter et al., 2009) and MOD_MESMA (Shi, 2012). Both these two methods are based on spectral mixture analysis. The difference between MODISCAG and MOD_MESMA was the endmember selection. For MODSCAG product, snow spectral endmembers of varying grain size was obtained both from a radiative transfer model and spectra of vegetation, rock and soil collected in the field and laboratory. MOD_MESMA was obtained from automated endmember extraction method using linear spectral mixture analysis. Its endmembers are selected in each image to enhance the computational efficiency of MESMA (Multiple Endmember Spectral Analysis). Landsat-8 Operatinal Land Imager (OLI) data from 2013-2015 was used to evaluate the performance of these three snow fraction products in Tibetan Plateau. The effect of land cover types including forest, grass and bare soil was analyzed to evaluate three products. In addition, the effects of relatively flat surface in internal plateau and high mountain areas of Himalaya were also evaluated on the impact of these snow fraction products. From our comparison, MODSCAG and MOD10A1 overestimated snow cover, while MOD_MESMA underestimated snow cover. And RMSE of MOD_MESMA at each land cover type including forest, grass and mountain area decreased with the spatial resolution increasing from 500m, 1km, 2km to 5km. The RMSE of MODSCAG and MOD10A1 is very similar. In Himalaya area, these two RMSEs of MODSCAG and MOD10A1 increased with the spatial resolution increasing from 500m to 5km. For forest, grass and bare soil, RMSE decreased from 500m to 1km, then increased from 1km to 2km.

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

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; DiGirolamo, Nicolo E.; Riggs, George A.

2011-01-01

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.

Continuous Change Detection and Classification (CCDC) of Land Cover Using All Available Landsat Data

NASA Astrophysics Data System (ADS)

Zhu, Z.; Woodcock, C. E.

2012-12-01

A new algorithm for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data is developed. This new algorithm is capable of detecting many kinds of land cover change as new images are collected and at the same time provide land cover maps for any given time. To better identify land cover change, a two step cloud, cloud shadow, and snow masking algorithm is used for eliminating "noisy" observations. Next, a time series model that has components of seasonality, trend, and break estimates the surface reflectance and temperature. The time series model is updated continuously with newly acquired observations. Due to the high variability in spectral response for different kinds of land cover change, the CCDC algorithm uses a data-driven threshold derived from all seven Landsat bands. When the difference between observed and predicted exceeds the thresholds three consecutive times, a pixel is identified as land cover change. Land cover classification is done after change detection. Coefficients from the time series models and the Root Mean Square Error (RMSE) from model fitting are used as classification inputs for the Random Forest Classifier (RFC). We applied this new algorithm for one Landsat scene (Path 12 Row 31) that includes all of Rhode Island as well as much of Eastern Massachusetts and parts of Connecticut. A total of 532 Landsat images acquired between 1982 and 2011 were processed. During this period, 619,924 pixels were detected to change once (91% of total changed pixels) and 60,199 pixels were detected to change twice (8% of total changed pixels). The most frequent land cover change category is from mixed forest to low density residential which occupies more than 8% of total land cover change pixels.

Snow cover dynamics in the Catalan Pyrenees range using remote sensing data from 2002 to 2008 period

NASA Astrophysics Data System (ADS)

Cea, C.; Cristóbal, J.; Pons, X.

2009-04-01

Snow cover dynamics in the Catalan Pyrenees range using remote sensing data from 2002 to 2008 period. C. Cea (1), J. Cristóbal (1), X. Pons (1, 2) (1) Department of Geography. Autonomous University of Barcelona. Cerdanyola del Vallès, 08193. Cristina.Cea@uab.cat, (2) Center for Ecological Research and Forestry Applications (CREAF) Cerdanyola del Vallès, 08193. Water resources and its management are essential in many alpine mountainous areas. Snow cover monitoring in the Mediterranean zone requires obtaining accurate snow cartography to estimate the volume of water derived from snow melting and species distribution modelling. Snow data is usually obtained by field campaigns, but to obtain a spatial and temporal cover of enough detail and quality it is necessary collect an important number of data. However, when a continuous surface is needed, Remote Sensing could provide better snow cover estimation due to its spatial and temporal resolution. The aim of this study is to map snow cover and analyse its spatial and temporal dynamics using medium and coarse remote sensing data at a regional scale over an heterogeneous area, the Catalan Pyrenees (NE of the Iberian Peninsula). The seasonal snow cover period is from October to June. In this period, regular snowfalls usually take place from December to April, although during the rest of the period, punctual but important episodes of snowfalls are frequent. To perform this analysis, a set of 96 Landsat images (36 Landsat-5 TM and 60 Landsat-7 ETM+) of path 197 and 198 and rows 31 and 32 from January 2002 to April 2007, and 90 Terra-MODIS images from October 2007 to July 2008, with a different percentage of cloudiness, have been chosen. The computation of the Landsat-5 TM and Landsat-7 ETM+ data used in snow cover mapping has been carried out by means of the following methodologies. Images have been geometrically corrected by means of techniques based on first order polynomials taking into account the effect of the relief of the land surface using a Digital Elevation Model. Radiometric correction (non-thermal bands) has been done following the methodology which allows us to reduce the number of undesired artefacts that are due to the effects of the atmosphere or to the differential illumination. Finally, cloud removal has been carried out by means of a semi-automatic methodology. In the case of MODIS images, these have been imported by reading all the necessary metadata to document and interpret them. These products have already been radiometrically and geometrically corrected by USGS in a sinusoidal projection. Snow cover mapping has been carried out by means of the Normalized Snow Cover Index (NDSI), one of the most widely methodologies used. This methodology proposes a normalized index using green band 2 and Medium Infrared band because of snow reflectance is higher in the visible bands than in the medium infrared bands. NDSI pixels greater than 0.4 are select as snow cover. Although this threshold also selects water bodies, we have obtained optimal results using a mask of water bodies and generating a pre-boundary snow mask around the snow cover. In shadow cast areas, we have used a hybrid classification to obtain the snow cover. Preliminary results show the snow surface evolution of the Pyrenean basis during the hydrological period, as well as the differences between the different years of study. The obtained data shows how the basins with Mediterranean influence, placed in the east, receive less nival contribution than those with Atlantic influence, situated in the west. In addition, this data allow establishing the beginning and the ending of the snowfall cycle. Eastern basins usually have a shorter period than west basins, where the snow line is lower. The snow cover area average of the studied period is about 1200 km2 and stands out that for the particular period 2006-2007, this area was only about 55% of the average, due to the lack of snow precipitation. It affected the winter sports, an important sector in this area, the vegetation and the extensive livestock, just as the volume of water that flows towards river basins and reservoir when snow melts. Technical characteristics are different between both sensors. On the one hand, Landsat spatial resolution is better to obtain accuracy cartography, however it is not suitable to determine the frequency of snow falls. On the one other hand, MODIS with its daily temporal resolution allows better temporal monitoring, but with coarse spatial resolution.

NASA sea ice and snow validation plan for the Defense Meteorological Satellite Program special sensor microwave/imager

NASA Technical Reports Server (NTRS)

Cavalieri, Donald J. (Editor); Swift, Calvin T. (Editor)

1987-01-01

This document addresses the task of developing and executing a plan for validating the algorithm used for initial processing of sea ice data from the Special Sensor Microwave/Imager (SSMI). The document outlines a plan for monitoring the performance of the SSMI, for validating the derived sea ice parameters, and for providing quality data products before distribution to the research community. Because of recent advances in the application of passive microwave remote sensing to snow cover on land, the validation of snow algorithms is also addressed.

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and forest surface types during dormant and snow-covered periods

Treesearch

Zhuosen Wang; Crystal B. Schaaf; Alan H. Strahler; Mark J. Chopping; Miguel O. Román; Yanmin Shuai; Curtis E. Woodcock; David Y. Hollinger; David R. Fitzjarrald

2014-01-01

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...

Wet Snow Mapping in Southern Ontario with Sentinel-1A Observations

NASA Astrophysics Data System (ADS)

Chen, H.; Kelly, R. E. J.

2017-12-01

Wet snow is defined as snow with liquid water present in an ice-water mix. It is can be an indicator for the onset of the snowmelt period. Knowledge about the extent of wet snow area can be of great importance for the monitoring of seasonal snowmelt runoff with climate-induced changes in snowmelt duration having implications for operational hydrological and ecological applications. Spaceborne microwave remote sensing has been used to observe seasonal snow under all-weather conditions. Active microwave observations of snow at C-band are sensitive to wet snow due to the high dielectric contrast with non-wet snow surfaces and synthetic aperture radar (SAR) is now openly available to identify and map the wet snow areas globally at relatively fine spatial resolutions ( 100m). In this study, a semi-automated workflow is developed from the change detection method of Nagler et al. (2016) using multi-temporal Sentinel-1A (S1A) dual-polarization observations of Southern Ontario. Weather station data and visible-infrared satellite observations are used to refine the wet snow area estimates. Wet snow information from National Operational Hydrologic Remote Sensing Center (NOHRSC) is used to compare with the S1A estimates. A time series of wet snow maps shows the variations in backscatter from wet snow on a pixel basis. Different land cover types in Southern Ontario are assessed with respect to their impacts on wet snow estimates. While forests and complex land surfaces can impact the ability to map wet snow, the approach taken is robust and illustrates the strong sensitivity of the approach to wet snow backscattering characteristics. The results indicate the feasibility of the change detection method on non-mountainous large areas and address the usefulness of Sentinel-1A data for wet snow mapping.

Mapping snow cover using multi-source satellite data on big data platforms

NASA Astrophysics Data System (ADS)

Lhermitte, Stef

2017-04-01

Snowmelt is an important and dynamically changing water resource in mountainous regions around the world. In this framework, remote sensing data of snow cover data provides an essential input for hydrological models to model the water contribution from remote mountain areas and to understand how this water resource might alter as a result of climate change. Traditionally, however, many of these remote sensing products show a trade-off between spatial and temporal resolution (e.g., 16-day Landsat at 30m vs. daily MODIS at 500m resolution). With the advent of Sentinel-1 and 2 and the PROBA-V 100m products this trade-off can partially be tackled by having data that corresponds more closely to the spatial and temporal variations in snow cover typically observed over complex mountain areas. This study provides first a quantitative analysis of the trade-offs between the state-of-the-art snow cover mapping methodologies for Landsat, MODIS, PROBA-V, Sentinel-1 and 2 and applies them on big data platforms such as Google Earth Engine (GEE), RSS (ESA Research Service & Support) CloudToolbox, and the PROBA-V Mission Exploitation Platform (MEP). Second, it combines the different sensor data-cubes in one multi-sensor classification approach using newly developed spatio-temporal probability classifiers within the big data platform environments. Analysis of the spatio-temporal differences in derived snow cover areas from the different sensors reveals the importance of understanding the spatial and temporal scales at which variations occur. Moreover, it shows the importance of i) temporal resolution when monitoring highly dynamical properties such as snow cover and of ii) differences in satellite viewing angles over complex mountain areas. Finally, it highlights the potential and drawbacks of big data platforms for combining multi-source satellite data for monitoring dynamical processes such as snow cover.

Comparison of the Snow Simulations in Community Land Model Using Two Snow Cover Fraction Parameterizations

NASA Astrophysics Data System (ADS)

Xie, Zhipeng; Hu, Zeyong

2016-04-01

Snow cover is an important component of local- and regional-scale energy and water budgets, especially in mountainous areas. This paper evaluates the snow simulations by using two snow cover fraction schemes in CLM4.5 (NY07 is the original snow-covered area parameterization used in CLM4, and SL12 is the default scheme in CLM4.5). Off-line simulations are carried out forced by the China Meteorological forcing dataset from January 1, 2001 to December 31, 2010 over the Tibetan Plateau. Simulated snow cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product, the daily snow depth dataset of China, and China Meteorological Administration (CMA) in-situ snow depth and SWE observations. The comparison results indicate significant differences existing between those two SCF parameterizations simulations. Overall, the SL12 formulation shows a certain improvement compared to the NY07 scheme used in CLM4, with the percentage of correctly modeled snow/no snow being 75.8% and 81.8% when compared with the IMS snow product, respectively. Yet, this improvement varies both temporally and spatially. Both these two snow cover schemes overestimated the snow depth, in comparison with the daily snow depth dataset of China, the average biases of simulated snow depth are 7.38cm (8.77cm), 6.97cm (8.2cm) and 5.49cm (5.76cm) NY07 (and SL12) in the snow accumulation period (September through next February), snowmelt period (March through May) and snow-free period (June through August), respectively. When compared with the CMA in-situ snow depth observations, averaged biases are 3.18cm (4.38cm), 2.85cm (4.34cm) and 0.34cm (0.34cm) for NY07 (SL12), respectively. Though SL12 does worse snow depth simulation than NY07, the simulated SWE by SL12 is better than that by NY07, with average biases being 2.64mm, 6.22mm, 1.33mm for NY07, and 1.47mm, 2.63mm, 0.31mm for SL12, respectively. This study demonstrates that future improvements on snow simulation over the Tibetan Plateau are in urgent need for better representing the variability of snow in CLM. Furthermore, these findings lay a foundation for follow-up studies on the modification of snow cover parameterization in the land surface model. Keywords: snow cover, CLM, Tibetan Plateau, simulation.

Measuring snow cover using satellite imagery during 1973 and 1974 melt season: North Santiam, Boise, and Upper Snake Basins, phase 1. [LANDSAT satellites, imaging techniques

NASA Technical Reports Server (NTRS)

Wiegman, E. J.; Evans, W. E.; Hadfield, R.

1975-01-01

Measurements are examined of snow coverage during the snow-melt season in 1973 and 1974 from LANDSAT imagery for the three Columbia River Subbasins. Satellite derived snow cover inventories for the three test basins were obtained as an alternative to inventories performed with the current operational practice of using small aircraft flights over selected snow fields. The accuracy and precision versus cost for several different interactive image analysis procedures was investigated using a display device, the Electronic Satellite Image Analysis Console. Single-band radiance thresholding was the principal technique employed in the snow detection, although this technique was supplemented by an editing procedure involving reference to hand-generated elevation contours. For each data and view measured, a binary thematic map or "mask" depicting the snow cover was generated by a combination of objective and subjective procedures. Photographs of data analysis equipment (displays) are shown.

Automated detection of snow avalanche deposits: segmentation and classification of optical remote sensing imagery

NASA Astrophysics Data System (ADS)

Lato, M. J.; Frauenfelder, R.; Bühler, Y.

2012-09-01

Snow avalanches in mountainous areas pose a significant threat to infrastructure (roads, railways, energy transmission corridors), personal property (homes) and recreational areas as well as for lives of people living and moving in alpine terrain. The impacts of snow avalanches range from delays and financial loss through road and railway closures, destruction of property and infrastructure, to loss of life. Avalanche warnings today are mainly based on meteorological information, snow pack information, field observations, historically recorded avalanche events as well as experience and expert knowledge. The ability to automatically identify snow avalanches using Very High Resolution (VHR) optical remote sensing imagery has the potential to assist in the development of accurate, spatially widespread, detailed maps of zones prone to avalanches as well as to build up data bases of past avalanche events in poorly accessible regions. This would provide decision makers with improved knowledge of the frequency and size distributions of avalanches in such areas. We used an object-oriented image interpretation approach, which employs segmentation and classification methodologies, to detect recent snow avalanche deposits within VHR panchromatic optical remote sensing imagery. This produces avalanche deposit maps, which can be integrated with other spatial mapping and terrain data. The object-oriented approach has been tested and validated against manually generated maps in which avalanches are visually recognized and digitized. The accuracy (both users and producers) are over 0.9 with errors of commission less than 0.05. Future research is directed to widespread testing of the algorithm on data generated by various sensors and improvement of the algorithm in high noise regions as well as the mapping of avalanche paths alongside their deposits.

Building a Snow Data Management System using Open Source Software (and IDL)

NASA Astrophysics Data System (ADS)

Goodale, C. E.; Mattmann, C. A.; Ramirez, P.; Hart, A. F.; Painter, T.; Zimdars, P. A.; Bryant, A.; Brodzik, M.; Skiles, M.; Seidel, F. C.; Rittger, K. E.

2012-12-01

At NASA's Jet Propulsion Laboratory free and open source software is used everyday to support a wide range of projects, from planetary to climate to research and development. In this abstract I will discuss the key role that open source software has played in building a robust science data processing pipeline for snow hydrology research, and how the system is also able to leverage programs written in IDL, making JPL's Snow Data System a hybrid of open source and proprietary software. Main Points: - The Design of the Snow Data System (illustrate how the collection of sub-systems are combined to create a complete data processing pipeline) - Discuss the Challenges of moving from a single algorithm on a laptop, to running 100's of parallel algorithms on a cluster of servers (lesson's learned) - Code changes - Software license related challenges - Storage Requirements - System Evolution (from data archiving, to data processing, to data on a map, to near-real-time products and maps) - Road map for the next 6 months (including how easily we re-used the snowDS code base to support the Airborne Snow Observatory Mission) Software in Use and their Software Licenses: IDL - Used for pre and post processing of data. Licensed under a proprietary software license held by Excelis. Apache OODT - Used for data management and workflow processing. Licensed under the Apache License Version 2. GDAL - Geospatial Data processing library used for data re-projection currently. Licensed under the X/MIT license. GeoServer - WMS Server. Licensed under the General Public License Version 2.0 Leaflet.js - Javascript web mapping library. Licensed under the Berkeley Software Distribution License. Python - Glue code and miscellaneous data processing support. Licensed under the Python Software Foundation License. Perl - Script wrapper for running the SCAG algorithm. Licensed under the General Public License Version 3. PHP - Front-end web application programming. Licensed under the PHP License Version 3.01

Evaluating terrain based criteria for snow avalanche exposure ratings using GIS

NASA Astrophysics Data System (ADS)

Delparte, Donna; Jamieson, Bruce; Waters, Nigel

2010-05-01

Snow avalanche terrain in backcountry regions of Canada is increasingly being assessed based upon the Avalanche Terrain Exposure Scale (ATES). ATES is a terrain based classification introduced in 2004 by Parks Canada to identify "simple", "challenging" and "complex" backcountry areas. The ATES rating system has been applied to well over 200 backcountry routes, has been used in guidebooks, trailhead signs and maps and is part of the trip planning component of the AVALUATOR™, a simple decision-support tool for backcountry users. Geographic Information Systems (GIS) offers a means to model and visualize terrain based criteria through the use of digital elevation model (DEM) and land cover data. Primary topographic variables such as slope, aspect and curvature are easily derived from a DEM and are compatible with the equivalent evaluation criteria in ATES. Other components of the ATES classification are difficult to extract from a DEM as they are not strictly terrain based. An overview is provided of the terrain variables that can be generated from DEM and land cover data; criteria from ATES which are not clearly terrain based are identified for further study or revision. The second component of this investigation was the development of an algorithm for inputting suitable ATES criteria into a GIS, thereby mimicking the process avalanche experts use when applying the ATES classification to snow avalanche terrain. GIS based classifications were compared to existing expert assessments for validity. The advantage of automating the ATES classification process through GIS is to assist avalanche experts with categorizing and mapping remote backcountry terrain.

Mapping lichen color-groups in western Arctic Alaska using seasonal Landsat composites

NASA Astrophysics Data System (ADS)

Nelson, P.; Macander, M. J.; Swingley, C. S.

2016-12-01

Mapping lichens at a landscape scale has received increased recent interest due to fears that terricolous lichen mats, primary winter caribou forage, may be decreasing across the arctic and boreal zones. However, previous efforts have produced taxonomically coarse, total lichen cover maps or have covered relatively small spatial extents. Here we attempt to map lichens of differing colors as species proxies across northwestern Alaska to produce the finest taxonomic and spatial- grained lichen maps covering the largest spatial extent to date. Lichen community sampling in five western Alaskan National Parks and Preserves from 2007-2012 generated 328 FIA-style 34.7 m radius plots on which species-level macrolichen community structure and abundance was estimated. Species were coded by color and plot lichen cover was aggregated by plot as the sum of the cover of each species in a color group. Ten different lichen color groupings were used for modeling to deduce which colors were most detectable. Reflectance signatures of each plot were extracted from a series of Landsat composites (circa 2000-2010) partitioned into two-week intervals from June 1 to Sept. 15. Median reflectance values for each band in each pixel were selected based on filtering criteria to reduce likelihood of snow cover. Lichen color group cover was regressed against plot reflectance plus additional abiotic predictors in two different data mining algorithms. Brown and grey lichens had the best models explaining approximately 40% of lichen cover in those color groups. Both data mining techniques produced similarly good fitting models. Spatial patterns of lichen color-group cover show distinctly different ecological patterns of these color-group species proxies.

Improving snow density estimation for mapping SWE with Lidar snow depth: assessment of uncertainty in modeled density and field sampling strategies in NASA SnowEx

NASA Astrophysics Data System (ADS)

Raleigh, M. S.; Smyth, E.; Small, E. E.

2017-12-01

The spatial distribution of snow water equivalent (SWE) is not sufficiently monitored with either remotely sensed or ground-based observations for water resources management. Recent applications of airborne Lidar have yielded basin-wide mapping of SWE when combined with a snow density model. However, in the absence of snow density observations, the uncertainty in these SWE maps is dominated by uncertainty in modeled snow density rather than in Lidar measurement of snow depth. Available observations tend to have a bias in physiographic regime (e.g., flat open areas) and are often insufficient in number to support testing of models across a range of conditions. Thus, there is a need for targeted sampling strategies and controlled model experiments to understand where and why different snow density models diverge. This will enable identification of robust model structures that represent dominant processes controlling snow densification, in support of basin-scale estimation of SWE with remotely-sensed snow depth datasets. The NASA SnowEx mission is a unique opportunity to evaluate sampling strategies of snow density and to quantify and reduce uncertainty in modeled snow density. In this presentation, we present initial field data analyses and modeling results over the Colorado SnowEx domain in the 2016-2017 winter campaign. We detail a framework for spatially mapping the uncertainty in snowpack density, as represented across multiple models. Leveraging the modular SUMMA model, we construct a series of physically-based models to assess systematically the importance of specific process representations to snow density estimates. We will show how models and snow pit observations characterize snow density variations with forest cover in the SnowEx domains. Finally, we will use the spatial maps of density uncertainty to evaluate the selected locations of snow pits, thereby assessing the adequacy of the sampling strategy for targeting uncertainty in modeled snow density.

Mapping and assessing variability in the Antarctic marginal ice zone, pack ice and coastal polynyas in two sea ice algorithms with implications on breeding success of snow petrels

NASA Astrophysics Data System (ADS)

Stroeve, Julienne C.; Jenouvrier, Stephanie; Campbell, G. Garrett; Barbraud, Christophe; Delord, Karine

2016-08-01

Sea ice variability within the marginal ice zone (MIZ) and polynyas plays an important role for phytoplankton productivity and krill abundance. Therefore, mapping their spatial extent as well as seasonal and interannual variability is essential for understanding how current and future changes in these biologically active regions may impact the Antarctic marine ecosystem. Knowledge of the distribution of MIZ, consolidated pack ice and coastal polynyas in the total Antarctic sea ice cover may also help to shed light on the factors contributing towards recent expansion of the Antarctic ice cover in some regions and contraction in others. The long-term passive microwave satellite data record provides the longest and most consistent record for assessing the proportion of the sea ice cover that is covered by each of these ice categories. However, estimates of the amount of MIZ, consolidated pack ice and polynyas depend strongly on which sea ice algorithm is used. This study uses two popular passive microwave sea ice algorithms, the NASA Team and Bootstrap, and applies the same thresholds to the sea ice concentrations to evaluate the distribution and variability in the MIZ, the consolidated pack ice and coastal polynyas. Results reveal that the seasonal cycle in the MIZ and pack ice is generally similar between both algorithms, yet the NASA Team algorithm has on average twice the MIZ and half the consolidated pack ice area as the Bootstrap algorithm. Trends also differ, with the Bootstrap algorithm suggesting statistically significant trends towards increased pack ice area and no statistically significant trends in the MIZ. The NASA Team algorithm on the other hand indicates statistically significant positive trends in the MIZ during spring. Potential coastal polynya area and amount of broken ice within the consolidated ice pack are also larger in the NASA Team algorithm. The timing of maximum polynya area may differ by as much as 5 months between algorithms. These differences lead to different relationships between sea ice characteristics and biological processes, as illustrated here with the breeding success of an Antarctic seabird.

Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice and snow, and other materials: The USGS tricorder algorithm

NASA Technical Reports Server (NTRS)

Clark, Roger N.; Swayze, Gregg A.

1995-01-01

One of the challenges of Imaging Spectroscopy is the identification, mapping and abundance determination of materials, whether mineral, vegetable, or liquid, given enough spectral range, spectral resolution, signal to noise, and spatial resolution. Many materials show diagnostic absorption features in the visual and near infrared region (0.4 to 2.5 micrometers) of the spectrum. This region is covered by the modern imaging spectrometers such as AVIRIS. The challenge is to identify the materials from absorption bands in their spectra, and determine what specific analyses must be done to derive particular parameters of interest, ranging from simply identifying its presence to deriving its abundance, or determining specific chemistry of the material. Recently, a new analysis algorithm was developed that uses a digital spectral library of known materials and a fast, modified-least-squares method of determining if a single spectral feature for a given material is present. Clark et al. made another advance in the mapping algorithm: simultaneously mapping multiple minerals using multiple spectral features. This was done by a modified-least-squares fit of spectral features, from data in a digital spectral library, to corresponding spectral features in the image data. This version has now been superseded by a more comprehensive spectral analysis system called Tricorder.

Response of alpine vegetation growth dynamics to snow cover phenology on the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Wang, X.; Wu, C.

2017-12-01

Alpine vegetation plays a crucial role in global energy cycles with snow cover, an essential component of alpine land cover showing high sensitivity to climate change. The Tibetan Plateau (TP) has a typical alpine vegetation ecosystem and is rich of snow resources. With global warming, the snow of the TP has undergone significant changes that will inevitably affect the growth of alpine vegetation, but observed evidence of such interaction is limited. In particular, a comprehensive understanding of the responses of alpine vegetation growth to snow cover variability is still not well characterized on TP region. To investigate this, we calculated three indicators, the start (SOS) and length (LOS) of growing season, and the maximum of normalized difference vegetation index (NDVImax) as proxies of vegetation growth dynamics from the Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2000-2015. Snow cover duration (SCD) and melt (SCM) dates were also extracted during the same time frame from the combination of MODIS and the Interactive Multi-sensor Snow and Ice Mapping System (IMS) data. We found that the snow cover phenology had a strong control on alpine vegetation growth dynamics. Furthermore, the responses of SOS, LOS and NDVImax to snow cover phenology varied among plant functional types, eco-geographical zones, and temperature and precipitation gradients. The alpine steppes showed a much stronger negative correlation between SOS and SCD, and also a more evidently positive relationship between LOS and SCD than other types, indicating a longer SCD would lead to an earlier SOS and longer LOS. Most areas showed positive correlation between SOS and SCM, while a contrary response was also found in the warm but drier areas. Both SCD and SCM showed positive correlations with NDVImax, but the relationship became weaker with the increase of precipitation. Our findings provided strong evidences between vegetation growth and snow cover phenology, and changes in snow cover should be also considered when analyzing alpine vegetation growth dynamics in future.

Separating snow, clean and debris covered ice in the Upper Indus Basin, Hindukush-Karakoram-Himalayas, using Landsat images between 1998 and 2002

NASA Astrophysics Data System (ADS)

Khan, Asif; Naz, Bibi S.; Bowling, Laura C.

2015-02-01

The Hindukush Karakoram Himalayan mountains contain some of the largest glaciers of the world, and supply melt water from perennial snow and glaciers to the Upper Indus Basin (UIB) upstream of Tarbela dam, which constitutes greater than 80% of the annual flows, and caters to the needs of millions of people in the Indus Basin. It is therefore important to study the response of perennial snow and glaciers in the UIB under changing climatic conditions, using improved hydrological modeling, glacier mass balance, and observations of glacier responses. However, the available glacier inventories and datasets only provide total perennial-snow and glacier cover areas, despite the fact that snow, clean ice and debris covered ice have different melt rates and densities. This distinction is vital for improved hydrological modeling and mass balance studies. This study, therefore, presents a separated perennial snow and glacier inventory (perennial snow-cover on steep slopes, perennial snow-covered ice, clean and debris covered ice) based on a semi-automated method that combines Landsat images and surface slope information in a supervised maximum likelihood classification to map distinct glacier zones, followed by manual post processing. The accuracy of the presented inventory falls well within the accuracy limits of available snow and glacier inventory products. For the entire UIB, estimates of perennial and/or seasonal snow on steep slopes, snow-covered ice, clean and debris covered ice zones are 7238 ± 724, 5226 ± 522, 4695 ± 469 and 2126 ± 212 km2 respectively. Thus total snow and glacier cover is 19,285 ± 1928 km2, out of which 12,075 ± 1207 km2 is glacier cover (excluding steep slope snow-cover). Equilibrium Line Altitude (ELA) estimates based on the Snow Line Elevation (SLE) in various watersheds range between 4800 and 5500 m, while the Accumulation Area Ratio (AAR) ranges between 7% and 80%. 0 °C isotherms during peak ablation months (July and August) range between ∼ 5500 and 6200 m in various watersheds. These outputs can be used as input to hydrological models, to estimate spatially-variable degree day factors for hydrological modeling, to separate glacier and snow-melt contributions in river flows, and to study glacier mass balance, and glacier responses to changing climate.

Proceedings of the Annual Meeting of the Eastern Snow Conference (47th) Held in Bangor, Maine on 7-8 June 1990

DTIC Science & Technology

1990-06-01

western U.S. Ten additional windows contain basin boundary data sets to map snow cover for the Upper Midwest , the Great Lakes, New England, and... Midwest and in the East. The User’s Guide gives the data format. 4.3.2 Satellite Areal Extent of Snow Cover by Basin/Zone (MCI/AFOS-ASCII) Alphanumeric...acres) of lake shore land in the snowbelt and this perennial vine is susceptible to winter cold damage. County extension agents indicated that winter

Fluctuating snow line altitudes in the Hunza basin (Karakoram) using Landsat OLI imagery

NASA Astrophysics Data System (ADS)

Racoviteanu, Adina; Rittger, Karl; Brodzik, Mary J.; Painter, Thomas H.; Armstrong, Richard

2016-04-01

Snowline altitudes (SLAs) on glacier surfaces are needed for separating snow and ice as input for melt models. When measured at the end of the ablation season, SLAs are used for inferring stable-state glacier equilibrium line altitudes (ELAs). Direct measurements of snowlines are rarely possible particularly in remote, high altitude glacierized terrain, but remote sensing data can be used to separate these snow and ice surfaces. Snow lines are commonly visible on optical satellite images acquired at the end of the ablation season if the images are contrasted enough, and are manually digitized on screen using various satellite band combinations for visual interpretation, which is a time-consuming, subjective process. Here we use Landsat OLI imagery at 30 m resolution to estimate glacier SLAs for a subset of the Hunza basin in the Upper Indus in the Karakoram. Clean glacier ice surfaces are delineated using a standardized semi-automated band ratio algorithm with image segmentation. Within the glacier surface, snow and ice are separated using supervised classification schemes based on regions of interest, and glacier SLAs are extracted on the basis of these areas. SLAs are compared with estimates from a new automated method that relies on fractional snow covered area rather than on band ratio algorithms for delineating clean glacier ice surfaces, and on grain size (instead of supervised classification) for separating snow from glacier ice on the glacier surface. The two methods produce comparable snow/ice outputs. The fSCA-derived glacierized areas are slightly larger than the band ratio estimates. Some of the additional area is the result of better detection in shadows from spectral mixture analysis (true positive) while the rest is shallow water, which is spectrally similar to snow/ice (false positive). On the glacier surface, a thresholding the snow grain size image (grain size > 500μm) results in similar glacier ice areas derived from the supervised classification, but there is noise (snow) on edges of dirty ice/ moraines at the glacier termini and around rock outcrops on the glacier surface. Neither of the two methods distinguishes the debris-covered ice, so these were mapped separately using a combination of topographic indices (slope, terrain curvature), along with remote sensing surface temperature and texture data. Using average elevation of snow and ice areas, we calculate an ELA of 5260 m for 2013. We construct yearly time series of the ELAs around the centerlines of selected glaciers in the Hunza for the period 2000 - 2014 using Landsat imagery. We explore spatial trends in glacier ELAs within the region, as well as relationships between ELA and topographic characteristics extracted on a glacier-by-glacier basis from a digital elevation model.

Comparison of snow depth retrieval algorithm in Northeastern China based on AMSR2 and FY3B-MWRI data

NASA Astrophysics Data System (ADS)

Fan, Xintong; Gu, Lingjia; Ren, Ruizhi; Zhou, Tingting

2017-09-01

Snow accumulation has a very important influence on the natural environment and human activities. Meanwhile, improving the estimation accuracy of passive microwave snow depth (SD) retrieval is a hotspot currently. Northeastern China is a typical snow study area including many different land cover types, such as forest, grassland and farmland. Especially, there is relatively stable snow accumulation in January every year. The brightness temperatures which are observed by the Advanced Microwave Scanning Radiometer 2 (AMSR2) on GCOM-W1 and FengYun3B Microwave Radiation Imager (FY3B-MWRI) in the same period in 2013 are selected as the study data in the research. The results of snow depth retrieval using AMSR2 standard algorithm and Jiang's FY operational algorithm are compared in the research. Moreover, to validate the accuracy of the two algorithms, the retrieval results are compared with the SD data observed at the national meteorological stations in Northeastern China. Furthermore, the retrieval SD is also compared with AMSR2 and FY standard SD products, respectively. The root mean square errors (RMSE) results using AMSR2 standard algorithms and FY operational algorithm are close in the forest surface, which are 6.33cm and 6.28cm, respectively. However, The FY operational algorithm shows a better result than the AMSR2 standard algorithms in the grassland and farmland surface. The RMSE results using FY operational algorithm in the grassland and farmland surface are 2.44cm and 6.13cm, respectively.

Mapping Snow Grain Size over Greenland from MODIS

NASA Technical Reports Server (NTRS)

Lyapustin, Alexei; Tedesco, Marco; Wang, Yujie; Kokhanovsky, Alexander

2008-01-01

This paper presents a new automatic algorithm to derive optical snow grain size (SGS) at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Differently from previous approaches, snow grains are not assumed to be spherical but a fractal approach is used to account for their irregular shape. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived SGS shows a good sensitivity to snow metamorphism, including melting and snow precipitation events. Preprocessing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask (CM) is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust performance of CM algorithm in discrimination of clouds over bright snow and ice. As part of the validation analysis, SGS derived from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM\\I radiometer, which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS SGS was compared with predictions of the snow model CROCUS driven by measurements of the automatic whether stations of the Greenland Climate Network. We found that CROCUS grain size is on average a factor of two larger than MODIS-derived SGS. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of SGS for four permanent sites, the paper presents SGS maps over the Greenland ice sheet for the March-September period of 2004.

Evaluation of the Snow Simulations from the Community Land Model, Version 4 (CLM4)

NASA Technical Reports Server (NTRS)

Toure, Ally M.; Rodell, Matthew; Yang, Zong-Liang; Beaudoing, Hiroko; Kim, Edward; Zhang, Yongfei; Kwon, Yonghwan

2015-01-01

This paper evaluates the simulation of snow by the Community Land Model, version 4 (CLM4), the land model component of the Community Earth System Model, version 1.0.4 (CESM1.0.4). CLM4 was run in an offline mode forced with the corrected land-only replay of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land) and the output was evaluated for the period from January 2001 to January 2011 over the Northern Hemisphere poleward of 30 deg N. Simulated snow-cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the Canadian Meteorological Centre (CMC) daily snow analysis products, snow depth from the National Weather Service Cooperative Observer (COOP) program, and Snowpack Telemetry (SNOTEL) SWE observations. CLM4 SCF was converted into snow-cover extent (SCE) to compare with MODIS SCE. It showed good agreement, with a correlation coefficient of 0.91 and an average bias of -1.54 x 10(exp 2) sq km. Overall, CLM4 agreed well with IMS snow cover, with the percentage of correctly modeled snow-no snow being 94%. CLM4 snow depth and SWE agreed reasonably well with the CMC product, with the average bias (RMSE) of snow depth and SWE being 0.044m (0.19 m) and -0.010m (0.04 m), respectively. CLM4 underestimated SNOTEL SWE and COOP snow depth. This study demonstrates the need to improve the CLM4 snow estimates and constitutes a benchmark against which improvement of the model through data assimilation can be measured.

Remote sensing of snow and ice: A review of the research in the United States 1975 - 1978

NASA Technical Reports Server (NTRS)

Rango, A.

1979-01-01

Research work in the United States from 1975-1978 in the field of remote sensing of snow and ice is reviewed. Topics covered include snowcover mapping, snowmelt runoff forecasting, demonstration projects, snow water equivalent and free water content determination, glaciers, river and lake ice, and sea ice. A bibliography of 200 references is included.

Improving the Accuracy of the AFWA-NASA (ANSA) Blended Snow-Cover Product over the Lower Great Lakes Region

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Kumar, Sujay; Chien, Janety Y. L.; Riggs, George A.

2012-01-01

The Air Force Weather Agency (AFWA) -- NASA blended snow-cover product, called ANSA, utilizes Earth Observing System standard snow products from the Moderate- Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to map daily snow cover and snow-water equivalent (SWE) globally. We have compared ANSA-derived SWE with SWE values calculated from snow depths reported at 1500 National Climatic Data Center (NCDC) co-op stations in the Lower Great Lakes Basin. Compared to station data, the ANSA significantly underestimates SWE in densely-forested areas. We use two methods to remove some of the bias observed in forested areas to reduce the root-mean-square error (RMSE) between the ANSA- and station-derived SWE. First, we calculated a 5- year mean ANSA-derived SWE for the winters of 2005-06 through 2009-10, and developed a five-year mean bias-corrected SWE map for each month. For most of the months studied during the five-year period, the 5-year bias correction improved the agreement between the ANSA-derived and station-derived SWE. However, anomalous months such as when there was very little snow on the ground compared to the 5-year mean, or months in which the snow was much greater than the 5-year mean, showed poorer results (as expected). We also used a 7-day running mean (7DRM) bias correction method using days just prior to the day in question to correct the ANSA data. This method was more effective in reducing the RMSE between the ANSA- and co-op-derived SWE values, and in capturing the effects of anomalous snow conditions.

Machine learning based cloud mask algorithm driven by radiative transfer modeling

NASA Astrophysics Data System (ADS)

Chen, N.; Li, W.; Tanikawa, T.; Hori, M.; Shimada, R.; Stamnes, K. H.

2017-12-01

Cloud detection is a critically important first step required to derive many satellite data products. Traditional threshold based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and have difficulty over snow/ice covered areas. With the advance of computational power and machine learning techniques, we have developed a new algorithm based on a neural network classifier driven by extensive radiative transfer modeling. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over mid-latitude snow covered areas. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors.

Validation of Satellite Snow Cover Maps in North America and Norway

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Solberg, Rune; Riggs, George A.

2002-01-01

Satellite-derived snow maps from NASA's Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) have been produced since February of 2000. The global maps are available daily at 500-m resolution, and at a climate-modeling grid (CMG) resolution of 1/20 deg (approximately 5.6 km). We compared the 8-day composite CMG MODIS-derived global maps from November 1,2001, through March 21,2002, and daily CMG maps from February 26 - March 5,2002, with National Oceanic and Atmospheric Administration (NOAA) Interactive Multisensor Snow and Ice Mapping System (IMS) 25-km resolution maps for North America. For the Norwegian study area, national snow maps, based on synoptic measurements as well as visual interpretation of AVHRR images, published by the Det Norske Meteorologiske Institutt (Norwegian Meteorological Institute) (MI) maps, as well as Landsat ETM+ images were compared with the MODIS maps. The MODIS-derived maps agreed over most areas with the IMS or MI maps, however, there are important areas of disagreement between the maps, especially when the 8-day composite maps were used. It is concluded that MODIS daily CMG maps should be studied for validation purposes rather than the 8-day composite maps, despite the limitations imposed by cloud obscuration when using the daily maps.

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

NASA Technical Reports Server (NTRS)

Wang, Zhousen; Schaaf, Crystal B.; Strahler, Alan H.; Chopping, Mark J.; Roman, Miguel O.; Shuai, Yanmin; Woodcock, Curtis E.; Hollinger, David Y.; Fitzjarrald, David R.

2013-01-01

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.

Mapping snow depth in alpine terrain with remotely piloted aerial systems and structure-from-motion photogrammetry - first results from a pilot study

NASA Astrophysics Data System (ADS)

Adams, Marc; Fromm, Reinhard; Bühler, Yves; Bösch, Ruedi; Ginzler, Christian

2016-04-01

Detailed information on the spatio-temporal distribution of seasonal snow in the alpine terrain plays a major role for the hydrological cycle, natural hazard management, flora and fauna, as well as tourism. Current methods are mostly only valid on a regional scale or require a trade-off between the data's availability, cost and resolution. During a one-year pilot study, we investigated the potential of remotely piloted aerial systems (RPAS) and structure-from-motion photogrammetry for snow depth mapping. We employed multi-copter and fixed-wing RPAS, equipped with different low-cost, off-the shelf sensors, at four test sites in Austria and Switzerland. Over 30 flights were performed during the winter 2014/15, where different camera settings, filters and lenses, as well as data collection routines were tested. Orthophotos and digital surface models (DSM) where calculated from the imagery using structure-from-motion photogrammetry software. Snow height was derived by subtracting snow-free from snow-covered DSMs. The RPAS-results were validated against data collected using a variety of well-established remote sensing (i.e. terrestrial laser scanning, large frame aerial sensors) and in-situ measurement techniques. The results show, that RPAS i) are able to map snow depth within accuracies of 0.07-0.15 m root mean square error (RMSE), when compared to traditional in-situ data; ii) can be operated at lower cost, easier repeatability, less operational constraints and higher GSD than large frame aerial sensors on-board manned aircraft, while achieving significantly higher accuracies; iii) are able to acquire meaningful data even under harsh environmental conditions above 2000 m a.s.l. (turbulence, low temperature and high irradiance, low air density). While providing a first prove-of-concept, the study also showed future challenges and limitations of RPAS-based snow depth mapping, including a high dependency on correct co-registration of snow-free and snow-covered height measurements, as well as a significant impact of the underlying vegetation and illumination of the snow surface on the fidelity of the results.

Strategies for cloud-top phase determination: differentiation between thin cirrus clouds and snow in manual (ground truth) analyses

NASA Astrophysics Data System (ADS)

Hutchison, Keith D.; Etherton, Brian J.; Topping, Phillip C.

1996-12-01

Quantitative assessments on the performance of automated cloud analysis algorithms require the creation of highly accurate, manual cloud, no cloud (CNC) images from multispectral meteorological satellite data. In general, the methodology to create ground truth analyses for the evaluation of cloud detection algorithms is relatively straightforward. However, when focus shifts toward quantifying the performance of automated cloud classification algorithms, the task of creating ground truth images becomes much more complicated since these CNC analyses must differentiate between water and ice cloud tops while ensuring that inaccuracies in automated cloud detection are not propagated into the results of the cloud classification algorithm. The process of creating these ground truth CNC analyses may become particularly difficult when little or no spectral signature is evident between a cloud and its background, as appears to be the case when thin cirrus is present over snow-covered surfaces. In this paper, procedures are described that enhance the researcher's ability to manually interpret and differentiate between thin cirrus clouds and snow-covered surfaces in daytime AVHRR imagery. The methodology uses data in up to six AVHRR spectral bands, including an additional band derived from the daytime 3.7 micron channel, which has proven invaluable for the manual discrimination between thin cirrus clouds and snow. It is concluded that while the 1.6 micron channel remains essential to differentiate between thin ice clouds and snow. However, this capability that may be lost if the 3.7 micron data switches to a nighttime-only transmission with the launch of future NOAA satellites.

Observation of melt onset on multiyear Arctic sea ice using the ERS 1 synthetic aperture radar

NASA Technical Reports Server (NTRS)

Winebrenner, D. P.; Nelson, E. D.; Colony, R.; West, R. D.

1994-01-01

We present nearly coincident observations of backscattering from the Earth Remote-Sensing Satellite (ERS) 1 synthetic aperture radar (SAR) and of near-surface temperature from six drifting buoys in the Beaufort Sea, showing that the onset of melting in snow on multiyear sea ice is clearly detectable in the SAR data. Melt onset is marked by a clean, steep decrease in the backscattering cross section of multiyear ice at 5.3 GHz and VV polarization. We investigate the scattering physics responsible for the signature change and find that the cross section decrease is due solely to the appearance of liquid water in the snow cover overlying the ice. A thin layer of moist snow is sufficient to cause the observed decrease. We present a prototype algorithm to estimate the date of melt onset using the ERS 1 SAR and apply the algorithm first to the SAR data for which we have corresponding buoy temperatures. The melt onset dates estimated by the SAR algorithm agree with those obtained independently from the temperature data to within 4 days or less, with the exception of one case in which temperatures oscillated about 0 C for several weeks. Lastly, we apply the algorithm to the entire ERS 1 SAR data record acquired by the Alaska SAR Facility for the Beaufort Sea north of 73 deg N during the spring of 1992, to produce a map of the dates of melt onset over an area roughly 1000 km on a side. The progression of melt onset is primarily poleward but shows a weak meridional dependence at latitudes of approximately 76 deg-77 deg N. Melting begins in the southern part of the study region on June 13 and by June 20 has progressed to the northermost part of the region.

Wide-area mapping of snow water equivalent by Sentinel-1&2 data

NASA Astrophysics Data System (ADS)

Conde, Vasco; Nico, Giovanni; Catalao, Joao; Kontu, Anna; Gritsevich, Maria

2017-04-01

The mapping of snow physical properties over large mountain areas of remote areas is an important topic in both climatological studies and hydrological models where the effects of snow melting are modeled and used to forecast extreme flood events. Usually, these models are run using in-situ measurements of snow which are expensive and statistically not representative of the spatial distribution of snow properties due to slope orientation of terrain, local terrain morphology and height as well as vegetation cover. In this work we investigate the use of data acquired by Sentinel-1 and 2 missions using a C-band SAR and multispectral sensor, respectively. The Sentinel-1 SAR data are processed to estimate the Snow Water Equivalent (SWE) using both the radar amplitude and the output of the SAR interferometry processing. Both approaches need in-situ data to process SAR data and calibrate SWE estimates. The use of SAR amplitude to estimate the SWE is well established and the basic idea is that the radar signal backscattered by snow is related to the SWE so, after modeling the relationship between these two quantities at the site of in-situ measurements this relationship can be used to map the SWE at all site where the SAR amplitude information is available. The physical principle used by SAR interferometry is that of phase delay due to propagation in a non-dispersive medium. This implies that the snow is supposed to be dry in order to allow the propagation of the SAR signal. Sentinel-2 images have been used to get land-use maps and identify areas covered by vegetation. Finland has been chosen as a study region with in-situ measurements acquired thanks to the availability of rich database of in-situ measurements of SWE. Sentinel data used in this work have been acquired starting from November 2015. Publication supported by FCT- project UID/GEO/50019/2013 - Instituto Dom Luiz.

Assessing snow extent data sets over North America to inform and improve trace gas retrievals from solar backscatter

NASA Astrophysics Data System (ADS)

Cooper, Matthew J.; Martin, Randall V.; Lyapustin, Alexei I.; McLinden, Chris A.

2018-05-01

Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability and thus snow-covered scenes are often omitted from retrieval data sets; however, the high reflectance of snow is potentially advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America. We use a radiative transfer model to examine how an increase in surface reflectivity due to snow cover changes the sensitivity of satellite retrievals to NO2 in the lower troposphere. We find that a substantial fraction (> 50 %) of the TEMPO field of regard can be snow covered in January and that the average sensitivity to the tropospheric NO2 column substantially increases (doubles) when the surface is snow covered.We then evaluate seven existing satellite-derived or reanalysis snow extent products against ground station observations over North America to assess their capability of informing surface conditions for TEMPO retrievals. The Interactive Multisensor Snow and Ice Mapping System (IMS) had the best agreement with ground observations (accuracy of 93 %, precision of 87 %, recall of 83 %). Multiangle Implementation of Atmospheric Correction (MAIAC) retrievals of MODIS-observed radiances had high precision (90 % for Aqua and Terra), but underestimated the presence of snow (recall of 74 % for Aqua, 75 % for Terra). MAIAC generally outperforms the standard MODIS products (precision of 51 %, recall of 43 % for Aqua; precision of 69 %, recall of 45 % for Terra). The Near-real-time Ice and Snow Extent (NISE) product had good precision (83 %) but missed a significant number of snow-covered pixels (recall of 45 %). The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data set had strong performance metrics (accuracy of 91 %, precision of 79 %, recall of 82 %). We use the Fscore, which balances precision and recall, to determine overall product performance (F = 85 %, 82 (82) %, 81 %, 58 %, 46 (54) % for IMS, MAIAC Aqua (Terra), CMC, NISE, MODIS Aqua (Terra), respectively) for providing snow cover information for TEMPO retrievals from solar backscatter observations. We find that using IMS to identify snow cover and enable inclusion of snow-covered scenes in clear-sky conditions across North America in January can increase both the number of observations by a factor of 2.1 and the average sensitivity to the tropospheric NO2 column by a factor of 2.7.

Generation of Level 3 SMMR and SSM/I Brightness Temperatures for the Period 1978-1999

NASA Technical Reports Server (NTRS)

Partington, Kim

1999-01-01

The NOAA/NASA Pathfinder Program was initially designed to assure that certain key remote sensing data sets of particular significance to global change research were scientifically validated, consistently processed and made readily available to the research community at minimal cost. Through this Program the National Snow and Ice Data Center (NSIDC), University of Colorado has successfully processed, archived and distributed the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) Level 3 (EASE-Grid format) Pathfinder data sets for the period 1978 to 1999. These data are routinely distributed to approximately 150 researchers through various media including CD-ROM, 8 mm tape, ftp and the EOS Information Management System (IMS). At NSIDC these data are currently being applied in the development and validation of algorithms to derive snow water equivalent (NASA NAG5-6636), the mapping of frozen ground and the detection of the onset of melt over ice sheets, sea ice and snow cover. The EASE-Grid format, developed at NSIDC in conjunction with the SMMR-SSM/I Pathfinder project has also been applied to Advanced Very High Resolution Radiometer (AVHRR) and TOVS Pathfinder data, as well as ancillary data such as digital elevation, land cover classification and several in situ data sets. EASE-Grid will also be used for all land products derived from the NASA EOS AMSR-E instrument.

Changing Snow Cover and Stream Discharge in the Western United States - Wind River Range, Wyoming

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; DiGirolamo, Nicolo E.; Barton, Jonathan S.; Riggs, George A.

2011-01-01

Earlier onset of springtime weather has been documented in the western United States over at least the last 50 years. Because the majority (>70%) of the water supply in the western U.S. comes from snowmelt, analysis of the declining spring snowpack has important implications for the management of water resources. We studied ten years of Moderate-Resolution Imaging Spectroradiometer (MODIS) snow-cover products, 40 years of stream discharge and meteorological station data and 30 years of snow-water equivalent (SWE) SNOw Telemetry (SNOTEL) data in the Wind River Range (WRR), Wyoming. Results show increasing air temperatures for.the 40-year study period. Discharge 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. Changes in streamflow may be related to increasing air temperatures which are probably contributing to a reduction in snow cover, although no trend of either increasingly lower streamflow or earlier snowmelt was observed within the decade of the 2000s. And SWE on 1 April does not show an expected downward trend from 1980 to 2009. The extent of snow cover derived from the lowest-elevation zone of the WRR study area is strongly correlated (r=0.91) with stream discharge on 1 May during the decade of the 2000s. The strong relationship between snow cover and streamflow indicates that MODIS snow-cover maps can be used to improve management of water resources in the drought-prone western U.S.

Discharge forecasts in mountain basins based on satellite snow cover mapping. [Dinwoody Creek Basin, Wyoming and the Dischma Basin, Switzerland

NASA Technical Reports Server (NTRS)

Martinec, J.; Rango, A. (Principal Investigator)

1980-01-01

The author has identified the following significant results. A snow runoff model developed for European mountain basins was used with LANDSAT imagery and air temperature data to simulate runoff in the Rocky Mountains under conditions of large elevation range and moderate cloud cover (cloud cover of 40% or less during LANDSAT passes 70% of the time during a snowmelt season). Favorable results were obtained for basins with area not exceeding serval hundred square kilometers and with a significant component of subsurface runoff.

SWANN: The Snow Water Artificial Neural Network Modelling System

NASA Astrophysics Data System (ADS)

Broxton, P. D.; van Leeuwen, W.; Biederman, J. A.

2017-12-01

Snowmelt from mountain forests is important for water supply and ecosystem health. Along Arizona's Mogollon Rim, snowmelt contributes to rivers and streams that provide a significant water supply for hydro-electric power generation, agriculture, and human consumption in central Arizona. In this project, we are building a snow monitoring system for the Salt River Project (SRP), which supplies water and power to millions of customers in the Phoenix metropolitan area. We are using process-based hydrological models and artificial neural networks (ANNs) to generate information about both snow water equivalent (SWE) and snow cover. The snow-cover data is generated with ANNs that are applied to Landsat and MODIS satellite reflectance data. The SWE data is generated using a combination of gridded SWE estimates generated by process-based snow models and ANNs that account for variations in topography, forest cover, and solar radiation. The models are trained and evaluated with snow data from SNOTEL stations as well as from aerial LiDAR and field data that we collected this past winter in northern Arizona, as well as with similar data from other sites in the Southwest US. These snow data are produced in near-real time, and we have built a prototype decision support tool to deliver them to SRP. This tool is designed to provide daily-to annual operational monitoring of spatial and temporal changes in SWE and snow cover conditions over the entire Salt River Watershed (covering 17,000 km2), and features advanced web mapping capabilities and watershed analytics displayed as graphical data.

Sensitivity of modelled snow cover to turbulent flux parameterization and forcing data: a case study in a high altitude basin of the dry Andes, northern Chile

NASA Astrophysics Data System (ADS)

Kinnard, C.; Irarrazaval, I.; Campos, C.; Gascoin, S.; MacDonell, S.; Macdonell, S.; Herrero, J.

2016-12-01

Snow cover in the central-northern Andes of Chile is the main runoff source, providing water for the irrigation of cultures in the fertile valleys downstream. The prospect of adverse climate warming impacts on the hydrological cycle calls for a better understanding of the snow cover dynamics in response to climate, an aspect that has been little studied in the dry Andes. The heterogeneous and often thin snow cover, as well as the paucity of long-term hydrometeorological data makes snow modelling a challenging task in these regions. In this work we applied a physically-based, spatially-distributed snow model (Wimmed) to the La Laguna headwater catchment in the dry Andes (30°S, 70°W) during three hydrological years (2010-2013) when forcing data was available. Model testing at the point scale revealed a large sensitivity of simulated snow depths to the choice of snow roughness parameter (z0), which controls turbulent fluxes, while wind-induced snow erosion at the station in 2010 and 2011 complicated model validation. The inclusion of a mean wind speed map from a previous simulation with the WRF atmospheric model was found to improve the simulation results, while excluding the highest mountain ridge weather station had detrimental effects on the results. A snow roughness (z0) of 1 mm yielded the best comparison between the simulated and observed snow depth at the reference weather station, and between the simulated and MODIS-derived snow cover at the catchment scale. The simulation resulted in large sublimation losses (up to 4 mm day-1), corresponding to more than 80% of snow ablation in the catchment. While such high sublimation rates have been reported before in this region, remaining uncertainties in precipitation data and snow compaction processes call for more detailed studies and increased instrumentation in order to improve future modelling efforts.

Improving Understanding of Glacier Melt Contribution to High Asian River Discharge through Collaboration and Capacity Building with High Asian CHARIS Partner Institutions

NASA Astrophysics Data System (ADS)

Armstrong, Richard; Brodzik, Mary Jo; Armstrong, Betsy; Barrett, Andrew; Fetterer, Florence; Hill, Alice; Jodha Khalsa, Siri; Racoviteanu, Adina; Raup, Bruce; Rittger, Karl; Williams, Mark; Wilson, Alana; Ye, Qinghua

2017-04-01

The Contribution to High Asia Runoff from Ice & Snow (CHARIS) project uses remote sensing data combined with modeling from 2000 to the present to improve proportional estimates of melt from glaciers and seasonal snow surfaces. Based at the National Snow and Ice Data Center (NSIDC), University of Colorado, Boulder, USA, the CHARIS project objectives are twofold: 1) capacity-building efforts with CHARIS partners from eight High Asian countries to better forecast future availability and vulnerability of water resources in the region, and 2) improving our ability to systematically assess the role of glaciers and seasonal snow in the freshwater resources of High Asia. Capacity-building efforts include working with CHARIS partners from Bhutan, Nepal, India, Pakistan, Afghanistan, Kazakhstan, Kyrgyzstan and Tajikistan. Our capacity-building activities include training, data sharing, supporting fieldwork, graduate student education and infrastructure development. Because of the scarcity of in situ data in this High Asian region, we are using the wealth of available remote sensing data to characterize digital elevation, daily maps of fractional snow-cover, annual maps of glacier and permanent snow cover area and downscaled reanalysis temperature data in snow melt models to estimate the relative proportions of river runoff from glacierized and seasonally snow-covered surfaces. Current collaboration with Qinghua Ye, visiting scientist at NSIDC from the Institute of Tibetan Plateau Research, CAS, focuses on remote sensing methods to detect changes in the mountain cryosphere. Collaboration with our Asian partners supports the systematic analysis of the annual cycle of seasonal snow and glacier ice melt across the High Mountain Asia region. With our Asian partners, we have derived reciprocal benefits, learning from their specialized local knowledge and obtaining access to their in situ data. We expect that the improved understanding of runoff from snow and glacier surfaces will inform the development of adaptation and mitigation measures. The CHARIS Project is funded by USAID.

Shifting mountain snow patterns in a changing climate from remote sensing retrieval.

PubMed

Dedieu, J P; Lessard-Fontaine, A; Ravazzani, G; Cremonese, E; Shalpykova, G; Beniston, M

2014-09-15

Observed climate change has already led to a wide range of impacts on environmental systems and society. In this context, many mountain regions seem to be particularly sensitive to a changing climate, through increases in temperature coupled with changes in precipitation regimes that are often larger than the global average (EEA, 2012). In mid-latitude mountains, these driving factors strongly influence the variability of the mountain snow-pack, through a decrease in seasonal reserves and earlier melting of the snow pack. These in turn impact on hydrological systems in different watersheds and, ultimately, have consequences for water management. Snow monitoring from remote sensing provides a unique opportunity to address the question of snow cover regime changes at the regional scale. This study outlines the results retrieved from the MODIS satellite images over a time period of 10 hydrological years (2000-2010) and applied to two case studies of the EU FP7 ACQWA project, namely the upper Rhone and Po in Europe and the headwaters of the Syr Darya in Kyrgyzstan (Central Asia). The satellite data were provided by the MODIS Terra MOD-09 reflectance images (NASA) and MOD-10 snow products (NSIDC). Daily snow maps were retrieved over that decade and the results presented here focus on the temporal and spatial changes in snow cover. This paper highlights the statistical bias observed in some specific regions, expressed by the standard deviation values (STD) of annual snow duration. This bias is linked to the response of snow cover to changes in elevation and can be used as a signal of strong instability in regions sensitive to climate change: with alternations of heavy snowfalls and rapid snow melting processes. The interest of the study is to compare the methodology between the medium scales (Europe) and the large scales (Central Asia) in order to overcome the limits of the applied methodologies and to improve their performances. Results show that the yearly snow cover duration increases by 4-5 days per 100 m elevation during the accumulation period, depending of the watershed, while during the melting season the snow depletion rate is 0.3% per day of surface loss for the upper Rhone catchment, 0.4%/day for the Syr Darya headwater basins, and 0.6%/day for the upper Po, respectively. Then, the annual STD maps of snow cover indicate higher values (more than 45 days difference compared to the mean values) for (i) the Po foothill region at medium elevation (SE orientation) and (ii) the Kyrgyzstan high plateaux (permafrost areas). These observations cover only a time-period of 10 years, but exhibit a signal under current climate that is already consistent with the expected decline in snow in these regions in the course of the 21st century. Copyright © 2014 Elsevier B.V. All rights reserved.

Optimizing placements of ground-based snow sensors for areal snow cover estimation using a machine-learning algorithm and melt-season snow-LiDAR data

NASA Astrophysics Data System (ADS)

Oroza, C.; Zheng, Z.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

2016-12-01

We present a structured, analytical approach to optimize ground-sensor placements based on time-series remotely sensed (LiDAR) data and machine-learning algorithms. We focused on catchments within the Merced and Tuolumne river basins, covered by the JPL Airborne Snow Observatory LiDAR program. First, we used a Gaussian mixture model to identify representative sensor locations in the space of independent variables for each catchment. Multiple independent variables that govern the distribution of snow depth were used, including elevation, slope, and aspect. Second, we used a Gaussian process to estimate the areal distribution of snow depth from the initial set of measurements. This is a covariance-based model that also estimates the areal distribution of model uncertainty based on the independent variable weights and autocorrelation. The uncertainty raster was used to strategically add sensors to minimize model uncertainty. We assessed the temporal accuracy of the method using LiDAR-derived snow-depth rasters collected in water-year 2014. In each area, optimal sensor placements were determined using the first available snow raster for the year. The accuracy in the remaining LiDAR surveys was compared to 100 configurations of sensors selected at random. We found the accuracy of the model from the proposed placements to be higher and more consistent in each remaining survey than the average random configuration. We found that a relatively small number of sensors can be used to accurately reproduce the spatial patterns of snow depth across the basins, when placed using spatial snow data. Our approach also simplifies sensor placement. At present, field surveys are required to identify representative locations for such networks, a process that is labor intensive and provides limited guarantees on the networks' representation of catchment independent variables.

Facilitating the exploitation of ERTS-1 imagery using snow enhancement techniques. [geological fault maps of Massachusetts and Connecticut

NASA Technical Reports Server (NTRS)

Wobber, F. J. (Principal Investigator); Martin, K. R.; Amato, R. V.; Leshendok, T.

1973-01-01

The author has identified the following significant results. The applications of ERTS-1 imagery for geological fracture mapping regardless of season has been repeatedly confirmed. The enhancement provided by a differential cover of snow increases the number and length of fracture-lineaments which can be detected with ERTS-1 data and accelerates the fracture mapping process for a variety of practical applications. The geological mapping benefits of the program will be realized in geographic areas where data are most needed - complex glaciated terrain and areas of deep residual soils. ERTS-1 derived fracture-lineament maps which provide detail well in excess of existing geological maps are not available in the Massachusetts-Connecticut area. The large quantity of new data provided by ERTS-1 may accelerate and improve field mapping now in progress in the area. Numerous other user groups have requested data on the techniques. This represents a major change in operating philosophy for groups who to data judged that snow obscured geological detail.

Improvement and further development of SSM/I overland parameter algorithms using the WetNet workstation

NASA Technical Reports Server (NTRS)

Neale, Christopher M. U.; Mcdonnell, Jeffrey J.; Ramsey, Douglas; Hipps, Lawrence; Tarboton, David

1993-01-01

Since the launch of the DMSP Special Sensor Microwave/Imager (SSM/I), several algorithms have been developed to retrieve overland parameters. These include the present operational algorithms resulting from the Navy calibration/validation effort such as land surface type (Neale et al. 1990), land surface temperature (McFarland et al. 1990), surface moisture (McFarland and Neale, 1991) and snow parameters (McFarland and Neale, 1991). In addition, other work has been done including the classification of snow cover and precipitation using the SSM/I (Grody, 1991). Due to the empirical nature of most of the above mentioned algorithms, further research is warranted and improvements can probably be obtained through a combination of radiative transfer modelling to study the physical processes governing the microwave emissions at the SSM/I frequencies, and the incorporation of additional ground truth data and special cases into the regression data sets. We have proposed specifically to improve the retrieval of surface moisture and snow parameters using the WetNet SSM/I data sets along with ground truth information namely climatic variables from the NOAA cooperative network of weather stations as well as imagery from other satellite sensors such as the AVHRR and Thematic Mapper. In the case of surface moisture retrievals the characterization of vegetation density is of primary concern. The higher spatial resolution satellite imagery collected at concurrent periods will be used to characterize vegetation types and amounts which, along with radiative transfer modelling should lead to more physically based retrievals. Snow parameter retrieval algorithm improvement will initially concentrate on the classification of snowpacks (dry snow, wet snow, refrozen snow) and later on specific products such as snow water equivalent. Significant accomplishments in the past year are presented.

A Decade of High-Resolution Arctic Sea Ice Measurements from Airborne Altimetry

NASA Astrophysics Data System (ADS)

Duncan, K.; Farrell, S. L.; Connor, L. N.; Jackson, C.; Richter-Menge, J.

2017-12-01

Satellite altimeters carried on board ERS-1,-2, EnviSat, ICESat, CryoSat-2, AltiKa and Sentinel-3 have transformed our ability to map the thickness and volume of the polar sea ice cover, on seasonal and decadal time-scales. The era of polar satellite altimetry has coincided with a rapid decline of the Arctic ice cover, which has thinned, and transitioned from a predominantly multi-year to first-year ice cover. In conjunction with basin-scale satellite altimeter observations, airborne surveys of the Arctic Ocean at the end of winter are now routine. These surveys have been targeted to monitor regions of rapid change, and are designed to obtain the full snow and ice thickness distribution, across a range of ice types. Sensors routinely deployed as part of NASA's Operation IceBridge (OIB) campaigns include the Airborne Topographic Mapper (ATM) laser altimeter, the frequency-modulated continuous-wave snow radar, and the Digital Mapping System (DMS). Airborne measurements yield high-resolution data products and thus present a unique opportunity to assess the quality and characteristics of the satellite observations. We present a suite of sea ice data products that describe the snow depth and thickness of the Arctic ice cover during the last decade. Fields were derived from OIB measurements collected between 2009-2017, and from reprocessed data collected during ad-hoc sea ice campaigns prior to OIB. Our bespoke algorithms are designed to accommodate the heterogeneous sea ice surface topography, that varies at short spatial scales. We assess regional and inter-annual variability in the sea ice thickness distribution. Results are compared to satellite-derived ice thickness fields to highlight the sensitivities of satellite footprints to the tails of the thickness distribution. We also show changes in the dynamic forcing shaping the ice pack over the last eight years through an analysis of pressure-ridge sail-height distributions and surface roughness conditions. Variability is linked to the geographic location and extent of multi-year sea ice. Finally, we describe accessing our high-resolution data products at the NOAA Laboratory for Satellite Altimetry.

Operational applications of satellite snowcover observations in Rio Grande drainage of Colorado

NASA Technical Reports Server (NTRS)

Washicheck, J. N.; Mikesell, T.

1975-01-01

Various mapping techniques were tried and evaluated. There were many problems encountered such as distinquishing clouds from snow and snow under trees. A partial solution to some of the problems involves ground reconnaissance and low air flights. Snow areas, cloud cover, and total areas were planimetered after transferring imagery by use of zoom transfer scope. These determinations were then compared to areas determined by use of a density slicer. Considerable adjustment is required for these two values to compare. NOAA pictures were also utilized in the evaluation. Forest cover is one of the parameters used in the modeling process. The determination of this percentage is being explored.

Combined Study of Snow Depth Determination and Winter Leaf Area Index Retrieval by Unmanned Aerial Vehicle Photogrammetry

NASA Astrophysics Data System (ADS)

Lendzioch, Theodora; Langhammer, Jakub; Jenicek, Michal

2017-04-01

A rapid and robust approach using Unmanned Aerial Vehicle (UAV) digital photogrammetry was performed for evaluating snow accumulation over different small localities (e.g. disturbed forest and open area) and for indirect field measurements of Leaf Area Index (LAI) of coniferous forest within the Šumava National Park, Czech Republic. The approach was used to reveal impacts related to changes in forest and snowpack and to determine winter effective LAI for monitoring the impact of forest canopy metrics on snow accumulation. Due to the advancement of the technique, snow depth and volumetric changes of snow depth over these selected study areas were estimated at high spatial resolution (1 cm) by subtracting a snow-free digital elevation model (DEM) from a snow-covered DEM. Both, downward-looking UAV images and upward-looking digital hemispherical photography (DHP), and additional widely used LAI-2200 canopy analyser measurements were applied to determine the winter LAI, controlling interception and transmitting radiation. For the performance of downward-looking UAV images the snow background instead of the sky fraction was used. The reliability of UAV-based LAI retrieval was tested by taking an independent data set during the snow cover mapping campaigns. The results showed the potential of digital photogrammetry for snow depth mapping and LAI determination by UAV techniques. The average difference obtained between ground-based and UAV-based measurements of snow depth was 7.1 cm with higher values obtained by UAV. The SD of 22 cm for the open area seemed competitive with the typical precision of point measurements. In contrast, the average difference in disturbed forest area was 25 cm with lower values obtained by UAV and a SD of 36 cm, which is in agreement with other studies. The UAV-based LAI measurements revealed the lowest effective LAI values and the plant canopy analyser LAI-2200 the highest effective LAI values. The biggest bias of effective LAI was observed between LAI-2200 and UAV-based analyses. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

BOREAS HYD-4 Areal Snow Course Data

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Knapp, David E. (Editor); Metcalfe, John R.; Goodison, Barry E.; Walker, Anne; Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-4 team focused on collecting data during the 1994 winter focused field campaign (FFCW) to improve the understanding of winter processes within the boreal forest. Knowledge of snow cover and its variability in the boreal forest is fundamental if BOREAS is to achieve its goals of understanding the processes and states involved in the exchange of energy and water. The development and validation of remote sensing algorithms will provide the means to extend the knowledge of these processes and states from the local to the regional scale. A specific thrust of the hydrology research is the development and validation of snow cover algorithms from airborne passive microwave measurements. Airborne remote sensing data (gamma, passive microwave) were acquired along a series of flight lines established in the vicinity of the BOREAS study areas. Ground snow surveys were conducted along selected sections of these aircraft flight lines. These calibration segments were typically 10-20 km in length, and ground data were collected at one to two kilometer intervals. The data are provided in tabular ASCII files. The HYD-04 areal snow course data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

NASA Astrophysics Data System (ADS)

Kwok, Ron; Kurtz, Nathan T.; Brucker, Ludovic; Ivanoff, Alvaro; Newman, Thomas; Farrell, Sinead L.; King, Joshua; Howell, Stephen; Webster, Melinda A.; Paden, John; Leuschen, Carl; MacGregor, Joseph A.; Richter-Menge, Jacqueline; Harbeck, Jeremy; Tschudi, Mark

2017-11-01

Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air-snow (a-s) and snow-ice (s-i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two ground-based campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.

Analysis of Leaf Area Index and Fraction of PAR Absorbed by Vegetation Products from the Terra MODIS Sensor: 2000-2005

NASA Technical Reports Server (NTRS)

Yang, Wenze; Huang, Dong; Tan, Bin; Stroeve, Julienne C.; Shabanov, Nikolay V.; Knyazikhin, Yuri; Nemani, Ramakrishna R.; Myneni, Ranga B.

2006-01-01

The analysis of two years of Collection 3 and five years of Collection 4 Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data sets is presented in this article with the goal of understanding product quality with respect to version (Collection 3 versus 4), algorithm (main versus backup), snow (snow-free versus snow on the ground), and cloud (cloud-free versus cloudy) conditions. Retrievals from the main radiative transfer algorithm increased from 55% in Collection 3 to 67% in Collection 4 due to algorithm refinements and improved inputs. Anomalously high LAI/FPAR values observed in Collection 3 product in some vegetation types were corrected in Collection 4. The problem of reflectance saturation and too few main algorithm retrievals in broadleaf forests persisted in Collection 4. The spurious seasonality in needleleaf LAI/FPAR fields was traced to fewer reliable input data and retrievals during the boreal winter period. About 97% of the snow covered pixels were processed by the backup Normalized Difference Vegetation Index-based algorithm. Similarly, a majority of retrievals under cloudy conditions were obtained from the backup algorithm. For these reasons, the users are advised to consult the quality flags accompanying the LAI and FPAR product.

Quantifying widespread canopy cover decline through the course of a beetle kill epidemic in Colorado with remote sensing of snow

NASA Astrophysics Data System (ADS)

Baker, E. H.; Raleigh, M. S.; Molotch, N. P.

2014-12-01

Since the mid-1990s, outbreaks of aggressive bark beetle species have caused extensive forest morality across 600,000 km2 of North-American forests, killing over 17,800 km2 of forest in Colorado alone. This mortality has resulted in a widespread, spatially heterogeneous decline of forest canopies, which in turn exerts strong controls on the accumulation and melt of the snowpack. In the Western United States, where approximately 70-80% of total annual runoff originates as mountain snowmelt, it is important to monitor and quantify changes in forest canopy in snow-dominated catchments. To quantify annual values of forest canopy cover, this research develops a metric from time series of daily fractional snow covered area (FSCA) from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) snow covered area and grain size (MODSCAG) algorithm. In areas where soil and rock are completely snow-covered, a land pixel is composed only of forest canopy and snow. Following a snowfall event, FSCA initially rises rapidly, as snow is intercepted in the canopy, and then declines, as snow unloads from the canopy. The lower of these local minima form a threshold representative of snow-free canopy conditions, which serves as a spatially explicit metric of forest canopy. Investigation of a site in southern Colorado with over 40% spruce beetle mortality shows a statistically significant decrease of canopy cover, from 76 (±4)% pre-infestation to 55 (±8)% post-infestation (t=-5.1, p<0.01). Additionally, this yearly parameterization of forest canopy is well correlated (ρ=0.76, p<0.01) with an independent product of yearly crown mortality derived from U.S. Forest Service Aerial Detection Surveys. Future work will examine this relationship across varied ecologic settings and geographic locations, and incorporate field measurements of species-specific canopy change after beetle kill.

Monitoring Snow and Land Ice Using Satellite data in the GMES Project CryoLand

NASA Astrophysics Data System (ADS)

Bippus, Gabriele; Nagler, Thomas

2013-04-01

The main objectives of the project "CryoLand - GMES Service Snow and Land Ice" are to develop, implement and validate services for snow, glaciers and lake and river ice products as a Downstream Service within the Global Monitoring for Environment and Security (GMES) program of the European Commission. CryoLand exploits Earth Observation data from current optical and microwave sensors and of the upcoming GMES Sentinel satellite family. The project prepares also the basis for the cryospheric component of the GMES Land Monitoring services. The CryoLand project team consists of 10 partner organisations from Austria, Finland, Norway, Sweden, Switzerland and Romania and is funded by the 7th Framework Program of the European Commission. The CryoLand baseline products for snow include fractional snow extent from optical satellite data, the extent of melting snow from SAR data, and coarse resolution snow water equivalent maps from passive microwave data. Experimental products include maps of snow surface wetness and temperature. The products range from large scale coverage at medium resolution to regional products with high resolution, in order to address a wide user community. Medium resolution optical data (e.g. MODIS, in the near future Sentinel-3) and SAR (ENVISAT ASAR, in the near future Sentinel-1) are the main sources of EO data for generating large scale products in near real time. For generation of regional products high resolution satellite data are used. Glacier products are based on high resolution optical (e.g. SPOT-5, in the near future Sentinel-2) and SAR (TerraSAR-X, in the near future Sentinel-1) data and include glacier outlines, mapping of glacier facies, glacier lakes and ice velocity. The glacier products are generated on users demand. Current test areas are located in the Alps, Norway, Greenland and the Himalayan Mountains. The lake and river ice products include ice extent and its temporal changes and snow extent on ice. The algorithms for these products are in development. One major task of CryoLand is the performance assessment of the products, which is carried out in different environments, climate zones and land cover types, selected jointly with users. Accuracy assessment is done for test areas using in-situ data and very high resolution satellite data. This presentation gives an overview on the processing lines and demonstration products for snow, glacier and lake ice parameters including examples of the product accuracy assessment. An important point of the CryoLand project is the use of advanced information technology, which is applied to process and distribute snow and land ice products in near real time.

Snow mapping and land use studies in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1977-01-01

The author has identified the following significant results. A system was developed for operational snow and land use mapping, based on a supervised classification method using various classification algorithms and representation of the results in maplike form on color film with a photomation system. Land use mapping, under European conditions, was achieved with a stepwise linear discriminant analysis by using additional ratio variables. On fall images, signatures of built-up areas were often not separable from wetlands. Two different methods were tested to correlate the size of settlements and the population with an accuracy for the densely populated Swiss Plateau between +2 or -12%.

Discrimination Between Clouds and Snow in Landsat 8 Imagery: an Assessment of Current Methods and a New Approach

NASA Astrophysics Data System (ADS)

Stillinger, T.; Dozier, J.; Phares, N.; Rittger, K.

2015-12-01

Discrimination between snow and clouds poses a serious but tractable challenge to the consistent delivery of high-quality information on mountain snow from remote sensing. Clouds obstruct the surface from the sensor's view, and the similar optical properties of clouds and snow make accurate discrimination difficult. We assess the performance of the current Landsat 8 operational snow and cloud mask products (LDCM CCA and CFmask), along with a new method, using over one million manually identified snow and clouds pixels in Landsat 8 scenes. The new method uses physically based scattering models to generate spectra in each Landsat 8 band, at that scene's solar illumination, for snow and cloud particle sizes that cover the plausible range for each. The modeled spectra are compared to pixels' spectra via several independent ways to identify snow and clouds. The results are synthesized to create a final snow/cloud mask, and the method can be applied to any multispectral imager with bands covering the visible, near-infrared, and shortwave-infrared regions. Each algorithm we tested misidentifies snow and clouds in both directions to varying degrees. We assess performance with measures of Precision, Recall, and the F statistic, which are based on counts of true and false positives and negatives. Tests for significance in differences between spectra in the measured and modeled values among incorrectly identified pixels help ascertain reasons for misidentification. A cloud mask specifically designed to separate snow from clouds is a valuable tool for those interested in remotely sensing snow cover. Given freely available remote sensing datasets and computational tools to feasibly process entire mission histories for an area of interest, enabling researchers to reliably identify and separate snow and clouds increases the usability of the data for hydrological and climatological studies.

Validation of Globsnow-2 Snow Water Equivalent Over Eastern Canada

NASA Technical Reports Server (NTRS)

Larue, Fanny; Royer, Alain; De Seve, Danielle; Langlois, Alexandre; Roy, Alexandre R.; Brucker, Ludovic

2017-01-01

In Qubec, Eastern Canada, snowmelt runoff contributes more than 30% of the annual energy reserve for hydroelectricity production, and uncertainties in annual maximum snow water equivalent (SWE) over the region are one of the main constraints for improved hydrological forecasting. Current satellite-based methods for mapping SWE over Qubec's main hydropower basins do not meet Hydro-Qubec operational requirements for SWE accuracies with less than 15% error. This paper assesses the accuracy of the GlobSnow-2 (GS-2) SWE product, which combines microwave satellite data and in situ measurements, for hydrological applications in Qubec. GS-2 SWE values for a 30-year period (1980 to 2009) were compared with space- and time-matched values from a comprehensive dataset of in situ SWE measurements (a total of 38,990 observations in Eastern Canada). The root mean square error (RMSE) of the GS-2 SWE product is 94.1+/- 20.3 mm, corresponding to an overall relative percentage error (RPE) of 35.9%. The main sources of uncertainty are wet and deep snow conditions (when SWE is higher than 150 mm), and forest cover type. However, compared to a typical stand-alone brightness temperature channel difference algorithm, the assimilation of surface information in the GS-2 algorithm clearly improves SWE accuracy by reducing the RPE by about 30%. Comparison of trends in annual mean and maximum SWE between surface observations and GS-2 over 1980-2009 showed agreement for increasing trends over southern Qubec, but less agreement on the sign and magnitude of trends over northern Qubec. Extended at a continental scale, the GS-2 SWE trends highlight a strong regional variability.

Using mid-altitude regions as observatories of change in snow areas: the Natural Park of Cazorla, Segura y las Villas (South Spain) as study case for early snow regression.

NASA Astrophysics Data System (ADS)

Montilla, Soledad; Pimentel, Rafael; José Pérez-Palazón, María; Aguillar, Cristina; José Polo, María

2017-04-01

Snow plays a key role at the hydrological cycle in semiarid mountainous areas, modifying the energy and water balances that govern the regime of stored water in the snowpack, a key resource for the spring and summer river flow. The Natural and National Park of Sierra Nevada (SNNP), a coastal mountain range up to 3450 m a.s.l. in southern Spain, is a representative example of snow areas in Mediterranean-climate regions; its high altitudinal gradient results in a wide variety of eco-climatic environments, and it is part of the global monitoring network to study climate change. Both monitoring and modelling efforts have been performed to assess this variability and its significant scales; whereas increasing temperature trends have been found, no significant trends are observed so far regarding the precipitation regime both on a seasonal and annual basis, with a highly variable impact on the snow regime in this area, especially in the mid-altitude range. In this context, the study of the snow cover in the neighbouring Natural Park of Cazorla, Segura and Las Villas (CSLVNP), with similar climatic conditions but lower altitudes (up to 2107 m a.s.l.) is proposed as a parallel monitoring site for early warning of impacts of climate change on the snow regime. The CSLVNP is the most extensive protected area in Spain and it is located to the north of SNPN, with less influence of the Mediterranean Sea. This study carried out a first quantification of the snow importance in this area, which exhibits a large transitional zone with a dominant alpine environment, and its relationship with the observed local precipitation-temperature trends. For this, the snow cover fraction on a 30x30 m gridded resolution has been studied during a 5-yr period combining on-site meteorological observations and remote-sensing data analysis, and snow modelling by the distributed and physically based approach for Mediterranean regions proposed by Herrero et al. (2009; 2010). The analysis of the available series of satellite images Landsat 8 OLI/TIRS, Landsat 7 ETM+ and Landsat 4-5 TM were used to obtain snow cover fraction maps with 30x30 m resolution. The study period 2010-2015 was simulated with the distributed snow model and the results were compared against these snow map series. Additionally, the annual and seasonal trends of precipitation, mean daily temperature and global radiation were obtained from the available local data sets. Globally, the simulated results overestimate the snow presence in the study area, very likely due to the estimation of snowfall. However, on a local scale, the model performance improves in the region between 1750 and 2250 m altitude. On the other hand, those zones at lower altitudes, which are a transition of the clearly alpine environment above, present a high variability of results related to the spatial patterns of precipitation, temperature and radiation. Regarding the precipitation-temperature regime, an increasing 0.05 °/yr over the last 30 years (1970-2010) was found, but no significant conclusion can be achieved on precipitation trends. This is also observed in the SNNP, which confirms the potential representativeness of PNCSV as an early warning site. Further work is being carried out to improve the snow modelling at this site and generate longer snow cover fraction maps series and other characteristic variables of the snow in this area.

Evaluating MODIS snow products for modelling snowmelt runoff: case study of the Rio Grande headwaters

USDA-ARS?s Scientific Manuscript database

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM). Landsat Thematic Mapper (TM) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and ...

The Snow Data System at NASA JPL

NASA Astrophysics Data System (ADS)

Horn, J.; Painter, T. H.; Bormann, K. J.; Rittger, K.; Brodzik, M. J.; Skiles, M.; Burgess, A. B.; Mattmann, C. A.; Ramirez, P.; Joyce, M.; Goodale, C. E.; McGibbney, L. J.; Zimdars, P.; Yaghoobi, R.

2017-12-01

The Snow Data System at NASA JPL includes data processing pipelines built with open source software, Apache 'Object Oriented Data Technology' (OODT). Processing is carried out in parallel across a high-powered computing cluster. The pipelines use input data from satellites such as MODIS, VIIRS and Landsat. They apply algorithms to the input data to produce a variety of outputs in GeoTIFF format. These outputs include daily data for SCAG (Snow Cover And Grain size) and DRFS (Dust Radiative Forcing in Snow), along with 8-day composites and MODICE annual minimum snow and ice calculations. This poster will describe the Snow Data System, its outputs and their uses and applications. It will also highlight recent advancements to the system and plans for the future.

The Snow Data System at NASA JPL

NASA Astrophysics Data System (ADS)

Joyce, M.; Laidlaw, R.; Painter, T. H.; Bormann, K. J.; Rittger, K.; Brodzik, M. J.; Skiles, M.; Burgess, A. B.; Mattmann, C. A.; Ramirez, P.; Goodale, C. E.; McGibbney, L. J.; Zimdars, P.; Yaghoobi, R.

2016-12-01

The Snow Data System at NASA JPL includes data processing pipelines built with open source software, Apache 'Object Oriented Data Technology' (OODT). Processing is carried out in parallel across a high-powered computing cluster. The pipelines use input data from satellites such as MODIS, VIIRS and Landsat. They apply algorithms to the input data to produce a variety of outputs in GeoTIFF format. These outputs include daily data for SCAG (Snow Cover And Grain size) and DRFS (Dust Radiative Forcing in Snow), along with 8-day composites and MODICE annual minimum snow and ice calculations. This poster will describe the Snow Data System, its outputs and their uses and applications. It will also highlight recent advancements to the system and plans for the future.

Snow Water Equivalent estimation based on satellite observation

NASA Astrophysics Data System (ADS)

Macchiavello, G.; Pesce, F.; Boni, G.; Gabellani, S.

2009-09-01

The availability of remotely sensed images and them analysis is a powerful tool for monitoring the extension and typology of snow cover over territory where the in situ measurements are often difficult. Information on snow are fundamental for monitoring and forecasting the available water above all in regions at mid latitudes as Mediterranean where snowmelt may cause floods. The hydrological model requirements and the daily acquisitions of MODIS (Moderate Resolution Imaging Spectroradiometer), drove, in previous research activities, to the development of a method to automatically map the snow cover from multi-spectral images. But, the major hydrological parameter related to the snow pack is the Snow Water Equivalent (SWE). This represents a direct measure of stored water in the basin. Because of it, the work was focused to the daily estimation of SWE from MODIS images. But, the complexity of this aim, based only on optical data, doesn’t find any information in literature. Since, from the spectral range of MODIS data it is not possible to extract a direct relation between spectral information and the SWE. Then a new method, respectful of the physic of the snow, was defined and developed. Reminding that the snow water equivalent is the product of the three factors as snow density, snow depth and the snow covered areas, the proposed approach works separately on each of these physical behaviors. Referring to the physical characteristic of snow, the snow density is function of the snow age, then it was studied a new method to evaluate this. Where, a module for snow age simulation from albedo information was developed. It activates an age counter updated by new snow information set to estimate snow age from zero accumulation status to the end of melting season. The height of the snow pack, can be retrieved by adopting relation between vegetation and snow depth distributions. This computes snow height distribution by the relation between snow cover fraction and the forest canopy density. Finally, the SWE has to be calculated for the snow covered areas, detected by means of a previously developed decision tree classifier able to classify snow cover by self selecting rules in a statistically optimum way. The advantages introduced from this work are many. Firstly, applying a suitable method with data features, it is possible to automatically obtain snow cover description with high frequency. Moreover, the advantages of the modularity in the proposed approach allows to improve the three factors estimation in an independent way. Limitations lie into clouds problem that affects results by obscuring the observed territory, that is bounded by fusing temporal and spatial information. Then the spatial resolution of data, satisfactory with the scale of hydrological models, mismatch with the available in situ point information, causing difficulties for a method validation or calibration. However this working flow results computationally cost-effectiveness, robust to the radiometric noise of the original data, provides spatially extended and frequent information.

Multitemporal Accuracy and Precision Assessment of Unmanned Aerial System Photogrammetry for Slope-Scale Snow Depth Maps in Alpine Terrain

NASA Astrophysics Data System (ADS)

Adams, Marc S.; Bühler, Yves; Fromm, Reinhard

2017-12-01

Reliable and timely information on the spatio-temporal distribution of snow in alpine terrain plays an important role for a wide range of applications. Unmanned aerial system (UAS) photogrammetry is increasingly applied to cost-efficiently map the snow depth at very high resolution with flexible applicability. However, crucial questions regarding quality and repeatability of this technique are still under discussion. Here we present a multitemporal accuracy and precision assessment of UAS photogrammetry for snow depth mapping on the slope-scale. We mapped a 0.12 km2 large snow-covered study site, located in a high-alpine valley in Western Austria. 12 UAS flights were performed to acquire imagery at 0.05 m ground sampling distance in visible (VIS) and near-infrared (NIR) wavelengths with a modified commercial, off-the-shelf sensor mounted on a custom-built fixed-wing UAS. The imagery was processed with structure-from-motion photogrammetry software to generate orthophotos, digital surface models (DSMs) and snow depth maps (SDMs). Accuracy of DSMs and SDMs were assessed with terrestrial laser scanning and manual snow depth probing, respectively. The results show that under good illumination conditions (study site in full sunlight), the DSMs and SDMs were acquired with an accuracy of ≤ 0.25 and ≤ 0.29 m (both at 1σ), respectively. In case of poorly illuminated snow surfaces (study site shadowed), the NIR imagery provided higher accuracy (0.19 m; 0.23 m) than VIS imagery (0.49 m; 0.37 m). The precision of the UASSDMs was 0.04 m for a small, stable area and below 0.33 m for the whole study site (both at 1σ).

Preliminary Evaluation of the AFWA-NASA (ANSA) Blended Snow-Cover Product over the Lower Great Lakes Region

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Riggs, George A.; Kelly, Richard E. J.; Chien, Janet Y. L.; Montesano, Paul M.

2009-01-01

The Air Force Weather Agency (AFWA) - NASA (ANSA) blended-snow product utilizes EOS standard snow products from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to map daily snow cover and snow-water equivalent (SWE) globally. We have compared ANSA-derived SWE. with SWE values calculated from snow depths reported at approx.1500 National Climatic Data Center (NCDC) coop stations in the Lower Great Lakes basin. Our preliminary results show that conversion of snow depth to SWE is very sensitive to the choice of snow density (we used either 0.2 or 03 as conversion factors). We found overall better agreement between the ANSA-derived SWE and the co-op station data when we use a snow density of 0.3 to convert the snow depths to SWE. In addition, we show that the ANSA underestimates SWE in densely-forested areas, using January and February 2008 ANSA and co-op data. Furthermore, apparent large SWE changes from one day to the next may be caused by thaw-re-freeze events, and do not always represent a real change in SWE. In the near future we will continue the analysis in the 2006-07 and 2007-08 snow seasons.

Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

NASA Astrophysics Data System (ADS)

Zhu, Zhe; Gallant, Alisa L.; Woodcock, Curtis E.; Pengra, Bruce; Olofsson, Pontus; Loveland, Thomas R.; Jin, Suming; Dahal, Devendra; Yang, Limin; Auch, Roger F.

2016-12-01

The U.S. Geological Survey's Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4-8. Training data were selected from map products of the U.S. Geological Survey's Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening "Function of mask" (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability, and cloud probability improved the accuracy of land cover classification. Compared to the original strategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification accuracies substantially (15-percentage point increase in overall accuracy and 4-percentage point increase in minimum accuracy).

Turbulent Surface Flux Measurements over Snow-Covered Sea Ice

NASA Astrophysics Data System (ADS)

Andreas, E. L.; Fairall, C. W.; Grachev, A. A.; Guest, P. S.; Jordan, R. E.; Persson, P. G.

2006-12-01

Our group has used eddy correlation to make over 10,000 hours of measurements of the turbulent momentum and heat fluxes over snow-covered sea ice in both the Arctic and the Antarctic. Polar sea ice is an ideal site for studying fundamental processes for turbulent exchange over snow. Both our Arctic and Antarctic sites---in the Beaufort Gyre and deep into the Weddell Sea, respectively---were expansive, flat areas with continuous snow cover; and both were at least 300 km from any topography that might have complicated the atmospheric flow. In this presentation, we will review our measurements of the turbulent fluxes of momentum and sensible and latent heat. In particular, we will describe our experiences making turbulence instruments work in the fairly harsh polar, marine boundary layer. For instance, several of our Arctic sites were remote from our main camp and ran unattended for a week at a time. Besides simply making flux measurements, we have been using the data to develop a bulk flux algorithm and to study fundamental turbulence processes in the atmospheric surface layer. The bulk flux algorithm predicts the turbulent surface fluxes from mean meteorological quantities and, thus, will find use in data analyses and models. For example, components of the algorithm are already embedded in our one- dimensional mass and energy budget model SNTHERM. Our fundamental turbulence studies have included deducing new scaling regimes in the stable boundary layer; examining the Monin-Obukhov similarity functions, especially in stable stratification; and evaluating the von Kármán constant with the largest atmospheric data set ever applied to such a study. During this presentation, we will highlight some of this work.

Arctic multiyear ice classification and summer ice cover using passive microwave satellite data

NASA Astrophysics Data System (ADS)

Comiso, J. C.

1990-08-01

The ability to classify and monitor Arctic multiyear sea ice cover using multispectral passive microwave data is studied. Sea ice concentration maps during several summer minima have been analyzed to obtain estimates of ice surviving the summer. The results are compared with multiyear ice concentrations derived from data the following winter, using an algorithm that assumes a certain emissivity for multiyear ice. The multiyear ice cover inferred from the winter data is approximately 25 to 40% less than the summer ice cover minimum, suggesting that even during winter when the emissivity of sea ice is most stable, passive microwave data may account for only a fraction of the total multiyear ice cover. The difference of about 2×106 km2 is considerably more than estimates of advection through Fram Strait during the intervening period. It appears that as in the Antarctic, some multiyear ice floes in the Arctic, especially those near the summer marginal ice zone, have first-year ice or intermediate signatures in the subsequent winter. A likely mechanism for this is the intrusion of seawater into the snow-ice interface, which often occurs near the marginal ice zone or in areas where snow load is heavy. Spatial variations in melt and melt ponding effects also contribute to the complexity of the microwave emissivity of multiyear ice. Hence the multiyear ice data should be studied in conjunction with the previous summer ice data to obtain a more complete characterization of the state of the Arctic ice cover. The total extent and actual areas of the summertime Arctic pack ice were estimated to be 8.4×106 km2 and 6.2×106 km2, respectively, and exhibit small interannual variability during the years 1979 through 1985, suggesting a relatively stable ice cover.

Microwave Observations of Snow-Covered Freshwater Lake Ice obtained during the Great Lakes Winter EXperiment (GLAWEX), 2017

NASA Astrophysics Data System (ADS)

Gunn, G. E.; Hall, D. K.; Nghiem, S. V.

2017-12-01

Studies observing lake ice using active microwave acquisitions suggest that the dominant scattering mechanism in ice is caused by double-bounce of the signal off vertical tubular bubble inclusions. Recent polarimetric SAR observations and target decomposition algorithms indicate single-bounce interactions may be the dominant source of returns, and in the absence of field observations, has been hypothesized to be the result of roughness at the ice-water interface on the order of incident wavelengths. This study presents in-situ physical observations of snow-covered lake ice in western Michigan and Wisconsin acquired during the Great Lakes Winter EXperiment in 2017 (GLAWEX'17). In conjunction with NASA's SnowEx airborne snow campaign in Colorado (http://snow.nasa.gov), C- (Sentinel-1, RADARSAT-2) and X-band (TerraSAR-X) synthetic aperture radar (SAR) observations were acquired coincidently to surface physical snow and ice observations. Small/large scale roughness features at the ice-water interface are quantified through auger transects and used as an input variable in lake ice backscatter models to assess the relative contributions from different scattering mechanisms.

A passive microwave snow depth algorithm with a proxy for snow metamorphism

USGS Publications Warehouse

Josberger, E.G.; Mognard, N.M.

2002-01-01

Passive microwave brightness temperatures of snowpacks depend not only on the snow depth, but also on the internal snowpack properties, particularly the grain size, which changes through the winter. Algorithms that assume a constant grain size can yield erroneous estimates of snow depth or water equivalent. For snowpacks that are subject to temperatures well below freezing, the bulk temperature gradient through the snowpack controls the metamorphosis of the snow grains. This study used National Weather Service (NWS) station measurements of snow depth and air temperature from the Northern US Great Plains to determine temporal and spatial variability of the snow depth and bulk snowpack temperature gradient. This region is well suited for this study because it consists primarily of open farmland or prairie, has little relief, is subject to very cold temperatures, and has more than 280 reporting stations. A geostatistical technique called Kriging was used to grid the randomly spaced snow depth measurements. The resulting snow depth maps were then compared with the passive microwave observations from the Special Sensor Microwave Imager (SSM/I). Two snow seasons were examined: 1988-89, a typical snow year, and 1996-97, a record year for snow that was responsible for extensive flooding in the Red River Basin. Inspection of the time series of snow depth and microwave spectral gradient (the difference between the 19 and 37 GHz bands) showed that while the snowpack was constant, the spectral gradient continued to increase. However, there was a strong correlation (0.6 < R2 < 0.9) between the spectral gradient and the cumulative bulk temperature gradient through the snowpack (TGI). Hence, TGI is an index of grain size metamorphism that has occurred within the snowpack. TGI time series from 21 representative sites across the region and the corresponding SSM/I observations were used to develop an algorithm for snow depth that requires daily air temperatures. Copyright ?? 2002 John Wiley & Sons, Ltd.

Optical Algorithm for Cloud Shadow Detection Over Water

DTIC Science & Technology

2013-02-01

REPORT DATE (DD-MM-YYYY) 05-02-2013 2. REPORT TYPE Journal Article 3. DATES COVERED (From ■ To) 4. TITLE AND SUBTITLE Optical Algorithm for Cloud...particularly over humid tropical regions. Throughout the year, about two-thirds of the Earth’s surface is always covered by clouds [1]. The problem...V. Khlopenkov and A. P. Trishchenko, "SPARC: New cloud, snow , cloud shadow detection scheme for historical I-km AVHHR data over Canada," / Atmos

The classification of the Arctic Sea ice types and the determination of surface temperature using advanced very high resolution radiometer data

NASA Technical Reports Server (NTRS)

Massom, Robert; Comiso, Josefino C.

1994-01-01

The accurate quantification of new ice and open water areas and surface temperatures within the sea ice packs is a key to the realistic parameterization of heat, moisture, and turbulence fluxes between ocean and atmosphere in the polar regions. Multispectral NOAA advanced very high resolution radiometer/2 (AVHRR/2) satellite images are analyzed to evaluate how effectively the data can be used to characterize sea ice in the Bering and Greenland seas, both in terms of surface type and physical temperature. The basis of the classification algorithm, which is developed using a late wintertime Bering Sea ice cover data, is that frequency distributions of 10.8- micrometers radiances provide four distinct peaks, represeting open water, new ice, young ice, and thick ice with a snow cover. The results are found to be spatially and temporally consistent. Possible sources of ambiguity, especially associated with wider temporal and spatial application of the technique, are discussed. An ice surface temperature algorithm is developed for the same study area by regressing thermal infrared data from 10.8- and 12.0- micrometers channels against station air temperatures, which are assumed to approximate the skin temperatures of adjacent snow and ice. The standard deviations of the results when compared with in situ data are about 0.5 K over leads and polynyas to about 0.5-1.5 K over thick ice. This study is based upon a set of in situ data limited in scope and coverage. Cloud masks are applied using a thresholding technique that utilizes 3.74- and 10.8- micrometers channel data. The temperature maps produced show coherence with surface features like new ice and leads, and consistency with corresponding surface type maps. Further studies are needed to better understand the effects of both the spatial and temporal variability in emissivity, aerosol and precipitable atmospheric ice particle distribution, and atmospheric temperature inversions.

Do we need a dynamic snow depth threshold when comparing hydrological models with remote sensing products in mountain catchments?

NASA Astrophysics Data System (ADS)

Engel, Michael; Bertoldi, Giacomo; Notarnicola, Claudia; Comiti, Francesco

2017-04-01

To assess the performance of simulated snow cover of hydrological models, it is common practice to compare simulated data with observed ones derived from satellite images such as MODIS. However, technical and methodological limitations such as data availability of MODIS products, its spatial resolution or difficulties in finding appropriate parameterisations of the model need to be solved previously. Another important assumption usually made is the threshold of minimum simulated snow depth, generally set to 10 mm of snow depth, to respect the MODIS detection thresholds for snow cover. But is such a constant threshold appropriate for complex alpine terrain? How important is the impact of different snow depth thresholds on the spatial and temporal distribution of the pixel-based overall accuracy (OA)? To address this aspect, we compared the snow covered area (SCA) simulated by the GEOtop 2.0 snow model to the daily composite 250 m EURAC MODIS SCA in the upper Saldur basin (61 km2, Eastern Italian Alps) during the period October 2011 - October 2013. Initially, we calibrated the snow model against snow depths and snow water equivalents at point scale, taken from measurements at different meteorological stations. We applied different snow depth thresholds (0 mm, 10 mm, 50 mm, and 100 mm) to obtain the simulated snow cover and assessed the changes in OA both in time (during the entire evaluation period, accumulation and melting season) and space (entire catchment and specific areas of topographic characteristics such as elevation, slope, aspect, landcover, and roughness). Results show remarkable spatial and temporal differences in OA with respect to different snow depth thresholds. Inaccuracies of simulated and observed SCA during the accumulation season September to November 2012 were located in areas with north-west aspect, slopes of 30° or little elevation differences at sub-pixel scale (-0.25 to 0 m). We obtained best agreements with MODIS SCA for a snow depth threshold of 100 mm, leading to increased OA (> 0.8) in 13‰ of the catchment area. SCA agreement in January 2012 and 2013 was slightly limited by MODIS sensor detection due to shading effects and low illumination in areas exposed north-west to north. On the contrary, during the melting season in April 2013 and after the September 2013 snowfall event seemed to depend more on parameterisation than on snow depth thresholds. In contrast, inaccuracies during the melting season March to June 2013 could hardly be attributed to topographic characteristics and different snow depth thresholds but rather on model parameterisation. We identified specific conditions (p.e. specific snowfall events in autumn 2012 and spring 2013) when either MODIS data or the hydrological model was less accurate, thus justifying the need for improvements of precision in the snow cover detection algorithms or in the model's process description. In consequence, our study observations could support future snow cover evaluations in mountain areas, where spatially and temporally dynamic snow depth thresholds are transferred from the catchment scale to the regional scale. Keywords: snow cover, snow modelling, MODIS, snow depth sensitivity, alpine catchment

Snow observations in Mount Lebanon (2011-2016)

NASA Astrophysics Data System (ADS)

Fayad, Abbas; Gascoin, Simon; Faour, Ghaleb; Fanise, Pascal; Drapeau, Laurent; Somma, Janine; Fadel, Ali; Bitar, Ahmad Al; Escadafal, Richard

2017-08-01

We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km2 and an elevation up to 3088 m). The dataset consists of (1) continuous meteorological and snow height observations, (2) snowpack field measurements, and (3) medium-resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA, 2296 m; LAQ, 1840 m; and CED, 2834 m a.s.l.) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 min intervals for the snow seasons (November-June) between 2011 and 2016 for MZA and between 2014 and 2016 for CED and LAQ. Precipitation data were filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a.s.l. during the two snow seasons of 2014-2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011-2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m-3. We find that the relationship between snow depth and snow density is specific to the Mediterranean climate. The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a.s.l.) and 62 % for high mountain regions (elevation 2200-2900 m a.s.l.). The dataset is suitable for the investigation of snow dynamics and for the forcing and validation of energy balance models. Therefore, this dataset bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the eastern Mediterranean. The DOI for the data is https://doi.org/10.5281/zenodo.583733.

Estimating snow depth of alpine snowpack via airborne multifrequency passive microwave radiance observations: Colorado, USA

NASA Astrophysics Data System (ADS)

Kim, R. S.; Durand, M. T.; Li, D.; Baldo, E.; Margulis, S. A.; Dumont, M.; Morin, S.

2017-12-01

This paper presents a newly-proposed snow depth retrieval approach for mountainous deep snow using airborne multifrequency passive microwave (PM) radiance observation. In contrast to previous snow depth estimations using satellite PM radiance assimilation, the newly-proposed method utilized single flight observation and deployed the snow hydrologic models. This method is promising since the satellite-based retrieval methods have difficulties to estimate snow depth due to their coarse resolution and computational effort. Indeed, this approach consists of particle filter using combinations of multiple PM frequencies and multi-layer snow physical model (i.e., Crocus) to resolve melt-refreeze crusts. The method was performed over NASA Cold Land Processes Experiment (CLPX) area in Colorado during 2002 and 2003. Results showed that there was a significant improvement over the prior snow depth estimates and the capability to reduce the prior snow depth biases. When applying our snow depth retrieval algorithm using a combination of four PM frequencies (10.7,18.7, 37.0 and 89.0 GHz), the RMSE values were reduced by 48 % at the snow depth transects sites where forest density was less than 5% despite deep snow conditions. This method displayed a sensitivity to different combinations of frequencies, model stratigraphy (i.e. different number of layering scheme for snow physical model) and estimation methods (particle filter and Kalman filter). The prior RMSE values at the forest-covered areas were reduced by 37 - 42 % even in the presence of forest cover.

Relationships between nocturnal winter road slipperiness, cloud cover and surface temperature

NASA Astrophysics Data System (ADS)

Grimbacher, T.; Schmid, W.

2003-04-01

Ice and Snow are important risks for road traffic. In this study we show several events of slipperiness in Switzerland, mainly caused by rain or snow falling on a frozen surface. Other reasons for slippery conditions are frost or freezing dew in clear nights and nocturnal clearing after precipitation, which goes along with radiative cooling. The main parameters of road weather forecasts are precipitation, cloudiness and surface temperature. Precipitation is well predictable with weather radars and radar nowcasting algorithms. Temperatures are often taken from numerical weather prediction models, but because of changes in cloud cover these model values are inaccurate in terms of predicting the onset of freezing. Cloudiness, especially the advection, formation and dissipation of clouds and their interaction with surface temperatures, is one of the major unsolved problems of road weather forecasts. Cloud cover and the temperature difference between air and surface temperature are important parameters of the radiation balance. In this contribution, we show the relationship between them, proved at several stations all over Switzerland. We found a quadratic correlation coefficient of typically 60% and improved it considering other meteorological parameters like wind speed and surface water. The acquired relationship may vary from one station to another, but we conclude that temperature difference is a signature for nocturnal cloudiness. We investigated nocturnal cloudiness for two cases from winters 2002 and 2003 in the canton of Lucerne in central Switzerland. There, an ultra-dense combination of two networks with together 55 stations within 50x50 km^2 is operated, measuring air and surface temperature, wind and other road weather parameters. With the aid of our equations, temperature differences detected from this network were converted into cloud maps. A comparison between precipitation seen by radar, cloud maps and surface temperatures shows that there are similar structures in all data. Depending on the situation, we also identified additional effects influencing the temperature differences, for instance the advection of could air or the influence of melting heat at or after a snow event. All these findings help to further understand the phenomena, and hence will contribute to a better predictability of winter road slipperiness.

Coupling of a Simple 3-Layer Snow Model to GISS GCM

NASA Astrophysics Data System (ADS)

Aleinov, I.

2001-12-01

Appropriate simulation of the snow cover dynamics is an important issue for the General Circulation Models (GCMs). The presence of snow has a significant impact on ground albedo and on heat and moisture balance. A 3-layer snow model similar to the one proposed by Lynch-Stieglitz was developed with the purpose of using it inside the GCM developed in the NASA Goddard Institute for Space Studies (GISS). The water transport between the layers is modeled explicitly while the heat balance is computed implicitly between the snow layers and semi-implicitly on the surface. The processes of melting and refreezing and compactification of layers under the gravitational force are modeled appropriately. It was noticed that implicit computation of the heat transport can cause a significant under- or over-estimation of the incoming heat flux when the temperature of the upper snow layer is equal to 0 C. This may lead in particular to delayed snow melting in spring. To remedy this problem a special flux-control algorithm was added to the model, which checks computed flux for possible errors and if such are detected the heat transport is recomputed again with the appropriate corrections. The model was tested off-line with Sleepers River forcing data and exhibited a good agreement between simulated and observed quantities for snow depth, snow density and snow temperature. The model was then incorporated into the GISS GCM. Inside the GCM the model is driven completely by the data simulated by other parts of the GCM. The screening effect of the vegetation is introduced by means of masking depth. For a thin snowpack a fractional cover is implemented so that the total thickness of the the snow is never less then 10 cm (rather, the areal fraction of the snow cover decreases when it melts). The model was tested with 6 year long GCM speed-up runs. It proved to be stable and produced reasonable results for the global snow cover. In comparison to the old GISS GCM snow model (which was incorporating the snow into the first soil layer) the new snow model has better insulating properties, thus preventing the ground from overcooling in winter. It also provides better simulation for water retention and release by the snow which results in more physical ground water runoff.

A Supplementary Clear-Sky Snow and Ice Recognition Technique for CERES Level 2 Products

NASA Technical Reports Server (NTRS)

Radkevich, Alexander; Khlopenkov, Konstantin; Rutan, David; Kato, Seiji

2013-01-01

Identification of clear-sky snow and ice is an important step in the production of cryosphere radiation budget products, which are used in the derivation of long-term data series for climate research. In this paper, a new method of clear-sky snow/ice identification for Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. The algorithm's goal is to enhance the identification of snow and ice within the Clouds and the Earth's Radiant Energy System (CERES) data after application of the standard CERES scene identification scheme. The input of the algorithm uses spectral radiances from five MODIS bands and surface skin temperature available in the CERES Single Scanner Footprint (SSF) product. The algorithm produces a cryosphere rating from an aggregated test: a higher rating corresponds to a more certain identification of the clear-sky snow/ice-covered scene. Empirical analysis of regions of interest representing distinctive targets such as snow, ice, ice and water clouds, open waters, and snow-free land selected from a number of MODIS images shows that the cryosphere rating of snow/ice targets falls into 95% confidence intervals lying above the same confidence intervals of all other targets. This enables recognition of clear-sky cryosphere by using a single threshold applied to the rating, which makes this technique different from traditional branching techniques based on multiple thresholds. Limited tests show that the established threshold clearly separates the cryosphere rating values computed for the cryosphere from those computed for noncryosphere scenes, whereas individual tests applied consequently cannot reliably identify the cryosphere for complex scenes.

Contribution of high resolution remote sensing data to the modeling of the snow cover the in Atlas Mountains

NASA Astrophysics Data System (ADS)

Baba, Wassim; Gascoin, Simon; Hanich, Lahoucine; Kinnard, Christophe

2017-04-01

Snow melt from the Atlas Mountains watersheds represent an important water resource for the semi-arid, cultivated, lowlands. Due to the high incoming solar radiation and low precipitation, the spatial-temporal variability of the snowpack is expected to be strongly influenced by the topography. We explore this hypothesis using a distributed energy balance snow model (SnowModel) in the experimental watershed of the Rheraya River in Morocco (225 km2). The digital elevation model (DEM) in SnowModel is used for the computation of the gridded meteorological forcing from the automatic weather stations data. We acquired three Pléiades stereo pairs in to produce an accurate, high resolution DEM of the Rheraya watershed at 4 m posting. Then, the DEM was resampled to different spatial resolutions (8 m, 30 m, 90 m, 250 m and 500 m) to simulate the snowpack evolution over 2008-2009 snow season. As validation data we used a time series of 15 maps of the snow cover area (SCA) from Formosat-2 imagery over the same snow season in the upper Rheraya watershed. These maps have a resolution of 8 m, which enables to capture small-scale variability in the snow cover. We found that the simulations at 90 m, 30 m and 8 m yield similar results at the catchment scale, with significant differences in areas of very steep topography only. From February to April, an overall good agreement was observed between the simulated SCA and the Formosat-2 SCA at 8 m and 90 m. Before the melting season, true positive (TP) column of confusion matrix is close to 1, but it drops to 0.6 during the melting season. Heidke Skill Score is higher than 0.7 for the most of the validation dates and averages 0.8. On the contrary, 500 m simulation underestimates the SCA throughout the snow season and the TP score is always inferior to the one obtained at 8 m and 90 m. We further analyzed the effect of topography by comparing the distribution of meteorological and snowpack variables along north-south and east-west transects. This analysis indicates that the impact of the topography on the simulated SWE and snow melt is mainly driven by changes in the solar radiations and the precipitations.

Cartographic modelling of aerotechnogenic pollution in snow cover in the landscapes of the Kola Peninsula.

PubMed

Ratkin, N E; Asming, V E; Koshkin, V V

2001-01-01

The goal of this work was to develop computational techniques for sulphates, nickel and copper accumulation in the snow in the local pollution zone. The main task was to reveal the peculiarities of formation and pollution of snow cover on the region with complex cross-relief. A digital cartographic model of aerotechnogenic pollution of snow cover in the landscapes of the local zone has been developed, based on five-year experimental data. Data regarding annual emissions from the industrial complex, information about distribution of wind and the sum of precipitation from meteostation "Nikel" for the winter period, allowed the model to ensure: * material presentation in the form of maps of water capacity and accumulation of sulphates, nickel and copper in the snow over any winter period in retrospective; * calculation of water capacity and accumulation of pollutants for watersheds and other natural-territorial complexes; * solution of the opposite problem about the determination of the emissions of sulphates, nickel and copper from the enterprise by measuring snow pollution in datum points. The model can be used in other northern regions of the Russian Federation with similar physical-geographical and climatic conditions. The relationships between the sum of precipitation and water capacity in the landscapes of the same type and also the relationships between pollution content in snow and relief, pollution content in snow and distance from the source of emissions, were used as the basis for the model.

Snow Radar Derived Surface Elevations and Snow Depths Multi-Year Time Series over Greenland Sea-Ice During IceBridge Campaigns

NASA Astrophysics Data System (ADS)

Perkovic-Martin, D.; Johnson, M. P.; Holt, B.; Panzer, B.; Leuschen, C.

2012-12-01

This paper presents estimates of snow depth over sea ice from the 2009 through 2011 NASA Operation IceBridge [1] spring campaigns over Greenland and the Arctic Ocean, derived from Kansas University's wideband Snow Radar [2] over annually repeated sea-ice transects. We compare the estimates of the top surface interface heights between NASA's Atmospheric Topographic Mapper (ATM) [3] and the Snow Radar. We follow this by comparison of multi-year snow depth records over repeated sea-ice transects to derive snow depth changes over the area. For the purpose of this paper our analysis will concentrate on flights over North/South basin transects off Greenland, which are the closest overlapping tracks over this time period. The Snow Radar backscatter returns allow for surface and interface layer types to be differentiated between snow, ice, land and water using a tracking and classification algorithm developed and discussed in the paper. The classification is possible due to different scattering properties of surfaces and volumes at the radar's operating frequencies (2-6.5 GHz), as well as the geometries in which they are viewed by the radar. These properties allow the returns to be classified by a set of features that can be used to identify the type of the surface or interfaces preset in each vertical profile. We applied a Support Vector Machine (SVM) learning algorithm [4] to the Snow Radar data to classify each detected interface into one of four types. The SVM algorithm was trained on radar echograms whose interfaces were visually classified and verified against coincident aircraft data obtained by CAMBOT [5] and DMS [6] imaging sensors as well as the scanning ATM lidar. Once the interface locations were detected for each vertical profile we derived a range to each interface that was used to estimate the heights above the WGS84 ellipsoid for direct comparisons with ATM. Snow Radar measurements were calibrated against ATM data over areas free of snow cover and over GPS land surveyed areas of Thule and Sondrestrom air bases. The radar measurements were compared against the ATM and the GPS measurements that were located in the estimated radar footprints, which resulted in an overall error of ~ 0.3 m between the radar and ATM. The agreement between ATM and GPS survey is within +/- 0.1 m. References: [1] http://www.nasa.gov/mission_pages/icebridge/ [2] Panzer, B. et. al, "An ultra-wideband, microwave radar for measuring snow thickness on sea ice and mapping near-surface internal layers in polar firn," Submitted to J. of Glaciology Instr. and Tech., July 23, 2012. [3] Krabill, William B. 2009 and 2011, updated current year. IceBridge ATM L1B Qfit Elevation and Return Strength. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. [4] Chih-Chung Chang and Chih-Jen Lin. "Libsvm: a library for support vector machines", ACM Transactions on Intelligent Systems and Technology, 2:2:27:1-27:27, 2011. [5] Krabill, William B. 2009 and 2011, updated current year. IceBridge CAMBOT L1B Geolocated Images, [2009-04-25, 2011-04-15]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. [6] Dominguez, Roseanne. 2011, updated current year. IceBridge DMS L1B Geolocated and Orthorectified Images. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media

Clear-Sky Narrowband Albedo Datasets Derived from Modis Data

NASA Astrophysics Data System (ADS)

Chen, Y.; Minnis, P.; Sun-Mack, S.; Arduini, R. F.; Hong, G.

2013-12-01

Satellite remote sensing of clouds requires an accurate estimate of the clear-sky radiances for a given scene to detect clouds and aerosols and to retrieve their microphysical properties. Knowing the spatial and angular variability of clear-sky albedo is essential for predicting the clear-sky radiance at solar wavelengths. The Clouds and the Earth's Radiant Energy System (CERES) Project uses the near-infrared (NIR; 1.24, 1.6 or 2.13 μm) and visible (VIS; 0.63 μm) channels available on the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS) to help identify clouds and retrieve their properties. Generally, clear-sky albedo for a given surface type is determined for conditions when the vegetation is either thriving or dormant and free of snow. The clear-sky albedos are derived using a radiative transfer parameterization of the impact of the atmosphere, including aerosols, on the observed reflectances. This paper presents the method of generating monthly clear-sky overhead albedo maps for both snow-free and snow-covered surfaces of these channels using one year of MODIS (Moderate Resolution Imaging Spectroradiometer) CERES products. Maps of 1.24 and 1.6 μm are being used as the background to help retrieve cloud properties (e.g., effective particle size, optical depth) in CERES cloud retrievals in both snow-free and snow-covered conditions.

NOAA's National Snow Analyses

NASA Astrophysics Data System (ADS)

Carroll, T. R.; Cline, D. W.; Olheiser, C. M.; Rost, A. A.; Nilsson, A. O.; Fall, G. M.; Li, L.; Bovitz, C. T.

2005-12-01

NOAA's National Operational Hydrologic Remote Sensing Center (NOHRSC) routinely ingests all of the electronically available, real-time, ground-based, snow data; airborne snow water equivalent data; satellite areal extent of snow cover information; and numerical weather prediction (NWP) model forcings for the coterminous U.S. The NWP model forcings are physically downscaled from their native 13 km2 spatial resolution to a 1 km2 resolution for the CONUS. The downscaled NWP forcings drive an energy-and-mass-balance snow accumulation and ablation model at a 1 km2 spatial resolution and at a 1 hour temporal resolution for the country. The ground-based, airborne, and satellite snow observations are assimilated into the snow model's simulated state variables using a Newtonian nudging technique. The principle advantages of the assimilation technique are: (1) approximate balance is maintained in the snow model, (2) physical processes are easily accommodated in the model, and (3) asynoptic data are incorporated at the appropriate times. The snow model is reinitialized with the assimilated snow observations to generate a variety of snow products that combine to form NOAA's NOHRSC National Snow Analyses (NSA). The NOHRSC NSA incorporate all of the available information necessary and available to produce a "best estimate" of real-time snow cover conditions at 1 km2 spatial resolution and 1 hour temporal resolution for the country. The NOHRSC NSA consist of a variety of daily, operational, products that characterize real-time snowpack conditions including: snow water equivalent, snow depth, surface and internal snowpack temperatures, surface and blowing snow sublimation, and snowmelt for the CONUS. The products are generated and distributed in a variety of formats including: interactive maps, time-series, alphanumeric products (e.g., mean areal snow water equivalent on a hydrologic basin-by-basin basis), text and map discussions, map animations, and quantitative gridded products. The NOHRSC NSA products are used operationally by NOAA's National Weather Service field offices when issuing hydrologic forecasts and warnings including river and flood forecasts, water supply forecasts, and spring flood outlooks for the nation. Additionally, the NOHRSC NSA products are used by a wide variety of federal, state, local, municipal, private-sector, and general-public end-users with a requirement for real-time snowpack information. The paper discusses, in detail, the techniques and procedures used to create the NOHRSC NSA products and gives a number of examples of the real-time snow products generated and distributed over the NOHRSC web site (www.nohrsc.noaa.gov). Also discussed are major limitations of the approach, the most notable being deficiencies in observation of snow water equivalent. Snow observation networks generally lack the consistency and coverage needed to significantly improve confidence in snow model states through updating. Many regions of the world simply lack snow water equivalent observations altogether, a significant constraint on global application of the NSA approach.

Microwave signatures of snow and fresh water ice

NASA Technical Reports Server (NTRS)

Schmugge, T.; Wilheit, T. T.; Gloersen, P.; Meier, M. F.; Frank, D.; Dirmhirn, I.

1973-01-01

During March of 1971, the NASA Convair 990 Airborne Observatory carrying microwave radiometers in the wavelength range 0.8 to 21 cm was flown over dry snow with different substrata: Lake ice at Bear Lake in Utah; wet soil in the Yampa River Valley near Steamboat Springs, Colorado; and glacier ice, firm and wet snow on the South Cascade Glacier in Washington. The data presented indicate that the transparency of the snow cover is a function of wavelength. False-color images of microwave brightness temperatures obtained from a scanning radiometer operating at a wavelength of 1.55 cm demonstrate the capability of scanning radiometers for mapping snowfields.

Preliminary applications of Landsat images and aerial photography for determining land-use, geologic, and hydrologic characteristics, Yampa River basin, Colorado and Wyoming

USGS Publications Warehouse

Heimes, F.J.; Moore, G.K.; Steele, T.D.

1978-01-01

Expanded energy- and recreation-related activities in the Yampa River basin, Colorado and Wyoming, have caused a rapid increase in economic development which will result in increased demand and competition for natural resources. In planning for efficient allocation of the basin 's natural resources, Landsat images and small-scale color and color-infrared photographs were used for selected geologic, hydrologic and land-use applications within the Yampa River basin. Applications of Landsat data included: (1) regional land-use classification and mapping, (2) lineament mapping, and (3) areal snow-cover mapping. Results from the Landsat investigations indicated that: (1) Landsat land-use classification maps, at a regional level, compared favorably with areal land-use patterns that were defined from available ground information, (2) lineaments were mapped in sufficient detail using recently developed techniques for interpreting aerial photographs, (3) snow cover generally could be mapped for large areas with the exception of some densely forested areas of the basin and areas having a large percentage of winter-season cloud cover. Aerial photographs were used for estimation of turbidity for eight stream locations in the basin. Spectral reflectance values obtained by digitizing photographs were compared with measured turbidity values. Results showed strong correlations (variances explained of greater than 90 percent) between spectral reflectance obtained from color photographs and measured turbidity values. (Woodard-USGS)

Enhancement of the MODIS Snow and Ice Product Suite Utilizing Image Segmentation

NASA Technical Reports Server (NTRS)

Tilton, James C.; Hall, Dorothy K.; Riggs, George A.

2006-01-01

A problem has been noticed with the current NODIS Snow and Ice Product in that fringes of certain snow fields are labeled as "cloud" whereas close inspection of the data indicates that the correct labeling is a non-cloud category such as snow or land. This occurs because the current MODIS Snow and Ice Product generation algorithm relies solely on the MODIS Cloud Mask Product for the labeling of image pixels as cloud. It is proposed here that information obtained from image segmentation can be used to determine when it is appropriate to override the cloud indication from the cloud mask product. Initial tests show that this approach can significantly reduce the cloud "fringing" in modified snow cover labeling. More comprehensive testing is required to determine whether or not this approach consistently improves the accuracy of the snow and ice product.

Simulation of Surface Energy Fluxes and Snow Interception Using a Higher Order Closure Multi-Layer Soil-Vegetation-Atmospheric Model: The Effect of Canopy Shape and Structure

NASA Astrophysics Data System (ADS)

McGowan, L. E.; Dahlke, H. E.; Paw U, K. T.

2015-12-01

Snow cover is a critical driver of the Earth's surface energy budget, climate change, and water resources. Variations in snow cover not only affect the energy budget of the land surface but also represent a major water supply source. In California, US estimates of snow depth, extent, and melt in the Sierra Nevada are critical to estimating the amount of water available for both California agriculture and urban users. However, accurate estimates of snow cover and snow melt processes in forested area still remain a challenge. Canopy structure influences the vertical and spatiotemporal distribution of snow, and therefore ultimately determines the degree and extent by which snow alters both the surface energy balance and water availability in forested regions. In this study we use the Advanced Canopy-Atmosphere-Soil algorithm (ACASA), a multi-layer soil-vegetation-atmosphere numerical model, to simulate the effect of different snow-covered canopy structures on the energy budget, and temperature and other scalar profiles within different forest types in the Sierra Nevada, California. ACASA incorporates a higher order turbulence closure scheme which allows the detailed simulation of turbulent fluxes of heat and water vapor as well as the CO2 exchange of several layers within the canopy. As such ACASA can capture the counter gradient fluxes within canopies that may occur frequently, but are typically unaccounted for, in most snow hydrology models. Six different canopy types were modeled ranging from coniferous forests (e.g. most biomass near the ground) to top-heavy (e.g. most biomass near the top of the crown) deciduous forests to multi-layered forest canopies (e.g. mixture of young and mature trees). Preliminary results indicate that the canopy shape and structure associated with different canopy types fundamentally influence the vertical scalar profiles (including those of temperature, moisture, and wind speed) in the canopy and thus alter the interception and snow melt dynamics in forested land surfaces. The turbulent transport dynamics, including counter-gradient fluxes, and radiation features including land surface albedo, are discussed in the context of the snow energy balance.

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J. (Principal Investigator); Martin, K. R.; Amato, R. V.

1973-01-01

The author has identified the following significant results. Detection and analysis of fracture systems can be more effectively conducted utilizing snow cover as an enhancement tool. From analysis within the Great Barrington Test Site it appears that the use of aeromagnetic data effectively supplements lineament data acquired using ERTS imagery. Coincidence of lineaments derived from aeromagnetics with lineaments interpreted from ERTS imagery apparently indicate the presence of mineralized fracture systems and dikes. Utilizing both tools can increase the speed and efficiency of mineral exploration and geological mapping in areas where bedrock is obscured by a thick unconsolidated sediment cover.

Satellites: New global observing techniques for ice and snow. [using erts-1 and nimbus 5 satellite

NASA Technical Reports Server (NTRS)

Gloersen, P.; Salomonson, V. V.

1974-01-01

The relation of aereal extent of snow cover to the average monthly runoff in a given watershed was investigated by comparing runoff records with a series of snowcover maps. Studies using the high spatial resolution available with ERTS-1 imagery were carried out for the Wind River Mountains watersheds in Wyoming, where it was found that the empirical relationship varied with mean elevation of the watershed. In addition, digital image enhancement techniques are shown to be useful for identifying glacier features related to extent of snowcover, moraine characteristics, and debris average. Longer wavelength observations using sensors on board the Nimbus 5 Satellite are shown to be useful for indicating crystal size distributions and onset of melting on glacier snow cover.

LANDSAT-4 Science Characterization Early Results. Volume 4: Applications. [agriculture, soils land use, geology, hydrology, wetlands, water quality, biomass identification, and snow mapping

NASA Technical Reports Server (NTRS)

Barker, J. L. (Editor)

1985-01-01

The excellent quality of TM data allows researchers to proceed directly with applications analyses, without spending a significant amount of time applying various corrections to the data. The early results derived of TM data are discussed for the following applications: agriculture, land cover/land use, soils, geology, hydrology, wetlands biomass, water quality, and snow.

Impacts of Early Summer Eurasian Snow Cover Change on Atmospheric Circulation in Northern Mid-Latitudes

NASA Astrophysics Data System (ADS)

Nozawa, T.

2016-12-01

Recently, Japan Aerospace Exploration Agency (JAXA) has developed a new long-term snow cover extent (SCE) product using Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data spanning from 1980's to date. This new product (JAXA/SCE) has higher spatial resolution and smaller commission error compared with traditional SCE dataset of National Oceanic and Atmospheric Administration (NOAA/SCE). Continuity of the algorithm is another strong point in JAXA/SCE. According to the new JAXA/SCE dataset, the Eurasian SCE has been significantly retreating since 1980's, especially in late spring and early summer. Here, we investigate impacts of early summer Eurasian snow cover change on atmospheric circulation in Northern mid-latitudes, especially over the East Asia, using the new JAXA/SCE dataset and a few reanalysis data. We will present analyzed results on relationships between early summer SCE anomaly over the Eurasia and changes in atmospheric circulations such as upper level zonal jets (changes in strength, positions, etc.) over the East Asia.

Quantifying forest mortality with the remote sensing of snow

NASA Astrophysics Data System (ADS)

Baker, Emily Hewitt

Greenhouse gas emissions have altered global climate significantly, increasing the frequency of drought, fire, and pest-related mortality in forests across the western United States, with increasing area affected each year. Associated changes in forests are of great concern for the public, land managers, and the broader scientific community. These increased stresses have resulted in a widespread, spatially heterogeneous decline of forest canopies, which in turn exerts strong controls on the accumulation and melt of the snowpack, and changes forest-atmosphere exchanges of carbon, water, and energy. Most satellite-based retrievals of summer-season forest data are insufficient to quantify canopy, as opposed to the combination of canopy and undergrowth, since the signals of the two types of vegetation greenness have proven persistently difficult to distinguish. To overcome this issue, this research develops a method to quantify forest canopy cover using winter-season fractional snow covered area (FSCA) data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) snow covered area and grain size (MODSCAG) algorithm. In areas where the ground surface and undergrowth are completely snow-covered, a pixel comprises only forest canopy and snow. Following a snowfall event, FSCA initially rises, as snow is intercepted in the canopy, and then falls, as snow unloads. A select set of local minima in a winter F SCA timeseries form a threshold where canopy is snow-free, but forest understory is snow-covered. This serves as a spatially-explicit measurement of forest canopy, and viewable gap fraction (VGF) on a yearly basis. Using this method, we determine that MODIS-observed VGF is significantly correlated with an independent product of yearly crown mortality derived from spectral analysis of Landsat imagery at 25 high-mortality sites in northern Colorado. (r =0.96 +/-0.03, p =0.03). Additionally, we determine the lag timing between green-stage tree mortality and needlefall, showing that needlefall occurred an average of 2.6 +/- 1.2 years after green-stage mortality. We relate observed increases in the VGF with crown mortality, showing that a 1% increase in mortality area produces a 0.33 +/- 0.1 % increase in the VGF.

Freeze/thaw conditions at periglacial landforms in Kapp Linné, Svalbard, investigated using field observations, in situ, and radar satellite monitoring

NASA Astrophysics Data System (ADS)

Eckerstorfer, M.; Malnes, E.; Christiansen, H. H.

2017-09-01

In periglacial landscapes, snow dynamics and microtopography have profound implications of freeze-thaw conditions and thermal regime of the ground. We mapped periglacial landforms at Kapp Linné, central Svalbard, where we chose six widespread landforms (solifluction sheet, nivation hollow, palsa and peat in beach ridge depressions, raised marine beach ridge, and exposed bedrock ridge) as study sites. At these six landforms, we studied ground thermal conditions, freeze-thaw cycles, and snow dynamics using a combination of in situ monitoring and C-band radar satellite data in the period 2005-2012. Based on these physical parameters, the six studied landforms can be classified into raised, dry landforms with minor ground ice content and a thin, discontinuous snow cover and into wet landforms with high ice content located in the topographical depressions in-between with medium to thick snow cover. This results in a differential snow-melting period inferred from the C-band radar satellite data, causing the interseasonal and interlandform variability in the onset of ground surface thawing once the ground becomes snow free. Therefore, variability also exists in the period of thawed ground surface conditions. However, the length of the season with thawed ground surface conditions does not determine the mean annual ground surface temperature, it only correlates well with the active layer depths. From the C-band radar satellite data series, measured relative backscatter trends hint toward a decrease in snow cover through time and a more frequent presence of ice layers from mid-winter rain on snow events at Kapp Linné, Svalbard.

Investigation of the 3-D actinic flux field in mountainous terrain

PubMed Central

Wagner, J.E.; Angelini, F.; Blumthaler, M.; Fitzka, M.; Gobbi, G.P.; Kift, R.; Kreuter, A.; Rieder, H.E.; Simic, S.; Webb, A.; Weihs, P.

2011-01-01

During three field campaigns spectral actinic flux was measured from 290–500 nm under clear sky conditions in Alpine terrain and the associated O3- and NO2-photolysis frequencies were calculated and the measurement products were then compared with 1-D- and 3-D-model calculations. To do this 3-D-radiative transfer model was adapted for actinic flux calculations in mountainous terrain and the maps of the actinic flux field at the surface, calculated with the 3-D-radiative transfer model, are given. The differences between the 3-D- and 1-D-model results for selected days during the campaigns are shown, together with the ratios of the modeled actinic flux values to the measurements. In many cases the 1-D-model overestimates actinic flux by more than the measurement uncertainty of 10%. The results of using a 3-D-model generally show significantly lower values, and can underestimate the actinic flux by up to 30%. This case study attempts to quantify the impact of snow cover in combination with topography on spectral actinic flux. The impact of snow cover on the actinic flux was ~ 25% in narrow snow covered valleys, but for snow free areas there were no significant changes due snow cover in the surrounding area and it is found that the effect snow-cover at distances over 5 km from the point of interest was below 5%. Overall the 3-D-model can calculate actinic flux to the same accuracy as the 1-D-model for single points, but gives a much more realistic view of the surface actinic flux field in mountains as topography and obstruction of the horizon are taken into account. PMID:26412915

Development of a MODIS-Derived Surface Albedo Data Set: An Improved Model Input for Processing the NSRDB

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maclaurin, Galen; Sengupta, Manajit; Xie, Yu

A significant source of bias in the transposition of global horizontal irradiance to plane-of-array (POA) irradiance arises from inaccurate estimations of surface albedo. The current physics-based model used to produce the National Solar Radiation Database (NSRDB) relies on model estimations of surface albedo from a reanalysis climatalogy produced at relatively coarse spatial resolution compared to that of the NSRDB. As an input to spectral decomposition and transposition models, more accurate surface albedo data from remotely sensed imagery at finer spatial resolutions would improve accuracy in the final product. The National Renewable Energy Laboratory (NREL) developed an improved white-sky (bi-hemispherical reflectance)more » broadband (0.3-5.0 ..mu..m) surface albedo data set for processing the NSRDB from two existing data sets: a gap-filled albedo product and a daily snow cover product. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Terra and Aqua satellites have provided high-quality measurements of surface albedo at 30 arc-second spatial resolution and 8-day temporal resolution since 2001. The high spatial and temporal resolutions and the temporal coverage of the MODIS sensor will allow for improved modeling of POA irradiance in the NSRDB. However, cloud and snow cover interfere with MODIS observations of ground surface albedo, and thus they require post-processing. The MODIS production team applied a gap-filling methodology to interpolate observations obscured by clouds or ephemeral snow. This approach filled pixels with ephemeral snow cover because the 8-day temporal resolution is too coarse to accurately capture the variability of snow cover and its impact on albedo estimates. However, for this project, accurate representation of daily snow cover change is important in producing the NSRDB. Therefore, NREL also used the Integrated Multisensor Snow and Ice Mapping System data set, which provides daily snow cover observations of the Northern Hemisphere for the temporal extent of the NSRDB (1998-2015). We provide a review of validation studies conducted on these two products and describe the methodology developed by NREL to remap the data products to the NSRDB grid and integrate them into a seamless daily data set.« less

Operational satellites and the global monitoring of snow and ice

NASA Technical Reports Server (NTRS)

Walsh, John E.

1991-01-01

The altitudinal dependence of the global warming projected by global climate models is at least partially attributable to the albedo-temperature feedback involving snow and ice, which must be regarded as key variables in the monitoring for global change. Statistical analyses of data from IR and microwave sensors monitoring the areal coverage and extent of sea ice have led to mixed conclusions about recent trends of hemisphere sea ice coverage. Seasonal snow cover has been mapped for over 20 years by NOAA/NESDIS on the basis of imagery from a variety of satellite sensors. Multichannel passive microwave data show some promise for the routine monitoring of snow depth over unforested land areas.

Satellite geological and geophysical remote sensing of Iceland

NASA Technical Reports Server (NTRS)

Williams, R. S., Jr. (Principal Investigator)

1974-01-01

The author has identified the following significant results. ERTS-1 imagery provides sufficient resolution to discern two effects of geothermal activity at the Namafjall geothermal area: snowmelt anomalies and delineation of altered ground. The fallout pattern of tephra from Hekla's 1970 volcanic eruption can be mapped where sufficient depth of deposition destroyed the vegetation. Lava flows from the volcanic eruptions at Askja and Hekla can be delineated. Low sun-angle imagery of snow-covered terrain has permitted the mapping of new structural and volcanic features beneath the icecaps. Coastline changes on the islands of Surtsey and Heimaey can be mapped. Variations of sediment plumes from glacial rivers on the south coast give a qualitative indication of seasonal changes in melting rates of glaciers. ERTS-1 imagery has been shown to be especially amenable to portrayal of changing glaciological phenomena: surging glaciers, collapse features in icecaps caused by subglacial volcanic (?) and geothermal activity and resulting jokulhlaups, and variations in size of glacier-margin lakes. A fifth vegetation class has now been added: lichen-covered bedrock. The high latitude permits more precise analysis of landforms, vegetation distribution, occurrence of snow cover, glaciers, and geologic structure.

Airborne radar surveys of snow depth over Antarctic sea ice during Operation IceBridge

NASA Astrophysics Data System (ADS)

Panzer, B.; Gomez-Garcia, D.; Leuschen, C.; Paden, J. D.; Gogineni, P. S.

2012-12-01

Over the last decade, multiple satellite-based laser and radar altimeters, optimized for polar observations, have been launched with one of the major objectives being the determination of global sea ice thickness and distribution [5, 6]. Estimation of sea-ice thickness from these altimeters relies on freeboard measurements and the presence of snow cover on sea ice affects this estimate. Current means of estimating the snow depth rely on daily precipitation products and/or data from passive microwave sensors [2, 7]. Even a small uncertainty in the snow depth leads to a large uncertainty in the sea-ice thickness estimate. To improve the accuracy of the sea-ice thickness estimates and provide validation for measurements from satellite-based sensors, the Center for Remote Sensing of Ice Sheets deploys the Snow Radar as a part of NASA Operation IceBridge. The Snow Radar is an ultra-wideband, frequency-modulated, continuous-wave radar capable of resolving snow depth on sea ice from 5 cm to more than 2 meters from long-range, airborne platforms [4]. This paper will discuss the algorithm used to directly extract snow depth estimates exclusively using the Snow Radar data set by tracking both the air-snow and snow-ice interfaces. Prior work in this regard used data from a laser altimeter for tracking the air-snow interface or worked under the assumption that the return from the snow-ice interface was greater than that from the air-snow interface due to a larger dielectric contrast, which is not true for thick or higher loss snow cover [1, 3]. This paper will also present snow depth estimates from Snow Radar data during the NASA Operation IceBridge 2010-2011 Antarctic campaigns. In 2010, three sea ice flights were flown, two in the Weddell Sea and one in the Amundsen and Bellingshausen Seas. All three flight lines were repeated in 2011, allowing an annual comparison of snow depth. In 2011, a repeat pass of an earlier flight in the Weddell Sea was flown, allowing for a comparison of snow depths with two weeks elapsed between passes. [1] Farrell, S.L., et al., "A First Assessment of IceBridge Snow and Ice Thickness Data Over Arctic Sea Ice," IEEE Tran. Geoscience and Remote Sensing, Vol. 50, No. 6, pp. 2098-2111, June 2012. [2] Kwok, R., and G. F. Cunningham, "ICESat over Arctic sea ice: Estimation of snow depth and ice thickness," J. Geophys. Res., 113, C08010, 2008. [3] Kwok, R., et al., "Airborne surveys of snow depth over Arctic sea ice," J. Geophys. Res., 116, C11018, 2011. [4] Panzer, B., et al., "An ultra-wideband, microwave radar for measuring snow thickness on sea ice and mapping near-surface internal layers in polar firn," Submitted to J. Glaciology, July 23, 2012. [5] Wingham, D.J., et al., "CryoSat: A Mission to Determine the Fluctuations in Earth's Land and Marine Ice Fields," Advances in Space Research, Vol. 37, No. 4, pp. 841-871, 2006. [6] Zwally, H. J., et al., "ICESat's laser measurements of polar ice, atmosphere, ocean, and land," J. Geodynamics, Vol. 34, No. 3-4, pp. 405-445, Oct-Nov 2002. [7] Zwally, H. J., et al., "ICESat measurements of sea ice freeboard and estimates of sea ice thickness in the Weddell Sea," J. Geophys. Res., 113, C02S15, 2008.

Cloud-based Computing and Applications of New Snow Metrics for Societal Benefit

NASA Astrophysics Data System (ADS)

Nolin, A. W.; Sproles, E. A.; Crumley, R. L.; Wilson, A.; Mar, E.; van de Kerk, M.; Prugh, L.

2017-12-01

Seasonal and interannual variability in snow cover affects socio-environmental systems including water resources, forest ecology, freshwater and terrestrial habitat, and winter recreation. We have developed two new seasonal snow metrics: snow cover frequency (SCF) and snow disappearance date (SDD). These metrics are calculated at 500-m resolution using NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data (MOD10A1). SCF is the number of times snow is observed in a pixel over the user-defined observation period. SDD is the last date of observed snow in a water year. These pixel-level metrics are calculated rapidly and globally in the Google Earth Engine cloud-based environment. SCF and SDD can be interactively visualized in a map-based interface, allowing users to explore spatial and temporal snowcover patterns from 2000-present. These metrics are especially valuable in regions where snow data are sparse or non-existent. We have used these metrics in several ongoing projects. When SCF was linked with a simple hydrologic model in the La Laguna watershed in northern Chile, it successfully predicted summer low flows with a Nash-Sutcliffe value of 0.86. SCF has also been used to help explain changes in Dall sheep populations in Alaska where sheep populations are negatively impacted by late snow cover and low snowline elevation during the spring lambing season. In forest management, SCF and SDD appear to be valuable predictors of post-wildfire vegetation growth. We see a positive relationship between winter SCF and subsequent summer greening for several years post-fire. For western US winter recreation, we are exploring trends in SDD and SCF for regions where snow sports are economically important. In a world with declining snowpacks and increasing uncertainty, these metrics extend across elevations and fill data gaps to provide valuable information for decision-making. SCF and SDD are being produced so that anyone with Internet access and a Google account can access, visualize, and download the data with a minimum of technical expertise and no need for proprietary software.

Assessment of four methods to estimate surface UV radiation using satellite data, by comparison with ground measurements from four stations in Europe

NASA Astrophysics Data System (ADS)

Arola, Antti; Kalliskota, S.; den Outer, P. N.; Edvardsen, K.; Hansen, G.; Koskela, T.; Martin, T. J.; Matthijsen, J.; Meerkoetter, R.; Peeters, P.; Seckmeyer, G.; Simon, P. C.; Slaper, H.; Taalas, P.; Verdebout, J.

2002-08-01

Four different satellite-UV mapping methods are assessed by comparing them against ground-based measurements. The study includes most of the variability found in geographical, meteorological and atmospheric conditions. Three of the methods did not show any significant systematic bias, except during snow cover. The mean difference (bias) in daily doses for the Rijksinstituut voor Volksgezondheid en Milieu (RIVM) and Joint Research Centre (JRC) methods was found to be less than 10% with a RMS difference of the order of 30%. The Deutsches Zentrum für Luft- und Raumfahrt (DLR) method was assessed for a few selected months, and the accuracy was similar to the RIVM and JRC methods. It was additionally used to demonstrate how spatial averaging of high-resolution cloud data improves the estimation of UV daily doses. For the Institut d'Aéronomie Spatiale de Belgique (IASB) method the differences were somewhat higher, because of their original cloud algorithm. The mean difference in daily doses for IASB was about 30% or more, depending on the station, while the RMS difference was about 60%. The cloud algorithm of IASB has been replaced recently, and as a result the accuracy of the IASB method has improved. Evidence is found that further research and development should focus on the improvement of the cloud parameterization. Estimation of daily exposures is likely to be improved if additional time-resolved cloudiness information is available for the satellite-based methods. It is also demonstrated that further development work should be carried out on the treatment of albedo of snow-covered surfaces.

Response of Arctic Snow and Sea Ice Extents to Melt Season Atmospheric Forcing Across the Land-Ocean Boundary

NASA Astrophysics Data System (ADS)

Bliss, A. C.; Anderson, M. R.

2011-12-01

Little research has gone into studying the concurrent variations in the annual loss of continental snow cover and sea ice extent across the land-ocean boundary, however, the analysis of these data averaged spatially over three study regions located in North America and Eastern and Western Russia, reveals a distinct difference in the response of anomalous snow and sea ice conditions to the atmospheric forcing. This study compares the monthly continental snow cover and sea ice extent loss in the Arctic, during the melt season months (May-August) for the period 1979-2007, with regional atmospheric conditions known to influence summer melt including: mean sea level pressures, 925 hPa air temperatures, and mean 2 m U and V wind vectors from NCEP/DOE Reanalysis 2. The monthly hemispheric snow cover extent data used are from the Rutgers University Global Snow Lab and sea ice extents for this study are derived from the monthly passive microwave satellite Bootstrap algorithm sea ice concentrations available from the National Snow and Ice Data Center. Three case study years (1985, 1996, and 2007) are used to compare the direct response of monthly anomalous sea ice and snow cover areal extents to monthly mean atmospheric forcing averaged spatially over the extent of each study region. This comparison is then expanded for all summer months over the 29 year study period where the monthly persistence of sea ice and snow cover extent anomalies and changes in the sea ice and snow conditions under differing atmospheric conditions are explored further. The monthly anomalous atmospheric conditions are classified into four categories including: warmer temperatures with higher pressures, warmer temperatures with lower pressures, cooler temperatures with higher pressures, and cooler temperatures with lower pressures. Analysis of the atmospheric conditions surrounding anomalous loss of snow and ice cover over the independent study regions indicates that conditions of warmer temperatures advected via southerly winds are effective at forcing melt, while conditions of anomalously cool temperatures with persistent, strong northeasterly winds in the later melt season months are also effective at removing anomalous extents of sea ice cover, likely through ice divergence. Normalized sea ice extent anomalies, regardless of the snow cover, tend to persist in the same positive or negative directions (or remain near normal) from month to month over the summer season in 73.6% of cases from June to July, in 69% of cases from July to August, and in 54% of cases for the entire season (June-August) for the 29 year study period. However, when shifts in the sea ice extent anomaly directions from the conditions present in the early melt season occur, it is generally associated with a shift in the atmospheric conditions forcing the change in sea ice extent loss for the region.

Glaciological studies in the central Andes using AIRSAR/TOPSAR

NASA Technical Reports Server (NTRS)

Forster, Richard R.; Klein, Andrew G.; Blodgett, Troy A.; Isacks, Bryan L.

1993-01-01

The interaction of climate and topography in mountainous regions is dramatically expressed in the spatial distribution of glaciers and snowcover. Monitoring existing alpine glaciers and snow extent provides insight into the present mountain climate system and how it is changing, while mapping the positions of former glaciers as recorded in landforms such as cirques and moraines provide a record of the large past climate change associated with the last glacial maximum. The Andes are an ideal mountain range in which to study the response of snow and ice to past and present climate change. Their expansive latitudinal extent offers the opportunity to study glaciers in diverse climate settings from the tropical glaciers of Peru and Bolivia to the ice caps and tide-water glaciers of sub-polar Patagonia. SAR has advantages over traditional passive remote sensing instruments for monitoring present snow and ice and differentiating moraine relative ages. The cloud penetrating ability of SAR is indispensable for perennially cloud covered mountains. Snow and ice facies can be distinguished from SAR's response to surface roughness, liquid water content and grain size distribution. The combination of SAR with a coregestered high-resolution DEM (TOPSAR) provides a promising tool for measuring glacier change in three dimensions, thus allowing ice volume change to be measured directly. The change in moraine surface roughness over time enables SAR to differentiate older from younger moraines. Polarimetric SAR data have been used to distinguish snow and ice facies and relatively date moraines. However, both algorithms are still experimental and require ground truth verification. We plan to extend the SAR classification of snow and ice facies and moraine age beyond the ground truth sites to throughout the Cordillera Real to provide a regional view of past and present snow and ice. The high resolution DEM will enhance the SAR moraine dating technique by discriminating relative ages based on moraine slope degradation.

Application of Satellite SAR Imagery in Mapping the Active Layer of Arctic Permafrost

NASA Technical Reports Server (NTRS)

Zhang, Ting-Jun; Li, Shu-Sun

2003-01-01

The objective of this project is to map the spatial variation of the active layer over the arctic permafrost in terms of two parameters: (i) timing and duration of thaw period and (ii) differential frost heave and thaw settlement of the active layer. To achieve this goal, remote sensing, numerical modeling, and related field measurements are required. Tasks for the University of Colorado team are to: (i) determine the timing of snow disappearance in spring through changes in surface albedo (ii) simulate the freezing and thawing processes of the active layer and (iii) simulate the impact of snow cover on permafrost presence.

Snow-covered Landsat time series stacks improve automated disturbance mapping accuracy in forested landscapes

Treesearch

Kirk M. Stueve; Ian W. Housman; Patrick L. Zimmerman; Mark D. Nelson; Jeremy B. Webb; Charles H. Perry; Robert A. Chastain; Dale D. Gormanson; Chengquan Huang; Sean P. Healey; Warren B. Cohen

2011-01-01

Accurate landscape-scale maps of forests and associated disturbances are critical to augment studies on biodiversity, ecosystem services, and the carbon cycle, especially in terms of understanding how the spatial and temporal complexities of damage sustained from disturbances influence forest structure and function. Vegetation change tracker (VCT) is a highly automated...

Accuracy Assessments and Validation of an Expanded UV Irradiance Database from Satellite Total Ozone Mapping Spectrometer (TOMS)

NASA Technical Reports Server (NTRS)

Krotkov, N. A.; Herman, J.; Fioletov, V.; Seftor, C.; Larko, D.; Vasilkov, A.

2004-01-01

The TOMS UV irradiance database (1978 to 2000) has been expanded to include 5 new products (noon irradiance at 305, 310, 324, and 380 nm, and noon erythemal-weighted irradiance), in addition to the existing erythemal daily exposure, which permit direct Comparisons with ground-based measurements from UV spectrometers. Sensitivity studies are conducted to estimate uncertainties of the new TOMS UV irradiance data due to algorithm apriori assumptions. Comparisons with Brewer spectrometers as well as filter radiometers are used to review of the sources of known errors. Inability to distinguish between snow and cloud cover using only TOMS data results in large errors in estimating surface UV using snow climatology. A correction is suggested for the case when the regional snow albedo is known from an independent source. The summer-time positive bias between TOMS UV estimations and Brewer measurements can be seen at all wavelengths. This suggests the difference is not related to ozone absorption effects. We emphasize that uncertainty of boundary layer UV aerosol absorption properties remains a major source of error in modeling UV irradiance in clear sky conditions. Neglecting aerosol absorption by the present TOMS algorithm results in a positive summertime bias in clear-sky UV estimations over many locations. Due to high aerosol variability the bias is strongly site dependent. Data from UV-shadow-band radiometer and well-calibrated CIMEL sun-sky radiometer are used to quantify the bias at NASA/GSFC site in Greenbelt, MD. Recommendations are given to enable potential users to better account for local conditions by combining standard TOMS UV data with ancillary ground measurements.

Surface Snow Density of East Antarctica Derived from In-Situ Observations

NASA Astrophysics Data System (ADS)

Tian, Y.; Zhang, S.; Du, W.; Chen, J.; Xie, H.; Tong, X.; Li, R.

2018-04-01

Models based on physical principles or semi-empirical parameterizations have used to compute the firn density, which is essential for the study of surface processes in the Antarctic ice sheet. However, parameterization of surface snow density is often challenged by the description of detailed local characterization. In this study we propose to generate a surface density map for East Antarctica from all the filed observations that are available. Considering that the observations are non-uniformly distributed around East Antarctica, obtained by different methods, and temporally inhomogeneous, the field observations are used to establish an initial density map with a grid size of 30 × 30 km2 in which the observations are averaged at a temporal scale of five years. We then construct an observation matrix with its columns as the map grids and rows as the temporal scale. If a site has an unknown density value for a period, we will set it to 0 in the matrix. In order to construct the main spatial and temple information of surface snow density matrix we adopt Empirical Orthogonal Function (EOF) method to decompose the observation matrix and only take first several lower-order modes, because these modes already contain most information of the observation matrix. However, there are a lot of zeros in the matrix and we solve it by using matrix completion algorithm, and then we derive the time series of surface snow density at each observation site. Finally, we can obtain the surface snow density by multiplying the modes interpolated by kriging with the corresponding amplitude of the modes. Comparative analysis have done between our surface snow density map and model results. The above details will be introduced in the paper.

Evaluation of Moderate-Resolution Imaging Spectroradiometer (MODIS) Snow Albedo Product (MCD43A) over Tundra

NASA Technical Reports Server (NTRS)

Wang, Zhuosen; Schaaf, Crystal B.; Chopping, Mark J.; Strahler, Alan H.; Wang, Jindi; Roman, Miguel O.; Rocha, Adrian V.; Woodcock, Curtis E.; Shuai, Yanmin

2012-01-01

This study assesses the MODIS standard Bidirectional Reflectance Distribution Function (BRDF)/Albedo product, and the daily Direct Broadcast BRDF/Albedo algorithm at tundra locations under large solar zenith angles and high anisotropic diffuse illumination and multiple scattering conditions. These products generally agree with ground-based albedo measurements during the snow cover period when the Solar Zenith Angle (SZA) is less than 70deg. An integrated validation strategy, including analysis of the representativeness of the surface heterogeneity, is performed to decide whether direct comparisons between field measurements and 500- m satellite products were appropriate or if the scaling of finer spatial resolution airborne or spaceborne data was necessary. Results indicate that the Root Mean Square Errors (RMSEs) are less than 0.047 during the snow covered periods for all MCD43 albedo products at several Alaskan tundra areas. The MCD43 1- day daily albedo product is particularly well suited to capture the rapidly changing surface conditions during the spring snow melt. Results also show that a full expression of the blue sky albedo is necessary at these large SZA snow covered areas because of the effects of anisotropic diffuse illumination and multiple scattering. In tundra locations with dark residue as a result of fire, the MODIS albedo values are lower than those at the unburned site from the start of snowmelt.

Predicting the distribution and ecological niche of unexploited snow crab (Chionoecetes opilio) populations in Alaskan waters: a first open-access ensemble model.

PubMed

Hardy, Sarah M; Lindgren, Michael; Konakanchi, Hanumantharao; Huettmann, Falk

2011-10-01

Populations of the snow crab (Chionoecetes opilio) are widely distributed on high-latitude continental shelves of the North Pacific and North Atlantic, and represent a valuable resource in both the United States and Canada. In US waters, snow crabs are found throughout the Arctic and sub-Arctic seas surrounding Alaska, north of the Aleutian Islands, yet commercial harvest currently focuses on the more southerly population in the Bering Sea. Population dynamics are well-monitored in exploited areas, but few data exist for populations further north where climate trends in the Arctic appear to be affecting species' distributions and community structure on multiple trophic levels. Moreover, increased shipping traffic, as well as fisheries and petroleum resource development, may add additional pressures in northern portions of the range as seasonal ice cover continues to decline. In the face of these pressures, we examined the ecological niche and population distribution of snow crabs in Alaskan waters using a GIS-based spatial modeling approach. We present the first quantitative open-access model predictions of snow-crab distribution, abundance, and biomass in the Chukchi and Beaufort Seas. Multi-variate analysis of environmental drivers of species' distribution and community structure commonly rely on multiple linear regression methods. The spatial modeling approach employed here improves upon linear regression methods in allowing for exploration of nonlinear relationships and interactions between variables. Three machine-learning algorithms were used to evaluate relationships between snow-crab distribution and environmental parameters, including TreeNet, Random Forests, and MARS. An ensemble model was then generated by combining output from these three models to generate consensus predictions for presence-absence, abundance, and biomass of snow crabs. Each algorithm identified a suite of variables most important in predicting snow-crab distribution, including nutrient and chlorophyll-a concentrations in overlying waters, temperature, salinity, and annual sea-ice cover; this information may be used to develop and test hypotheses regarding the ecology of this species. This is the first such quantitative model for snow crabs, and all GIS-data layers compiled for this project are freely available from the authors, upon request, for public use and improvement.

How Does Snow Persistence Relate to Annual Streamflow in Mountain Watersheds of the Western U.S. With Wet Maritime and Dry Continental Climates?

NASA Astrophysics Data System (ADS)

Hammond, John C.; Saavedra, Freddy A.; Kampf, Stephanie K.

2018-04-01

With climate warming, many regions are experiencing changes in snow accumulation and persistence. These changes are known to affect streamflow volume, but the magnitude of the effect varies between regions. This research evaluates whether variables derived from remotely sensed snow cover can be used to estimate annual streamflow at the small watershed scale across the western U.S., a region with a wide range of climate types. We compared snow cover variables derived from MODIS, snow persistence (SP), and snow season (SS), to more commonly utilized metrics, snow fraction (fraction of precipitation falling as snow, SF), and peak snow water equivalent (SWE). Each variable represents different information about snow, and this comparison assesses similarities and differences between the snow metrics. Next, we evaluated how two snow variables, SP and SWE, related to annual streamflow (Q) for 119 USGS reference watersheds and examined whether these relationships varied for wet/warm (precipitation surplus) and dry/cold (precipitation deficit) watersheds. Results showed high correlations between all snow variables, but the slopes of these relationships differed between climates, with wet/warm watersheds displaying lower SF and higher SWE for the same SP. In dry/cold watersheds, both SP and SNODAS SWE correlated with Q spatially across all watersheds and over time within individual watersheds. We conclude that SP can be used to map spatial patterns of annual streamflow generation in dry/cold parts of the region. Applying this approach to the Upper Colorado River Basin demonstrates that 50% of streamflow comes from areas >3,000 masl. If the relationship between SP and Q is similar in other dry/cold regions, this approach could be used to estimate annual streamflow in ungauged basins.

Improving Running Times for the Determination of Fractional Snow-Covered Area from Landsat TM/ETM+ via Utilization of the CUDA® Programming Paradigm

NASA Astrophysics Data System (ADS)

McGibbney, L. J.; Rittger, K.; Painter, T. H.; Selkowitz, D.; Mattmann, C. A.; Ramirez, P.

2014-12-01

As part of a JPL-USGS collaboration to expand distribution of essential climate variables (ECV) to include on-demand fractional snow cover we describe our experience and implementation of a shift towards the use of NVIDIA's CUDA® parallel computing platform and programming model. In particular the on-demand aspect of this work involves the improvement (via faster processing and a reduction in overall running times) for determination of fractional snow-covered area (fSCA) from Landsat TM/ETM+. Our observations indicate that processing tasks associated with remote sensing including the Snow Covered Area and Grain Size Model (SCAG) when applied to MODIS or LANDSAT TM/ETM+ are computationally intensive processes. We believe the shift to the CUDA programming paradigm represents a significant improvement in the ability to more quickly assert the outcomes of such activities. We use the TMSCAG model as our subject to highlight this argument. We do this by describing how we can ingest a LANDSAT surface reflectance image (typically provided in HDF format), perform spectral mixture analysis to produce land cover fractions including snow, vegetation and rock/soil whilst greatly reducing running time for such tasks. Within the scope of this work we first document the original workflow used to assert fSCA for Landsat TM and it's primary shortcomings. We then introduce the logic and justification behind the switch to the CUDA paradigm for running single as well as batch jobs on the GPU in order to achieve parallel processing. Finally we share lessons learned from the implementation of myriad of existing algorithms to a single set of code in a single target language as well as benefits this ultimately provides scientists at the USGS.

Spatiotemporal Variability and in Snow Phenology over Eurasian Continent druing 1966-2012

NASA Astrophysics Data System (ADS)

Zhong, X.; Zhang, T.; Wang, K.; Zheng, L.; Wang, H.

2016-12-01

Snow cover is a key part of the cryosphere, which is a critical component of the global climate system. Snow cover phenology critically effects on the surface energy budget, the surface albedo and hydrological processes. In this study, the climatology and spatiotemporal variability of snow cover phenology were investigated using the long-term (1966-2012) ground-based measurements of daily snow depth from 1103 stations across the Eurasian Continent. The results showed that the distributions of the first date, last date, snow cover duration and number of snow cover days generally represented the latitudinal zonality over the Eurasian Continent, and there were significant elevation gradient patterns in the Tibetan Plateau. The first date of snow cover delayed by about 1.2 day decade-1, the last date of snow cover advanced with the rate of -1.2 day decade-1, snow cover duration and number of snow cover days shortened by about 2.7and 0.6 day decade-1, respectively, from 1966 through 2012. Compared with precipitation, the correlation between snow cover phenology and air temperature was more significant. The changes in snow cover duration were mainly controlled by the changes of air temperature in autumn and spring. The shortened number of snow cover days was affected by rising temperature during the cold season except for the air temperature in autumn and spring.

[Effects of snow cover on water soluble and organic solvent soluble components during foliar litter decomposition in an alpine forest].

PubMed

Xu, Li-Ya; Yang, Wan-Qin; Li, Han; Ni, Xiang-Yin; He, Jie; Wu, Fu-Zhong

2014-11-01

Seasonal snow cover may change the characteristics of freezing, leaching and freeze-thaw cycles in the scenario of climate change, and then play important roles in the dynamics of water soluble and organic solvent soluble components during foliar litter decomposition in the alpine forest. Therefore, a field litterbag experiment was conducted in an alpine forest in western Sichuan, China. The foliar litterbags of typical tree species (birch, cypress, larch and fir) and shrub species (willow and azalea) were placed on the forest floor under different snow cover thickness (deep snow, medium snow, thin snow and no snow). The litterbags were sampled at snow formation stage, snow cover stage and snow melting stage in winter. The results showed that the content of water soluble components from six foliar litters decreased at snow formation stage and snow melting stage, but increased at snow cover stage as litter decomposition proceeded in the winter. Besides the content of organic solvent soluble components from azalea foliar litter increased at snow cover stage, the content of organic solvent soluble components from the other five foliar litters kept a continue decreasing tendency in the winter. Compared with the content of organic solvent soluble components, the content of water soluble components was affected more strongly by snow cover thickness, especially at snow formation stage and snow cover stage. Compared with the thicker snow covers, the thin snow cover promoted the decrease of water soluble component contents from willow and azalea foliar litter and restrain the decrease of water soluble component content from cypress foliar litter. Few changes in the content of water soluble components from birch, fir and larch foliar litter were observed under the different thicknesses of snow cover. The results suggested that the effects of snow cover on the contents of water soluble and organic solvent soluble components during litter decomposition would be controlled by litter quality.

On-Board Cryospheric Change Detection By The Autonomous Sciencecraft Experiment

NASA Astrophysics Data System (ADS)

Doggett, T.; Greeley, R.; Castano, R.; Cichy, B.; Chien, S.; Davies, A.; Baker, V.; Dohm, J.; Ip, F.

2004-12-01

The Autonomous Sciencecraft Experiment (ASE) is operating on-board Earth Observing - 1 (EO-1) with the Hyperion hyper-spectral visible/near-IR spectrometer. ASE science activities include autonomous monitoring of cryopsheric changes, triggering the collection of additional data when change is detected and filtering of null data such as no change or cloud cover. This would have application to the study of cryospheres on Earth, Mars and the icy moons of the outer solar system. A cryosphere classification algorithm, in combination with a previously developed cloud algorithm [1] has been tested on-board ten times from March through August 2004. The cloud algorithm correctly screened out three scenes with total cloud cover, while the cryosphere algorithm detected alpine snow cover in the Rocky Mountains, lake thaw near Madison, Wisconsin, and the presence and subsequent break-up of sea ice in the Barrow Strait of the Canadian Arctic. Hyperion has 220 bands ranging from 400 to 2400 nm, with a spatial resolution of 30 m/pixel and a spectral resolution of 10 nm. Limited on-board memory and processing speed imposed the constraint that only partially processed Level 0.5 data with dark image subtraction and gain factors applied, but not full radiometric calibration. In addition, a maximum of 12 bands could be used for any stacked sequence of algorithms run for a scene on-board. The cryosphere algorithm was developed to classify snow, water, ice and land, using six Hyperion bands at 427, 559, 661, 864, 1245 and 1649 nm. Of these, only 427 nm does overlap with the cloud algorithm. The cloud algorithm was developed with Level 1 data, which introduces complications because of the incomplete calibration of SWIR in Level 0.5 data, including a high level of noise in the 1377 nm band used by the cloud algorithm. Development of a more robust cryosphere classifier, including cloud classification specifically adapted to Level 0.5, is in progress for deployment on EO-1 as part of continued ASE operations. [1] Griffin, M.K. et al., Cloud Cover Detection Algorithm For EO-1 Hyperion Imagery, SPIE 17, 2003.

The use of the LANDSAT data collection system and imagery in reservoir management and operation. [Maine, Vermont, New Hamphire, Canada, St. John River, Beech Ridge, Merrimack River, and Franklin Falls

NASA Technical Reports Server (NTRS)

Cooper, S. (Principal Investigator); Buckelew, T. D.; Mckim, H. L.; Merry, C. J.

1977-01-01

The author has identified the following significant results. An increase in the data collection system's (DCS) ability to function in the flood control mission with no additional manpower was demonstrated during the storms which struck New England during April and May of 1975 and August 1976. It was found that for this watershed, creditable flood hydrographs could be generated from DCS data. It was concluded that an ideal DCS for reservoir regulation would draw features from LANDSAT and GOES. MSS grayscale computer printout and a USGS topographic map were compared, yielding an optimum computer classification map of the wetland areas of the Merrimack River estuary. A classification accuracy of 75% was obtained for the wetlands unit, taking into account the misclassified and the unclassified pixels. The MSS band 7 grayscale printouts of the Franklin Falls reservoir showed good agreement to USGS topographic maps in total area of water depicted at the low water reservoir stage and at the maximum inundation level. Preliminary analysis of the LANDSAT digital data using the GISS computer algorithms showed that the radiance of snow cover/vegetation varied from approximately 20 mW/sq cm sr in nonvegetated areas to less than 4 mW/sq cm sr for densely covered forested area.

White water: Fifty years of snow research in WRR and the outlook for the future

NASA Astrophysics Data System (ADS)

Sturm, Matthew

2015-07-01

Over the past 50 years, 239 papers related to snow have been published in Water Resources Research (WRR). Seminal papers on virtually every facet of snow physics and snow water resources have appeared in the journal. These include papers on drifting snow, the snow surface energy balance, the effect of grain size on albedo, chemical elution, water movement through snow, and canopy interception. In particular, papers in WRR have explored the distribution of snow across different landscapes, providing data, process knowledge, and the basis for virtually all of the distributed snow models in use today. In this paper, I review these key contributions and provide some personal thoughts on what is likely to be the focus and nature of papers published in the next few decades, a period that is likely to see an increasing ability to map snow cover in detail, which should serve as a basis for the further development and improvement of snow models. It will also be an uncertain future, with profound changes in snow climatology predicted. I expect WRR will continue to play a key role in documenting and understanding these important cryospheric changes.

Computer-aided analysis of Skylab scanner data for land use mapping, forestry and water resource applications

NASA Technical Reports Server (NTRS)

Hoffer, R. M.

1975-01-01

Skylab data were obtained over a mountainous test site containing a complex association of cover types and rugged topography. The application of computer-aided analysis techniques to the multispectral scanner data produced a number of significant results. Techniques were developed to digitally overlay topographic data (elevation, slope, and aspect) onto the S-192 MSS data to provide a method for increasing the effectiveness and accuracy of computer-aided analysis techniques for cover type mapping. The S-192 MSS data were analyzed using computer techniques developed at Laboratory for Applications of Remote Sensing (LARS), Purdue University. Land use maps, forest cover type maps, snow cover maps, and area tabulations were obtained and evaluated. These results compared very well with information obtained by conventional techniques. Analysis of the spectral characteristics of Skylab data has conclusively proven the value of the middle infrared portion of the spectrum (about 1.3-3.0 micrometers), a wavelength region not previously available in multispectral satellite data.

1D-Var multilayer assimilation of X-band SAR data into a detailed snowpack model

NASA Astrophysics Data System (ADS)

Phan, X. V.; Ferro-Famil, L.; Gay, M.; Durand, Y.; Dumont, M.; Morin, S.; Allain, S.; D'Urso, G.; Girard, A.

2014-10-01

The structure and physical properties of a snowpack and their temporal evolution may be simulated using meteorological data and a snow metamorphism model. Such an approach may meet limitations related to potential divergences and accumulated errors, to a limited spatial resolution, to wind or topography-induced local modulations of the physical properties of a snow cover, etc. Exogenous data are then required in order to constrain the simulator and improve its performance over time. Synthetic-aperture radars (SARs) and, in particular, recent sensors provide reflectivity maps of snow-covered environments with high temporal and spatial resolutions. The radiometric properties of a snowpack measured at sufficiently high carrier frequencies are known to be tightly related to some of its main physical parameters, like its depth, snow grain size and density. SAR acquisitions may then be used, together with an electromagnetic backscattering model (EBM) able to simulate the reflectivity of a snowpack from a set of physical descriptors, in order to constrain a physical snowpack model. In this study, we introduce a variational data assimilation scheme coupling TerraSAR-X radiometric data into the snowpack evolution model Crocus. The physical properties of a snowpack, such as snow density and optical diameter of each layer, are simulated by Crocus, fed by the local reanalysis of meteorological data (SAFRAN) at a French Alpine location. These snowpack properties are used as inputs of an EBM based on dense media radiative transfer (DMRT) theory, which simulates the total backscattering coefficient of a dry snow medium at X and higher frequency bands. After evaluating the sensitivity of the EBM to snowpack parameters, a 1D-Var data assimilation scheme is implemented in order to minimize the discrepancies between EBM simulations and observations obtained from TerraSAR-X acquisitions by modifying the physical parameters of the Crocus-simulated snowpack. The algorithm then re-initializes Crocus with the modified snowpack physical parameters, allowing it to continue the simulation of snowpack evolution, with adjustments based on remote sensing information. This method is evaluated using multi-temporal TerraSAR-X images acquired over the specific site of the Argentière glacier (Mont-Blanc massif, French Alps) to constrain the evolution of Crocus. Results indicate that X-band SAR data can be taken into account to modify the evolution of snowpack simulated by Crocus.

Snow cover and snow goose Anser caerulescens caerulescens distribution during spring migration

USGS Publications Warehouse

Hupp, Jerry W.; Zacheis, Amy B.; Anthony, R. Michael; Robertson, Donna G.; Erickson, Wallace P.; Palacios, Kelly C.

2001-01-01

Arctic geese often use spring migration stopover areas when feeding habitats are partially snow covered. Melting of snow during the stopover period causes spatial and temporal variability in distribution and abundance of feeding habitat. We recorded changes in snow cover and lesser snow goose Anser caerulescens caerulescens distribution on a spring migration stopover area in south-central Alaska during aerial surveys in 1993-1994. Our objectives were to determine whether geese selected among areas with different amounts of snow cover and to assess how temporal changes in snow cover affected goose distribution. We also measured temporal changes in chemical composition of forage species after snow melt. We divided an Arc/Info coverage of the approximately 210 km2 coastal stopover area into 2-km2 cells, and measured snow cover and snow goose use of cells. Cells that had 10-49.9% snow cover were selected by snow geese, whereas cells that lacked snow cover were avoided. In both years, snow cover diminished along the coast between mid-April and early May. Flock distribution changed as snow geese abandoned snow-free areas in favour of cells where snow patches were interspersed with bare ground. Snow-free areas may have been less attractive to geese because available forage had been quickly exploited as bare ground was exposed, and because soils became drier making extraction of underground forage more difficult. Fiber content of two forage species increased whereas non-structural carbohydrate concentrations of forage plants appeared to diminish after snow melt, but changes in nutrient concentrations likely occurred too slowly to account for abandonment of snow-free areas by snow geese.

Snow cover in the Siberian forest-steppe

NASA Technical Reports Server (NTRS)

Zykov, I. V.

1985-01-01

A study is made of the snow cover on an experimental agricultural station in Mariinsk in the winter of 1945 to 1946. Conditions of snow cover formation, and types and indicators of snow cover are discussed. Snow cover structure and conditions and nature of thawing are described.

Influence of snow cover changes on surface radiation and heat balance based on the WRF model

NASA Astrophysics Data System (ADS)

Yu, Lingxue; Liu, Tingxiang; Bu, Kun; Yang, Jiuchun; Chang, Liping; Zhang, Shuwen

2017-10-01

The snow cover extent in mid-high latitude areas of the Northern Hemisphere has significantly declined corresponding to the global warming, especially since the 1970s. Snow-climate feedbacks play a critical role in regulating the global radiation balance and influencing surface heat flux exchange. However, the degree to which snow cover changes affect the radiation budget and energy balance on a regional scale and the difference between snow-climate and land use/cover change (LUCC)-climate feedbacks have been rarely studied. In this paper, we selected Heilongjiang Basin, where the snow cover has changed obviously, as our study area and used the WRF model to simulate the influences of snow cover changes on the surface radiation budget and heat balance. In the scenario simulation, the localized surface parameter data improved the accuracy by 10 % compared with the control group. The spatial and temporal analysis of the surface variables showed that the net surface radiation, sensible heat flux, Bowen ratio, temperature and percentage of snow cover were negatively correlated and that the ground heat flux and latent heat flux were positively correlated with the percentage of snow cover. The spatial analysis also showed that a significant relationship existed between the surface variables and land cover types, which was not obviously as that for snow cover changes. Finally, six typical study areas were selected to quantitatively analyse the influence of land cover types beneath the snow cover on heat absorption and transfer, which showed that when the land was snow covered, the conversion of forest to farmland can dramatically influence the net radiation and other surface variables, whereas the snow-free land showed significantly reduced influence. Furthermore, compared with typical land cover changes, e.g., the conversion of forest into farmland, the influence of snow cover changes on net radiation and sensible heat flux were 60 % higher than that of land cover changes, indicating the importance of snow cover changes in the surface-atmospheric feedback system.

Snowscape Ecology: Linking Snow Properties to Wildlife Movements and Demography

NASA Astrophysics Data System (ADS)

Prugh, L.; Verbyla, D.; van de Kerk, M.; Mahoney, P.; Sivy, K. J.; Liston, G. E.; Nolin, A. W.

2017-12-01

Snow enshrouds up to one third of the global land mass annually and exerts a major influence on animals that reside in these "snowscapes," (landscapes covered in snow). Dynamic snowscapes may have especially strong effects in arctic and boreal regions where dry snow persists for much of the year. Changes in temperature and hydrology are transforming northern regions, with profound implications for wildlife that are not well understood. We report initial findings from a NASA ABoVE project examining effects of snow properties on Dall sheep (Ovis dalli dalli). We used the MODSCAG snow fraction product to map spring snowline elevations and snow-off dates from 2000-2015 throughout the global range of Dall sheep in Alaska and northwestern Canada. We found a negative effect of spring snow cover on Dall sheep recruitment that increased with latitude. Using meteorological data and a daily freeze/thaw status product derived from passive microwave remote sensing from 1983-2012, we found that sheep survival rates increased in years with higher temperatures, less winter precipitation, fewer spring freeze-thaw events, and more winter freeze-thaw events. To examine the effects of snow depth and density on sheep movements, we used location data from GPS-collared sheep and a snowpack evolution model (SnowModel). We found that sheep selected for shallow, fluffy snow at high elevations, but they selected for denser snow as depth increased. Our field measurements identified a critical snow density threshold of 329 (± 18 SE) kg/m3 to support the weight of Dall sheep. Thus, sheep may require areas of shallow, fluffy snow for foraging, while relying on hard-packed snow for winter travel. These findings highlight the importance of multiple snowscape properties on wildlife movements and demography. The integrated study of snow properties and ecological processes, which we call snowscape ecology, will greatly improve global change forecasting.

Relationship Between Satellite-Derived Snow Cover and Snowmelt-Runoff Timing and Stream Power in the Wind River Range, Wyoming

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; DiGirolamo, Nicolo E.; Riggs, George A.

2010-01-01

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 (>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 streamflow management. 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 six streams in the 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 using MODIS snow-cover maps 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. MODIS-derived snow cover (percent of basin covered) measured on 30 April explains over 89% of the variance in discharge for maximum monthly streamflow in the decade of the 2000s using Spearman rank correlation analysis. We also investigated stream power for Bull Lake Creek Above Bull Lake from 1970 to 2009; a statistically-significant end toward reduced stream power was found (significant at the 90% confidence level). Observed changes in streamflow and stream power may be related to increasing weekly maximum air temperature measured during the 40-year study period. The strong relationship between percent of basin covered and streamflow indicates that MODIS data is useful for predicting streamflow, leading to improved reservoir management

Relationship Between Satellite-Derived Snow Cover and Snowmelt-Runoff Timing and Stream Power in the Wind River Range, Wyoming

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Riggs, George A.; DiGirolano, Nocolo E.

2010-01-01

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 (>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 streamflow management. 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 a are studied. Streamflow data from six streams in the 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 using MODIS snow-cover maps 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. MODIS- derived snow cover (percent of basin covered) measured on 30 April explains over 89% of the variance in discharge for maximum monthly streamflow in the decade of the 2000s using Spearman rank correlation analysis. We also investigated stream power for Bull Lake Creek Above Bull Lake from 1970 to 2009; a statistically-significant trend toward reduced stream power was found (significant at the 90% confidence level). Observed changes in streamflow and stream power may be related to increasing weekly maximum air temperature measured during the 40-year study period. The strong relationship between percent of basin covered and streamflow indicates that MODIS data is useful for predicting streamflow, leading to improved reservoir management.

Time Lapse Camera Images for Observation and Visualization of Snow Lines on two Benchmark Glaciers in High Asia - Zhadang and Halji

NASA Astrophysics Data System (ADS)

Schröter, B.; Buchroithner, M. F.; Pieczonka, T.

2015-12-01

Glaciers are characteristic elements of high mountain environments and represent key indicators for the ongoing climate change. The covering snowpack considerably affects the glacier-ice surface temperature and thus the meltdown of the glaciers which in recent decades has been accelerating worldwide. Therefore, the detailed investigation of the transient snow is of high importance. Zhadang Glacier is located in the Nyainqentanglha Mountain Range on the central part of the Tibetan Plateau (30°28.24' N, 90°38.69' E). The glacier is debris-free and flows from 6,090 to 5,515 m a.s.l. Recent measurements have shown that the whole glacier is below the ELA and experiences significant glacier volume loss. In May 2010 two terrestrial cameras had been installed there and were continuously operating until September 2012 generating 6,225 images of the glacier with a frequency of three resp. six images per day. In order to use this dataset for snow line mapping all images had to be georeferenced and orthorectified. The biggest challenge was the problem of shifting camera positions due to deformations of the ground and hence the offset in the image coordinates. This was resolved by combining the manual orthorectification of one image per week with a subsequent spline interpolation to determine the changed image coordinates. The actual orthorectification was finally realized by applying a fully automated batch processing of all images. The most favorable image of each day was chosen for the manual snow line mapping process. The final aim was the calculation of the mean elevation of the snow line for every day of the data collecting period materialized by intersecting the mapped snow lines with resampled SRTM 3 data. Considering the fact that there were several weeks either with full snow cover or without any snow this aim could be achieved. The findings have been used for the evaluation of a glacier mass balance model developed at RWTH Aachen, Germany, showing a high level of congruence. Another result is the proof of intense ablation due to snow drift and sublimation during the winter months. In 2014 a similar camera system was installed near Halji Glacier in northwestern Nepal on the southern edge of the Tibetan Plateau (30°15.80' N, 81°28.16' E; 5,730 - 5,270 m a.s.l.; ELA = 5,660 m a.s.l.). The images are currently being processed.

Spring Snow Melt Timing and Changes over Arctic Lands

NASA Technical Reports Server (NTRS)

Foster, J. L.; Robinson, D. A.; Hall, D. K.; Estilow, T. W.

2006-01-01

Spring snow cover over Arctic lands has, on average, melted approximately 4-7 days earlier since the late 1980s compared to the previous 20 years. The earlier disappearance of snow has been identified in non-mountainous regions at the 60 deg and 70 deg N parallels over Eurasia and North America using visible satellite observations of continental snow cover extent (SCE) mapped by the National Oceanic and Atmospheric Administration. The change was greater in the farthest north continental locations. Northern hemisphere SCE declined by almost 10% (May) to 20% (June) between the two intervals. At latitude 70 deg N, eight segments of longitude (each 10 deg in width) show significant (negative) trends. However, only two longitudinal segments at 60 deg N show significant trends, (one positive and one negative). SCE changes coincide with increasing spring warmth and the earlier diminution of sea ice in the last several decades. However, while sea ice has continued to decrease during this recent interval, snowmelt dates in the Arctic changed in a step-like fashion during the mid to late 1980s and have remained much the same since that time.

Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

NASA Astrophysics Data System (ADS)

Ehrlich, André; Bierwirth, Eike; Istomina, Larysa; Wendisch, Manfred

2017-09-01

The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C. In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff, C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice edge are analysed. The retrieved values of τ, reff, C, and reff, S show a continuous transition of cloud properties across snow-covered sea ice and open water and are consistent with estimates based on satellite data. It is shown that the uncertainties of the trispectral retrieval increase for high values of τ, and low reff, S but nevertheless allow the effective snow grain size in cloud-covered areas to be estimated.

Ground penetrating radar detection of subsnow slush on ice-covered lakes in interior Alaska

NASA Astrophysics Data System (ADS)

Gusmeroli, A.; Grosse, G.

2012-12-01

Lakes are abundant throughout the pan-Arctic region. For many of these lakes ice cover lasts for up to two thirds of the year. The frozen cover allows human access to these lakes, which are therefore used for many subsistence and recreational activities, including water harvesting, fishing, and skiing. Safe traveling condition onto lakes may be compromised, however, when, after significant snowfall, the weight of the snow acts on the ice and causes liquid water to spill through weak spots and overflow at the snow-ice interface. Since visual detection of subsnow slush is almost impossible our understanding on overflow processes is still very limited and geophysical methods that allow water and slush detection are desirable. In this study we demonstrate that a commercially available, lightweight 1 GHz, ground penetrating radar system can detect and map extent and intensity of overflow. The strength of radar reflections from wet snow-ice interfaces are at least twice as much in strength than returns from dry snow-ice interface. The presence of overflow also affects the quality of radar returns from the base of the lake ice. During dry conditions we were able to profile ice thickness of up to 1 m, conversely, we did not retrieve any ice-water returns in areas affected by overflow.

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

NASA Astrophysics Data System (ADS)

Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kucera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús

2011-09-01

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.

Exploitation of ERTS-1 imagery utilizing snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J.; Martin, K. R.

1973-01-01

Photogeological analysis of ERTS-simulation and ERTS-1 imagery of snowcovered terrain within the ERAP Feather River site and within the New England (ERTS) test area provided new fracture detail which does not appear on available geological maps. Comparative analysis of snowfree ERTS-1 images has demonstrated that MSS Bands 5 and 7 supply the greatest amount of geological fracture detail. Interpretation of the first snow-covered ERTS-1 images in correlation with ground snow depth data indicates that a heavy blanket of snow (more than 9 inches) accentuates major structural features while a light "dusting", (less than 1 inch) accentuates more subtle topographic expressions. An effective mail-based method for acquiring timely ground-truth (snowdepth) information was established and provides a ready correlation of fracture detail with snow depth so as to establish the working limits of the technique. The method is both efficient and inexpensive compared with the cost of similarly scaled direct field observations.

Distributed Assimilation of Satellite-based Snow Extent for Improving Simulated Streamflow in Mountainous, Dense Forests: An Example Over the DMIP2 Western Basins

NASA Technical Reports Server (NTRS)

Yatheendradas, Soni; Peters-Lidard, Christa D.; Koren, Victor; Cosgrove, Brian A.; DeGoncalves, Luis G. D.; Smith, Michael; Geiger, James; Cui, Zhengtao; Borak, Jordan; Kumar, Sujay V.;

The Snow Data System at NASA JPL

NASA Astrophysics Data System (ADS)

Laidlaw, R.; Painter, T. H.; Mattmann, C. A.; Ramirez, P.; Bormann, K.; Brodzik, M. J.; Burgess, A. B.; Rittger, K.; Goodale, C. E.; Joyce, M.; McGibbney, L. J.; Zimdars, P.

2014-12-01

NASA JPL's Snow Data System has a data-processing pipeline powered by Apache OODT, an open source software tool. The pipeline has been running for several years and has successfully generated a significant amount of cryosphere data, including MODIS-based products such as MODSCAG, MODDRFS and MODICE, with historical and near-real time windows and covering regions such as the Artic, Western US, Alaska, Central Europe, Asia, South America, Australia and New Zealand. The team continues to improve the pipeline, using monitoring tools such as Ganglia to give an overview of operations, and improving fault-tolerance with automated recovery scripts. Several alternative adaptations of the Snow Covered Area and Grain size (SCAG) algorithm are being investigated. These include using VIIRS and Landsat TM/ETM+ satellite data as inputs. Parallel computing techniques are being considered for core SCAG processing, such as using the PyCUDA Python API to utilize multi-core GPU architectures. An experimental version of MODSCAG is also being developed for the Google Earth Engine platform, a cloud-based service.

Distributed snow modeling suitable for use with operational data for the American River watershed.

NASA Astrophysics Data System (ADS)

Shamir, E.; Georgakakos, K. P.

2004-12-01

The mountainous terrain of the American River watershed (~4300 km2) at the Western slope of the Northern Sierra Nevada is subject to significant variability in the atmospheric forcing that controls the snow accumulation and ablations processes (i.e., precipitation, surface temperature, and radiation). For a hydrologic model that attempts to predict both short- and long-term streamflow discharges, a plausible description of the seasonal and intermittent winter snow pack accumulation and ablation is crucial. At present the NWS-CNRFC operational snow model is implemented in a semi distributed manner (modeling unit of about 100-1000 km2) and therefore lump distinct spatial variability of snow processes. In this study we attempt to account for the precipitation, temperature, and radiation spatial variability by constructing a distributed snow accumulation and melting model suitable for use with commonly available sparse data. An adaptation of the NWS-Snow17 energy and mass balance that is used operationally at the NWS River Forecast Centers is implemented at 1 km2 grid cells with distributed input and model parameters. The input to the model (i.e., precipitation and surface temperature) is interpolated from observed point data. The surface temperature was interpolated over the basin based on adiabatic lapse rates using topographic information whereas the precipitation was interpolated based on maps of climatic mean annual rainfall distribution acquired from PRISM. The model parameters that control the melting rate due to radiation were interpolated based on aspect. The study was conducted for the entire American basin for the snow seasons of 1999-2000. Validation of the Snow Water Equivalent (SWE) prediction is done by comparing to observation from 12 snow Sensors. The Snow Cover Area (SCA) prediction was evaluated by comparing to remotely sensed 500m daily snow cover derived from MODIS. The results that the distribution of snow over the area is well captured and the quantity compared to the snow gauges are well estimated in the high elevation.

[Snow cover pollution monitoring in Ufa].

PubMed

Daukaev, R A; Suleĭmanov, R A

2008-01-01

The paper presents the results of examining the snow cover polluted with heavy metals in the large industrial town of Ufa. The level of man-caused burden on the snow cover of the conventional parts of the town was estimated and compared upon exposure to a wide range of snow cover pollutants. The priority snow cover pollutants were identified among the test heavy metals.

Geologic Mapping and Mineral Resource Assessment of the Healy and Talkeetna Mountains Quadrangles, Alaska Using Minimal Cloud- and Snow-Cover ASTER Data

USGS Publications Warehouse

Hubbard, Bernard E.; Rowan1, Lawrence C.; Dusel-Bacon, Cynthia; Eppinger, Robert G.

2007-01-01

On July 8, 2003, ASTER acquired satellite imagery of a 60 km-wide swath of parts of two 1:250,000 Alaska quadrangles, under favorable conditions of minimal cloud- and snow-cover. Rocks from eight different lithotectonic terranes are exposed within the swath of data, several of which define permissive tracts for various mineral deposit types such as: volcanic-hosted massive sulfides (VMS) and porphyry copper and molybdenum. Representative rock samples collected from 13 different lithologic units from the Bonnifield mining district within the Yukon-Tanana terrane (YTT), plus hydrothermally altered VMS material from the Red Mountain prospect, were analyzed to produce a spectral library spanning the VNIR-SWIR (0.4 - 2.5 ?m) through the TIR (8.1 - 11.7 ?m). Comparison of the five-band ASTER TIR emissivity and decorrelation stretch data to available geologic maps indicates that rocks from the YTT display the greatest range and diversity of silica composition of the mapped terranes, ranging from mafic rocks to silicic quartzites. The nine-band ASTER VNIR-SWIR reflectance data and spectral matched-filter processing were used to map several lithologic sequences characterized by distinct suites of minerals that exhibit diagnostic spectral features (e.g. chlorite, epidote, amphibole and other ferrous-iron bearing minerals); other sequences were distinguished by their weathering characteristics and associated hydroxyl- and ferric-iron minerals, such as illite, smectite, and hematite. Smectite, kaolinite, opaline silica, jarosite and/or other ferric iron minerals defined narrow (< 250 m diameter) zonal patterns around Red Mountain and other potential VMS targets. Using ASTER we identified some of the known mineral deposits in the region, as well as mineralogically similar targets that may represent potential undiscovered deposits. Some known deposits were not identified and may have been obscured by vegetation- or snow-cover, or were too small to be resolved.

Evaluation of an assimilation scheme to estimate snow water equivalent in the High Atlas of Morocco.

NASA Astrophysics Data System (ADS)

Baba, W. M.; Baldo, E.; Gascoin, S.; Margulis, S. A.; Cortés, G.; Hanich, L.

2017-12-01

The snow melt from the Atlas mountains represents a crucial water resource for crop irrigation in Morocco. Due to the paucity of in situ measurements, and the high spatial variability of the snow cover in this semi-arid region, assimilation of snow cover area (SCA) from high resolution optical remote sensing into a snowpack energy-balance model is considered as a promising method to estimate the snow water equivalent (SWE) and snow melt at catchment scales. Here we present a preliminary evaluation of an uncalibrated particle batch smoother data assimilation scheme (Margulis et al., 2015, J. Hydrometeor., 16, 1752-1772) in the High-Atlas Rheraya pilot catchment (225 km2) over a snow season. This approach does not require in situ data since it is based on MERRA-2 reanalyses data and satellite fractional snow cover area data. We compared the output of this prior/posterior ensemble data assimilation system to output from the distributed snowpack evolution model SnowModel (Liston and Elder, 2006, J. Hydrometeor. 7, 1259-1276). SnowModel was forced with in situ meteorological data from five automatic weather stations (AWS) and some key parameters (precipitation correction factor and rain-snow phase transition parameters) were calibrated using a time series of 8-m resolution SCA maps from Formosat-2. The SnowModel simulation was validated using a continuous snow height record at one high elevation AWS. The results indicate that the open loop simulation was reasonably accurate (compared to SnowModel results) in spite of the coarse resolution of the MERRA-2 forcing. The assimilation of Formosat-2 SCA further improved the simulation in terms of the peak SWE and SWE evolution over the melt season. During the accumulation season, the differences between the modeled and estimated (posterior) SWE were more substantial. The differences appear to be due to some observed precipitation events not being captured in MERRA-2. Further investigation will determine whether additional improvement in the posterior estimates result from a calibration of uncertainty input parameters based on the in situ meteorological data. The positive preliminary results pave the way for a SWE reanalysis at the scale of the Atlas mountains using data from wide swath sensors such as Landsat and Sentinel-2.

Mapping known and potential mineral occurrences and host rocks in the Bonnifield Mining District using minimal cloud- and snow-cover ASTER data: Chapter E in Recent U.S. Geological Survey studies in the Tintina Gold Province, Alaska, United States, and Yukon, Canada--results of a 5-year project

USGS Publications Warehouse

Hubbard, Bernard E.; Dusel-Bacon, Cynthia; Rowan, Lawrence C.; Eppinger, Robert G.; Gough, Larry P.; Day, Warren C.

2007-01-01

On July 8, 2003, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor acquired satellite imagery of a 60-kilometer-wide swath covering a portion of the Bonnifield mining district within the southernmost part of the Tintina Gold Province, Alaska, under unusually favorable conditions of minimal cloud and snow cover. Although rocks from more than eight different lithotectonic terranes are exposed within the extended swath of data, we focus on volcanogenic massive sulfides (VMS) and porphyry deposits within the Yukon-Tanana terrane (YTT), the largest Mesozoic accretionary terrane exposed between the Denali fault system to the south of Fairbanks and the Tintina fault system to the north of Fairbanks. Comparison of thermal-infrared region (TIR) decorrelation stretch data to available geologic maps indicates that rocks from the YTT contain a wide range of rock types ranging in composition from mafic metavolcanic rocks to felsic rock types such as metarhyolites, pelitic schists, and quartzites. The nine-band ASTER visible-near-infrared region--short-wave infrared region (VNIR-SWIR) reflectance data and spectral matched-filter processing were used to map hydrothermal alteration patterns associated with VMS and porphyry deposit types. In particular, smectite, kaolinite, opaline silica, jarosite and (or) other ferric iron minerals defined narrow (less than 250-meter diameter) zonal patterns around Red Mountain and other potential VMS targets. Using ASTER we identified some of the known mineral deposits in the region, as well as mineralogically similar targets that may represent potential undiscovered deposits. Some known deposits were not identified and may have been obscured by vegetation or snow cover or were too small to be resolved.

Influence of Western Tibetan Plateau Summer Snow Cover on East Asian Summer Rainfall

NASA Astrophysics Data System (ADS)

Wang, Zhibiao; Wu, Renguang; Chen, Shangfeng; Huang, Gang; Liu, Ge; Zhu, Lihua

2018-03-01

The influence of boreal winter-spring eastern Tibetan Plateau snow anomalies on the East Asian summer rainfall variability has been the focus of previous studies. The present study documents the impacts of boreal summer western and southern Tibetan Plateau snow cover anomalies on summer rainfall over East Asia. Analysis shows that more snow cover in the western and southern Tibetan Plateau induces anomalous cooling in the overlying atmospheric column. The induced atmospheric circulation changes are different corresponding to more snow cover in the western and southern Tibetan Plateau. The atmospheric circulation changes accompanying the western Plateau snow cover anomalies are more obvious over the midlatitude Asia, whereas those corresponding to the southern Plateau snow cover anomalies are more prominent over the tropics. As such, the western and southern Tibetan Plateau snow cover anomalies influence the East Asian summer circulation and precipitation through different pathways. Nevertheless, the East Asian summer circulation and precipitation anomalies induced by the western and southern Plateau snow cover anomalies tend to display similar distribution so that they are more pronounced when the western and southern Plateau snow cover anomalies work in coherence. Analysis indicates that the summer snow cover anomalies over the Tibetan Plateau may be related to late spring snow anomalies due to the persistence. The late spring snow anomalies are related to an obvious wave train originating from the western North Atlantic that may be partly associated with sea surface temperature anomalies in the North Atlantic Ocean.

Numerical run-out modelling used for reassessment of existing permanent avalanche paths in the Krkonose Mts., Czechia

NASA Astrophysics Data System (ADS)

Blahut, Jan; Klimes, Jan; Balek, Jan; Taborik, Petr; Juras, Roman; Pavlasek, Jiri

2015-04-01

Run-out modelling of snow avalanches is being widely applied in high mountain areas worldwide. This study presents application of snow avalanche run-out calculation applied to mid-mountain ranges - the Krkonose, Jeseniky and Kralicky Sneznik Mountains. All mentioned mountain ranges lie in the northern part of Czechia, close to the border with Poland. Its highest peak reaches only 1602 m a.s.l. However, climatic conditions and regular snowpack presence are the reason why these mountain ranges experience considerable snow avalanche activity every year, sometimes resulting in injuries or even fatalities. Within the aim of an applied project dealing with snow avalanche hazard prediction a re-assessment of permanent snow avalanche paths has been performed based on extensive statistics covering period from 1961/62 till present. On each avalanche path different avalanches with different return periods were modelled using the RAMMS code. As a result, an up-to-date snow avalanche hazard map was prepared.

[Effect of different snow depth and area on the snow cover retrieval using remote sensing data].

PubMed

Jiang, Hong-bo; Qin, Qi-ming; Zhang, Ning; Dong, Heng; Chen, Chao

2011-12-01

For the needs of snow cover monitoring using multi-source remote sensing data, in the present article, based on the spectrum analysis of different depth and area of snow, the effect of snow depth on the results of snow cover retrieval using normalized difference snow index (NDSI) is discussed. Meanwhile, taking the HJ-1B and MODIS remote sensing data as an example, the snow area effect on the snow cover monitoring is also studied. The results show that: the difference of snow depth does not contribute to the retrieval results, while the snow area affects the results of retrieval to some extents because of the constraints of spatial resolution.

A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011

USGS Publications Warehouse

Jin, Suming; Yang, Limin; Zhu, Zhe; Homer, Collin G.

2017-01-01

Monitoring and mapping land cover changes are important ways to support evaluation of the status and transition of ecosystems. The Alaska National Land Cover Database (NLCD) 2001 was the first 30-m resolution baseline land cover product of the entire state derived from circa 2001 Landsat imagery and geospatial ancillary data. We developed a comprehensive approach named AKUP11 to update Alaska NLCD from 2001 to 2011 and provide a 10-year cyclical update of the state's land cover and land cover changes. Our method is designed to characterize the main land cover changes associated with different drivers, including the conversion of forests to shrub and grassland primarily as a result of wildland fire and forest harvest, the vegetation successional processes after disturbance, and changes of surface water extent and glacier ice/snow associated with weather and climate changes. For natural vegetated areas, a component named AKUP11-VEG was developed for updating the land cover that involves four major steps: 1) identify the disturbed and successional areas using Landsat images and ancillary datasets; 2) update the land cover status for these areas using a SKILL model (System of Knowledge-based Integrated-trajectory Land cover Labeling); 3) perform decision tree classification; and 4) develop a final land cover and land cover change product through the postprocessing modeling. For water and ice/snow areas, another component named AKUP11-WIS was developed for initial land cover change detection, removal of the terrain shadow effects, and exclusion of ephemeral snow changes using a 3-year MODIS snow extent dataset from 2010 to 2012. The overall approach was tested in three pilot study areas in Alaska, with each area consisting of four Landsat image footprints. The results from the pilot study show that the overall accuracy in detecting change and no-change is 90% and the overall accuracy of the updated land cover label for 2011 is 86%. The method provided a robust, consistent, and efficient means for capturing major disturbance events and updating land cover for Alaska. The method has subsequently been applied to generate the land cover and land cover change products for the entire state of Alaska.

Monitoring Snow on ice as Critical Habitat for Ringed Seals

NASA Astrophysics Data System (ADS)

Kelly, B. P.; Moran, J.; Douglas, D. C.; Nghiem, S. V.

2007-12-01

Ringed seals are the primary prey of polar bears, and they are found in all seasonally ice covered seas of the northern hemisphere as well as in several freshwater lakes. The presence of snow covered sea ice is essential for successful ringed seal reproduction. Ringed seals abrade holes in the ice allowing them to surface and breathe under the snow cover. Where snow accumulates to sufficient depths, ringed seals excavate subnivean lairs above breathing holes. They rest, give birth, and nurse their young in those lairs. Temperatures within the lairs remain within a few degrees of freezing, well within the zone of thermal neutrality for newborn ringed seals, even at ambient temperatures of -30° C. High rates of seal mortality have been recorded when early snow melt caused lairs to collapse exposing newborn seals to predators and to subsequent extreme cold events. As melt onset dates come earlier in the Arctic Ocean, ringed seal populations (and the polar bears that depend upon them) will be increasingly challenged. We determined dates of lair abandonment by ringed seals fitted with radio transmitters in the Beaufort Sea (n = 60). We compared abandonment dates to melt onset dates measured in the field, as well as estimated dates derived from active (Ku-band backscatter) and passive (SSM/I) microwave satellite imagery. Date of snow melt significantly improved models of environmental influences on the timing of lair abandonment. We used an algorithm based on multi-channel means and variances of passive microwave data to detect melt onset dates. Those melt onset dates predicted the date of lair abandonment ± 3 days (r 2 = 0.982, p = 0.001). The predictive power of passive microwave proxies combined with their historical record suggest they could serve to monitor critical changes to ringed seal habitat.

Correlation signatures of wet soils and snows. [algorithm development and computer programming

NASA Technical Reports Server (NTRS)

Phillips, M. R.

1972-01-01

Interpretation, analysis, and development of algorithms have provided the necessary computational programming tools for soil data processing, data handling and analysis. Algorithms that have been developed thus far, are adequate and have been proven successful for several preliminary and fundamental applications such as software interfacing capabilities, probability distributions, grey level print plotting, contour plotting, isometric data displays, joint probability distributions, boundary mapping, channel registration and ground scene classification. A description of an Earth Resources Flight Data Processor, (ERFDP), which handles and processes earth resources data under a users control is provided.

An interdisciplinary analysis of Colorado Rocky Mountain environments using ADP techniques. [San Juan Mts. and Indian Peaks

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator)

1974-01-01

The author has identified the following significant results. Good ecological, classification accuracy (90-95%) can be achieved in areas of rugged relief on a regional basis for Level 1 cover types (coniferous forest, deciduous forest, grassland, cropland, bare rock and soil, and water) using computer-aided analysis techniques on ERTS/MSS data. Cost comparisons showed that a Level 1 cover type map and a table of areal estimates could be obtained for the 443,000 hectare San Juan Mt. test site for less than 0.1 cent per acre, whereas photointerpretation techniques would cost more than 0.4 cent per acre. Results of snow cover mapping have conclusively proven that the areal extent of snow in mountainous terrain can be rapidly and economically mapped by using ERTS/MSS data and computer-aided analysis techniques. A distinct relationship between elevation and time of freeze or thaw was observed, during mountain lake mapping. Basic lithologic units such as igneous, sedimentary, and unconsolidated rock materials were successfully identified. Geomorphic form, which is exhibited through spatial and textual data, can only be inferred from ERTS data. Data collection platform systems can be utilized to produce satisfactory data from extremely inaccessible locations that encounter very adverse weather conditions, as indicated by results obtained from a DCP located at 3,536 meters elevation that encountered minimum temperatures of -25.5 C and wind speeds of up to 40.9m/sec (91 mph), but which still performed very reliably.

Integration of MODIS-derived metrics to assess interannual variability in snowpack, lake ice, and NDVI in southwest Alaska

USGS Publications Warehouse

Reed, Bradley C.; Budde, Michael E.; Spencer, Page; Miller, Amy E.

2009-01-01

Impacts of global climate change are expected to result in greater variation in the seasonality of snowpack, lake ice, and vegetation dynamics in southwest Alaska. All have wide-reaching physical and biological ecosystem effects in the region. We used Moderate Resolution Imaging Spectroradiometer (MODIS) calibrated radiance, snow cover extent, and vegetation index products for interpreting interannual variation in the duration and extent of snowpack, lake ice, and vegetation dynamics for southwest Alaska. The approach integrates multiple seasonal metrics across large ecological regions. Throughout the observation period (2001-2007), snow cover duration was stable within ecoregions, with variable start and end dates. The start of the lake ice season lagged the snow season by 2 to 3??months. Within a given lake, freeze-up dates varied in timing and duration, while break-up dates were more consistent. Vegetation phenology varied less than snow and ice metrics, with start-of-season dates comparatively consistent across years. The start of growing season and snow melt were related to one another as they are both temperature dependent. Higher than average temperatures during the El Ni??o winter of 2002-2003 were expressed in anomalous ice and snow season patterns. We are developing a consistent, MODIS-based dataset that will be used to monitor temporal trends of each of these seasonal metrics and to map areas of change for the study area.

Indices for estimating fractional snow cover in the western Tibetan Plateau

NASA Astrophysics Data System (ADS)

Shreve, Cheney M.; Okin, Gregory S.; Painter, Thomas H.

Snow cover in the Tibetan Plateau is highly variable in space and time and plays a key role in ecological processes of this cold-desert ecosystem. Resolution of passive microwave data is too low for regional-scale estimates of snow cover on the Tibetan Plateau, requiring an alternate data source. Optically derived snow indices allow for more accurate quantification of snow cover using higher-resolution datasets subject to the constraint of cloud cover. This paper introduces a new optical snow index and assesses four optically derived MODIS snow indices using Landsat-based validation scenes: MODIS Snow-Covered Area and Grain Size (MODSCAG), Relative Multiple Endmember Spectral Mixture Analysis (RMESMA), Relative Spectral Mixture Analysis (RSMA) and the normalized-difference snow index (NDSI). Pearson correlation coefficients were positively correlated with the validation datasets for all four optical snow indices, suggesting each provides a good measure of total snow extent. At the 95% confidence level, linear least-squares regression showed that MODSCAG and RMESMA had accuracy comparable to validation scenes. Fusion of optical snow indices with passive microwave products, which provide snow depth and snow water equivalent, has the potential to contribute to hydrologic and energy-balance modeling in the Tibetan Plateau.

Research on snow cover monitoring of Northeast China using Fengyun Geostationary Satellite

NASA Astrophysics Data System (ADS)

Wu, Tong; Gu, Lingjia; Ren, Ruizhi; Zhou, TIngting

2017-09-01

Snow cover information has great significance for monitoring and preventing snowstorms. With the development of satellite technology, geostationary satellites are playing more important roles in snow monitoring. Currently, cloud interference is a serious problem for obtaining accurate snow cover information. Therefore, the cloud pixels located in the MODIS snow products are usually replaced by cloud-free pixels around the day, which ignores snow cover dynamics. FengYun-2(FY-2) is the first generation of geostationary satellite in our country which complements the polar orbit satellite. The snow cover monitoring of Northeast China using FY-2G data in January and February 2016 is introduced in this paper. First of all, geometric and radiometric corrections are carried out for visible and infrared channels. Secondly, snow cover information is extracted according to its characteristics in different channels. Multi-threshold judgment methods for the different land types and similarity separation techniques are combined to discriminate snow and cloud. Furthermore, multi-temporal data is used to eliminate cloud effect. Finally, the experimental results are compared with the MOD10A1 and MYD10A1 (MODIS daily snow cover) product. The MODIS product can provide higher resolution of the snow cover information in cloudless conditions. Multi-temporal FY-2G data can get more accurate snow cover information in cloudy conditions, which is beneficial for monitoring snowstorms and climate changes.

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

NASA Astrophysics Data System (ADS)

Zhou, Lu; Xu, Shiming; Liu, Jiping; Wang, Bin

2018-03-01

The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

[Monitoring on spatial and temporal changes of snow cover in the Heilongjiang Basin based on remote sensing].

PubMed

Yu, Ling-Xue; Zhang, Shu-Wen; Guan, Cong; Yan, Feng-Qin; Yang, Chao-Bin; Bu, Kun; Yang, Jiu-Chun; Chang, Li-Ping

2014-09-01

This paper extracted and verified the snow cover extent in Heilongjiang Basin from 2003 to 2012 based on MODIS Aqua and Terra data, and the seasonal and interannual variations of snow cover extent were analyzed. The result showed that the double-star composite data reduced the effects of clouds and the overall accuracy was more than 91%, which could meet the research requirements. There existed significant seasonal variation of snow cover extent. The snow cover area was almost zero in July and August while in January it expanded to the maximum, which accounted for more than 80% of the basin. According to the analysis on the interannual variability of snow cover, the maximum winter snow cover areas in 2003-2004 and 2009-2010 (>180 x 10(4) km2) were higher than that of 2011 (150 x 10(4) km2). Meanwhile, there were certain correlations between the interannual fluctuations of snow cover and the changes of average annual temperature and precipitation. The year with the low snow cover was corresponding to less annual rainfall and higher average temperature, and vice versa. The spring snow cover showed a decreasing trend from 2003 to 2012, which was closely linked with decreasing precipitation and increasing temperature.

Putting the Capital 'A' in CoCoRAHS: A Pilot Program to Measure Albedo using the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) Network

NASA Astrophysics Data System (ADS)

Burakowski, E. A.; Stampone, M. D.; Wake, C. P.; Dibb, J. E.

2012-12-01

The Community Collaborative Rain, Hail, and Snow (CoCoRaHS) Network, started in 1998 as a community-based network of volunteer weather observer in Colorado, is the single largest provider of daily precipitation observations in the United States. We embrace the CoCoRaHS mission to use low-cost measurement tools, provide training and education, and utilize an interactive website to collect high quality albedo data for research and education applications. We trained a select sub-set of CoCoRaHS's eighteen most enthusiastic, self-proclaimed 'weather nuts' in the state of New Hampshire to collect surface albedo, snow depth, and snow density measurements between 23-Nov-2011 and 15-Mar-2012. At less than 700 per observer, the low-cost albedo data falls within ±0.05 of albedo values collected from a First Class Kipp and Zonen Albedometer (CMA6) at local solar noon. CoCoRaHS albedo values range from 0.99 for fresh snow to 0.34 for shallow, aged snow. Snow-free albedo ranges from 0.09 to 0.39, depending on ground cover. Albedo is found to increase logarithmically with snow depth and decrease linearly with snow density. The latter relationship with snow density is inferred to be a proxy for increasing snow grain size as snowpack ages and compacts, supported by spectral albedo measurements collected with an ASD FieldSpec4 spectrometer. The newly established albedo network also serves as a development test bed for interactive online mapping and graphing applications for CoCoRaHS observers to investigate spatial and temporal patterns in albedo, snow depth, and snow density (www.cocorahs-albedo.org). The 2012-2013 field season will include low-cost infrared temperature guns (<40 each) to investigate the relationship between surface albedo and skin temperature. We have also recruited middle- and high-schools as volunteer observers and are working with the teachers to develop curriculum and lesson plans that utilize the low-cost measurement tools provided by CoCoRAHS. CoCoRAHS data will provide critical spatially distributed measurements of surface data that will be used to validate and improve land surface modeling of New Hampshire climate under different land cover scenarios. Building on the success of the first season, the newly established albedo network shows promise to put the capital 'A' in CoCoRAHS.Figure 1. (a) Map of Community Collaborative Rain, Hail, and Snow (CoCoRAHS) volunteers participating in the pilot albedo project, and (b) CoCoRAHS snow measurement kit.

Complexity in Climatic Controls on Plant Species Distribution: Satellite Data Reveal Unique Climate for Giant Sequoia in the California Sierra Nevada

NASA Astrophysics Data System (ADS)

Waller, Eric Kindseth

A better understanding of the environmental controls on current plant species distribution is essential if the impacts of such diverse challenges as invasive species, changing fire regimes, and global climate change are to be predicted and important diversity conserved. Climate, soil, hydrology, various biotic factors fire, history, and chance can all play a role, but disentangling these factors is a daunting task. Increasingly sophisticated statistical models relying on existing distributions and mapped climatic variables, among others, have been developed to try to answer these questions. Any failure to explain pattern with existing mapped climatic variables is often taken as a referendum on climate as a whole, rather than on the limitations of the particular maps or models. Every location has a unique and constantly changing climate so that any distribution could be explained by some aspect of climate. Chapter 1 of this dissertation reviews some of the major flaws in species distribution modeling and addresses concerns that climate may therefore not be predictive of, or even relevant to, species distributions. Despite problems with climate-based models, climate and climate-derived variables still have substantial merit for explaining species distribution patterns. Additional generation of relevant climate variables and improvements in other climate and climate-derived variables are still needed to demonstrate this more effectively. Satellite data have a long history of being used for vegetation mapping and even species distribution mapping. They have great potential for being used for additional climatic information, and for improved mapping of other climate and climate-derived variables. Improving the characterization of cloud cover frequency with satellite data is one way in which the mapping of important climate and climate-derived variables can be improved. An important input to water balance models, solar radiation maps could be vastly improved with a better mapping of spatial and temporal patterns in cloud cover. Chapter 2 of this dissertation describes the generation of custom daily cloud cover maps from Advanced Very High Resolution Radiometer (AVHRR) satellite data from 1981-1999 at ~5 km resolution and Moderate Resolution Imagine Spectroradiomter (MODIS) satellite reflectance data at ~500 meter resolution for much of the western U.S., from 2000 to 2012. Intensive comparisons of reflectance spectra from a variety of cloud and snow-covered scenes from the southwestern United States allowed the generation of new rules for the classification of clouds and snow in both the AVHRR and MODIS data. The resulting products avoid many of the problems that plague other cloud mapping efforts, such as the tendency for snow cover and bright desert soils to be mapped as cloud. This consistency in classification across cover types is critically important for any distribution modeling of a plant species that might be dependent on cloud cover. In Chapter 3, monthly cloud frequencies derived from the daily classifications were used directly in species distribution models for giant sequoia and were found to be the strongest predictors of giant sequoia distribution. A high frequency of cloud cover, especially in the spring, differentiated the climate of the west slope of the southern Sierra Nevada, where giant sequoia are prolific, from central and northern parts of the range, where the tree is rare and generally absent. Other mapped cloud products, contaminated by confusion with high elevation snow, would likely not have found this important result. The result illustrates the importance of accuracy in mapping as well as the importance of previously overlooked aspects of climate for species distribution modeling. But it also raises new questions about why the clouds form where they do and whether they might be associated with other aspects of climate important to giant sequoia distribution. What are the exact climatic mechanisms governing the distribution? Detailed aspects of the local climate warranted more investigation. Chapter 4 investigates the climate associated with the frequent cloud formation over the western slopes of the southern Sierra Nevada: the "sequoia belt". This region is climatically distinct in a number of ways, all of which could be factors in influencing the distribution of giant sequoia and other species. Satellite and micrometeorological flux tower data reveal characteristics of the sequoia belt that were not evident with surface climate measurements and maps derived from them. Results have implications for species distributions everywhere, but especially in rugged mountains, where climates are complex and poorly mapped. Chapter 5 summarizes some of the main conclusions from the work and suggests directions for related future research. (Abstract shortened by UMI.).

Seasonal Snow Extent and Snow Mass in South America using SMMR and SSM/I Passive Microwave Data (1979-2006)

NASA Technical Reports Server (NTRS)

Foster, J. L.; Hall, D. K.; Kelly, R. E. J.; Chiu, L.

2008-01-01

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite and the Special Sensor Microwave Imagers (SSM/I) onboard Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2006, both snow cover extent and snow water equivalent (snow mass) were investigated during the coldest months (May-September), primarily in the Patagonia area of Argentina and in the Andes of Chile, Argentina and Bolivia, where most of the seasonal snow is found. Since winter temperatures in this region are often above freezing, the coldest winter month was found to be the month having the most extensive snow cover and usually the month having the deepest snow cover as well. Sharp year-to-year differences were recorded using the passive microwave observations. The average snow cover extent for July, the month with the greatest average extent during the 28-year period of record, is 321,674 km(exp 2). In July of 1984, the average monthly snow cover extent was 701,250 km(exp 2) the most extensive coverage observed between 1979 and 2006. However, in July of 1989, snow cover extent was only 120,000 km(exp 2). The 28-year period of record shows a sinusoidal like pattern for both snow cover and snow mass, though neither trend is significant at the 95% level.

LANDSAT-D investigations in snow hydrology. [Sierra Nevada Mountains, California

NASA Technical Reports Server (NTRS)

Dozier, J.

1983-01-01

Two tapes of the southern Sierra Nevada study area were received and the TM data are being registered to digital data. The spectral, spatial, temporal, and economic characteristics of data from LANDSAT 4 TM and MSS were compared with those of NOAA-r AVHRR data for snow cover mapping. An atmospheric radiative transfer model which accounts for both the zenith and aximuth variation in the radiative field is presented and its advantages are discussed. A Monte Carlo method for solving the atmospheric correction problem for an inhomogeneous surface is also considered.

Observation of Snow cover glide on Sub-Alpine Coniferous Forests in Mount Zao, Northeastern Japan

NASA Astrophysics Data System (ADS)

Sasaki, A.; Suzuki, K.

2017-12-01

This is the study to clarify the snow cover glide behavior in the sub-alpine coniferous forests on Mount Zao, Northeastern Japan, in the winter of 2014-2015. We installed the glide-meter which is sled type, and measured the glide motion on the slope of Abies mariesii forest and its surrounding slope. In addition, we observed the air temperature, snow depth, density of snow, and snow temperature to discuss relationship between weather conditions and glide occurrence. The snow cover of the 2014-15 winter started on November 13th and disappeared on April 21st. The maximum snow depth was 242 cm thick, it was recorded at February 1st. The snow cover glide in the surrounding slope was occurred first at February 10th, although maximum snow depth recorded on February 1st. The glide motion in the surrounding slope is continuing and its velocity was 0.4 cm per day. The glide in the surrounding slope stopped at March 16th. The cumulative amount of the glide was 21.1 cm. The snow cover glide in the A. mariesii forest was even later occurred first at February 21st. The glide motion of it was intermittent and extremely small. On sub-alpine zone of Mount Zao, snow cover glide intensity is estimated to be 289 kg/m2 on March when snow water equivalent is maximum. At same period, maximum snow cover glide intensity is estimated to be about 1000 kg/m2 at very steep slopes where the slope angle is about 35 degree. Although potential of snow cover glide is enough high, the snow cover glide is suppressed by stem of A. mariesii trees, in the sub-alpine coniferous forest.

Automated Mapping of Flood Events in the Mississippi River Basin Utilizing NASA Earth Observations

NASA Technical Reports Server (NTRS)

Bartkovich, Mercedes; Baldwin-Zook, Helen Blue; Cruz, Dashiell; McVey, Nicholas; Ploetz, Chris; Callaway, Olivia

2017-01-01

The Mississippi River Basin is the fourth largest drainage basin in the world, and is susceptible to multi-level flood events caused by heavy precipitation, snow melt, and changes in water table levels. Conducting flood analysis during periods of disaster is a challenging endeavor for NASA's Short-term Prediction Research and Transition Center (SPoRT), Federal Emergency Management Agency (FEMA), and the U.S. Geological Survey's Hazards Data Distribution Systems (USGS HDDS) due to heavily-involved research and lack of manpower. During this project, an automated script was generated that performs high-level flood analysis to relieve the workload for end-users. The script incorporated Landsat 8 Operational Land Imager (OLI) tiles and utilized computer-learning techniques to generate accurate water extent maps. The script referenced the Moderate Resolution Imaging Spectroradiometer (MODIS) land-water mask to isolate areas of flood induced waters. These areas were overlaid onto the National Land Cover Database's (NLCD) land cover data, the Oak Ridge National Laboratory's LandScan data, and Homeland Infrastructure Foundation-Level Data (HIFLD) to determine the classification of areas impacted and the population density affected by flooding. The automated algorithm was initially tested on the September 2016 flood event that occurred in Upper Mississippi River Basin, and was then further tested on multiple flood events within the Mississippi River Basin. This script allows end users to create their own flood probability and impact maps for disaster mitigation and recovery efforts.

Satellite image maps of Pakistan

USGS Publications Warehouse

,

1997-01-01

Georeferenced Landsat satellite image maps of Pakistan are now being made available for purchase from the U.S. Geological Survey (USGS). The first maps to be released are a series of Multi-Spectral Scanner (MSS) color image maps compiled from Landsat scenes taken before 1979. The Pakistan image maps were originally developed by USGS as an aid for geologic and general terrain mapping in support of the Coal Resource Exploration and Development Program in Pakistan (COALREAP). COALREAP, a cooperative program between the USGS, the United States Agency for International Development, and the Geological Survey of Pakistan, was in effect from 1985 through 1994. The Pakistan MSS image maps (bands 1, 2, and 4) are available as a full-country mosaic of 72 Landsat scenes at a scale of 1:2,000,000, and in 7 regional sheets covering various portions of the entire country at a scale of 1:500,000. The scenes used to compile the maps were selected from imagery available at the Eros Data Center (EDC), Sioux Falls, S. Dak. Where possible, preference was given to cloud-free and snow-free scenes that displayed similar stages of seasonal vegetation development. The data for the MSS scenes were resampled from the original 80-meter resolution to 50-meter picture elements (pixels) and digitally transformed to a geometrically corrected Lambert conformal conic projection. The cubic convolution algorithm was used during rotation and resampling. The 50-meter pixel size allows for such data to be imaged at a scale of 1:250,000 without degradation; for cost and convenience considerations, however, the maps were printed at 1:500,000 scale. The seven regional sheets have been named according to the main province or area covered. The 50-meter data were averaged to 150-meter pixels to generate the country image on a single sheet at 1:2,000,000 scale

Influence of Projected Changes in North American Snow Cover Extent on Mid-Latitude Cyclone Progression

NASA Astrophysics Data System (ADS)

Clare, R. M.; Desai, A. R.; Martin, J. E.; Notaro, M.; Vavrus, S. J.

2017-12-01

It has long been hypothesized that snow cover and snow extent have an influence on the development or steering of synoptic mid-latitude cyclones (MLCs). Rydzik and Desai (2014) showed a robust statistical relationship among snow cover extent, generation of low-level baroclinicity, and MLC tracks. Though snow cover extent is highly variable year to year, the changing global climate is expected to continue an already observed pattern of poleward retreat of mean snow cover in North America, particularly in late winter and spring. For this experiment, large ensemble simulations with the Weather Research and Forecasting model (WRF) were forced with output from the Community Earth System Model (CESM) to test the effect contributed solely by snow cover and the projected effects of a changing climate. Our experiment induces an adjustment to the extent of snow cover in North America according to CESM RCP 8.5 projections for each decade from 2020 to 2100 before and during several cases of MLCs moving east across the Great Plains near the snow line. To evaluate mechanisms of pre-existing and current snow influence on MLCs, model cases are started with snow line adjustment occurring from three days prior up to the storm's arrival over the Great Plains. We demonstrate that snow cover changes do alter MLC intensity and path via modification of low-level potential vorticity.

Seasonal Snow Extent and Snow Mass in South America Using SMMR and SSM/I Passive Microwave Data (1979-2003)

NASA Technical Reports Server (NTRS)

Foster, J. L.; Hall, D. K.; Chiu, L.; Kelly, R. E.; Powell, H.; Chiu, L.

2007-01-01

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-satellite and the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2003, both snow cover extent and snow depth (snow mass) were investigated during coldest months (May-September), primarily in the Patagonia area of Argentina and in Chile. Most of the seasonal snow in South America is in the Patagonia region of Argentina. Since winter temperatures in this region are often above freezing, the coldest winter month was found to be the month having the most extensive snow cover and also usually the month having the deepest snow cover as well. Sharp year-to-year differences were recorded using the passive microwave observations. The average snow cover extent for July, the month with the greatest average snow extent during the 25-year period of record, is 320,700 km(exp 2). In July of 1984, the average monthly snow cover was 701,250 km(exp 2) - the most extensive coverage observed between 1979 and 2003. However, in July of 1989, snow cover extent was only 120 km(exp 2). The 25-year period of record shows a sinusoidal like pattern, though there appears to be no obvious trend in either increasing or decreasing snow extent or snow mass between 1979 and 2003.

An evaluation of terrain-based downscaling of fractional snow covered area data sets based on LiDAR-derived snow data and orthoimagery

NASA Astrophysics Data System (ADS)

Cristea, Nicoleta C.; Breckheimer, Ian; Raleigh, Mark S.; HilleRisLambers, Janneke; Lundquist, Jessica D.

2017-08-01

Reliable maps of snow-covered areas at scales of meters to tens of meters, with daily temporal resolution, are essential to understanding snow heterogeneity, melt runoff, energy exchange, and ecological processes. Here we develop a parsimonious downscaling routine that can be applied to fractional snow covered area (fSCA) products from satellite platforms such as the Moderate Resolution Imaging Spectroradiometer (MODIS) that provide daily ˜500 m data, to derive higher-resolution snow presence/absence grids. The method uses a composite index combining both the topographic position index (TPI) to represent accumulation effects and the diurnal anisotropic heat (DAH, sun exposure) index to represent ablation effects. The procedure is evaluated and calibrated using airborne-derived high-resolution data sets across the Tuolumne watershed, CA using 11 scenes in 2014 to downscale to 30 m resolution. The average matching F score was 0.83. We then tested our method's transferability in time and space by comparing against the Tuolumne watershed in water years 2013 and 2015, and over an entirely different site, Mt. Rainier, WA in 2009 and 2011, to assess applicability to other topographic and climatic conditions. For application to sites without validation data, we recommend equal weights for the TPI and DAH indices and close TPI neighborhoods (60 and 27 m for downscaling to 30 and 3 m, respectively), which worked well in both our study areas. The method is less effective in forested areas, which still requires site-specific treatment. We demonstrate that the procedure can even be applied to downscale to 3 m resolution, a very fine scale relevant to alpine ecohydrology research.

Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm

USGS Publications Warehouse

Grippa, M.; Mognard, N.; Le, Toan T.; Josberger, E.G.

2004-01-01

One of the major challenges in determining snow depth (SD) from passive microwave measurements is to take into account the spatiotemporal variations of the snow grain size. Static algorithms based on a constant snow grain size cannot provide accurate estimates of snow pack thickness, particularly over large regions where the snow pack is subjected to big spatial temperature variations. A recent dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from the Special Sensor Microwave/Imager (SSM/I) over the Northern Great Plains (NGP) in the US. In this paper, we develop a combined dynamic and static algorithm to estimate snow depth from 13 years of SSM/I observations over Central Siberia. This region is characterised by extremely cold surface air temperatures and by the presence of permafrost that significantly affects the ground temperature. The dynamic algorithm is implemented to take into account these effects and it yields accurate snow depths early in the winter, when thin snowpacks combine with cold air temperatures to generate rapid crystal growth. However, it is not applicable later in the winter when the grain size growth slows. Combining the dynamic algorithm to a static algorithm, with a temporally constant but spatially varying coefficient, we obtain reasonable snow depth estimates throughout the entire snow season. Validation is carried out by comparing the satellite snow depth monthly averages to monthly climatological data. We show that the location of the snow depth maxima and minima is improved when applying the combined algorithm, since its dynamic portion explicitly incorporate the thermal gradient through the snowpack. The results obtained are presented and evaluated for five different vegetation zones of Central Siberia. Comparison with in situ measurements is also shown and discussed. ?? 2004 Elsevier Inc. All rights reserved.

NOAA WEATHER SATELLITES

Science.gov Websites

extent of snow cover. In addition, satellite sensors detect ice fields and map the movement of sea and greater danger near shore or any shallow waters? NATIONAL WEATHER SERVICE SATELLITE PRODUCTS NOAA's operational weather satellite system is composed of two types of satellites: geostationary operational

Monitoring snow cover variability (2000-2014) in the Hengduan Mountains based on cloud-removed MODIS products with an adaptive spatio-temporal weighted method

NASA Astrophysics Data System (ADS)

Li, Xinghua; Fu, Wenxuan; Shen, Huanfeng; Huang, Chunlin; Zhang, Liangpei

2017-08-01

Monitoring the variability of snow cover is necessary and meaningful because snow cover is closely connected with climate and ecological change. In this work, 500 m resolution MODIS daily snow cover products from 2000 to 2014 were adopted to analyze the status in Hengduan Mountains. In order to solve the spatial discontinuity caused by clouds in the products, we propose an adaptive spatio-temporal weighted method (ASTWM), which is based on the initial result of a Terra and Aqua combination. This novel method simultaneously considers the temporal and spatial correlations of the snow cover. The simulated experiments indicate that ASTWM removes clouds completely, with a robust overall accuracy (OA) of above 93% under different cloud fractions. The spatio-temporal variability of snow cover in the Hengduan Mountains was investigated with two indices: snow cover days (SCD) and snow fraction. The results reveal that the annual SCD gradually increases and the coefficient of variation (CV) decreases with elevation. The pixel-wise trends of SCD first rise and then drop in most areas. Moreover, intense intra-annual variability of the snow fraction occurs from October to March, during which time there is abundant snow cover. The inter-annual variability, which mainly occurs in high elevation areas, shows an increasing trend before 2004/2005 and a decreasing trend after 2004/2005. In addition, the snow fraction responds to the two climate factors of air temperature and precipitation. For the intra-annual variability, when the air temperature and precipitation decrease, the snow cover increases. Besides, precipitation plays a more important role in the inter-annual variability of snow cover than temperature.

Snow Cover Distribution and Variation using MODIS in the Himalayas of India

NASA Astrophysics Data System (ADS)

Mondal, A.; Lakshmi, V.; Jain, S. K.; Kansara, P. H.

2017-12-01

Snow cover variation plays a big role in river discharge, permafrost distribution and mass balance of glaciers in mountainous watersheds. Spatial distribution and temporal variation of snow cover varies with elevation and climate. We study the spatial distribution and temporal change of snow cover that has been observed using Terra Moderate Resolution Imaging Spectrometer (MODIS) product (MOD10A2 version 5) from 2001 to 2016. This MODIS product is based on normalized-difference snow index (NDSI) using band 4 (0.545-0.565 μm) and band 6 (1.628-1.652 μm). The spatial resolution of MOD10A2 is 500 m and composited over 8 days. The study area is the Indian Himalayas, major snow covered part of which is located in the states of Jammu and Kashmir, Himachal Pradesh, Uttarakhand, West Bengal, Sikkim, Assam and Arunachal Pradesh. Distribution and variation in snow cover is examined on monthly and annual time scales in this study. The temporal changes in snow cover has been compared with terrain attributes (elevation, slope and aspect). The snow cover depletion and accumulation have been observed during April-August and September-March. The snow cover is highest in the March and lowest in the August in the Himachal region. This study will be helpful to identify the amount of water stored in the glaciers of the Indian Himalaya and also important for water resources management of river basins, which are located in this area. Key words: Snow cover, MODIS, NDSI, terrain attribute

Impact of the snow cover scheme on snow distribution and energy budget modeling over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Xie, Zhipeng; Hu, Zeyong; Xie, Zhenghui; Jia, Binghao; Sun, Genhou; Du, Yizhen; Song, Haiqing

2018-02-01

This paper presents the impact of two snow cover schemes (NY07 and SL12) in the Community Land Model version 4.5 (CLM4.5) on the snow distribution and surface energy budget over the Tibetan Plateau. The simulated snow cover fraction (SCF), snow depth, and snow cover days were evaluated against in situ snow depth observations and a satellite-based snow cover product and snow depth dataset. The results show that the SL12 scheme, which considers snow accumulation and snowmelt processes separately, has a higher overall accuracy (81.8%) than the NY07 (75.8%). The newer scheme performs better in the prediction of overall accuracy compared with the NY07; however, SL12 yields a 15.1% underestimation rate while NY07 overestimated the SCF with a 15.2% overestimation rate. Both two schemes capture the distribution of the maximum snow depth well but show large positive biases in the average value through all periods (3.37, 3.15, and 1.48 cm for NY07; 3.91, 3.52, and 1.17 cm for SL12) and overestimate snow cover days compared with the satellite-based product and in situ observations. Higher altitudes show larger root-mean-square errors (RMSEs) in the simulations of snow depth and snow cover days during the snow-free period. Moreover, the surface energy flux estimations from the SL12 scheme are generally superior to the simulation from NY07 when evaluated against ground-based observations, in particular for net radiation and sensible heat flux. This study has great implications for further improvement of the subgrid-scale snow variations over the Tibetan Plateau.

Land Surface Temperature Measurements from EOD MODIS Data

NASA Technical Reports Server (NTRS)

Wan, Zheng-Ming

1998-01-01

We made more tests of the version 2.0 daily Level 2 and Level 3 Land-Surface Temperature (LST) code (PGE 16) jointly with the MODIS Science Data Support Team (SDST). After making minor changes a few times, the PGE16 code has been successfully integrated and tested by MODIS SDST, and recently has passed the inspection at the Goddard Distributed Active Archive Center (DAAC). We conducted a field campaign in the area of Mono Lake, California on March 10, 1998, in order to validate the MODIS LST algorithm in cold and dry conditions. Two MODIS Airborne Simulator (MAS) flights were completed during the field campaign, one before noon, and another around 10 pm PST. The weather condition for the daytime flight was perfect: clear sky, the column water vapor measured by radiosonde around 0.3 cm, and wind speed less than a half meter per second. The quality of MAS data is good for both day and night flights. We analyzed the noise equivalent temperature difference (NE(delta)T) and the calibration accuracy of the seven MAS thermal infrared (TIR) bands, that are used in the MODIS day/night LST algorithm, with daytime MAS data over four flat homogeneous study areas: two on Grant Lake (covered with ice and snow, respectively), one on Mono Lake, and another on the snow field site where we made field measurements. NE(delta)T ranges from 0.2 to 0.6 k for bands 42, 45, 46, and 48. It ranges from 0.8 to 1.1 K for bands 30-32. The day and night MAS data have been used to retrieve surface temperature and emissivities in these bands. A simple method to correct the effect of night thin cirrus has been incorporated into the day/night LST algorithm in dry atmospheric conditions. We compared the retrieved surface temperatures with those measured with TIR spectrometer, radiometers and thermistors in the snow test site, and the retrieved emissivity images with topographic map. The daytime LST values match well within 1 K. The night LST retrieved from MAS data is 3.3 K colder than those from field measurements most likely because of the effect of haze at night. The good agreement among the regional averaged surface temperatures obtained from LST values retrieved at different resolutions increased our confidence in the MODIS day/night LST algorithm.

Some relationships among air, snow, and soil temperatures and soil frost

Treesearch

George Hart; Howard W. Lull

1963-01-01

Each winter gives examples of the insulating properties of snow cover. Seeds and soil fauna are protected from the cold by snow. Underground water pipes are less likely to freeze under snow cover. And, according to many observers, the occurrence, penetration, and thaw of soil frost are affected by snow cover. The depth of snow necessary to protect soil from freezing...

Spatiotemporal variability of snow cover and snow water equivalent in the last three decades over Eurasia

NASA Astrophysics Data System (ADS)

Zhang, Yinsheng; Ma, Ning

2018-04-01

Changes in the extent and amount of snow cover in Eurasia are of great interest because of their vital impacts on the global climate system and regional water resource management. This study investigated the spatial and temporal variability of the snow cover extent (SCE) and snow water equivalent (SWE) of the continental Eurasia using the Northern Hemisphere Equal-Area Scalable Earth Grid (EASE-Grid) Weekly SCE data for 1972-2006 and the Global Monthly EASE-Grid SWE data for 1979-2004. The results indicated that, in general, the spatial extent of snow cover significantly decreased during spring and summer, but varied little during autumn and winter over Eurasia in the study period. The date at which snow cover began to disappear in spring has significantly advanced, whereas the timing of snow cover onset in autumn did not vary significantly during 1972-2006. The snow cover persistence period declined significantly in the western Tibetan Plateau as well as partial area of Central Asia and northwestern Russia, but varied little in other parts of Eurasia. "Snow-free breaks" (SFBs) with intermittent snow cover in the cold season were principally observed in the Tibetan Plateau and Central Asia, causing a low sensitivity of snow cover persistence period to the timings of snow cover onset and disappearance over the areas with shallow snow. The averaged SFBs were 1-14 weeks during the study period and the maximum intermittence could even reach 25 weeks in certain years. At a seasonal scale, SWE usually peaked in February or March, but fell gradually since April across Eurasia. Both annual mean and annual maximum SWE decreased significantly during 1979-2004 in most parts of Eurasia except for eastern Siberia as well as northwestern and northeastern China. The possible cross-platform inconsistencies between two passive microwave radiometers may cause uncertainties in the detected trends of SWE here, suggesting an urgent need of producing a long-term, more homogeneous SWE product in future.

Comparing different snow products to assess spatio-temporal snow cover patterns in the Central Taurus Mountains, Turkey

NASA Astrophysics Data System (ADS)

Sturm, K.; Helmschrot, J.

2013-12-01

Snow and its spatial and temporal patterns are important for catchment hydrology in the semi-arid eastern Mediterranean. Since most of the annual rainfall is stored as snow during winter and released during drier conditions in spring and summer, downstream regions of the Taurus Mountains relying on snow water temporarily stored in reservoirs for agricultural use are heavily dependent on the timing of snowmelt discharge. Runoff is controlled by the amount of accumulated snow, its distribution, and the climatic conditions controlling spring snowmelt. Thus, knowledge about spatial and temporal snow cover dynamics is essential for sustainable water resources management. The lack of observations in high-altitude regions reinforces the application of different snow products for a better assessment of spatio-temporal snow cover patterns. To better assess the quality of such products, simulated daily snow cover and EO-based snow cover products were compared for the Egribuk subcatchment, in the Central Taurus Mountains, Turkey. Daily information on snow cover, depths, and snow water equivalent was derived from distributed hydrological modeling using the J2000 model. Furthermore, 8-day MODIS snow cover data from Terra (MOD10A2) and Aqua (MYD10A2) satellites at a spatial resolution of 500 m were synchronized to receive cloud-free images. From this effort, 253 images covering the period between 07/04/2002 and 12/27/2007 were used for further analyses. The products were analyzed individually to determine the number of snow-covered days in relation to freezing days, spring snowmelt onsets, and temporal patterns, reflecting the effect of altitude on the percentage snow-covered area (SCA) along a topographic gradient at various time-steps. Monthly and 8-day spatial patterns of a single snow season were also examined. When SCA peaks at all altitudes, in February and March, the results of both products show a good agreement regarding SCA extent. In contrast, the extent of SCA differs notably during snow accumulation and ablation periods, the highest deviations occurring in December, April, and May. The highest SCA inconsistencies are observed in the low and mid altitudes, whereas the higher elevations are snow-covered very early in the snow season as modeled by J2000. During these periods, J2000 simulates a significantly larger SCA than MODIS. The analysis of individual time steps suggests that the J2000 daily model does capture individual snow events, whereas the MODIS products fail to do so due to their temporal resolution. Furthermore, acquisition time and inner-daily melt and re-freezing effects may affect SCA estimates from MODIS data. In other cases, differences can clearly be associated to insufficient model input data, primarily due to limited spatial precipitation and temperature data. Our study indicates that individual products might provide inconsistent information on temporal and spatial snow cover. We recommend considering a combined analysis of different snow products in order to provide reliable information on snow cover dynamics, in particular in eastern Mediterranean high-altitude environments.

Validation of Airborne FMCW Radar Measurements of Snow Thickness Over Sea Ice in Antarctica

NASA Technical Reports Server (NTRS)

Galin, Natalia; Worby, Anthony; Markus, Thorsten; Leuschen, Carl; Gogineni, Prasad

2012-01-01

Antarctic sea ice and its snow cover are integral components of the global climate system, yet many aspects of their vertical dimensions are poorly understood, making their representation in global climate models poor. Remote sensing is the key to monitoring the dynamic nature of sea ice and its snow cover. Reliable and accurate snow thickness data are currently a highly sought after data product. Remotely sensed snow thickness measurements can provide an indication of precipitation levels, predicted to increase with effects of climate change in the polar regions. Airborne techniques provide a means for regional-scale estimation of snow depth and distribution. Accurate regional-scale snow thickness data will also facilitate an increase in the accuracy of sea ice thickness retrieval from satellite altimeter freeboard estimates. The airborne data sets are easier to validate with in situ measurements and are better suited to validating satellite algorithms when compared with in situ techniques. This is primarily due to two factors: better chance of getting coincident in situ and airborne data sets and the tractability of comparison between an in situ data set and the airborne data set averaged over the footprint of the antennas. A 28-GHz frequency modulated continuous wave (FMCW) radar loaned by the Center for Remote Sensing of Ice Sheets to the Australian Antarctic Division is used to measure snow thickness over sea ice in East Antarctica. Provided with the radar design parameters, the expected performance parameters of the radar are summarized. The necessary conditions for unambiguous identification of the airsnow and snowice layers for the radar are presented. Roughnesses of the snow and ice surfaces are found to be dominant determinants in the effectiveness of layer identification for this radar. Finally, this paper presents the first in situ validated snow thickness estimates over sea ice in Antarctica derived from an FMCW radar on a helicopterborne platform.

Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier

USGS Publications Warehouse

Friesz, Aaron M.; Wylie, Bruce K.; Howard, Daniel M.

2017-01-01

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contribute to the availability of consistent CONUS crop cover maps by extending temporal coverage of the NASS CDL archive back eight additional years to 2000 by creating annual NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million records to train a classification tree algorithm and develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000–2013 at 250 m spatial resolution. The CCM and the maps for years 2008–2013 were assessed for accuracy relative to resampled NASS CDLs. The CCM performed well against a withheld test data set with a model prediction accuracy of over 90%. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains; however, the model did show a bias towards the ‘Other’ crop cover class, which caused frequent misclassifications of pixels around the periphery of large crop cover patch clusters and of pixels that form small, sparsely dispersed crop cover patches.

Development of remote sensing technology in New Zealand, part 1. Seismotectonic, structural, volcanologic and geomorphic study of New Zealand, part 2. Indigenous forest assessment, part 3. Mapping land use and environmental studies in New Zealand, part 4. New Zealand forest service LANDSAT projects, part 5. Vegetation map and landform map of Aupouri Peninsula, Northland, part 6. Geographical applications of LANDSAT mapping, part 7

NASA Technical Reports Server (NTRS)

Probine, M. C.; Suggate, R. P.; Mcgreevy, M. G.; Stirling, I. F. (Principal Investigator)

1977-01-01

The author has identified the following significant results. Inspection of pixels obtained from LANDSAT of New Zealand revealed that not only can ships and their wakes be detected, but that information on the size, state of motion, and direction of movement was inferred by calculating the total number of pixels occupied by the vessel and wake, the orientation of these pixels, and the sum of their radiance values above the background level. Computer enhanced images showing the Waimihia State Forest and much of Kaingaroa State Forest on 22 December 1975 were examined. Most major forest categories were distinguished on LANDSAT imagery. However, the LANDSAT imagery seemed to be most useful for updating and checking existing forest maps, rather than making new maps with many forest categories. Snow studies were performed using two basins: Six Mile Creek and Mt. Robert. The differences in radiance levels indicated that a greater areal snow cover in Six Mile Creek Basin with the effect of lower radiance values from vegetation/snow regions. A comparison of the two visible bands (MSS 4 and 5) demonstrate this difference for the two basins.

Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming

USGS Publications Warehouse

Euskirchen, E.S.; McGuire, A.D.; Chapin, F.S.

2007-01-01

The warming associated with changes in snow cover in northern high-latitude terrestrial regions represents an important energy feedback to the climate system. Here, we simulate snow cover-climate feedbacks (i.e. changes in snow cover on atmospheric heating) across the Pan-arctic over two distinct warming periods during the 20th century, 1910-1940 and 1970-2000. We offer evidence that increases in snow cover-climate feedbacks during 1970-2000 were nearly three times larger than during 1910-1940 because the recent snow-cover change occurred in spring, when radiation load is highest, rather than in autumn. Based on linear regression analysis, we also detected a greater sensitivity of snow cover-climate feedbacks to temperature trends during the more recent time period. Pan-arctic vegetation types differed substantially in snow cover-climate feedbacks. Those with a high seasonal contrast in albedo, such as tundra, showed much larger changes in atmospheric heating than did those with a low seasonal contrast in albedo, such as forests, even if the changes in snow-cover duration were similar across the vegetation types. These changes in energy exchange warrant careful consideration in studies of climate change, particularly with respect to associated shifts in vegetation between forests, grasslands, and tundra. ?? 2007 Blackwell Publishing Ltd.

Subpixel Snow-covered Area Including Differentiated Grain Size from AVIRIS Data Over the Sierra Nevada Mountain Range

NASA Astrophysics Data System (ADS)

Hill, R.; Calvin, W. M.; Harpold, A. A.

2016-12-01

Mountain snow storage is the dominant source of water for humans and ecosystems in western North America. Consequently, the spatial distribution of snow-covered area is fundamental to both hydrological, ecological, and climate models. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were collected along the entire Sierra Nevada mountain range extending from north of Lake Tahoe to south of Mt. Whitney during the 2015 and 2016 snow-covered season. The AVIRIS dataset used in this experiment consists of 224 contiguous spectral channels with wavelengths ranging 400-2500 nanometers at a 15-meter spatial pixel size. Data from the Sierras were acquired on four days: 2/24/15 during a very low snow year, 3/24/16 near maximum snow accumulation, and 5/12/16 and 5/18/16 during snow ablation and snow loss. Previous retrieval of subpixel snow-covered area in alpine regions used multiple snow endmembers due to the sensitivity of snow spectral reflectance to grain size. We will present a model that analyzes multiple endmembers of varying snow grain size, vegetation, rock, and soil in segmented regions along the Sierra Nevada to determine snow-cover spatial extent, snow sub-pixel fraction and approximate grain size or melt state. The root mean squared error will provide a spectrum-wide assessment of the mixture model's goodness-of-fit. Analysis will compare snow-covered area and snow-cover depletion in the 2016 year, and annual variation from the 2015 year. Field data were also acquired on three days concurrent with the 2016 flights in the Sagehen Experimental Forest and will support ground validation of the airborne data set.

Interannual changes in snow cover and its impact on ground surface temperatures in Livingston Island (Antarctica)

NASA Astrophysics Data System (ADS)

Nieuwendam, Alexandre; Ramos, Miguel; Vieira, Gonçalo

2015-04-01

In permafrost areas the seasonal snow cover is an important factor on the ground thermal regime. Snow depth and timing are important in ground insulation from the atmosphere, creating different snow patterns and resulting in spatially variable ground temperatures. The aim of this work is to characterize the interactions between ground thermal regimes and snow cover and the influence on permafrost spatial distribution. The study area is the ice-free terrains of northwestern Hurd Peninsula in the vicinity of the Spanish Antarctic Station "Juan Carlos I" and Bulgarian Antarctic Station "St. Kliment Ohridski". Air and ground temperatures and snow thickness data where analysed from 4 sites along an altitudinal transect in Hurd Peninsula from 2007 to 2012: Nuevo Incinerador (25 m asl), Collado Ramos (110 m), Ohridski (140 m) and Reina Sofia Peak (275 m). The data covers 6 cold seasons showing different conditions: i) very cold with thin snow cover; ii) cold with a gradual increase of snow cover; iii) warm with thick snow cover. The data shows three types of periods regarding the ground surface thermal regime and the thickness of snow cover: a) thin snow cover and short-term fluctuation of ground temperatures; b) thick snow cover and stable ground temperatures; c) very thick snow cover and ground temperatures nearly constant at 0°C. a) Thin snow cover periods: Collado Ramos and Ohridski sites show frequent temperature variations, alternating between short-term fluctuations and stable ground temperatures. Nuevo Incinerador displays during most of the winter stable ground temperatures; b) Cold winters with a gradual increase of the snow cover: Nuevo Incinerador, Collado Ramos and Ohridski sites show similar behavior, with a long period of stable ground temperatures; c) Thick snow cover periods: Collado Ramos and Ohridski show long periods of stable ground, while Nuevo Incinerador shows temperatures close to 0°C since the beginning of the winter, due to early snow cover, which prevents cooling. Reina Sofia shows a very different behavior from the other sites, with a frequent stabilization of ground temperatures during all the winters, and last until late-fall. This situation could be related to the structure, and physical and thermal properties of snow cover. The analysis of the Freezing Degree Days (FDDs) and freezing n-factor reveals significant interannual variations. Ohridski shows the highest FDDs values followed by Reina Sofia. Nuevo Incinerador showed the lowest FDDs values. The freezing n-factor shows highest values at Ohridski, followed by Collado Ramos and Reina Sofia with very similar values. Nuevo Incinerador shows the lowest n-factor values. Snow cover doesn't insulate the ground from freezing, but depending on its thickness, density and the amount of heat in the ground, it decreases ground temperatures amplitudes and increases delays relative to air temperature changes. Even where snow cover remains several centimeters thick for several months, slow decrease of bottom temperature is possible, reaching a minimum value at the end of the winter. The results demonstrate that Reina Sofia and Ohridski sites, because of the seasonal behavior, FDDs and freezing n-factor, demonstrate higher winter ground cooling. This research was funded by PERMANTAR-3 (PTDC/AAG-GLO/3908/2012) project (Fundação para a Ciência e a Tecnologia of Portugal)

A novel multi-temporal approach to wet snow retrieval with Sentinel-1 images (Conference Presentation)

NASA Astrophysics Data System (ADS)

Marin, Carlo; Callegari, Mattia; Notarnicola, Claudia

2016-10-01

Snow is one of the most relevant natural water resources present in nature. It stores water in winter and releases it in spring during the melting season. Monitoring snow cover and its variability is thus of great importance for a proactive management of water-resources. Of particular interest is the identification of snowmelt processes, which could significantly support water administration, flood prediction and prevention. In the past years, remote sensing has demonstrated to be an essential tool for providing accurate inputs to hydrological models concerning the spatial and temporal variability of snow. Even though the analysis of snow pack can be conducted in the visible, near-infrared and short-wave infrared spectrum, the presence of clouds during the melting season, which may be pervasive in some parts of the World (e.g., polar regions), renders impossible the regular acquisition of information needed for the operational purposes. Therefore, the use of the microwave sensors, which signal can penetrate the clouds, can be an asset for the detection of snow proprieties. In particular, the SAR images have demonstrated to be effective and robust measurements to identify the wet snow. Among the several methods presented in the literature, the best results in wet snow mapping have been achieved by the bi-temporal change detection approach proposed by Nagler and Rott [1], or its slight improvements presented afterwards (e.g., [2]). Nonetheless, with the introduction of the Sentinel-1 by ESA, which provides free-of-charge SAR images every 6 days over the same geographical area with a resolution of 20m, the scientists have the opportunity to better investigate and improve the state-of-the-art methods for wet snow detection. In this work, we propose a novel method based on a supervised learning approach able to exploit both the experience of the state-of-the-art algorithms and the high multi-temporal information provided by the Sentinel-1 data. In detail, this is done by training the proposed method with examples extracted by [1] and refine this information by deriving additional training for the complex cases where the state-of-the-art algorithm fails. In addition, the multi-temporal information is fully exploited by modelling it as a series of statistical moments. Indeed, with a proper time sampling, statistical moments can describe the shape of the probability density function (pdf) of the backscattering time series ([3-4]). Given the description of the shape of the multi-temporal VV and VH backscattering pdfs, it is not necessary to explicitly identify which time instants in the time series are to be assigned to the reference image as done in the bi-temporal approach. This information is implicit in the shape of the pdf and it is used in the training procedure for solving the wet snow detection problem based on the available training samples. The proposed approach is designed to work in an alpine environment and it is validated considering ground truth measurements provided by automatic weather stations that record snow depth and snow temperature over 10 sites deployed in the South Tyrol region in northern Italy. References: [1] Nagler, T.; Rott, H., "Retrieval of wet snow by means of multitemporal SAR data," in Geoscience and Remote Sensing, IEEE Transactions on , vol.38, no.2, pp.754-765, Mar 2000. [2] Storvold, R., Malnes, E., and Lauknes, I., "Using ENVISAT ASAR wideswath data to retrieve snow covered area in mountainous regions", EARSeL eProceedings 4, 2/2006 [3] Inglada, J and Mercier, G., "A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis," in IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1432-1445, May 2007. [4] Bujor, F., Trouve, E., Valet, L., Nicolas J. M., and Rudant, J. P., "Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 10, pp. 2073-2084, Oct. 2004.

Potential slab avalanche release area identification from estimated winter terrain: a multi-scale, fuzzy logic approach

NASA Astrophysics Data System (ADS)

Veitinger, Jochen; Purves, Ross Stuart; Sovilla, Betty

2016-10-01

Avalanche hazard assessment requires a very precise estimation of the release area, which still depends, to a large extent, on expert judgement of avalanche specialists. Therefore, a new algorithm for automated identification of potential avalanche release areas was developed. It overcomes some of the limitations of previous tools, which are currently not often applied in hazard mitigation practice. By introducing a multi-scale roughness parameter, fine-scale topography and its attenuation under snow influence is captured. This allows the assessment of snow influence on terrain morphology and, consequently, potential release area size and location. The integration of a wind shelter index enables the user to define release area scenarios as a function of the prevailing wind direction or single storm events. A case study illustrates the practical usefulness of this approach for the definition of release area scenarios under varying snow cover and wind conditions. A validation with historical data demonstrated an improved estimation of avalanche release areas. Our method outperforms a slope-based approach, in particular for more frequent avalanches; however, the application of the algorithm as a forecasting tool remains limited, as snowpack stability is not integrated. Future research activity should therefore focus on the coupling of the algorithm with snowpack conditions.

Distributed Wind Research | Wind | NREL

Science.gov Websites

evaluation, and improve wind turbine and wind power plant performance. A photo of a snowy road leading to a single wind turbine surrounded by snow-covered pine trees against blue sky. Capabilities NREL's power plant and small wind turbine development. Algorithms and programs exist for simulating, designing

[Evaluation of pollution of an urban area by level of heavy metals in snow cover].

PubMed

Stepanova, N V; Khamitova, R Ia; Petrova, R S

2003-01-01

The goal of this study was to systematize various methodological approaches to evaluating the contamination of the snow cover with heavy metals (HM) by using Kazan, an industrial city with diversified industry, as an example. The findings suggest that it is necessary to characterize the contamination of the snow cover by the actual entrance of an element per area unit of the snow cover for a definite period of time rather than by the concentration of TM in the volume unit of snow water (mg/l), which minimizes the uncertainties with spatial and temporary snow cover variations. The index of the maximum allowable entrance, which is of practical value, may be used to objectively calibrate the pollution of the snow cover, by estimating the amount of a coming element and its toxicity.

Monitoring Food Security Indicators from Remote Sensing and Predicting Cereal Production in Afghanistan

NASA Astrophysics Data System (ADS)

Pervez, M. S.; Budde, M. E.; Rowland, J.

2015-12-01

We extract percent of basin snow covered areas above 2500m elevation from Moderate Resolution Imaging Spectroradiometer (MODIS) 500-meter 8-day snow cover composites to monitor accumulation and depletion of snow in the basin. While the accumulation and depletion of snow cover extent provides an indication of the temporal progression of the snow pack, it does not provide insight into available water for irrigation. Therefore, we use snow model results from the National Operational Hydrologic Remote Sensing Center to quantify snow water equivalent and volume of water available within the snowpack for irrigation. In an effort to understand how water availability, along with its inter-annual variability, relates to the food security of the country, we develop a simple, effective, and easy-to-implement model to identify irrigated areas across the country on both annual and mid-season basis. The model is based on applying thresholds to peak growing season vegetation indices—derived from 250-meter MODIS images—in a decision-tree classifier to separate irrigated crops from non-irrigated vegetation. The spatial distribution and areal estimates of irrigated areas from these maps compare well with irrigated areas classified from multiple snap shots of the landscape from Landsat 5 optical and thermal images over selected locations. We observed that the extents of irrigated areas varied depending on the availability of snowmelt and can be between 1.35 million hectares in a year with significant water deficit and 2.4 million hectares in a year with significant water surplus. The changes in the amount of available water generally can contribute up to a 30% change in irrigated areas. We also observed that the strong correlation between inter-annual variability of irrigated areas and the variability in the country's cereal production could be utilized to predict an annual estimate of cereal production, providing early indication of food security scenarios for the country.

How snowmelt changed due to climate change in an ungauged catchment on the Tibetan Plateau?

NASA Astrophysics Data System (ADS)

Wang, Rui; Yao, Zhijun

2017-04-01

Snow variability is an integrated indicator of climate change, and it has important impacts on runoff regimes and water availability in high altitude catchments. Remote sensing techniques can make it possible to quantitatively detect the snow cover changes and associated hydrological effects in those poorly gauged regions. In this study, the spatial-temporal variations of snow cover and snow melting time in the Tuotuo River basin, which is the headwater of the Yangtze River, were evaluated based on satellite information from MODIS snow cover product, and the snow melting equivalent and its contribution to the total runoff and baseflow were estimated by using degree-day model. The results showed that the snow cover percentage and the tendency of snow cover variability increased with rising altitude. From 2000 to 2012, warmer and wetter climate change resulted in an increase of the snow cover area. Since the 1960s, the start time for snow melt has become earlier by 0.9 3 d/10a and the end time of snow melt has become later by 0.6 2.3 d/10a. Under the control of snow cover and snow melting time, the equivalent of snow melting runoff in the Tuotuo River basin has been fluctuating. The average contributions of snowmelt to baseflow and total runoff were 19.6 % and 6.8 %, respectively. Findings from this study will serve as a reference for future research in areas where observational data are deficient and for planning of future water management strategies for the source region of the Yangtze River.

CREST-SAFE: Snow LST validation, wetness profiler creation, and depth/SWE product development

NASA Astrophysics Data System (ADS)

Perez Diaz, C. L.; Lakhankar, T.; Romanov, P.; Khanbilvardi, R.; Munoz Barreto, J.; Yu, Y.

2017-12-01

CREST-SAFE: Snow LST validation, wetness profiler creation, and depth/SWE product development The Field Snow Research Station (also referred to as Snow Analysis and Field Experiment, SAFE) is operated by the NOAA Center for Earth System Sciences and Remote Sensing Technologies (CREST) in the City University of New York (CUNY). The field station is located within the premises of the Caribou Municipal Airport (46°52'59'' N, 68°01'07'' W) and in close proximity to the National Weather Service (NWS) Regional Forecast Office. The station was established in 2010 to support studies in snow physics and snow remote sensing. The Visible Infrared Imager Radiometer Suite (VIIRS) Land Surface Temperature (LST) Environmental Data Record (EDR) and Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (provided by the Terra and Aqua Earth Observing System satellites) were validated using in situ LST (T-skin) and near-surface air temperature (T-air) observations recorded at CREST-SAFE for the winters of 2013 and 2014. Results indicate that T-air correlates better than T-skin with VIIRS LST data and that the accuracy of nighttime LST retrievals is considerably better than that of daytime. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and night-time values. Results indicate that, although all the data sets showed high correlation with ground measurements, day values yielded slightly higher accuracy ( 1°C). Additionally, we created a liquid water content (LWC)-profiling instrument using time-domain reflectometry (TDR) at CREST-SAFE and tested it during the snow melt period (February-April) immediately after installation in 2014. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Lastly, to improve on global snow cover mapping, a snow product capable of estimating snow depth and snow water equivalent (SWE) using microwave remote sensing and the CREST Snow Depth Regression Tree Model (SDRTM) was developed. Data from AMSR2 onboard the JAXA GCOM-W1 satellite is used to produce daily global snow depth and SWE maps in automated fashion at a 10-km resolution.

A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

NASA Technical Reports Server (NTRS)

Tedesco, Marco; Jeyaratnam, Jeyavinoth

2016-01-01

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

Rapid heat-flowing surveying of geothermal areas, utilizing individual snowfalls as calorimeters

USGS Publications Warehouse

White, Donald E.

1969-01-01

Local differences in rate of heat transfer in vapor and by conduction through the ground in hot spring areas are difficult and time-consuming to measure quantitatively. Individual heavy snowfalls provide a rapid low-cost means of measuring total heat flow from such ground. After a favorable snowfall (heavy, brief duration, little wind, air temperature near 0°C), contacts between snow-covered and snow-free ground are mapped on a suitable base. Each mapped contact, as time elapses after a specific snowfall, is a heat-flow contour representing a decreasing rate of flow. Calibration of each mapped contact or snow line is made possible by the fact that snow remains on insulated surfaces (such as the boardwalks of Yellowstone's thermal areas) long after it has melted on adjacent warm ground. Heat-flow contours mapped to date range from 450 to 5500 μcal/cm2 sec, or 300 to 3700 times the world average of conductive heat flow. The very high rates of heat flow (2000 to > 10,000 μcal/cm2 sec) are probably too high, and the lower heat flows determinable by the method (2 sec) may be too low. Values indicated by the method are, however, probably within a factor of 2 of the total conductive and convective heat flow. Thermal anomalies from infrared imagery are similar in shape to heat-flow contours of a test area near Old Faithful geyser. Snowfall calorimetry provides a rapid means for evaluating the imagery and computer-derived products of the infrared data in terms of heat flow.

A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance

NASA Astrophysics Data System (ADS)

Swenson, S. C.; Lawrence, D. M.

2011-11-01

One function of the Community Land Model (CLM4) is the determination of surface albedo in the Community Earth System Model (CESM1). Because the typical spatial scales of CESM1 simulations are large compared to the scales of variability of surface properties such as snow cover and vegetation, unresolved surface heterogeneity is parameterized. Fractional snow-covered area, or snow-covered fraction (SCF), within a CLM4 grid cell is parameterized as a function of grid cell mean snow depth and snow density. This parameterization is based on an analysis of monthly averaged SCF and snow depth that showed a seasonal shift in the snow depth-SCF relationship. In this paper, we show that this shift is an artifact of the monthly sampling and that the current parameterization does not reflect the relationship observed between snow depth and SCF at the daily time scale. We demonstrate that the snow depth analysis used in the original study exhibits a bias toward early melt when compared to satellite-observed SCF. This bias results in a tendency to overestimate SCF as a function of snow depth. Using a more consistent, higher spatial and temporal resolution snow depth analysis reveals a clear hysteresis between snow accumulation and melt seasons. Here, a new SCF parameterization based on snow water equivalent is developed to capture the observed seasonal snow depth-SCF evolution. The effects of the new SCF parameterization on the surface energy budget are described. In CLM4, surface energy fluxes are calculated assuming a uniform snow cover. To more realistically simulate environments having patchy snow cover, we modify the model by computing the surface fluxes separately for snow-free and snow-covered fractions of a grid cell. In this configuration, the form of the parameterized snow depth-SCF relationship is shown to greatly affect the surface energy budget. The direct exposure of the snow-free surfaces to the atmosphere leads to greater heat loss from the ground during autumn and greater heat gain during spring. The net effect is to reduce annual mean soil temperatures by up to 3°C in snow-affected regions.

A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance

NASA Astrophysics Data System (ADS)

Swenson, S. C.; Lawrence, D. M.

2012-11-01

One function of the Community Land Model (CLM4) is the determination of surface albedo in the Community Earth System Model (CESM1). Because the typical spatial scales of CESM1 simulations are large compared to the scales of variability of surface properties such as snow cover and vegetation, unresolved surface heterogeneity is parameterized. Fractional snow-covered area, or snow-covered fraction (SCF), within a CLM4 grid cell is parameterized as a function of grid cell mean snow depth and snow density. This parameterization is based on an analysis of monthly averaged SCF and snow depth that showed a seasonal shift in the snow depth-SCF relationship. In this paper, we show that this shift is an artifact of the monthly sampling and that the current parameterization does not reflect the relationship observed between snow depth and SCF at the daily time scale. We demonstrate that the snow depth analysis used in the original study exhibits a bias toward early melt when compared to satellite-observed SCF. This bias results in a tendency to overestimate SCF as a function of snow depth. Using a more consistent, higher spatial and temporal resolution snow depth analysis reveals a clear hysteresis between snow accumulation and melt seasons. Here, a new SCF parameterization based on snow water equivalent is developed to capture the observed seasonal snow depth-SCF evolution. The effects of the new SCF parameterization on the surface energy budget are described. In CLM4, surface energy fluxes are calculated assuming a uniform snow cover. To more realistically simulate environments having patchy snow cover, we modify the model by computing the surface fluxes separately for snow-free and snow-covered fractions of a grid cell. In this configuration, the form of the parameterized snow depth-SCF relationship is shown to greatly affect the surface energy budget. The direct exposure of the snow-free surfaces to the atmosphere leads to greater heat loss from the ground during autumn and greater heat gain during spring. The net effect is to reduce annual mean soil temperatures by up to 3°C in snow-affected regions.

Sensitivity of the snowmelt runoff model to underestimates of remotely sensed snow covered area

USDA-ARS?s Scientific Manuscript database

Three methods for estimating snow covered area (SCA) from Terra MODIS data were used to derive conventional depletion curves for input to the Snowmelt Runoff Model (SRM). We compared the MOD10 binary and fractional snow cover products and a method for estimating sub-pixel snow cover using spectral m...

Single-pass Airborne InSAR for Wide-swath, High-Resolution Cryospheric Surface Topography Mapping

NASA Astrophysics Data System (ADS)

Moller, D.; Hensley, S.; Wu, X.; Muellerschoen, R.

2014-12-01

In May 2009 a mm-wave single-pass interferometric synthetic aperture radar (InSAR) for the first time demonstrated ice surface topography swath-mapping in Greenland. This was achieved with the airborne Glacier and Ice Surface Topography Interferometer (GLISTIN-A). Ka-band (35.6GHz) was chosen for high-precision topographic mapping from a compact sensor with minimal surface penetration. In recent years, the system was comprehensively upgraded for improved performance, stability and calibration. In April 2013, after completing the upgrades, GLISTIN-A flew a brief campaign to Alaska. The primary purpose was to demonstrate the InSAR's ability to generate high-precision, high resolution maps of ice surface topography with swaths in excess of 10km. Comparison of GLISTIN-A's elevations over glacial ice with lidar verified the precision requirements and established elevation accuracies to within 2 m without tie points. Feature tracking of crevasses on Columbia Glacier using data acquired with a 3-day separation exhibit an impressive velocity mapping capability. Furthermore, GLISTIN-A flew over the Beaufort sea to determine if we could not only map sea ice, but also measure freeboard. Initial analysis has established we can measure sea-ice freeboard using height differences from the top of the sea-ice and the sea surface in open leads. In the future, a campaign with lidar is desired for a quantitative validation. Another proof-of-concept collection mapped snow-basins for hydrology. Snow depth measurements using summer and winter collections in the Sierras were compared with lidar measurements. Unsurprisingly when present, trees complicate the interpretation, but additional filtering and processing is in work. For each application, knowledge of the interferometric penetration is important for scientific interpretation. We present analytical predictions and experimental data to upper bound the elevation bias of the InSAR measurements over snow and snow-covered ice.

Dynamics of active layer in wooded palsas of northern Quebec

NASA Astrophysics Data System (ADS)

Jean, Mélanie; Payette, Serge

2014-02-01

Palsas are organic or mineral soil mounds having a permafrost core. Palsas are widespread in the circumpolar discontinuous permafrost zone. The annual dynamics and evolution of the active layer, which is the uppermost layer over the permafrost table and subjected to the annual freeze-thaw cycle, are influenced by organic layer thickness, snow depth, vegetation type, topography and exposure. This study examines the influence of vegetation types, with an emphasis on forest cover, on active layer dynamics of palsas in the Boniface River watershed (57°45‧ N, 76°00‧ W). In this area, palsas are often colonized by black spruce trees (Picea mariana (Mill.) B.S.P.). Thaw depth and active layer thickness were monitored on 11 wooded or non-wooded mineral and organic palsas in 2009, 2010 and 2011. Snow depth, organic layer thickness, and vegetation types were assessed. The mapping of a palsa covered by various vegetation types and a large range of organic layer thickness were used to identify the factors influencing the spatial patterns of thaw depth and active layer. The active layer was thinner and the thaw rate slower in wooded palsas, whereas it was the opposite in more exposed sites such as forest openings, shrubs and bare ground. Thicker organic layers were associated with thinner active layers and slower thaw rates. Snow depth was not an important factor influencing active layer dynamics. The topography of the mapped palsa was uneven, and the environmental factors such as organic layer, snow depth, and vegetation types were heterogeneously distributed. These factors explain a part of the spatial variation of the active layer. Over the 3-year long study, the area of one studied palsa decreased by 70%. In a context of widespread permafrost decay, increasing our understanding of factors that influence the dynamics of wooded and non-wooded palsas and understanding of the role of vegetation cover will help to define the response of discontinuous permafrost landforms to changing climatic conditions.

Changes in snow cover over Northern Eurasia in the last few decades

NASA Astrophysics Data System (ADS)

Bulygina, O. N.; Razuvaev, V. N.; Korshunova, N. N.

2009-10-01

Daily snow depth (SD) and snow cover extent around 820 stations are used to analyse variations in snow cover characteristics in Northern Eurasia, a region that encompasses the Russian Federation. These analyses employ nearly five times more stations than in the previous studies and temporally span forty years. A representative judgement on the changes of snow depth over most of Russia is presented here for the first time. The number of days with greater than 50% of the near-station territory covered with snow, and the number of days with the snow depth greater than 1.0 cm, are used to characterize the duration of snow cover (SCD) season. Linear trends of the number of days and snow depth are calculated for each station from 1966 to 2007. This investigation reveals regional features in the change of snow cover characteristics. A decrease in the duration of snow cover is demonstrated in the northern regions of European Russia and in the mountainous regions of southern Siberia. An increase in SCD is found in Yakutia and in the Far East. In the western half of the Russian Federation, the winter-averaged SD is shown to increase, with the maximum trends being observed in Northern West Siberia. In contrast, in the mountainous regions of southern Siberia, the maximum SD decreases as the SCD decreases. While both snow cover characteristics (SCD and SD) play an important role in the hydrological cycle, ecosystems dynamics and societal wellbeing are quite different roles and the differences in their systematic changes (up to differences in the signs of changes) deserve further attention.

Seasonal Snow Extent and Snow Volume in South America Using SSM/I Passive Microwave Data

NASA Technical Reports Server (NTRS)

Foster, James L.; Chang, A. T. C.; Hall, D. K.; Kelly, R.; Houser, Paul (Technical Monitor)

2001-01-01

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1992-1998, both snow cover extent and snow depth (snow mass) were investigated during the winter months (May-August) in the Patagonia region of Argentina. Since above normal temperatures in this region are typically above freezing, the coldest winter month was found to be not only the month having the most extensive snow cover but also the month having the deepest snows. For the seven-year period of this study, the average snow cover extent (May-August) was about 0.46 million sq km and the average monthly snow mass was about 1.18 x 10(exp 13) kg. July 1992 was the month having the greatest snow extent (nearly 0.8 million sq km) and snow mass (approximately 2.6 x 10(exp 13) kg).

Small-area snow surveys on the northern plains of North Dakota

USGS Publications Warehouse

Emerson, Douglas G.; Carroll, T.R.; Steppuhn, Harold

1985-01-01

Snow-cover data are needed for many facets of hydrology. The variation in snow cover over small areas is the focus of this study. The feasibility of using aerial surveys to obtain information on the snow water equivalent of the snow cover in order to minimize the necessity of labor intensive ground snow surveys was- evaluated. A low-flying aircraft was used to measure attenuations of natural terrestrial gamma radiation by snow cover. Aerial and ground snow surveys of eight 1-mile snow courses and one 4-mile snow course were used in the evaluation, with ground snow surveys used as the base to evaluate aerial data. Each of the 1-mile snow courses consisted of a single land use and all had the same terrain type (plane). The 4-mile snow course consists of a variety of land uses and the same terrain type (plane). Using the aerial snow-survey technique, the snow water equivalent of the 1-mile snow courses was. measured with three passes of the aircraft. Use of more than one pass did not improve the results. The mean absolute difference between the aerial- and ground-measured snow water equivalents for the 1-mile snow courses was 26 percent (0.77 inches). The aerial snow water equivalents determined for the 1-mile snow courses were used to estimate the variations in the snow water equivalents over the 4-mile snow course. The weighted mean absolute difference for the 4-mile snow course was 27 percent (0.8 inches). Variations in snow water equivalents could not be verified adequately by segmenting the aerial snow-survey data because of the uniformity found in the snow cover. On the 4-mile snow coirse, about two-thirds of the aerial snow-survey data agreed with the ground snow-survey data within the accuracy of the aerial technique ( + 0.5 inch of the mean snow water equivalent).

Variations in snow cover seasonality across the Kyrgyz Republic from 2000 to 2016 revealed through MODIS Terra and Aqua snow products

NASA Astrophysics Data System (ADS)

Tomaszewska, M. A.; Henebry, G. M.

2017-12-01

The vertical transhumance practiced by herders in the highlands of Kyrgyzstan is vulnerable to environmental change. Herd movements and pasture conditions are both affected by spatial and temporal variations in snow cover and the timing of snowmelt. Early growing season soil moisture conditions affect the phenology and growth of vegetation, especially in the high elevation pastures used for summer forage. To evaluate snow seasonality, we examined three snow cover variables—the first day of snow (FDoS), the last day of snow (LDoS), and the duration of snow cover (DoSC) over 17 years based on 8-day snow product from MODIS Terra and Aqua (MOD/MYD10A2) across the Kyrgyz Republic (KYR). To track the "snow season" efficiently in the presence of snow-capped peaks, we start each snow season at day of year (DOY) 169, approximately the summer solstice, and extend to DOY 168 of the following year. To track the interannual variation of these variables, we applied two nonparametric statistics: the Mann-Kendall trend test and the Theil-Sen linear trend estimator. Our preliminary results focusing on four rayons in two oblasts indicate both large swaths of positive and negative significant trends over the different regions of the country. Positive trends in FDoS, meaning later snow arrival, were detected in parts of central KYR. Negative trends in FDoS meaning earlier arrival were detected at lower elevations in southwestern KYR. Earlier snowmelt (negative trend in LDoS) in eastern KYR resulted in a shorter snow season (negative trend in DoSC); in contrast, later snowmelt in southwestern KYR (positive trend in LDoS) resulted in a longer period of snow cover (positive trend of DoSC). We extend the analysis to the entire country and explore the influence of terrain attribites (elevation, slope, and aspect) and MODIS IGBP land cover type (MCD12Q1) on trends in snow cover seasonality. Additionally, we ran the trend tests for the Terra and Aqua snow products separately to evaluate the effect of overpass time on snow cover retrieval.

Snowmelt Pattern and Lake Ice Phenology around Tibetan Plateau Estimated from Enhanced Resolution Passive Microwave Data

NASA Astrophysics Data System (ADS)

Xiong, C.; Shi, J.; Wang, T.

2017-12-01

Snow and ice is very sensitive to the climate change. Rising air temperature will cause the snowmelt time change. In contrast, the change in snow state will have feedback on climate through snow albedo. The snow melt timing is also correlated with the associated runoff. Ice phenology describes the seasonal cycle of lake ice cover and includes freeze-up and breakup periods and ice cover duration, which is an important weather and climate indicator. It is also important for lake-atmosphere interactions and hydrological and ecological processes. The enhanced resolution (up to 3.125 km) passive microwave data is used to estimate the snowmelt pattern and lake ice phenology on and around Tibetan Plateau. The enhanced resolution makes the estimation of snowmelt and lake ice phenology in more spatial detail compared to previous 25 km gridded passive microwave data. New algorithm based on smooth filters and change point detection was developed to estimate the snowmelt and lake ice freeze-up and break-up timing. Spatial and temporal pattern of snowmelt and lake ice phonology are estimated. This study provides an objective evidence of climate change impact on the cryospheric system on Tibetan Plateau. The results show significant earlier snowmelt and lake ice break-up in some regions.

Monitoring of snowpack dynamics in mountainous terrain by cosmic-ray neutron sensing compared to Terrestrial Laser Scanning observations

NASA Astrophysics Data System (ADS)

Schattan, P.; Baroni, G.; Schrön, M.; Köhli, M.; Oswald, S. E.; Huttenlau, M.; Achleitner, S.

2017-12-01

Monitoring a mountain snowpack in a representative domain of several hectares is challenging due to its high heterogeneity in time and space. Recent studies have suggested cosmic-ray neutron sensing (CRNS) as a promising method for monitoring snow representatively at these scales. Little is known however about the depth of sensitivity, the effects of fractional snow coverage in complex terrain or the influence of snow density profiles. Therefore, a field campaign in the Austrian Alps was conducted from March 2014 to June 2016. The main scope was to evaluate the characteristics of CRNS for monitoring a snowpack in a relatively wet and mountainous environment. During the experiment, the study site experienced a peak snow accumulation in terms of snow water equivalent (SWE) of up to 600 mm in the 2014/2015 winter season. Snow depth (SD) and SWE measurements from an automatic weather station were compared to CRNS neutron counts. Several spatially distributed Terrestrial Laser Scanning (TLS)-based SD and SWE maps were additionally used to cope with the spatial heterogeneity of the site. Furthermore, an URANOS neutron transport model was set up to provide additional insights into the response of CRNS to the presence of a complex snowpack. Therein, spatially distributed SWE scenarios and different snow density assumptions are used for hypothesis testing. The field measurements revealed an unexpectedly high potential of CRNS for monitoring heterogeneous snowpack dynamics beyond shallow snowpacks. A clear, nonlinear relation was found for both SD and SWE with neutron counts. In contrast to previous studies suggesting signal saturation at around 100 mm of SWE, complete signal saturation was observed only for SWE values beyond 500 to 600 mm. In addition, first modelling results highlight the effects of snow density profiles, small-scale changes in SWE, and the complex patterns of fractional snow cover on neutron counts. Understanding the interactions between neutrons and snow cover in complex terrain potentially improves the transferability of the results to other locations.

CO2 flux over young and snow-covered Arctic pack ice in winter and spring

NASA Astrophysics Data System (ADS)

Nomura, Daiki; Granskog, Mats A.; Fransson, Agneta; Chierici, Melissa; Silyakova, Anna; Ohshima, Kay I.; Cohen, Lana; Delille, Bruno; Hudson, Stephen R.; Dieckmann, Gerhard S.

2018-06-01

Rare CO2 flux measurements from Arctic pack ice show that two types of ice contribute to the release of CO2 from the ice to the atmosphere during winter and spring: young, thin ice with a thin layer of snow and older (several weeks), thicker ice with thick snow cover. Young, thin sea ice is characterized by high salinity and high porosity, and snow-covered thick ice remains relatively warm ( > -7.5 °C) due to the insulating snow cover despite air temperatures as low as -40 °C. Therefore, brine volume fractions of these two ice types are high enough to provide favorable conditions for gas exchange between sea ice and the atmosphere even in mid-winter. Although the potential CO2 flux from sea ice decreased due to the presence of the snow, the snow surface is still a CO2 source to the atmosphere for low snow density and thin snow conditions. We found that young sea ice that is formed in leads without snow cover produces CO2 fluxes an order of magnitude higher than those in snow-covered older ice (+1.0 ± 0.6 mmol C m-2 day-1 for young ice and +0.2 ± 0.2 mmol C m-2 day-1 for older ice).

Remote sensing, hydrological modeling and in situ observations in snow cover research: A review

NASA Astrophysics Data System (ADS)

Dong, Chunyu

2018-06-01

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

Estimating the Thickness of Sea Ice Snow Cover in the Weddell Sea from Passive Microwave Brightness Temperatures

NASA Technical Reports Server (NTRS)

Arrigo, K. R.; vanDijken, G. L.; Comiso, J. C.

1996-01-01

Passive microwave satellite observations have frequently been used to observe changes in sea ice cover and concentration. Comiso et al. showed that there may also be a direct relationship between the thickness of snow cover (h(sub s)) on ice and microwave emissivity at 90 GHz. Because the in situ experiment of experiment of Comiso et al. was limited to a single station, the relationship is re-examined in this paper in a more general context and using more extensive in situ microwave observations and measurements of h from the Weddell Sea 1986 and 1989 winter cruises. Good relationships were found to exist between h(sub s) sand the emissivity at 90 GHz - 10 GHz and the emissivity at 90 GHz - 18.7 GHz when the standard deviation of h(sub s) was less than 50% of the mean and when h(sub s) was less than 0.25 m. The reliance of these relationships on h(sub s) is most likely caused by the limited penetration through the snow of radiation at 90 GHz. When the algorithm was applied to the Special Sensor Microwave/Imager (SSM/I) satellite data from the Weddell Sea, the resulting mean h(sub s) agreed within 5% of the mean calculated from greater than 1400 in situ observations.

[Spatiotemporal dynamics of land cover in northern Tibetan Plateau with responses to climate change].

PubMed

Song, Chun-qiao; You, Song-cai; Ke, Ling-hong; Liu, Gao-huan; Zhong, Xin-ke

2011-08-01

By using the 2001-2008 MOMS land cover products (MCDl2Ql) and based on the modified classification scheme embodied the characteristics of land cover in northern Tibetan Plateau, the annual land cover type maps of the Plateau were drawn, with the dynamic changes of each land cover type analyzed by classification statistics, dynamic transfer matrix, and landscape pattern indices. In 2001-2008, due to the acceleration of global climate warming, the areas of glacier and snow-covered land in the Plateau decreased rapidly, and the melted snow water gathered into low-lying valley or basin, making the lake level raised and the lake area enlarged. Some permanent wetlands were formed because of partially submersed grassland. The vegetation cover did not show any evident meliorated or degraded trend. From 2001 to 2004, as the climate became warmer and wetter, the spatial distribution of desert began to shrink, and the proportions of sparse grassland and grassland increased. From 2006 to 2007, due to the warmer and drier climate, the desert bare land increased, and the sparse grassland decreased. From 2001 to 2008, both the landscape fragmentation degree and the land cover heterogeneity decreased, and the differences in the proportions of all land cover types somewhat enlarged.

Cloud Detection of Optical Satellite Images Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Lee, Kuan-Yi; Lin, Chao-Hung

2016-06-01

Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection accuracy of the proposed method is better than related methods.

[Effects of seasonal snow cover on soil nitrogen transformation in alpine ecosystem: a review].

PubMed

Liu, Lin; Wu, Yan; He, Yi-xin; Wu, Ning; Sun, Geng; Zhang, Lin; Xu, Jun-jun

2011-08-01

Seasonal snow cover has pronounced effects on the soil nitrogen concentration and transformation in alpine ecosystem. Snowfall is an important form of nitrogen deposition, which directly affects the content of soil available nitrogen. Different depths and different duration of snow cover caused by snowfall may lead the heterogeneity of abiotic factors (soil temperature and moisture) and biotic factors (soil microbes, alpine plants, and alpine animals), and further, produce complicated effects on the mineralization and immobilization of soil nitrogen. This paper introduced in emphasis the inherent mechanisms of soil nitrogen mineralization and leaching under the effects of frequent freeze-thaw events during the durative melting of snow cover, and summarized the main research results of field in situ experiments about the effects of seasonal snow cover on soil nitrogen in alpine ecosystem based on the possible changes in snow cover in the future. Some suggestions with regard to the effects of seasonal snow cover on soil nitrogen were put forward.

Snow cover distribution over elevation zones in a mountainous catchment

NASA Astrophysics Data System (ADS)

Panagoulia, D.; Panagopoulos, Y.

2009-04-01

A good understanding of the elevetional distribution of snow cover is necessary to predict the timing and volume of runoff. In a complex mountainous terrain the snow cover distribution within a watershed is highly variable in time and space and is dependent on elevation, slope, aspect, vegetation type, surface roughness, radiation load, and energy exchange at the snow-air interface. Decreases in snowpack due to climate change could disrupt the downstream urban and agricultural water supplies, while increases could lead to seasonal flooding. Solar and longwave radiation are dominant energy inputs driving the ablation process. Turbulent energy exchange at the snow cover surface is important during the snow season. The evaporation of blowing and drifting snow is strongly dependent upon wind speed. Much of the spatial heterogeneity of snow cover is the result of snow redistribution by wind. Elevation is important in determining temperature and precipitation gradients along hillslopes, while the temperature gradients determine where precipitation falls as rain and snow and contribute to variable melt rates within the hillslope. Under these premises, the snow accumulation and ablation (SAA) model of the US National Weather Service (US NWS) was applied to implement the snow cover extent over elevation zones of a mountainous catchment (the Mesochora catchment in Western-Central Greece), taking also into account the indirectly included processes of sublimation, interception, and snow redistribution. The catchment hydrology is controlled by snowfall and snowmelt and the simulated discharge was computed from the soil moisture accounting (SMA) model of the US NWS and compared to the measured discharge. The elevationally distributed snow cover extent presented different patterns with different time of maximization, extinction and return during the year, producing different timing of discharge that is a crucial factor for the control and management of water resources systems.

Analyzing dynamics of snow distribution and melt runoff in a meso-scaled watershed using the AgroEcoSystem-Watershed (AgES-W) model

NASA Astrophysics Data System (ADS)

Kunz, A.; Helmschrot, J.; Green, T. R.

2013-12-01

The seasonal snow cover in the western mountain regions of the United States functions as the primary supply and storage of water. Water management in these areas is often based on empirical relationships between point measurements of snow water equivalent (SWE) at selected sites and associated stream discharge. With a climate shifting towards more rain and less snow, due to the global warming, the patterns of snow deposition, and consequently the timing of melt, soil water content and the flow in streams and rivers will most likely alter. As a consequence, the established relationships between measured SWE and runoff will become unstable and unreliable, and consequently impacting the water resource management in this area. To better assess and understand the spatial and temporal dimension of altered snow cover on runoff generation in the intermountain region of the western United States, we set up the distributed hydrological AgroEcoSystem-Watershed (AgES-W) model for the Reynolds Creek Experimental Watershed (239 km2) in the Owyhee Mountains of Idaho. The study area with elevations ranging from 1101 to 2241 m is dominated by granitic and volcanic rocks and lake sediments. Deep moist soils allowing for mountain big sagebrush aspen and subalpine fir are found at higher elevations, whereas shallow, arid soils supporting sagebrush-grassland communities are common at lower elevations. Precipitation in the region varies from 230 mm at the lower elevations in the north up to 1100 mm in the higher regions at the southern margin south. The mean annual streamflow at the outlet is 0.56 m3/s. Since the Reynolds Creek Experimental Watershed (RCEW) was selected as a test basin in 1959, a comprehensive hydro-climatological network provides long-term records of daily snow, precipitation, temperature and streamflow measurements. Thus, we used a 30-year data record to calibrate and validate the AgES-W model to three nested sub-basins within the test site. First results show declining discharge volumes for RCEW, while volumes remain fairly constant for the 0.4 km2 Reynolds Mountain East (RME) headwater basin. Comparing simulated snow cover with snow-depth records measured across RME, the model was initially tested regarding its reliability to estimate spatio-temporal snow cover. AgES-W was able to simulate snow-depth dynamics quite well (>0.7 Nash-Sutcliffe Efficiency) for single measurement points, which were cross-validated using additional measurement points as well as stream discharge. The obtained parameter set was then used to model snow distribution for the entire RME basin for a period of 12 years. Applying the calibrated model to all catchments, we analyzed temporal shifts of seasonal runoff within and between the three nested subwatersheds to identify possible changes in the spatio-temporal pattern of snow accumulation and snowmelt. The model results were further used to analyze and map simulated snow water equivalents along a topographic gradient to identify spatial shifts of the snowline during the last 30 years. First results for RME indicate a decline of snow-covered area based on the course of monthly averages, with the largest declines in January and February.

Effect of snow cover on soil frost penetration

NASA Astrophysics Data System (ADS)

Rožnovský, Jaroslav; Brzezina, Jáchym

2017-12-01

Snow cover occurrence affects wintering and lives of organisms because it has a significant effect on soil frost penetration. An analysis of the dependence of soil frost penetration and snow depth between November and March was performed using data from 12 automated climatological stations located in Southern Moravia, with a minimum period of measurement of 5 years since 2001, which belong to the Czech Hydrometeorological institute. The soil temperatures at 5 cm depth fluctuate much less in the presence of snow cover. In contrast, the effect of snow cover on the air temperature at 2 m height is only very small. During clear sky conditions and no snow cover, soil can warm up substantially and the soil temperature range can be even higher than the range of air temperature at 2 m height. The actual height of snow is also important - increased snow depth means lower soil temperature range. However, even just 1 cm snow depth substantially lowers the soil temperature range and it can therefore be clearly seen that snow acts as an insulator and has a major effect on soil frost penetration and soil temperature range.

Distribution and variability of total mercury in snow cover-a case study from a semi-urban site in Poznań, Poland.

PubMed

Siudek, Patrycja

2016-12-01

In the present paper, the inter-seasonal Hg variability in snow cover was examined based on multivariate statistical analysis of chemical and meteorological data. Samples of freshly fallen snow cover were collected at the semi-urban site in Poznań (central Poland), during 3-month field measurements in winter 2013. It was showed that concentrations of atmospherically deposited Hg were highly variable in snow cover, from 0.43 to 12.5 ng L -1 , with a mean value of 4.62 ng L -1 . The highest Hg concentration in snow cover coincided with local intensification of fossil fuel burning, indicating large contribution from various anthropogenic sources such as commercial and domestic heating, power generation plants, and traffic-related pollution. Moreover, the variability of Hg in collected snow samples was associated with long-range transport of pollutants, nocturnal inversion layer, low boundary layer height, and relatively low air temperature. For three snow episodes, Hg concentration in snow cover was attributed to southerly advection, suggesting significant contribution from the highly polluted region of Poland (Upper Silesia) and major European industrial hotspots. However, the peak Hg concentration was measured in samples collected during predominant N to NE advection of polluted air masses and after a relatively longer period without precipitation. Such significant contribution to the higher Hg accumulation in snow cover was associated with intensive emission from anthropogenic sources (coal combustion) and atmospheric conditions in this area. These results suggest that further measurements are needed to determine how the Hg transformation paths in snow cover change in response to longer/shorter duration of snow cover occurrence and to determine the interactions between mercury and absorbing carbonaceous aerosols in the light of climate change.

Land Surface Properties near Terra Nova Bay, East Antarctica, Analyzed by Time-series Height, Coherence and Amplitude Maps Derived from COSMO-SkyMed One-day Tandem Pairs

NASA Astrophysics Data System (ADS)

Ji, Y.; Han, H.; Lee, H.

2014-12-01

Analysis of the surface properties of Antarctica is very important to study the change of environment and climate in the polar region. Synthetic aperture radar (SAR) has been widely used to study Antarctic surface properties because it is independent of sun altitude and atmospheric conditions. Interferometric SAR (InSAR) observes surface topography and deformation, by calculating the phase differences between two or more SAR images obtained over same area. InSAR technique can be used for height mapping in stable areas with a few meter accuracy. However, the InSAR-derived height map can have errors if the phase differences due to surface deformation or change of the scattering center by microwave penetration into snow are misinterpreted as the elevation. In this study, we generated the height maps around Terra Nova Bay in East Antarctica from 13 COSMO-SkyMed one-day tandem InSAR pairs obtained from December 2010 to January 2012. By analyzing the height maps averaged over the 13 interferograms and its standard deviation (STD) map, we could classify the surface types into glacier, mountains and basin areas covered with snow. The mountain areas showed very small STD because its surface property is unchanged with time, except for the small STD values caused by the errors from the unwrapping processing, satellite orbit or atmospheric phase distortion. Over the basin areas, however, the STD of the height was much larger than the mountain area due to the variation of scattering center either from the change in surface property such as snowfall and sublimation or by the surface displacement of snow mass that are too slow. A year-long constant motion of such slow-creeping snow body was positively identified by its linear relationship between the misinterpreted elevation and the baseline perpendicular component of InSAR pair. Analysis of time-series coherence maps and amplitude maps have also contributed to clarify the surface properties and its changes due to various environmental factors such as snow fall, wind, sublimation, and the freezing-thawing processes in this Antarctic land surface. Acknowledgement - This research was supported by National Research Foundation of Korea through NRF-2013R1A1A2008062 and NRF-2013M1A3A3A02041853.

Operational Applications of Satellite Snowcover Observations

NASA Technical Reports Server (NTRS)

Rango, A. (Editor); Peterson, R. (Editor)

1980-01-01

The history of remote sensing of snow cover is reviewed and the following topics are covered: various techniques for interpreting LANDSAT and NOAA satellite data; the status of future systems for continuing snow hydrology applications; the use of snow cover observations in streamflow forecasts by Applications Systems Verification and Transfer participants and selected foreign investigators; and the benefits of using satellite snow cover data in runoff prediction.

Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

NASA Technical Reports Server (NTRS)

Kumar, Uttam; Nemani, Ramakrishna R.; Ganguly, Sangram; Kalia, Subodh; Michaelis, Andrew

2017-01-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS-national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91 percent was achieved, which is a 6 percent improvement in unmixing based classification relative to per-pixel-based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

NASA Astrophysics Data System (ADS)

Ganguly, S.; Kumar, U.; Nemani, R. R.; Kalia, S.; Michaelis, A.

2017-12-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS - national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91% was achieved, which is a 6% improvement in unmixing based classification relative to per-pixel based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

Numerical Modeling of Coupled Water Flow and Heat Transport in Soil and Snow

NASA Astrophysics Data System (ADS)

Kelleners, T.

2015-12-01

A numerical model is developed to calculate coupled water flow and heat transport in seasonally frozen soil and snow. Both liquid water flow and water vapor flow are included. The effect of dissolved ions on soil water freezing point depression is included by combining an expression for osmotic head with the Clapeyron equation and the van Genuchten soil water retention function. The coupled water flow and heat transport equations are solved using the Thomas algorithm and Picard iteration. Ice pressure is always assumed zero and frost heave is neglected. The new model is tested using data from a high-elevation rangeland soil that is subject to significant soil freezing and a mountainous forest soil that is snow-covered for about 8 months of the year. Soil hydraulic parameters are mostly based on measurements and only vegetation parameters are fine-tuned to match measured and calculated soil water content, soil & snow temperature, and snow height. Modeling statistics for both systems show good performance for temperature, intermediate performance for snow height, and relatively low performance for soil water content, in accordance with earlier results with an older version of the model.

Altitude-dependent influence of snow cover on alpine land surface phenology

NASA Astrophysics Data System (ADS)

Xie, Jing; Kneubühler, Mathias; Garonna, Irene; Notarnicola, Claudia; De Gregorio, Ludovica; De Jong, Rogier; Chimani, Barbara; Schaepman, Michael E.

2017-05-01

Snow cover impacts alpine land surface phenology in various ways, but our knowledge about the effect of snow cover on alpine land surface phenology is still limited. We studied this relationship in the European Alps using satellite-derived metrics of snow cover phenology (SCP), namely, first snow fall, last snow day, and snow cover duration (SCD), in combination with land surface phenology (LSP), namely, start of season (SOS), end of season, and length of season (LOS) for the period of 2003-2014. We tested the dependency of interannual differences (Δ) of SCP and LSP metrics with altitude (up to 3000 m above sea level) for seven natural vegetation types, four main climatic subregions, and four terrain expositions. We found that 25.3% of all pixels showed significant (p < 0.05) correlation between ΔSCD and ΔSOS and 15.3% between ΔSCD and ΔLOS across the entire study area. Correlations between ΔSCD and ΔSOS as well as ΔSCD and ΔLOS are more pronounced in the northern subregions of the Alps, at high altitudes, and on north and west facing terrain—or more generally, in regions with longer SCD. We conclude that snow cover has a greater effect on alpine phenology at higher than at lower altitudes, which may be attributed to the coupled influence of snow cover with underground conditions and air temperature. Alpine ecosystems may therefore be particularly sensitive to future change of snow cover at high altitudes under climate warming scenarios.

In-situ Microwave Brightness Temperature Variability from Ground-based Radiometer Measurements at Dome C in Antarctica Induced by Wind-formed Features

NASA Technical Reports Server (NTRS)

Royer, A.; Picard, G.; Arnaud, L.; Brucker, L.; Fily, M..

2014-01-01

Space-borne microwave radiometers are among the most useful tools to study snow and to collect information on the Antarctic climate. They have several advantages over other remote sensing techniques: high sensitivity to snow properties of interest (temperature, grain size, density), subdaily coverage in the polar regions, and their observations are independent of cloud conditions and solar illumination. Thus, microwave radiometers are widely used to retrieve information over snow-covered regions. For the Antarctic Plateau, many studies presenting retrieval algorithms or numerical simulations have assumed, explicitly or not, that the subpixel-scale heterogeneity is negligible and that the retrieved properties were representative of whole pixels. In this presentation, we investigate the spatial variations of brightness temperature over arange of a few kilometers in the Dome C area (Antarctic Plateau).

On the impact of snow cover on daytime pollution dispersion

NASA Astrophysics Data System (ADS)

Segal, M.; Garratt, J. R.; Pielke, R. A.; Hildebrand, P.; Rogers, F. A.; Cramer, J.; Schanot, A.

A preliminary evaluation of the impact of snow cover on daytime pollutant dispersion conditions is made by using conceptual, scaling, and observational analyses. For uniform snow cover and synoptically unperturbed sunny conditions, observations indicate a considerate suppression of the surface sensible heat flux, the turbulence, and the development of the daytime atmospheric boundary layer (ABL) when compared to snow-free conditions. However, under conditions of non-uniform snow cover, as in urban areas, or associated with vegetated areas or bare ground patches, a milder effect on pollutant dispersion conditions would be expected. Observed concentrations of atmospheric particles within the ABL, and surface pollutant concentrations in urban areas, reflect the impact of snow cover on the modification of ABL characteristics.

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts.

PubMed

Bokhorst, Stef; Pedersen, Stine Højlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W; Brown, Ross D; Ehrich, Dorothee; Essery, Richard L H; Heilig, Achim; Ingvander, Susanne; Johansson, Cecilia; Johansson, Margareta; Jónsdóttir, Ingibjörg Svala; Inga, Niila; Luojus, Kari; Macelloni, Giovanni; Mariash, Heather; McLennan, Donald; Rosqvist, Gunhild Ninis; Sato, Atsushi; Savela, Hannele; Schneebeli, Martin; Sokolov, Aleksandr; Sokratov, Sergey A; Terzago, Silvia; Vikhamar-Schuler, Dagrun; Williamson, Scott; Qiu, Yubao; Callaghan, Terry V

2016-09-01

Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.

Changing Arctic Snow Cover: A Review of Recent Developments and Assessment of Future Needs for Observations, Modelling, and Impacts

NASA Technical Reports Server (NTRS)

Bokhorst, Stef; Pedersen, Stine Hojlund; Brucker, Ludovic; Anisimov, Oleg; Bjerke, Jarle W.; Brown, Ross D.; Ehrich, Dorothee; Essery, Richard L. H.; Heilig, Achim; Ingvander, Susanne;

Complex responses of spring alpine vegetation phenology to snow cover dynamics over the Tibetan Plateau, China.

PubMed

Wang, Siyuan; Wang, Xiaoyue; Chen, Guangsheng; Yang, Qichun; Wang, Bin; Ma, Yuanxu; Shen, Ming

2017-09-01

Snow cover dynamics are considered to play a key role on spring phenological shifts in the high-latitude, so investigating responses of spring phenology to snow cover dynamics is becoming an increasingly important way to identify and predict global ecosystem dynamics. In this study, we quantified the temporal trends and spatial variations of spring phenology and snow cover across the Tibetan Plateau by calibrating and analyzing time series of the NOAA AVHRR-derived normalized difference vegetation index (NDVI) during 1983-2012. We also examined how snow cover dynamics affect the spatio-temporal pattern of spring alpine vegetation phenology over the plateau. Our results indicated that 52.21% of the plateau experienced a significant advancing trend in the beginning of vegetation growing season (BGS) and 34.30% exhibited a delaying trend. Accordingly, the snow cover duration days (SCD) and snow cover melt date (SCM) showed similar patterns with a decreasing trend in the west and an increasing trend in the southeast, but the start date of snow cover (SCS) showed an opposite pattern. Meanwhile, the spatial patterns of the BGS, SCD, SCS and SCM varied in accordance with the gradients of temperature, precipitation and topography across the plateau. The response relationship of spring phenology to snow cover dynamics varied within different climate, terrain and alpine plant community zones, and the spatio-temporal response patterns were primarily controlled by the long-term local heat-water conditions and topographic conditions. Moreover, temperature and precipitation played a profound impact on diverse responses of spring phenology to snow cover dynamics. Copyright © 2017 Elsevier B.V. All rights reserved.

Soil erosion by snow gliding - a first quantification attempt in a sub-alpine area, Switzerland

NASA Astrophysics Data System (ADS)

Meusburger, K.; Leitinger, G.; Mabit, L.; Mueller, M. H.; Walter, A.; Alewell, C.

2014-03-01

Snow processes might be one important driver of soil erosion in Alpine grasslands and thus the unknown variable when erosion modelling is attempted. The aim of this study is to assess the importance of snow gliding as soil erosion agent for four different land use/land cover types in a sub-alpine area in Switzerland. We used three different approaches to estimate soil erosion rates: sediment yield measurements in snow glide deposits, the fallout radionuclide 137Cs, and modelling with the Revised Universal Soil Loss Equation (RUSLE). The RUSLE model is suitable to estimate soil loss by water erosion, while the 137Cs method integrates soil loss due to all erosion agents involved. Thus, we hypothesise that the soil erosion rates determined with the 137Cs method are higher and that the observed discrepancy between the soil erosion rate of RUSLE and the 137Cs method is related to snow gliding and sediment concentrations in the snow glide deposits. Cumulative snow glide distance was measured for the sites in the winter 2009/10 and modelled for the surrounding area with the Spatial Snow Glide Model (SSGM). Measured snow glide distance ranged from 2 to 189 cm, with lower values at the north facing slopes. We observed a reduction of snow glide distance with increasing surface roughness of the vegetation, which is important information with respect to conservation planning and expected land use changes in the Alps. Our hypothesis was confirmed: the difference of RUSLE and 137Cs erosion rates was related to the measured snow glide distance (R2= 0.64; p < 0.005) and snow sediment yields (R2 = 0.39; p = 0.13). A high difference (lower proportion of water erosion compared to total net erosion) was observed for high snow glide rates and vice versa. The SSGM reproduced the relative difference of the measured snow glide values under different land uses and land cover types. The resulting map highlighted the relevance of snow gliding for large parts of the investigated area. Based on these results, we conclude that snow gliding is a key process impacting soil erosion pattern and magnitude in sub-alpine areas with similar topographic and climatic conditions.

A multidisciplinary study of earth resources imagery of Australia, Antarctica and Papua, New Guinea

NASA Technical Reports Server (NTRS)

Fisher, N. H. (Principal Investigator)

1975-01-01

The author has identified the following significant results. A thirteen category recognition map was prepared, showing forest, water, grassland, and exposed rock types. Preliminary assessment of classification accuracies showed that water, forest, meadow, and Niobrara shale were the most accurately mapped classes. Unsatisfactory results, were obtained in an attempt to discrimate sparse forest cover over different substrates. As base elevation varied from 7,000 to 13,000 ft, with an atmospheric visibility of 48 km, no changes in water and forest recognition were observed. Granodiorite recognition accuracy decreased monotonically as base elevation increased, even though the training set location was at 10,000 ft elevation. For snow varying in base elevation from 9400 to 8420 ft, recognition decreases from 99% at the 9400 ft training set elevation to 88% at 8420 ft. Calculations of expected radiance at the ERTS sensor from snow reflectance measured at the site and from Turner model calculations of irradiance, transmission and path radiance, reveal that snow signals should not be clipped, assuming that calculations and ERTS calibration constants were correct.

Deriving Snow-Cover Depletion Curves for Different Spatial Scales from Remote Sensing and Snow Telemetry Data

NASA Technical Reports Server (NTRS)

Fassnacht, Steven R.; Sexstone, Graham A.; Kashipazha, Amir H.; Lopez-Moreno, Juan Ignacio; Jasinski, Michael F.; Kampf, Stephanie K.; Von Thaden, Benjamin C.

2015-01-01

During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow-covered area (SCA) once snow-free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry stations were related to SCA derived from moderate-resolution imaging spectro radiometer images to produce snow-cover depletion curves. The snow depletion curves were created for an 80,000 sq km domain across southern Wyoming and northern Colorado encompassing 54 snow telemetry stations. Eight yearly snow depletion curves were compared, and it is shown that the slope of each is a function of the amount of snow received. Snow-cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A stations peak SWE was much more important than the main topographic variables that included location, elevation, slope, and modelled clear sky solar radiation. The threshold SWE mostly illustrated inter-annual consistency.

Decorrelation of L-band and C-band interferometry to volcanic risk prevention

NASA Astrophysics Data System (ADS)

Malinverni, E. S.; Sandwell, D.; Tassetti, A. N.; Cappelletti, L.

2013-10-01

SAR has several strong key features: fine spatial resolution/precision and high temporal pass frequency. Moreover, the InSAR technique allows the accurate detection of ground deformations. This high potential technology can be invaluable to study volcanoes: it provides important information on pre-eruption surface deformation, improving the understanding of volcanic processes and the ability to predict eruptions. As a downside, SAR measurements are influenced by artifacts such as atmospheric effects or bad topographic data. Correlation gives a measure of these interferences, quantifying the similarity of the phase of two SAR images. Different approaches exists to reduce these errors but the main concern remain the possibility to correlate images with different acquisition times: snow-covered or heavily-vegetated areas produce seasonal changes on the surface. Minimizing the time between passes partly limits decorrelation. Though, images with a short temporal baseline aren't always available and some artifacts affecting correlation are timeindependent. This work studies correlation of pairs of SAR images focusing on the influence of surface and climate conditions, especially snow coverage and temperature. Furthermore, the effects of the acquisition band on correlation are taken into account, comparing L-band and C-band images. All the chosen images cover most of the Yellowstone caldera (USA) over a span of 4 years, sampling all the seasons. Interferograms and correlation maps are generated. To isolate temporal decorrelation, pairs of images with the shortest baseline are chosen. Correlation maps are analyzed in relation to snow depth and temperature. Results obtained with ENVISAT and ERS satellites (C-band) are compared with the ones from ALOS (L-band). Results show a good performance during winter and a bad attitude towards wet snow (spring and fall). During summer both L-band and C-band maintain a good coherence with L-band performing better over vegetation.

New estimates of changes in snow cover over Russia in recent decades

NASA Astrophysics Data System (ADS)

Bulygina, O.; Korshunova, N.; Razuvaev, V.; Groisman, P. Y.

2017-12-01

Snow covers plays critical roles in the energy and water balance of the Earth through its unique physical properties (high reflectivity and low thermal conductivity) and water storage. The main objective of this research is to monitoring snow cover change in Russia. The estimates of changes of major snow characteristics (snow cover duration, maximum winter snow depth, snow water equivalent) are described. Apart from the description of long-term averages of snow characteristics, the estimates of their change that are averaged over quasi-homogeneous climatic regions are derived and regional differences in the change of snow characteristics are studied. We used in our study daily snow observations for 820 Russian meteorological station from 1966 to 2017. All of these meteorological stations are of unprotected type. The water equivalent is analyzed from snow course survey data at 958 meteorological stations from 1966 to 2017. The time series are prepared by RIHMI-WDC. Regional analysis of snow cover data was carried out using quasi-homogeneous climatic regions. The area-averaging technique using station values converted to anomalies with respect to a common reference period (in this study, 1981-2010). Anomalies were arithmetically averaged first within 1°N x 2°E grid cells and thereafter by a weighted average value derived over the quasi-homogeneous climatic regions. This approach provides a more uniform spatial field for averaging. By using a denser network of meteorological stations, bringing into consideration snow course data and, we managed to specify changes in all observed major snow characteristics and to obtain estimates generalized for quasi-homogeneous climatic regions. The detected changes in the dates of the establishment and disappearance of the snow cover.

Impact of snow gliding on soil redistribution for a sub-alpine area in Switzerland

NASA Astrophysics Data System (ADS)

Meusburger, K.; Leitinger, G.; Mabit, L.; Mueller, M. H.; Alewell, C.

2013-07-01

The aim of this study is to assess the importance of snow gliding as soil erosion agent for four different land use/land cover types in a sub-alpine area in Switzerland. The 14 investigated sites are located close to the valley bottom at approximately 1500 m a.s.l., while the elevation of the surrounding mountain ranges is about 2500 m a.s.l. We used two different approaches to estimate soil erosion rates: the fallout radionuclide 137Cs and the Revised Universal Soil Loss Equation (RUSLE). The RUSLE model is suitable to estimate soil loss by water erosion, while the 137Cs method integrates soil loss due to all erosion agents involved. Thus, we hypothesise that the soil erosion rates determined with the 137Cs method are higher and that the observed discrepancy between the erosion rate of RUSLE and the 137Cs method is related to snow gliding. Cumulative snow glide distance was measured for the sites in the winter 2009/2010 and modelled for the surrounding area with the Spatial Snow Glide Model (SSGM). Measured snow glide distance range from 0 to 189 cm with lower values for the north exposed slopes. We observed a reduction of snow glide distance with increasing surface roughness of the vegetation, which is an important information with respect to conservation planning and expected land use changes in the Alps. Our hypothesis was confirmed, the difference of RUSLE and 137Cs erosion rates was correlated to the measured snow glide distance (R2 = 0.73; p < 0.005). A high difference (lower proportion of water erosion compared to total net erosion) was observed for high snow glide rates and vice versa. The SSGM reproduced the relative difference of the measured snow glide values between different land use/land cover types. The resulting map highlights the relevance of snow gliding for large parts of the investigated area. Based on these results, we conclude that snow gliding is a key process impacting soil erosion pattern and magnitude in sub-alpine areas with similar topographic and climatic conditions.

Laws of distribution of the snow cover on the greater Caucasus (Soviet Union)

NASA Technical Reports Server (NTRS)

Gurtovaya, Y. Y.; Sulakvelidze, G. K.; Yashina, A. V.

1985-01-01

The laws of the distribution of the snow cover on the mountains of the greater Caucasus are discussed. It is shown that an extremely unequal distribution of the snow cover is caused by the complex orography of this territory, the diversity of climatic conditions and by the difference in altitude. Regions of constant, variable and unstable snow cover are distinguished because of the clearly marked division into altitude layers, each of which is characterized by climatic differences in the nature of the snow accumulation.

Land cover and land use mapping of the iSimangaliso Wetland Park, South Africa: comparison of oblique and orthogonal random forest algorithms

NASA Astrophysics Data System (ADS)

Bassa, Zaakirah; Bob, Urmilla; Szantoi, Zoltan; Ismail, Riyad

2016-01-01

In recent years, the popularity of tree-based ensemble methods for land cover classification has increased significantly. Using WorldView-2 image data, we evaluate the potential of the oblique random forest algorithm (oRF) to classify a highly heterogeneous protected area. In contrast to the random forest (RF) algorithm, the oRF algorithm builds multivariate trees by learning the optimal split using a supervised model. The oRF binary algorithm is adapted to a multiclass land cover and land use application using both the "one-against-one" and "one-against-all" combination approaches. Results show that the oRF algorithms are capable of achieving high classification accuracies (>80%). However, there was no statistical difference in classification accuracies obtained by the oRF algorithms and the more popular RF algorithm. For all the algorithms, user accuracies (UAs) and producer accuracies (PAs) >80% were recorded for most of the classes. Both the RF and oRF algorithms poorly classified the indigenous forest class as indicated by the low UAs and PAs. Finally, the results from this study advocate and support the utility of the oRF algorithm for land cover and land use mapping of protected areas using WorldView-2 image data.

Mapping and evaluation of snow avalanche risk using GIS technique in Rodnei National Park

NASA Astrophysics Data System (ADS)

Covǎsnianu, Adrian; Grigoraş, Ioan-Rǎducu; Covǎsnianu, Liliana-Elena; Iordache, Iulian; Balin, Daniela

2010-05-01

The study consisted in a precise mapping project (GPS field campaign, on-screen digitization of the topographic maps at 1:25.000 scale and updated with ASTER mission) of the Rodnei National Park area (Romanian Carpathians) with a focus on snow avalanche risk survey. Parameters taken into account were slope, aspect, altitude, landforms and roughness resulted from a high resolute numerical terrain model obtained by ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) mission. The resulted digital surface model with a spatial resolution of 10 m covered a total area of 187 square kilometers and was improved by the help of Topo to Raster tool. All these parameters were calibrated after a model applied onto Tatra Massive and also Ceahlău Mountain. The results were adapted and interpreted in accordance with European avalanche hazard scale. This work was made in the context of the elaboration of Risk Map and is directly concerning both the security of tourism activities but also the management of the Rodnei Natural Park. The extension of this method to similar mountain areas is ongoing.

Blowing snow detection from ground-based ceilometers: application to East Antarctica

NASA Astrophysics Data System (ADS)

Gossart, Alexandra; Souverijns, Niels; Gorodetskaya, Irina V.; Lhermitte, Stef; Lenaerts, Jan T. M.; Schween, Jan H.; Mangold, Alexander; Laffineur, Quentin; van Lipzig, Nicole P. M.

2017-12-01

Blowing snow impacts Antarctic ice sheet surface mass balance by snow redistribution and sublimation. However, numerical models poorly represent blowing snow processes, while direct observations are limited in space and time. Satellite retrieval of blowing snow is hindered by clouds and only the strongest events are considered. Here, we develop a blowing snow detection (BSD) algorithm for ground-based remote-sensing ceilometers in polar regions and apply it to ceilometers at Neumayer III and Princess Elisabeth (PE) stations, East Antarctica. The algorithm is able to detect (heavy) blowing snow layers reaching 30 m height. Results show that 78 % of the detected events are in agreement with visual observations at Neumayer III station. The BSD algorithm detects heavy blowing snow 36 % of the time at Neumayer (2011-2015) and 13 % at PE station (2010-2016). Blowing snow occurrence peaks during the austral winter and shows around 5 % interannual variability. The BSD algorithm is capable of detecting blowing snow both lifted from the ground and occurring during precipitation, which is an added value since results indicate that 92 % of the blowing snow is during synoptic events, often combined with precipitation. Analysis of atmospheric meteorological variables shows that blowing snow occurrence strongly depends on fresh snow availability in addition to wind speed. This finding challenges the commonly used parametrizations, where the threshold for snow particles to be lifted is a function of wind speed only. Blowing snow occurs predominantly during storms and overcast conditions, shortly after precipitation events, and can reach up to 1300 m a. g. l. in the case of heavy mixed events (precipitation and blowing snow together). These results suggest that synoptic conditions play an important role in generating blowing snow events and that fresh snow availability should be considered in determining the blowing snow onset.

Mapping snow depth distribution in forested terrain using unmanned aerial vehicles and structure-from-motion

NASA Astrophysics Data System (ADS)

Webster, C.; Bühler, Y.; Schirmer, M.; Stoffel, A.; Giulia, M.; Jonas, T.

2017-12-01

Snow depth distribution in forests exhibits strong spatial heterogeneity compared to adjacent open sites. Measurement of snow depths in forests is currently limited to a) manual point measurements, which are sparse and time-intensive, b) ground-penetrating radar surveys, which have limited spatial coverage, or c) airborne LiDAR acquisition, which are expensive and may deteriorate in denser forests. We present the application of unmanned aerial vehicles in combination with structure-from-motion (SfM) methods to photogrammetrically map snow depth distribution in forested terrain. Two separate flights were carried out 10 days apart across a heterogeneous forested area of 900 x 500 m. Corresponding snow depth maps were derived using both, LiDAR-based and SfM-based DTM data, obtained during snow-off conditions. Manual measurements collected following each flight were used to validate the snow depth maps. Snow depths were resolved at 5cm resolution and forest snow depth distribution structures such as tree wells and other areas of preferential melt were represented well. Differential snow depth maps showed maximum ablation in the exposed south sides of trees and smaller differences in the centre of gaps and on the north side of trees. This new application of SfM to map snow depth distribution in forests demonstrates a straightforward method for obtaining information that was previously only available through manual spatially limited ground-based measurements. These methods could therefore be extended to more frequent observation of snow depths in forests as well as estimating snow accumulation and depletion rates.

Multirisk analysis along the Road 7, Mendoza Province, Argentina

NASA Astrophysics Data System (ADS)

Wick, Emmanuel; Baumann, Valérie; Michoud, Clément; Derron, Marc-Henri; Jaboyedoff, Michel; Rune Lauknes, Tom; Marengo, Hugo; Rosas, Mario

2010-05-01

The National Road 7 crosses Argentina from East to West, linking Buenos Aires to the Chile border. This road is an extremely important corridor crossing the Andes Cordillera, but it is exposed to numerous natural hazards, such as rockfalls, debris flows and snow avalanches. The study area is located in the Mendoza Province, between Potrerillos and Las Cuevas in the Chilean border. This study has for main goals to achieve a regional mapping of geohazards susceptibility along the Road 7 corridor using modern remote sensing and numerical modelling techniques completed by field investigations. The main topics are: - Detection and monitoring of deep-seated gravitational slope deformations by time-series satellite radar interferometry (InSAR) methods. The area of interest is mountainous with almost no vegetation permitting an optimized InSAR processing. Our results are based on applying the small-baseline subset (SBAS) method to a time-series of Envisat ASAR images. - Rockfalls susceptibility mapping is realized using statistical analysis of the slope angle distribution, including external knowledge on the geology and land cover, to detect the potential source areas (quantitative DEM analysis). The run-outs are assessed with numerical methods based on the shallow angle method with Conefall. A second propagation is performed using the alpha-beta methodology (3D numerical modelling) with RAS and is compared to the first one. - Debris flow susceptibility mapping is realized using DF-IGAR to detect starting and spreading areas. Slope, flow accumulations, contributive surfaces, plan curvature, geological and land use dataset are used. The spreading is simulated by a multiple flow algorithm (rules the path that the debris flow will follow) coupled to a run-out distance calculation (energy-based). - Snow avalanches susceptibility mapping is realized using DF-IGAR to map sources areas and propagations. To detect the sources areas, slope, altitude, land-use and minimum surfaces are needed. DF-IGAR simulates the spreading by means of the "Perla" methodology. Furthermore, RAS performs the spreading based on the "alpha-beta" method. All these methods are based on Aster and SRTM DEM (grid 30 m) and observations of both optical and radar satellite imagery (Aster, Quickbird, Worldview, Ikonos, Envisat ASAR) and aerial photographs. Several field campaigns are performed to calibrate the regional models with adapted parameters. Susceptibility maps of the entire area for rockfalls, debris flows and snow avalanches at a scale of 1:100'000 are created. Those maps and the field investigations are cross-checked to identify and prioritize hotspots. It appears that numerous road sectors are subject to highly active phenomena. Some mitigation works already exist but they are often under-dimensioned, inadequate or neglected. Recommendations for priority and realistic mitigation measures along the endangered road sectors identified are proposed.

A Semi-Automated Machine Learning Algorithm for Tree Cover Delineation from 1-m Naip Imagery Using a High Performance Computing Architecture

NASA Astrophysics Data System (ADS)

Basu, S.; Ganguly, S.; Nemani, R. R.; Mukhopadhyay, S.; Milesi, C.; Votava, P.; Michaelis, A.; Zhang, G.; Cook, B. D.; Saatchi, S. S.; Boyda, E.

2014-12-01

Accurate tree cover delineation is a useful instrument in the derivation of Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree cover delineation in high to coarse resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR datasets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree cover estimates for the whole of Continental United States, using a High Performance Computing Architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field (CRF), which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the state of California, which covers a total of 11,095 NAIP tiles and spans a total geographical area of 163,696 sq. miles. Our framework produced correct detection rates of around 85% for fragmented forests and 70% for urban tree cover areas, with false positive rates lower than 3% for both regions. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR high-resolution canopy height model shows the effectiveness of our algorithm in generating accurate high-resolution tree cover maps.

Utilizing ERTS-1 imagery for tectonic analysis through study of the Bighorn Mountains region

NASA Technical Reports Server (NTRS)

Hoppin, R. A. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Comparisons of imagery of three seasons, late summer-fall, winter, and spring indicate that for this region fall imagery is the best for overall geologic analysis. Winter scenes with light to moderate snow cover provide excellent topographic detail owing to snow enhancement, lower sun angle, and clarity of the atmosphere. Spring imagery has considerable reduction of tonal contrast owing to the low reflecting heavy green grass cover which subdues lithologic effects; heavy snow cover in the uplands masks topography. Mapping of geologic formations is impractical in most cases. Separation into tonal units can provide some general clues on structure. A given tonal unit can include parts of several geologic formations and different stratigraphic units can have the same tonal signature. Drainage patterns and anomalies provide the most consistent clues for detecting folds, monoclines, and homoclines. Vegetation only locally reflects lithology and structure. False color infrared 9 x 9 transparencies are the most valuable single imagery. Where these can be supplemented by U-2 color infrared for more detailed work, a tremendous amount of information is available. Adequately field checking such a large area just in one scene is the major logistic problem even in a fairly well known region.

Unusual radar echoes from the Greenland ice sheet

NASA Technical Reports Server (NTRS)

Rignot, E. J.; Vanzyl, J. J.; Ostro, S. J.; Jezek, K. C.

1993-01-01

In June 1991, the NASA/Jet Propulsion Laboratory airborne synthetic-aperture radar (AIRSAR) instrument collected the first calibrated data set of multifrequency, polarimetric, radar observations of the Greenland ice sheet. At the time of the AIRSAR overflight, ground teams recorded the snow and firn (old snow) stratigraphy, grain size, density, and temperature at ice camps in three of the four snow zones identified by glaciologists to characterize four different degrees of summer melting of the Greenland ice sheet. The four snow zones are: (1) the dry-snow zone, at high elevation, where melting rarely occurs; (2) the percolation zone, where summer melting generates water that percolates down through the cold, porous, dry snow and then refreezes in place to form massive layers and pipes of solid ice; (3) the soaked-snow zone where melting saturates the snow with liquid water and forms standing lakes; and (4) the ablation zone, at the lowest elevations, where melting is vigorous enough to remove the seasonal snow cover and ablate the glacier ice. There is interest in mapping the spatial extent and temporal variability of these different snow zones repeatedly by using remote sensing techniques. The objectives of the 1991 experiment were to study changes in radar scattering properties across the different melting zones of the Greenland ice sheet, and relate the radar properties of the ice sheet to the snow and firn physical properties via relevant scattering mechanisms. Here, we present an analysis of the unusual radar echoes measured from the percolation zone.

Snow mechanics and avalanche formation: field experiments on the dynamic response of the snow cover

NASA Astrophysics Data System (ADS)

Schweizer, Jürg; Schneebeli, Martin; Fierz, Charles; Föhn, Paul M. B.

1995-11-01

Knowledge about snow mechanics and snow avalanche formation forms the basis of any hazard mitigation measures. The crucial point is the snow stability. The most relevant mechanical properties - the compressive, tensile and shear strength of the individual snow layers within the snow cover - vary substantially in space and time. Among other things the strength of the snow layers depends strongly on the state of stress and the strain rate. The evaluation of the stability of the snow cover is hence a difficult task involving many extrapolations. To gain insight in the release mechanism of slab avalanches triggered by skiers, the skier's impact is measured with a load cell at different depths within the snow cover and for different snow conditions. The study focused on the effects of the dynamic loading and of the damping by snow compaction. In accordance with earlier finite-element (FE) calculations the results show the importance of the depth of the weak layer or interface and the snow conditions, especially the sublayering. In order to directly measure the impact force and to study the snow properties in more detail, a new instrument, called rammrutsch was developed. It combines the properties of the rutschblock with the defined impact properties of the rammsonde. The mechanical properties are determined using (i) the impact energy of the rammrutsch and (ii) the deformations of the snow cover measured with accelerometers and digital image processing of video sequences. The new method is well suited to detect and to measure the mechanical processes and properties of the fracturing layers. The duration of one test is around 10 minutes and the method seems appropriate for determining the spatial variability of the snow cover. A series of experiments in a forest opening showed a clear difference in the snow stability between sites below trees and ones in the free field of the opening.

Absence of snow cover reduces understory plant cover and alters plant community composition in boreal forests.

PubMed

Kreyling, Juergen; Haei, Mahsa; Laudon, Hjalmar

2012-02-01

Snow regimes affect biogeochemistry of boreal ecosystems and are altered by climate change. The effects on plant communities, however, are largely unexplored despite their influence on relevant processes. Here, the impact of snow cover on understory community composition and below-ground production in a boreal Picea abies forest was investigated using a long-term (8-year) snow cover manipulation experiment consisting of the treatments: snow removal, increased insulation (styrofoam pellets), and control. The snow removal treatment caused longer (118 vs. 57 days) and deeper soil frost (mean minimum temperature -5.5 vs. -2.2°C) at 10 cm soil depth in comparison to control. Understory species composition was strongly altered by the snow cover manipulations; vegetation cover declined by more than 50% in the snow removal treatment. In particular, the dominant dwarf shrub Vaccinium myrtillus (-82%) and the most abundant mosses Pleurozium schreberi (-74%) and Dicranum scoparium (-60%) declined strongly. The C:N ratio in V. myrtillus leaves and plant available N in the soil indicated no altered nitrogen nutrition. Fine-root biomass in summer, however, was negatively affected by the reduced snow cover (-50%). Observed effects are attributed to direct frost damage of roots and/ or shoots. Besides the obvious relevance of winter processes on plant ecology and distribution, we propose that shifts in the vegetation caused by frost damage may be an important driver of the reported alterations in biogeochemistry in response to altered snow cover. Understory plant performance clearly needs to be considered in the biogeochemistry of boreal systems in the face of climate change.

On the Impact of Snow Salinity on CryoSat-2 First-Year Sea Ice Thickness Retrievals

NASA Astrophysics Data System (ADS)

Nandan, V.; Yackel, J.; Geldsetzer, T.; Mahmud, M.

2017-12-01

European Space Agency's Ku-band altimeter CryoSat-2 (CS-2) has demonstrated its potential to provide extensive basin-scale spatial and temporal measurements of Arctic sea ice freeboard. It is assumed that CS-2 altimetric returns originate from the snow/sea ice interface (assumed to be the main scattering horizon). However, in newly formed thin ice ( 0.6 m) through to thick first-year sea ice (FYI) ( 2 m), upward wicking of brine into the snow cover from the underlying sea ice surface produces saline snow layers, especially in the bottom 6-8 cm of a snow cover. This in turn modifies the brine volume at/or near the snow/sea ice interface, altering the dielectric and scattering properties of the snow cover, leading to strong Ku-band microwave attenuation within the upper snow volume. Such significant reductions in Ku-band penetration may substantially affect CS-2 FYI freeboard retrievals. Therefore, the goal of this study is to evaluate a theoretical approach to estimate snow salinity induced uncertainty on CS-2 Arctic FYI freeboard measurements. Using the freeboard-to-thickness hydrostatic equilibrium equation, we quantify the error differences between the CS-2 FYI thickness, (assuming complete penetration of CS-2 radar signals to the snow/FYI interface), and the FYI thickness based on the modeled Ku-band main scattering horizon for different snow cover cases. We utilized naturally occurring saline and non-saline snow cover cases ranging between 6 cm to 32 cm from the Canadian Arctic, observed during late-winter from 1993 to 2017, on newly-formed ice ( 0.6 m), medium ( 1.5 m) and thick FYI ( 2 m). Our results suggest that irrespective of the thickness of the snow cover overlaying FYI, the thickness of brine-wetted snow layers and actual FYI freeboard strongly influence the amount with which CS-2 FYI freeboard estimates and thus thickness calculations are overestimated. This effect is accentuated for increasingly thicker saline snow covers overlaying newly-formed ice, which accounted to an overestimated FYI thickness by 250%, when compared to 80% overestimations on thinner saline snow covers, and the error reduces with increase in FYI thickness. Our study recommends the CS-2 sea ice community to add snow salinity as a potential error source, affecting CS-2 Arctic FYI freeboard and thickness retrievals.

Snow cover and temperature relationships in North America and Eurasia

NASA Technical Reports Server (NTRS)

Foster, J.; Owe, M.; Rango, A.

1983-01-01

In this study the snow cover extent during the autumn months in both North America and Eurasia has been related to the ensuing winter temperature as measured at several locations near the center of each continent. The relationship between autumn snow cover and the ensuing winter temperatures was found to be much better for Eurasia than for North America. For Eurasia the average snow cover extent during the autumn explained as much as 52 percent of the variance in the winter (December-February) temperatures compared to only 12 percent for North America. However, when the average winter snow cover was correlated with the average winter temperature it was found that the relationship was better for North America than for Eurasia. As much as 46 percent of the variance in the winter temperature was explained by the winter snow cover in North America compared to only 12 percent in Eurasia.

Scaling and Numerical Model Evaluation of Snow-Cover Effects on the Generation and Modification of Daytime Mesoscale Circulations.

NASA Astrophysics Data System (ADS)

Segal, M.; Garratt, J. R.; Pielke, R. A.; Ye, Z.

1991-04-01

Consideration of the sensible heat flux characteristics over a snow surface suggests a significant diminution in the magnitude of the flux, compared to that over a snow-free surface under the same environmental conditions. Consequently, the existence of snow-covered mesoscale areas adjacent to snow-free areas produces horizontal thermal gradients in the lower atmosphere during the daytime, possibly resulting in a `snow breeze.' In addition, suppression of the daytime thermally induced upslope flow over snow-covered slopes is likely to occur. The present paper provides scaling and modeling evaluations of these situations, with quantification of the generated and modified circulations. These evaluations suggest that under ideal situations involved with uniform snow cover over large areas, particularly in late winter and early spring, a noticeable `snow breeze' is likely to develop. Additionally: suppression of the daytime thermally induced upslope flow is significant and may even result in a daytime drainage flow. The effects of bare ground patchiness in the snow cover on these circulations are also explored, both for flat terrain and slope-flow situations. A patchiness fraction greater than 0.5 is found to result in a noticeably reduced snow-breeze circulation, while a patchiness fraction of only 0.1 caused the simulated daytime drainage flow over slopes to he reversed.

Modeling the influence of snow cover temperature and water content on wet-snow avalanche runout

NASA Astrophysics Data System (ADS)

Valero, Cesar Vera; Wever, Nander; Christen, Marc; Bartelt, Perry

2018-03-01

Snow avalanche motion is strongly dependent on the temperature and water content of the snow cover. In this paper we use a snow cover model, driven by measured meteorological data, to set the initial and boundary conditions for wet-snow avalanche calculations. The snow cover model provides estimates of snow height, density, temperature and liquid water content. This information is used to prescribe fracture heights and erosion heights for an avalanche dynamics model. We compare simulated runout distances with observed avalanche deposition fields using a contingency table analysis. Our analysis of the simulations reveals a large variability in predicted runout for tracks with flat terraces and gradual slope transitions to the runout zone. Reliable estimates of avalanche mass (height and density) in the release and erosion zones are identified to be more important than an exact specification of temperature and water content. For wet-snow avalanches, this implies that the layers where meltwater accumulates in the release zone must be identified accurately as this defines the height of the fracture slab and therefore the release mass. Advanced thermomechanical models appear to be better suited to simulate wet-snow avalanche inundation areas than existing guideline procedures if and only if accurate snow cover information is available.

Landsat - What is operational in water resources

NASA Technical Reports Server (NTRS)

Middleton, E. M.; Munday, J. C., Jr.

1981-01-01

Applications of Landsat data in hydrology and water quality measurement were examined to determine which applications are operational. In hydrology, the principal applications have been surface water inventory, and land cover analysis for (1) runoff modeling and (2) abatement planning for non-point pollution and erosion. In water quality measurement, the principal applications have been: (1) trophic state assessment, and (2) measurement of turbidity and suspended sediment. The following applications were found to be operational: mapping of surface water, snow cover, and land cover (USGS Level 1) for watershed applications; measurement of turbidity, Secchi disk depth, suspended sediment concentration, and water depth.

The Snow Data System at NASA JPL

NASA Astrophysics Data System (ADS)

Laidlaw, R.; Painter, T. H.; Mattmann, C. A.; Ramirez, P.; Brodzik, M. J.; Rittger, K.; Bormann, K. J.; Burgess, A. B.; Zimdars, P.; McGibbney, L. J.; Goodale, C. E.; Joyce, M.

2015-12-01

The Snow Data System at NASA JPL includes a data processing pipeline built with open source software, Apache 'Object Oriented Data Technology' (OODT). It produces a variety of data products using inputs from satellites such as MODIS, VIIRS and Landsat. Processing is carried out in parallel across a high-powered computing cluster. Algorithms such as 'Snow Covered Area and Grain-size' (SCAG) and 'Dust Radiative Forcing in Snow' (DRFS) are applied to satellite inputs to produce output images that are used by many scientists and institutions around the world. This poster will describe the Snow Data System, its outputs and their uses and applications, along with recent advancements to the system and plans for the future. Advancements for 2015 include automated daily processing of historic MODIS data for SCAG (MODSCAG) and DRFS (MODDRFS), automation of SCAG processing for VIIRS satellite inputs (VIIRSCAG) and an updated version of SCAG for Landsat Thematic Mapper inputs (TMSCAG) that takes advantage of Graphics Processing Units (GPUs) for faster processing speeds. The pipeline has been upgraded to use the latest version of OODT and its workflows have been streamlined to enable computer operators to process data on demand. Additional products have been added, such as rolling 8-day composites of MODSCAG data, a new version of the MODSCAG 'annual minimum ice and snow extent' (MODICE) product, and recoded MODSCAG data for the 'Satellite Snow Product Intercomparison and Evaluation Experiment' (SnowPEx) project.

Domain-averaged snow depth over complex terrain from flat field measurements

NASA Astrophysics Data System (ADS)

Helbig, Nora; van Herwijnen, Alec

2017-04-01

Snow depth is an important parameter for a variety of coarse-scale models and applications, such as hydrological forecasting. Since high-resolution snow cover models are computational expensive, simplified snow models are often used. Ground measured snow depth at single stations provide a chance for snow depth data assimilation to improve coarse-scale model forecasts. Snow depth is however commonly recorded at so-called flat fields, often in large measurement networks. While these ground measurement networks provide a wealth of information, various studies questioned the representativity of such flat field snow depth measurements for the surrounding topography. We developed two parameterizations to compute domain-averaged snow depth for coarse model grid cells over complex topography using easy to derive topographic parameters. To derive the two parameterizations we performed a scale dependent analysis for domain sizes ranging from 50m to 3km using highly-resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees. The first, simpler parameterization uses a commonly applied linear lapse rate. For the second parameterization, we first removed the obvious elevation gradient in mean snow depth, which revealed an additional correlation with the subgrid sky view factor. We evaluated domain-averaged snow depth derived with both parameterizations using flat field measurements nearby with the domain-averaged highly-resolved snow depth. This revealed an overall improved performance for the parameterization combining a power law elevation trend scaled with the subgrid parameterized sky view factor. We therefore suggest the parameterization could be used to assimilate flat field snow depth into coarse-scale snow model frameworks in order to improve coarse-scale snow depth estimates over complex topography.

Comparison of different methods to retrieve optical-equivalent snow grain size in central Antarctica

NASA Astrophysics Data System (ADS)

Carlsen, Tim; Birnbaum, Gerit; Ehrlich, André; Freitag, Johannes; Heygster, Georg; Istomina, Larysa; Kipfstuhl, Sepp; Orsi, Anaïs; Schäfer, Michael; Wendisch, Manfred

2017-11-01

The optical-equivalent snow grain size affects the reflectivity of snow surfaces and, thus, the local surface energy budget in particular in polar regions. Therefore, the specific surface area (SSA), from which the optical snow grain size is derived, was observed for a 2-month period in central Antarctica (Kohnen research station) during austral summer 2013/14. The data were retrieved on the basis of ground-based spectral surface albedo measurements collected by the COmpact RAdiation measurement System (CORAS) and airborne observations with the Spectral Modular Airborne Radiation measurement sysTem (SMART). The snow grain size and pollution amount (SGSP) algorithm, originally developed to analyze spaceborne reflectance measurements by the MODerate Resolution Imaging Spectroradiometer (MODIS), was modified in order to reduce the impact of the solar zenith angle on the retrieval results and to cover measurements in overcast conditions. Spectral ratios of surface albedo at 1280 and 1100 nm wavelength were used to reduce the retrieval uncertainty. The retrieval was applied to the ground-based and airborne observations and validated against optical in situ observations of SSA utilizing an IceCube device. The SSA retrieved from CORAS observations varied between 27 and 89 m2 kg-1. Snowfall events caused distinct relative maxima of the SSA which were followed by a gradual decrease in SSA due to snow metamorphism and wind-induced transport of freshly fallen ice crystals. The ability of the modified algorithm to include measurements in overcast conditions improved the data coverage, in particular at times when precipitation events occurred and the SSA changed quickly. SSA retrieved from measurements with CORAS and MODIS agree with the in situ observations within the ranges given by the measurement uncertainties. However, SSA retrieved from the airborne SMART data slightly underestimated the ground-based results.

Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China

NASA Astrophysics Data System (ADS)

Huang, Xiaodong; Deng, Jie; Ma, Xiaofang; Wang, Yunlong; Feng, Qisheng; Hao, Xiaohua; Liang, Tiangang

2016-10-01

By combining optical remote sensing snow cover products with passive microwave remote sensing snow depth (SD) data, we produced a MODIS (Moderate Resolution Imaging Spectroradiometer) cloudless binary snow cover product and a 500 m snow depth product. The temporal and spatial variations of snow cover from December 2000 to November 2014 in China were analyzed. The results indicate that, over the past 14 years, (1) the mean snow-covered area (SCA) in China was 11.3 % annually and 27 % in the winter season, with the mean SCA decreasing in summer and winter seasons, increasing in spring and fall seasons, and not much change annually; (2) the snow-covered days (SCDs) showed an increase in winter, spring, and fall, and annually, whereas they showed a decrease in summer; (3) the average SD decreased in winter, summer, and fall, while it increased in spring and annually; (4) the spatial distributions of SD and SCD were highly correlated seasonally and annually; and (5) the regional differences in the variation of snow cover in China were significant. Overall, the SCD and SD increased significantly in south and northeast China, and decreased significantly in the north of Xinjiang province. The SCD and SD increased on the southwest edge and in the southeast part of the Tibetan Plateau, whereas it decreased in the north and northwest regions.

Estimate of temperature change due to ice and snow accretion in the boreal forest regions

NASA Astrophysics Data System (ADS)

Sugiura, K.; Nagai, S.; Suzuki, R.; Eicken, H.; Maximov, T. C.

2016-12-01

Previous research has demonstrated that there is a wide difference between the surface albedo in winter/spring in snow-covered forest regions in various global climate models. If the forest is covered with snow, the surface albedo would increase. In this study, we carried out field observations to monitor the frequency of ice and snow accretion in the boreal forest regions. The time-lapse digital camera was set up on each side of the observation towers at the site located to the north of Fairbanks (USA) and at the site located to the north of Yakutsk (Russia). It was confirmed that both forests were not necessarily covered with snow without a break from the start of continuous snow cover until the end. In addition, the boreal forest at the Yakutsk site is covered with snow in comparison with the boreal forest at the Fairbanks site for a long term such as for about five month. Using a one-dimensional mathematics model about the energy flow including atmospheric multiple scattering, we estimated temperature change due to ice and snow accretion in the boreal forest regions. The result show that the mean surface temperature rises approximately 0.5 [oC] when the boreal forest is not covered with snow. In this presentation, we discuss the snow albedo parameterization in the boreal forest regions and the one-dimensional mathematics model to provide a basis for a better understanding of the role of snow in the climate system.

The Effect of Climate Change on Snow Pack at Sleepers River, Vermont, USA

NASA Astrophysics Data System (ADS)

Shanley, J. B.; Chalmers, A.; Denner, J.; Clark, S.

2017-12-01

Sleepers River Research Watershed, a U.S. Geological Survey Water, Energy, and Biogeochemical Budgets (WEBB) site in northeastern Vermont, has a 58-year record (since 1959) of snow depth and snow water equivalence (SWE), one of the longest continuous records in eastern North America. Snow measurements occur weekly during the winter at the watershed using an Adirondack type snow tube sampler. Sleepers River averages about 1100 mm of precipitation annually of which 20 to 30 percent falls as snow. Snow cover typically persists from December to April. Length of snow cover and snow depth vary with elevation, aspect, and cover type. Sites include open field, and hardwood and conifer stand clearings from 225 to 630 meters elevation. We evaluated changes in snow depth, snow cover duration, and SWE relative to elevation, soil frost depth, air temperature, total precipitation, and the El Niño - Southern Oscillation (ENSO) cycle. Overall, warmer winter temperatures have resulted in more midwinter thaws, more rain during the winter, and more variable soil frost depth. Trends in snowpack amount and duration were compared to winter-spring streamflow center-of-mass to evaluate if shifts in the snow pack regime were leading to earlier snowmelt.

Our sense of Snow: the myth of John Snow in medical geography.

PubMed

McLeod, K S

2000-04-01

In 1854, Dr. John Snow identified the Broad Street pump as the source of an intense cholera outbreak by plotting the location of cholera deaths on a dot-map. He had the pump handle removed and the outbreak ended...or so one version of the story goes. In medical geography, the story of Snow and the Broad Street cholera outbreak is a common example of the discipline in action. While authors in other health-related disciplines focus on Snow's "shoe-leather epidemiology", his development of a water-borne theory of cholera transmission, and/or his pioneering role in anaesthesia, it is the dot-map that makes him a hero in medical geography. The story forms part of our disciplinary identity. Geographers have helped to shape the Snow narrative: the map has become part of the myth. Many of the published accounts of Snow are accompanied by versions of the map, but which map did Snow use? What happens to the meaning of our story when the determinative use of the map is challenged? In his book On the Mode of Communication of Cholera (2nd ed., John Churchill, London, 1855), Snow did not write that he used a map to identify the source of the outbreak. The map that accompanies his text shows cholera deaths in Golden Square (the subdistrict of London's Soho district where the outbreak occurred) from August 19 to September 30, a period much longer than the intense outbreak. What happens to the meaning of the myth when the causal connection between the pump's disengagement and the end of the outbreak is examined? Snow's data and text do not support this link but show that the number of cholera deaths was abating before the handle was removed. With the drama of the pump handle being questioned and the map, our artifact, occupying a more illustrative than central role, what is our sense of Snow?

The mortality rates and the space-time patterns of John Snow's cholera epidemic map.

PubMed

Shiode, Narushige; Shiode, Shino; Rod-Thatcher, Elodie; Rana, Sanjay; Vinten-Johansen, Peter

2015-06-17

Snow's work on the Broad Street map is widely known as a pioneering example of spatial epidemiology. It lacks, however, two significant attributes required in contemporary analyses of disease incidence: population at risk and the progression of the epidemic over time. Despite this has been repeatedly suggested in the literature, no systematic investigation of these two aspects was previously carried out. Using a series of historical documents, this study constructs own data to revisit Snow's study to examine the mortality rate at each street location and the space-time pattern of the cholera outbreak. This study brings together records from a series of historical documents, and prepares own data on the estimated number of residents at each house location as well as the space-time data of the victims, and these are processed in GIS to facilitate the spatial-temporal analysis. Mortality rates and the space-time pattern in the victims' records are explored using Kernel Density Estimation and network-based Scan Statistic, a recently developed method that detects significant concentrations of records such as the date and place of victims with respect to their distance from others along the street network. The results are visualised in a map form using a GIS platform. Data on mortality rates and space-time distribution of the victims were collected from various sources and were successfully merged and digitised, thus allowing the production of new map outputs and new interpretation of the 1854 cholera outbreak in London, covering more cases than Snow's original report and also adding new insights into their space-time distribution. They confirmed that areas in the immediate vicinity of the Broad Street pump indeed suffered from excessively high mortality rates, which has been suspected for the past 160 years but remained unconfirmed. No distinctive pattern was found in the space-time distribution of victims' locations. The high mortality rates identified around the Broad Street pump are consistent with Snow's theory about cholera being transmitted through contaminated water. The absence of a clear space-time pattern also indicates the water-bourne, rather than the then popular belief of air bourne, nature of cholera. The GIS data constructed in this study has an academic value and would cater for further research on Snow's map.

Comparison of snow melt properties across multiple spatial scales and landscape units in interior sub-Arctic boreal Alaskan watersheds

NASA Astrophysics Data System (ADS)

Bennett, K. E.; Cherry, J. E.; Hiemstra, C. A.; Bolton, W. R.

2013-12-01

Interior sub-Arctic Alaskan snow cover is rapidly changing and requires further study for correct parameterization in physically based models. This project undertook field studies during the 2013 snow melt season to capture snow depth, snow temperature profiles, and snow cover extent to compare with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor at four different sites underlain by discontinuous permafrost. The 2013 melt season, which turned out to be the latest snow melt period on record, was monitored using manual field measurements (SWE, snow depth data collection), iButtons to record temperature of the snow pack, GoPro cameras to capture time lapse of the snow melt, and low level orthoimagery collected at ~1500 m using a Navion L17a plane mounted with a Nikon D3s camera. Sites were selected across a range of landscape conditions, including a north facing black spruce hill slope, a south facing birch forest, an open tundra site, and a high alpine meadow. Initial results from the adjacent north and south facing sites indicate a highly sensitive system where snow cover melts over just a few days, illustrating the importance of high resolution temporal data capture at these locations. Field observations, iButtons and GoPro cameras show that the MODIS data captures the melt conditions at the south and the north site with accuracy (2.5% and 6.5% snow cover fraction present on date of melt, respectively), but MODIS data for the north site is less variable around the melt period, owing to open conditions and sparse tree cover. However, due to the rapid melt rate trajectory, shifting the melt date estimate by a day results in a doubling of the snow cover fraction estimate observed by MODIS. This information can assist in approximating uncertainty associated with remote sensing data that is being used to populate hydrologic and snow models (the Sacramento Soil Moisture Accounting model, coupled with SNOW-17, and the Variable Infiltration Capacity hydrologic model) and provide greater understanding of error and resultant model sensitivities associated with regional observations of snow cover across the sub-Arctic boreal landscape.

A method to generate surface UV radiation maps over Europe using GOME, Meteosat, and ancillary geophysical data

NASA Astrophysics Data System (ADS)

Verdebout, Jean

2000-02-01

This paper presents a method for generating surface ultraviolet (UV) radiation maps over Europe, with a spatial resolution of 0.05°, and potentially on a half-hour basis. The UV irradiance is obtained by interpolation in a look-up table (LUT), the entries of which are solar zenith angle, total column ozone amount, cloud liquid water thickness, near-surface horizontal visibility, surface elevation, and UV albedo. Both satellite (Meteosat, GOME) and nonsatellite (synoptic observations, meteorological model results, digital elevation model) data are exploited to assign values to the influencing factors. With the help of another LUT simulating the visible signal, Meteosat data are processed to retrieve the cloud liquid water thickness. The radiative transfer calculations are performed with the UVspec code. A preliminary step consists in generating an effective surface Meteosat albedo map from a series of 10 consecutive days. In this process the well-known difficulty of distinguishing clouds from snow-covered surfaces is encountered. An attempt is made to partially resolve the ambiguity by using the Meteosat infrared channel and modeled snow cover data. After additional empirical cloud filtering, the effective albedo map is used as a baseline to estimate the cloud liquid water thickness. The UV surface albedo is assigned uniform values for land and sea/ocean, except in the presence of snow. In this case it is given a value proportional to the Meteosat effective albedo. The total column ozone is extracted from the level 3 GOME products. The aerosol optical thickness is mapped by gridding the daily measurements performed by ˜1000 ground stations. The digital elevation model is the GTOPO30 data set from the U.S. Geological Survey. European wide UV dose rate maps are presented for one day in April 1997, and the influence of the various factors is illustrated. A daily integrated dose map was also generated using 27 Meteosat acquisitions at half-hour intervals on the same day. The dose map produced in this way takes into account the evolution of the cloud field and is thought to be more accurate than if it were estimated from one data take, in particular at the relatively high spatial resolution of the product. Finally, a preliminary comparison of modeled dose rate and daily dose with measurements performed with a ground instrument is discussed.

Towards development of an operational snow on sea ice product

NASA Astrophysics Data System (ADS)

Stroeve, J.; Liston, G. E.; Barrett, A. P.; Tschudi, M. A.; Stewart, S.

2017-12-01

Sea ice has been visibly changing over the past couple of decades; most notably the annual minimum extent which has shown a distinct downward, and recently accelerating, trend. September mean sea ice extent was over 7×106 km2 in the 1980's, but has averaged less than 5×106 km2 in the last decade. Should this loss continue, there will be wide-ranging impacts on marine ecosystems, coastal communities, prospects for resource extraction and marine activity, and weather conditions in the Arctic and beyond. While changes in the spatial extent of sea ice have been routinely monitored since the 1970s, less is known about how the thickness of the ice cover has changed. While estimates of ice thickness across the Arctic Ocean have become available over the past 20 years based on data from ERS-1/2, Envisat, ICESat, CryoSat-2 satellites and Operation IceBridge aircraft campaigns, the variety of these different measurement approaches, sensor technologies and spatial coverage present formidable challenges. Key among these is that measurement techniques do not measure ice thickness directly - retrievals also require snow depth and density. Towards that end, a sophisticated snow accumulation model is tested in a Lagrangian framework to map daily snow depths across the Arctic sea ice cover using atmospheric reanalysis data as input. Accuracy of the snow accumulation is assessed through comparison with Operation IceBridge data and ice mass balance buoys (IMBs). Impacts on ice thickness retrievals are further discussed.

Investigating the effect and uncertainties of light absorbing impurities in snow and ice on snow melt and discharge generation using a hydrologic catchment model and satellite data

NASA Astrophysics Data System (ADS)

Matt, Felix; Burkhart, John F.

2017-04-01

Light absorbing impurities in snow and ice (LAISI) originating from atmospheric deposition enhance snow melt by increasing the absorption of short wave radiation. The consequences are a shortening of the snow cover duration due to increased snow melt and, with respect to hydrologic processes, a temporal shift in the discharge generation. However, the magnitude of these effects as simulated in numerical models have large uncertainties, originating mainly from uncertainties in the wet and dry deposition of light absorbing aerosols, limitations in the model representation of the snowpack, and the lack of observable variables required to estimate model parameters and evaluate the simulated variables connected with the representation of LAISI. This leads to high uncertainties in the additional energy absorbed by the snow due to the presence of LAISI, a key variable in understanding snowpack energy-balance dynamics. In this study, we assess the effect of LAISI on snow melt and discharge generation and the involved uncertainties in a high mountain catchment located in the western Himalayas by using a distributed hydrological catchment model with focus on the representation of the seasonal snow pack. The snow albedo is hereby calculated from a radiative transfer model for snow, taking the increased absorption of short wave radiation by LAISI into account. Meteorological forcing data is generated from an assimilation of observations and high resolution WRF simulations, and LAISI mixing ratios from deposition rates of Black Carbon simulated with the FLEXPART model. To asses the quality of our simulations and the related uncertainties, we compare the simulated additional energy absorbed by the snow due to the presence of LAISI to the MODIS Dust Radiative Forcing in Snow (MODDRFS) algorithm satellite product.

SNOWMIP2: An evaluation of forest snow process simulations

Treesearch

Richard Essery; Nick Rutter; John Pomeroy; Robert Baxter; Manfred Stahli; David Gustafsson; Alan Barr; Paul Bartlett; Kelly Elder

2009-01-01

Models of terrestrial snow cover, or snow modules within land surface models, are used in many meteorological, hydrological, and ecological applications. Such models were developed first, and have achieved their greatest sophistication, for snow in open areas; however, huge tracts of the Northern Hemisphere both have seasonal snow cover and are forested (Fig. 1)....

Satellite-observed snow cover variations over the Tibetan Plateau for the period 2001-2014

NASA Astrophysics Data System (ADS)

Long, D.; Chen, X.

2016-12-01

Snow is an integral component of the global climate system. Owing to its high albedo and thermal and water storage properties, snow has important linkages and feedbacks through its influence on surface energy and moisture fluxes, clouds, precipitation, hydrology, and atmospheric circulation. As the "Roof of the World" and the "Third Pole" with the highest mountains in middle latitudes, the Tibetan Plateau (TP) is one of the most hot spots in climate change and hydrological studies, in which seasonal snow cover is a critical aspect. Unlike large-scale snow cover and regional-scale glaciers over other cryospheric regions, changes in snow cover over the TP has been largely unknown due mostly to the quality of observations. Based on improved MODIS daily snow cover products, this study aims to quantify the distribution and changes in snow cover over the TP for the period 2001 to 2014. Results show that the spatial distribution of changes in snow cover fraction (SCF) over the 14-year study period exhibited a general negative trend over the TP driven primarily by increasing land surface temperature (LST), except some areas of the upper Golden-Sanded River and upper Brahmaputra River basins. However, decreased LST and increased precipitation in the accumulation season (September to the following February) resulted in increased SCF in the accumulation season, coinciding with large-scale cold snaps and heavy snowfall events at middle latitudes. Detailed analyses of the intra-annual variability of SCF in the TP regions show an increase in SCF in the accumulation season but a decrease in SCF in the melting season (March to August), indicating that the intra-annual amplitude of SCF increased during the study period and more snow cover was released as snowmelt in the spring season.

A satellite snow depth multi-year average derived from SSM/I for the high latitude regions

USGS Publications Warehouse

Biancamaria, S.; Mognard, N.M.; Boone, A.; Grippa, M.; Josberger, E.G.

2008-01-01

The hydrological cycle for high latitude regions is inherently linked with the seasonal snowpack. Thus, accurately monitoring the snow depth and the associated aerial coverage are critical issues for monitoring the global climate system. Passive microwave satellite measurements provide an optimal means to monitor the snowpack over the arctic region. While the temporal evolution of snow extent can be observed globally from microwave radiometers, the determination of the corresponding snow depth is more difficult. A dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from Special Sensor Microwave/Imager (SSM/I) brightness temperatures and was validated over the U.S. Great Plains and Western Siberia. The purpose of this study is to assess the dynamic algorithm performance over the entire high latitude (land) region by computing a snow depth multi-year field for the time period 1987-1995. This multi-year average is compared to the Global Soil Wetness Project-Phase2 (GSWP2) snow depth computed from several state-of-the-art land surface schemes and averaged over the same time period. The multi-year average obtained by the dynamic algorithm is in good agreement with the GSWP2 snow depth field (the correlation coefficient for January is 0.55). The static algorithm, which assumes a constant snow grain size in space and time does not correlate with the GSWP2 snow depth field (the correlation coefficient with GSWP2 data for January is - 0.03), but exhibits a very high anti-correlation with the NCEP average January air temperature field (correlation coefficient - 0.77), the deepest satellite snow pack being located in the coldest regions, where the snow grain size may be significantly larger than the average value used in the static algorithm. The dynamic algorithm performs better over Eurasia (with a correlation coefficient with GSWP2 snow depth equal to 0.65) than over North America (where the correlation coefficient decreases to 0.29). ?? 2007 Elsevier Inc. All rights reserved.

Relating C-band Microwave and Optical Satellite Observations as A Function of Snow Thickness on First-Year Sea Ice during the Winter to Summer Transition

NASA Astrophysics Data System (ADS)

Zheng, J.; Yackel, J.

2015-12-01

The Arctic sea ice and its snow cover have a direct impact on both the Arctic and global climate system through their ability to moderate heat exchange across the ocean-sea ice-atmosphere (OSA) interface. Snow cover plays a key role in the OSA interface radiation and energy exchange, as it controls the growth and decay of first-year sea ice (FYI). However, meteoric accumulation and redistribution of snow on FYI is highly stochastic over space and time, which makes it poorly understood. Previous studies have estimated local-scale snow thickness distributions using in-situ technique and modelling but it is spatially limited and challenging due to logistic difficulties. Moreover, snow albedo is also critical for determining the surface energy balance of the OSA during the critical summer ablation season. Even then, due to persistent and widespread cloud cover in the Arctic at various spatio-temporal scales, it is difficult and unreliable to remotely measure albedo of snow cover on FYI in the optical spectrum. Previous studies demonstrate that only large-scale sea ice albedo was successfully estimated using optical-satellite sensors. However, space-borne microwave sensors, with their capability of all-weather and 24-hour imaging, can provide enhanced information about snow cover on FYI. Daily spaceborne C-band scatterometer data (ASCAT) and MODIS data are used to investigate the the seasonal co-evolution of the microwave backscatter coefficient and optical albedo as a function of snow thickness on smooth FYI. The research focuses on snow-covered FYI near Cambridge Bay, Nunavut (Fig.1) during the winter to advanced-melt period (April-June, 2014). The ACSAT time series (Fig.2) show distinct increase in scattering at melt onset indicating the first occurrence of melt water in the snow cover. The corresponding albedo exhibits no decrease at this stage. We show how the standard deviation of ASCAT backscatter on FYI during winter can be used as a proxy for surface roughness and subsequent snow thickness (ie. Rougher surfaces acquire thicker snow covers) and then how this surface manifests into statistically distinguishable surface melt pond fractions which largely governs the optical derived albedo. Such relationships are useful for modelling the subsequent summer melt pond fraction and albedo from winter snow cover.

Snow cover and snowfall impact corticosterone and immunoglobulin a levels in a threatened steppe bird.

PubMed

Liu, Gang; Hu, Xiaolong; Kessler, Aimee Elizabeth; Gong, Minghao; Wang, Yihua; Li, Huixin; Dong, Yuqiu; Yang, Yuhui; Li, Linhai

2018-05-15

Birds use both the corticosterone stress response and immune system to meet physiological challenges during exposure to adverse climatic conditions. To assess the stress level and immune response of the Asian Great Bustard during conditions of severe winter weather, we measured fecal corticosterone (CORT) and Immunoglobulin A (IgA) before and after snowfall in a low snow cover year (2014) and a high snow cover year (2015). A total of 239 fecal samples were gathered from individuals in Tumuji Nature Reserve, located in eastern Inner Mongolia, China. We observed high CORT levels that rose further after snowfall both in high and low snow cover years. IgA levels increased significantly after snowfall in the low snow cover year, but decreased after snowfall in the high snow cover year. These results suggest that overwintering Asian Great Bustards are subjected to climatic stress during severe winter weather, and the hypothalamic-pituitary-adrenal axis and immune system react to this challenge. Extreme levels of stress, such as snowfall in already prolonged and high snow cover conditions may decrease immune function. Supplemental feeding should be considered under severe winter weather conditions for this endangered subspecies. Copyright © 2018 Elsevier Inc. All rights reserved.

Reduced Snow Cover Increases Wintertime Nitrous Oxide (N 2O) Emissions from an Agricultural Soil in the Upper U.S. Midwest

DOE PAGES

Ruan, Leilei; Robertson, G. Philip

2016-11-21

Throughout most of the northern hemisphere, snow cover decreased in almost every winter month from 1967 to 2012. Because snow is an effective insulator, snow cover loss has likely enhanced soil freezing and the frequency of soil freeze–thaw cycles, which can disrupt soil nitrogen dynamics including the production of nitrous oxide (N 2O). Here, we used replicated automated gas flux chambers deployed in an annual cropping system in the upper Midwest US for three winters (December–March, 2011–2013) to examine the effects of snow removal and additions on N 2O fluxes. Diminished snow cover resulted in increased N2O emissions each year;more » over the entire experiment, cumulative emissions in plots with snow removed were 69% higher than in ambient snow control plots and 95% higher than in plots that received additional snow (P < 0.001). Higher emissions coincided with a greater number of freeze–thaw cycles that broke up soil macroaggregates (250–8000 µm) and significantly increased soil inorganic nitrogen pools. We conclude that winters with less snow cover can be expected to accelerate N 2O fluxes from agricultural soils subject to wintertime freezing.« less

Snow-atmosphere coupling and its impact on temperature variability and extremes over North America

NASA Astrophysics Data System (ADS)

Diro, G. T.; Sushama, L.; Huziy, O.

2018-04-01

The impact of snow-atmosphere coupling on climate variability and extremes over North America is investigated using modeling experiments with the fifth generation Canadian Regional Climate Model (CRCM5). To this end, two CRCM5 simulations driven by ERA-Interim reanalysis for the 1981-2010 period are performed, where snow cover and depth are prescribed (uncoupled) in one simulation while they evolve interactively (coupled) during model integration in the second one. Results indicate systematic influence of snow cover and snow depth variability on the inter-annual variability of soil and air temperatures during winter and spring seasons. Inter-annual variability of air temperature is larger in the coupled simulation, with snow cover and depth variability accounting for 40-60% of winter temperature variability over the Mid-west, Northern Great Plains and over the Canadian Prairies. The contribution of snow variability reaches even more than 70% during spring and the regions of high snow-temperature coupling extend north of the boreal forests. The dominant process contributing to the snow-atmosphere coupling is the albedo effect in winter, while the hydrological effect controls the coupling in spring. Snow cover/depth variability at different locations is also found to affect extremes. For instance, variability of cold-spell characteristics is sensitive to snow cover/depth variation over the Mid-west and Northern Great Plains, whereas, warm-spell variability is sensitive to snow variation primarily in regions with climatologically extensive snow cover such as northeast Canada and the Rockies. Furthermore, snow-atmosphere interactions appear to have contributed to enhancing the number of cold spell days during the 2002 spring, which is the coldest recorded during the study period, by over 50%, over western North America. Additional results also provide useful information on the importance of the interactions of snow with large-scale mode of variability in modulating temperature extreme characteristics.

Radar Images of the Earth and the World Wide Web

NASA Technical Reports Server (NTRS)

Chapman, B.; Freeman, A.

1995-01-01

A perspective of NASA's Jet Propulsion Laboratory as a center of planetary exploration, and its involvement in studying the earth from space is given. Remote sensing, radar maps, land topography, snow cover properties, vegetation type, biomass content, moisture levels, and ocean data are items discussed related to earth orbiting satellite imaging radar. World Wide Web viewing of this content is discussed.

Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau

USGS Publications Warehouse

Wang, Kun; Zhang, Li; Qiu, Yubao; Ji, Lei; Tian, Feng; Wang, Cuizhen; Wang, Zhiyong

2013-01-01

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes. The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover. We used 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) datasets during 2001–2010 to examine the snow–vegetation relationships, specifically, (1) the influence of snow melting date on vegetation green-up date and (2) the effects of snow cover duration on vegetation greenness. The results showed that the alpine vegetation responded strongly to snow phenology (i.e., snow melting date and snow cover duration) over large areas of the Qinghai-Tibetan Plateau. Snow melting date and vegetation green-up date were significantly correlated (p < 0.1) in 39.9% of meadow areas (accounting for 26.2% of vegetated areas) and 36.7% of steppe areas (28.1% of vegetated areas). Vegetation growth was influenced by different seasonal snow cover durations (SCDs) in different regions. Generally, the December–February and March–May SCDs played a significantly role in vegetation growth, both positively and negatively, depending on different water source regions. Snow's positive impact on vegetation was larger than the negative impact.

Automatic snow extent extraction in alpine environments: short and medium term 2000-2006 analysis

NASA Astrophysics Data System (ADS)

Gamba, P.; Lisini, G.; Merlin, E.; Riva, F.

2007-10-01

Water resources in Northern Italy has dramatically shortened in the past 10 to 20 years, and recent phenomena connected to the climate change have further sharpened the trend. To match the observable and collected information with this experience and find methodologies to improve the water management cycle in the Lombardy Region, University of Milan Bicocca, Fondazione Lombardia per l'Ambiente and ARPA Lombardia are currently funding a project, named "Regional Impact of Climatic Change in Lombardy Water Resources: Modelling and Applications" (RICLIC-WARM). In the framework of this project, the analysis of the fraction of water available and provided to the whole regional network by the snow cover of the Alps will be investigated by means of remotely sensed data. While there are already a number of algorithms devoted to this task for data coming from various and different sensors in the visible and infrared regions, no operative comparison and analytical analysis of the advantages and drawbacks of using different data has been attempted. This idea will pave the way for a fusion of the available information as well as a multi-source mapping procedure which will be able to exploit successfully the huge quantity of data available for the past and the even larger amount that may be accessed in the future. To this aim, a comparison on selected dates for the whole 2000/2006 period was performed.

Evaluation of Spaceborne L-band Radiometer Measurements for Terrestrial Freeze/Thaw Retrievals in Canada

NASA Technical Reports Server (NTRS)

Roy, A.; Royer, A.; Derksen, C.; Brucker, L.; Langlois, A.; Mailon, A.; Kerr, Y.

2015-01-01

The landscape freeze/thaw (FT) state has an important impact on the surface energy balance, carbon fluxes, and hydrologic processes; the timing of spring melt is linked to active layer dynamics in permafrost areas. L-band (1.4 GHz) microwave emission could allow the monitoring of surface state dynamics due to its sensitivity to the pronounced permittivity difference between frozen and thawed soil. The aim of this paper is to evaluate the performance of both Aquarius and Soil Moisture and Ocean Salinity (SMOS) L-band passive microwave measurements using a polarization ratio-based algorithm for landscape FT monitoring. Weekly L-band satellite observations are compared with a large set of reference data at 48 sites across Canada spanning three environments: tundra, boreal forest, and prairies. The reference data include in situ measurements of soil temperature (Tsoil) and air temperature (Tair), and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover area (SCA) products. Results show generally good agreement between Lband FT detection and the surface state estimated from four reference datasets. The best apparent accuracies for all seasons are obtained using Tair as the reference. Aquarius radiometer 2 (incidence angle of 39.6) data gives the best accuracies (90.8), while for SMOS the best results (87.8 of accuracy) are obtained at higher incidence angles (55- 60). The FT algorithm identifies both freeze onset and end with a delay of about one week in tundra and two weeks in forest and prairies, when compared to Tair. The analysis shows a stronger FT signal at tundra sites due to the typically clean transitions between consistently frozen and thawed conditions (and vice versa) and the absence of surface vegetation. Results in the prairies were poorer because of the influence of vegetation growth in summer (which decreases the polarization ratio) and the high frequency of ephemeral thaw events during winter. Freeze onset and end maps created from the same algorithm applied to SMOS and Aquarius measurements characterize similar FT patterns over Canada. This study shows the potential of using L-band spaceborne observations for FT monitoring, but underlines some limitations due to ice crusts in the snowpack, liquid water content in snow cover during the spring freeze to thaw transition, and vegetation growth.

Snow cover monitoring over French Alps based on Spot-Vegetation S-10 products. Application to the Vercors area for the time period 1998-2008.

NASA Astrophysics Data System (ADS)

Bigot, S.; Dedieu, Jp.; Rome, S.

2009-04-01

Sylvain.bigot@ujf-grenoble.fr Jean-pierre.dedieu@hmg.inpg.fr Sandra.rome@ujf-grenoble.fr Estimation of the Snow Covered Area (SCA) is an important issue for meteorological application and hydrological modeling of runoff. With spectral bands in the visible, near and middle infrared, the SPOT-4 and -5 VEGETATION sensors are used to detect snow cover because of large differences between reflectance from snow covered and snow free surfaces. At the same time, it allows separation between snow and clouds. Moreover, the sensor provides a daily coverage of large areas. However, as the pixel size is 1km x 1km, a VGT pixel may be partially covered by snow, particularly in Alpine areas, where snow may not be present in valleys lying at lower altitudes. Also, variation of reflectance due to differential sunlit effects as a function of slope and aspect, as well as bidirectional effects may be present in images. Nevertheless, it is possible to estimate snow cover at the sub-pixel level with a relatively good accuracy and with very good results if the sub-pixel estimations are integrated for a few pixels relative to an entire watershed. Application of this approach in the French Alps is presented over the Vercors Natural Park area (N 44°.50' / E 05°.30'), based on 10-day Synthetic products for the 1998-2008 time period, and using the NDSII (Normalized Difference Snow/Ice Index) as numerical threshold. This work performs an analysis of climate impact on snow cover spatial and temporal variability, at mid-elevation mountain range (1500 m asl) under temperate climate conditions. The results indicates (i) a increasing temporal and spatial variability of snow coverage, and (ii) a high sensitivity to low variation of air temperature, often close to 1° C. This is the case in particular for the beginning and the end of the winter season. The regional snow cover depletion is both influenced by thermal positives anomalies (e.g. 2000 and 2006), and the general trend of rising atmospheric temperatures since the late 1980s.

"Proximal Sensing" capabilities for snow cover monitoring

NASA Astrophysics Data System (ADS)

Valt, Mauro; Salvatori, Rosamaria; Plini, Paolo; Salzano, Roberto; Giusti, Marco; Montagnoli, Mauro; Sigismondi, Daniele; Cagnati, Anselmo

2013-04-01

The seasonal snow cover represents one of the most important land cover class in relation to environmental studies in mountain areas, especially considering its variation during time. Snow cover and its extension play a relevant role for the studies on the atmospheric dynamics and the evolution of climate. It is also important for the analysis and management of water resources and for the management of touristic activities in mountain areas. Recently, webcam images collected at daily or even hourly intervals are being used as tools to observe the snow covered areas; those images, properly processed, can be considered a very important environmental data source. Images captured by digital cameras become a useful tool at local scale providing images even when the cloud coverage makes impossible the observation by satellite sensors. When suitably processed these images can be used for scientific purposes, having a good resolution (at least 800x600x16 million colours) and a very good sampling frequency (hourly images taken through the whole year). Once stored in databases, those images represent therefore an important source of information for the study of recent climatic changes, to evaluate the available water resources and to analyse the daily surface evolution of the snow cover. The Snow-noSnow software has been specifically designed to automatically detect the extension of snow cover collected from webcam images with a very limited human intervention. The software was tested on images collected on Alps (ARPAV webcam network) and on Apennine in a pilot station properly equipped for this project by CNR-IIA. The results obtained through the use of Snow-noSnow are comparable to the one achieved by photo-interpretation and could be considered as better as the ones obtained using the image segmentation routine implemented into image processing commercial softwares. Additionally, Snow-noSnow operates in a semi-automatic way and has a reduced processing time. The analysis of this kind of images could represent an useful element to support the interpretation of remote sensing images, especially those provided by high spatial resolution sensors. Keywords: snow cover monitoring, digital images, software, Alps, Apennines.

Study of aerosol effect on accelerated snow melting over the Tibetan Plateau during boreal spring

NASA Astrophysics Data System (ADS)

Lee, Woo-Seop; Bhawar, Rohini L.; Kim, Maeng-Ki; Sang, Jeong

2013-08-01

In the present study, a coupled atmosphere-ocean global climate model (CSIRO-Mk3.6) is used to investigate the role of aerosol forcing agents as drivers of snow melting trends in the Tibetan Plateau (TP) region. Anthropogenic aerosol-induced snow cover changes in a warming climate are calculated from the difference between historical run (HIST) and all forcing except anthropogenic aerosol (NoAA). Absorbing aerosols can influence snow cover by warming the atmosphere, reducing snow reflectance after deposition. The warming the rate of snow melt, exposing darker surfaces below to short-wave radiation sooner, and allowing them to heat up even faster in the Himalayas and TP. The results show a strong spring snow cover decrease over TP when absorbing anthropogenic aerosol forcing is considered, whereas snow cover fraction (SCF) trends in NoAA are weakly negative (but insignificant) during 1951-2005. The enhanced spring snow cover trends in HIST are due to overall effects of different forcing agents: When aerosol forcing (AERO) is considered, a significant reduction of SCF than average can be found over the western TP and Himalayas. The large decreasing trends in SCF over the TP, with the maximum reduction of SCF around 12-15% over the western TP and Himalayas slope. Also accelerated snow melting during spring is due to effects of aerosol on snow albedo, where aerosol deposition cause decreases snow albedo. However, the SCF change in the “NoAA” simulations was observed to be less.

An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data

USGS Publications Warehouse

Tan, B.; Morisette, J.T.; Wolfe, R.E.; Gao, F.; Ederer, G.A.; Nightingale, J.; Pedelty, J.A.

2011-01-01

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates. ?? 2010 IEEE.

An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics from MODIS Data

NASA Technical Reports Server (NTRS)

Tan, Bin; Morisette, Jeffrey T.; Wolfe, Robert E.; Gao, Feng; Ederer, Gregory A.; Nightingale, Joanne; Pedelty, Jeffrey A.

2012-01-01

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates.

Building a Snow Data System on the Apache OODT Open Technology Stack

NASA Astrophysics Data System (ADS)

Goodale, C. E.; Painter, T. H.; Mattmann, C. A.; Hart, A. F.; Ramirez, P.; Zimdars, P.; Bryant, A. C.; Snow Data System Team

2011-12-01

Snow cover and its melt dominate regional climate and hydrology in many of the world's mountainous regions. One-sixth of Earth's population depends on snow- or glacier-melt for water resources. Operationally, seasonal forecasts of snowmelt-generated streamflow are leveraged through empirical relations based on past snowmelt periods. These historical data show that climate is changing, but the changes reduce the reliability of the empirical relations. Therefore optimal future management of snowmelt derived water resources will require explicit physical models driven by remotely sensed snow property data. Toward this goal, the Snow Optics Laboratory at the Jet Propulsion Laboratory has initiated a near real-time processing pipeline to generate and publish post-processed snow data products within a few hours of satellite acquisition. To solve this challenge, a Scientific Data Management and Processing System was required and the JPL Team leveraged an open-source project called Object Oriented Data Technology (OODT). OODT was developed within NASA's Jet Propulsion Laboratory across the last 10 years. OODT has supported various scientific data management and processing projects, providing solutions in the Earth, Planetary, and Medical science fields. It became apparent that the project needed to be opened to a larger audience to foster and promote growth and adoption. OODT was open-sourced at the Apache Software Foundation in November 2010 and has a growing community of users and committers that are constantly improving the software. Leveraging OODT, the JPL Snow Data System (SnowDS) Team was able to install and configure a core Data Management System (DMS) that would download MODIS raw data files and archive the products in a local repository for post processing. The team has since built an online data portal, and an algorithm-processing pipeline using the Apache OODT software as the foundation. We will present the working SnowDS system with its core remote sensing components: the MODIS Snow Covered Area and Grain size model (MODSCAG) and the MODIS Dust Radiative Forcing in Snow (MOD-DRFS). These products will be delivered in near real time to water managers and the broader cryosphere and climate community beginning in Winter 2012. We will then present the challenges and opportunities we see in the future as the SnowDS matures and contributions are made back to the OODT project.

Ten Years of Cloud Optical and Microphysical Retrievals from MODIS

NASA Technical Reports Server (NTRS)

Platnick, Steven; King, Michael D.; Wind, Galina; Hubanks, Paul; Arnold, G. Thomas; Amarasinghe, Nandana

2010-01-01

The MODIS cloud optical properties algorithm (MOD06/MYD06 for Terra and Aqua MODIS, respectively) has undergone extensive improvements and enhancements since the launch of Terra. These changes have included: improvements in the cloud thermodynamic phase algorithm; substantial changes in the ice cloud light scattering look up tables (LUTs); a clear-sky restoral algorithm for flagging heavy aerosol and sunglint; greatly improved spectral surface albedo maps, including the spectral albedo of snow by ecosystem; inclusion of pixel-level uncertainty estimates for cloud optical thickness, effective radius, and water path derived for three error sources that includes the sensitivity of the retrievals to solar and viewing geometries. To improve overall retrieval quality, we have also implemented cloud edge removal and partly cloudy detection (using MOD35 cloud mask 250m tests), added a supplementary cloud optical thickness and effective radius algorithm over snow and sea ice surfaces and over the ocean, which enables comparison with the "standard" 2.1 11m effective radius retrieval, and added a multi-layer cloud detection algorithm. We will discuss the status of the MOD06 algorithm and show examples of pixellevel (Level-2) cloud retrievals for selected data granules, as well as gridded (Level-3) statistics, notably monthly means and histograms (lD and 2D, with the latter giving correlations between cloud optical thickness and effective radius, and other cloud product pairs).

Multi-resolution Changes in the Spatial Extent of Perennial Arctic Alpine Snow and Ice Fields with Potential Archaeological Significance in the Central Brooks Range, Alaska

NASA Astrophysics Data System (ADS)

Tedesche, M. E.; Freeburg, A. K.; Rasic, J. T.; Ciancibelli, C.; Fassnacht, S. R.

2015-12-01

Perennial snow and ice fields could be an important archaeological and paleoecological resource for Gates of the Arctic National Park and Preserve in the central Brooks Range mountains of Arctic Alaska. These features may have cultural significance, as prehistoric artifacts may be frozen within the snow and ice. Globally significant discoveries have been made recently as ancient artifacts and animal dung have been found in melting alpine snow and ice patches in the Southern Yukon and Northwest Territories in Canada, the Wrangell mountains in Alaska, as well as in other areas. These sites are melting rapidly, which results in quick decay of biological materials. The summer of 2015 saw historic lows in year round snow cover extent for most of Alaska. Twenty mid to high elevation sites, including eighteen perennial snow and ice fields, and two glaciers, were surveyed in July 2015 to quantify their areal extent. This survey was accomplished by using both low flying aircraft (helicopter), as well as with on the ground in-situ (by foot) measurements. By helicopter, visual surveys were conducted within tens of meters of the surface. Sites visited by foot were surveyed for extent of snow and ice coverage, melt water hydrologic parameters and chemistry, and initial estimates of depths and delineations between snow, firn, and ice. Imagery from both historic aerial photography and from 5m resolution IKONOS satellite information were correlated with the field data. Initial results indicate good agreement in permanent snow and ice cover between field surveyed data and the 1985 to 2011 Landsat imagery-based Northwest Alaska snow persistence map created by Macander et al. (2015). The most deviation between the Macander et al. model and the field surveyed results typically occurred as an overestimate of perennial extent on the steepest aspects. These differences are either a function of image classification or due to accelerated ablation rates in perennial snow and ice coverage between 2011 and 2015. Further work is ongoing to develop a model to guide archaeological and paleoecological snow and ice field surveys. This will entail a fine scale, empirically based model of accumulation and ablation to estimate changes in three dimensional geometries of historically perennial arctic alpine snow and ice fields in the study area.

Use of areal snow cover measurements from ERTS-1 imagery in snowmelt-runoff relationships in Arizona

NASA Technical Reports Server (NTRS)

Aul, J. S.; Ffolliott, P. F.

1975-01-01

Methods of interpreting ERTS-1 imagery to measure areal snow cover were analyzed. Relationship of areal snow cover and runoff were among the objectives in this study of ERTS-1 imagery use for forecasting snowmelt-runoff relationships.

Incorporation of the Mass Concentration and the New Snow Albedo Schemes into the Global Forecasting Model, GEOS-5 and the Impact of the New Schemes over Himalayan Glaciers

NASA Technical Reports Server (NTRS)

Yasunari, Teppei

2012-01-01

Recently the issue on glacier retreats comes up and many factors should be relevant to the issue. The absorbing aerosols such as dust and black carbon (BC) are considered to be one of the factors. After they deposited onto the snow surface, it will reduce snow albedo (called snow darkening effect) and probably contribute to further melting of glacier. The Goddard Earth Observing System version 5 (GEOS-5) has developed at NASA/GSFC. However, the original snowpack model used in the land surface model in the GEOS-5 did not consider the snow darkening effect. Here we developed the new snow albedo scheme which can consider the snow darkening effect. In addition, another scheme on calculating mass concentrations on the absorbing aerosols in snowpack was also developed, in which the direct aerosol depositions from the chemical transport model in the GEOS-5 were used. The scheme has been validated with the observed data obtained at backyard of the Institute of Low Temperature Science, Hokkaido University, by Dr. Teruo Aoki (Meteorological Research Institute) et aL including me. The observed data was obtained when I was Ph.D. candidate. The original GEOS-5during 2007-2009 over the Himalayas and Tibetan Plateau region showed more reductions of snow than that of the new GEOS-5 because the original one used lower albedo settings. On snow cover fraction, the new GEOS-5 simulated more realistic snow-covered area comparing to the MODIS snow cover fraction. The reductions on snow albedo, snow cover fraction, and snow water equivalent were seen with statistically significance if we consider the snow darkening effect comparing to the results without the snow darkening effect. In the real world, debris cover, inside refreezing process, surface flow of glacier, etc. affect glacier mass balance and the simulated results immediately do not affect whole glacier retreating. However, our results indicate that some surface melting over non debris covered parts of the glacier would be explained by the snow darkening effect. Further discussion and observations are necessary to assess the glacier issue.

Detection of Slope Instabilities Along the National Road 7, Mendoza Province, Argentina, Using Multi-Temporal InSAR

NASA Astrophysics Data System (ADS)

Michoud, Clément; Derron, Marc-Henri; Baumann, Valérie; Jaboyedoff, Michel; Rune Lauknes, Tom

2013-04-01

About 2'230 vehicles per day pass through the National Road 7 that link Buenos Aires to Santiago de Chile, crossing Andes Cordillera. This extremely important corridor, being the most important land pass between Argentina and Chile, is exposed to numerous natural hazards, such as snow avalanches, rockfalls and debris flows and remains closed by natural hazards several days per year. This goal of this study is to perform a regional mapping of geohazard susceptibilities along the Road 7 corridor, as started by Baumann et al. (2005), using modern remote sensing and numerical approaches with field checking. The area of interest is located in the Mendoza Province, between the villages Potrerillos and Las Cuevas near the Chilean border. The diversity of soil and rock conditions, the active geomorphological processes associated to post-glacial decompression, seasonal freeze and thaw and severe storms along the road corridor, increase the risk to natural hazard. With the support of the European Space Agency (ESA Category-1 Project 7154), we have in this study processed a large number of ERS and Envisat ASAR scenes, covering the period from 1995 to 2000. We applied both the small-baseline (SB) and the persistent scatterer (PSI) multi-temporal interferometric SAR (InSAR) techniques. The study area contains sparse vegetation, and the SB InSAR method is therefore well suited to map the area containing mainly distributed scatterers. Furthermore, PSI algorithms are also used for comparison for selected landslides in the inventory. Both approaches show a relatively good coherence within mountain areas, which is a good point for the landslide detections along the road. Indeed, the authors identified several large slope instabilities even active scree deposits. This inventory is finally compared with field observations and with existing susceptibility maps regarding snow avalanches, debris-flows and rockfalls. The final objective of this project is to develop a risk strategy that will help local authorities to manage the risk along this highway and also to provide guidelines.

Deriving Continuous Fields of Tree Cover at 1-m over the Continental United States From the National Agriculture Imagery Program (NAIP) Imagery to Reduce Uncertainties in Forest Carbon Stock Estimation

NASA Astrophysics Data System (ADS)

Ganguly, S.; Basu, S.; Mukhopadhyay, S.; Michaelis, A.; Milesi, C.; Votava, P.; Nemani, R. R.

2013-12-01

An unresolved issue with coarse-to-medium resolution satellite-based forest carbon mapping over regional to continental scales is the high level of uncertainty in above ground biomass (AGB) estimates caused by the absence of forest cover information at a high enough spatial resolution (current spatial resolution is limited to 30-m). To put confidence in existing satellite-derived AGB density estimates, it is imperative to create continuous fields of tree cover at a sufficiently high resolution (e.g. 1-m) such that large uncertainties in forested area are reduced. The proposed work will provide means to reduce uncertainty in present satellite-derived AGB maps and Forest Inventory and Analysis (FIA) based regional estimates. Our primary objective will be to create Very High Resolution (VHR) estimates of tree cover at a spatial resolution of 1-m for the Continental United States using all available National Agriculture Imaging Program (NAIP) color-infrared imagery from 2010 till 2012. We will leverage the existing capabilities of the NASA Earth Exchange (NEX) high performance computing and storage facilities. The proposed 1-m tree cover map can be further aggregated to provide percent tree cover at any medium-to-coarse resolution spatial grid, which will aid in reducing uncertainties in AGB density estimation at the respective grid and overcome current limitations imposed by medium-to-coarse resolution land cover maps. We have implemented a scalable and computationally-efficient parallelized framework for tree-cover delineation - the core components of the algorithm [that] include a feature extraction process, a Statistical Region Merging image segmentation algorithm and a classification algorithm based on Deep Belief Network and a Feedforward Backpropagation Neural Network algorithm. An initial pilot exercise has been performed over the state of California (~11,000 scenes) to create a wall-to-wall 1-m tree cover map and the classification accuracy has been assessed. Results show an improvement in accuracy of tree-cover delineation as compared to existing forest cover maps from NLCD, especially over fragmented, heterogeneous and urban landscapes. Estimates of VHR tree cover will complement and enhance the accuracy of present remote-sensing based AGB modeling approaches and forest inventory based estimates at both national and local scales. A requisite step will be to characterize the inherent uncertainties in tree cover estimates and propagate them to estimate AGB.

Validating SWE reconstruction using Airborne Snow Observatory measurements in the Sierra Nevada

NASA Astrophysics Data System (ADS)

Bair, N.; Rittger, K.; Davis, R. E.; Dozier, J.

2015-12-01

The Airborne Snow Observatory (ASO) program offers high resolution estimates of snow water equivalent (SWE) in several small basins across California during the melt season. Primarily, water managers use this information to model snowmelt runoff into reservoirs. Another, and potentially more impactful, use of ASO SWE measurements is in validating and improving satellite-based SWE estimates which can be used in austere regions with no ground-based snow or water measurements, such as Afghanistan's Hindu Kush. Using the entire ASO dataset to date (2013-2015) which is mostly from the Upper Tuolumne basin, but also includes measurements from 2015 in the Kings, Rush Creek, Merced, and Mammoth Lakes basins, we compare ASO measurements to those from a SWE reconstruction method. Briefly, SWE reconstruction involves downscaling energy balance forcings to compute potential melt energy, then using satellite-derived estimates of fractional snow covered area (fSCA) to estimate snow melt from potential melt. The snowpack can then be built in reverse, given a remotely-sensed date of snow disappearance (fSCA=0). Our model has improvements over previous iterations in that it: uses the full energy balance (compared to a modified degree-day) approach, models bulk and surface snow temperatures, accounts for ephemeral snow, and uses a remotely-sensed snow albedo adjusted for impurities. To check that ASO provides accurate snow measurements, we compare fSCA derived from ASO snow depth at 3 m resolution with fSCA from a spectral unmixing algorithm for LandSAT at 30 m, and from binary SCA estimates from Geoeye at 0.5 m from supervised classification. To conclude, we document how our reconstruction model has evolved over the years and provide specific examples where improvements have been made using ASO and other verification sources.

ESA GlobPermafrost - mapping the extent and thermal state of permafrost with satellite data

NASA Astrophysics Data System (ADS)

Westermann, Sebastian; Obu, Jaroslav; Aalstad, Kristoffer; Bartsch, Annett; Kääb, Andreas

2017-04-01

The ESA GlobPermafrost initiative (2016-2019) aims at developing, validating and implementing information products based on remote sensing data to support permafrost research. Mapping of permafrost extent and ground temperatures is conducted at 1 km scale using remotely sensed land surface temperatures (MODIS), snow water equivalent (ESA GlobSnow) and land cover (ESA CCI landcover) in conjunction with a simple ground thermal model (CryoGrid 1). The spatial variability of the ground thermal regime at scales smaller than the model resolution is explicitly taken into account by considering an ensemble of realizations with different model properties. The approach has been tested for the unglacierized land areas in the North Atlantic region, an area of more than 5 million km2. The results have been compared to in-situ temperature measurements in more than 100 boreholes, indicating an accuracy of approximately 2.5°C. Within GlobPermafrost, the scheme will be extended to cover the entire the circum-polar permafrost area. Here, we provide an evaluation of the first prototype covering "lowland" permafrost areas north of 40° latitude (available on www.globpermafrost.info in early 2017). We give a feasibility assessment for extending the scheme to global scale, including both mountain and Antarctic permafrost. Finally, we discuss the potential and limitations for estimating changes of permafrost extent on decadal timescales.

Spring floods prediction with the use of optical satellite data in Québec

NASA Astrophysics Data System (ADS)

Roy, A.; Royer, A.; Turcotte, R.

2009-04-01

The Centre d'expertise hydrique du Québec (CEHQ) operates a distributed hydrological model, which integrates a snow model, for the management of dams in the south of Québec. It appears that the estimation of the water quantity of snowmelt in spring remains a variable with a large uncertainty and induces generally to an important error in stream flow simulation. Therefore, the National snow and ice center (NSIDC) produces, from MODIS (Moderate Resolution Imaging Spectroradiometer) data, continuous and homogeneous spatial snow cover (snow/swow-free) data on the whole territory, but with a cloud contamination. This research aims to improve the prediction of spring floods and the estimation of the rate of discharge by integrating snow cover data in the CEHQ's snow model. The study is done on two watersheds: du Nord river watershed (45,8°N) and Aux Écorces river watershed (47,9°N). The snow model used in the study (SPH-AV) is an implementation developed by the CEHQ of the snowmelt model of HYDROLTEL in is hydrological forecast system to simulate the melted water. The melted water estimated is then used as input in the empirical hydrological model MOHYSE to simulate stream flow. MODIS data are considered valid only when the cloud cover on each pixel of the watersheds is less then 30%. A pixel by pixel correction is applied to the snow pack when there is a difference between satellite snow cover and modeled snow cover. In the case of model shows to much snow, a factor is applied on temperatures by iterative process (starting from the last valid MODIS data) to melt the snow. In the opposite case, the snow quantity added to the last valid MODIS data is found by iterative process so that the pixel's snow water equivalent is equal to the nonzero neighbor minimum value. The study shows, through the simulations done on the two watersheds, the interest of the use of snow/snow-free product for the operational update of snow water equivalent with the objective to improve spring snowmelt stream flow simulations. The binary aspect (snow/snowfree) of the data is however a limit. Alternatives are discussed (passive microwave data). Keywords : satellite snow cover data, MODIS, satellite data integration, snow model, hydrological model, stream flow simulation, flood.

Land surface temperature measurements from EOS MODIS data

NASA Technical Reports Server (NTRS)

Wan, Zhengming

1993-01-01

The task objectives of this reporting phase included: (1) completing the draft of the LST Algorithms Theoretical Basic Document by July 30, 1993; (2) making a detailed characterization of the thermal infrared measurement system including spectrometer, blackbody, and radiation sources; (3) making TIR spectral measurements of water and snow-cover surfaces with the MIDAC M2401 spectrometer; and (4) making conceptual and engineering design of an accessory system for spectrometric measurements at variable angles. These objectives are based on the requirements by the MODIS Science Team and the unique challenge in the development of MODIS LST algorithms: to acquire accurate spectral emissivity data of land covers in the near-term and to make ground validations of the LST product in the long-term with a TIR measurement system.

Soil erosion by snow gliding - a first quantification attempt in a subalpine area in Switzerland

NASA Astrophysics Data System (ADS)

Meusburger, K.; Leitinger, G.; Mabit, L.; Mueller, M. H.; Walter, A.; Alewell, C.

2014-09-01

Snow processes might be one important driver of soil erosion in Alpine grasslands and thus the unknown variable when erosion modelling is attempted. The aim of this study is to assess the importance of snow gliding as a soil erosion agent for four different land use/land cover types in a subalpine area in Switzerland. We used three different approaches to estimate soil erosion rates: sediment yield measurements in snow glide depositions, the fallout radionuclide 137Cs and modelling with the Revised Universal Soil Loss Equation (RUSLE). RUSLE permits the evaluation of soil loss by water erosion, the 137Cs method integrates soil loss due to all erosion agents involved, and the measurement of snow glide deposition sediment yield can be directly related to snow-glide-induced erosion. Further, cumulative snow glide distance was measured for the sites in the winter of 2009/2010 and modelled for the surrounding area and long-term average winter precipitation (1959-2010) with the spatial snow glide model (SSGM). Measured snow glide distance confirmed the presence of snow gliding and ranged from 2 to 189 cm, with lower values on the north-facing slopes. We observed a reduction of snow glide distance with increasing surface roughness of the vegetation, which is an important information with respect to conservation planning and expected and ongoing land use changes in the Alps. Snow glide erosion estimated from the snow glide depositions was highly variable with values ranging from 0.03 to 22.9 t ha-1 yr-1 in the winter of 2012/2013. For sites affected by snow glide deposition, a mean erosion rate of 8.4 t ha-1 yr-1 was found. The difference in long-term erosion rates determined with RUSLE and 137Cs confirms the constant influence of snow-glide-induced erosion, since a large difference (lower proportion of water erosion compared to total net erosion) was observed for sites with high snow glide rates and vice versa. Moreover, the difference between RUSLE and 137Cs erosion rates was related to the measured snow glide distance (R2 = 0.64; p < 0.005) and to the snow deposition sediment yields (R2 = 0.39; p = 0.13). The SSGM reproduced the relative difference of the measured snow glide values under different land uses and land cover types. The resulting map highlighted the relevance of snow gliding for large parts of the investigated area. Based on these results, we conclude that snow gliding appears to be a crucial and non-negligible process impacting soil erosion patterns and magnitude in subalpine areas with similar topographic and climatic conditions.

Data sets for snow cover monitoring and modelling from the National Snow and Ice Data Center

NASA Astrophysics Data System (ADS)

Holm, M.; Daniels, K.; Scott, D.; McLean, B.; Weaver, R.

2003-04-01

A wide range of snow cover monitoring and modelling data sets are pending or are currently available from the National Snow and Ice Data Center (NSIDC). In-situ observations support validation experiments that enhance the accuracy of remote sensing data. In addition, remote sensing data are available in near-real time, providing coarse-resolution snow monitoring capability. Time series data beginning in 1966 are valuable for modelling efforts. NSIDC holdings include SMMR and SSM/I snow cover data, MODIS snow cover extent products, in-situ and satellite data collected for NASA's recent Cold Land Processes Experiment, and soon-to-be-released ASMR-E passive microwave products. The AMSR-E and MODIS sensors are part of NASA's Earth Observing System flying on the Terra and Aqua satellites Characteristics of these NSIDC-held data sets, appropriateness of products for specific applications, and data set access and availability will be presented.

Estimating snow depth in real time using unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Mizinski, Bartlomiej; Witek, Matylda; Spallek, Waldemar; Szymanowski, Mariusz

2016-04-01

In frame of the project no. LIDER/012/223/L-5/13/NCBR/2014, financed by the National Centre for Research and Development of Poland, we elaborated a fully automated approach for estimating snow depth in real time in the field. The procedure uses oblique aerial photographs taken by the unmanned aerial vehicle (UAV). The geotagged images of snow-covered terrain are processed by the Structure-from-Motion (SfM) method which is used to produce a non-georeferenced dense point cloud. The workflow includes the enhanced RunSFM procedure (keypoint detection using the scale-invariant feature transform known as SIFT, image matching, bundling using the Bundler, executing the multi-view stereo PMVS and CMVS2 software) which is preceded by multicore image resizing. The dense point cloud is subsequently automatically georeferenced using the GRASS software, and the ground control points are borrowed from positions of image centres acquired from the UAV-mounted GPS receiver. Finally, the digital surface model (DSM) is produced which - to improve the accuracy of georeferencing - is shifted using a vector obtained through precise geodetic GPS observation of a single ground control point (GCP) placed on the Laboratory for Unmanned Observations of Earth (mobile lab established at the University of Wroclaw, Poland). The DSM includes snow cover and its difference with the corresponding snow-free DSM or digital terrain model (DTM), following the concept of the digital elevation model of differences (DOD), produces a map of snow depth. Since the final result depends on the snow-free model, two experiments are carried out. Firstly, we show the performance of the entire procedure when the snow-free model reveals a very high resolution (3 cm/px) and is produced using the UAV-taken photographs and the precise GCPs measured by the geodetic GPS receiver. Secondly, we perform a similar exercise but the 1-metre resolution light detection and ranging (LIDAR) DSM or DTM serves as the snow-free model. Thus, the main objective of the paper is to present the performance of the new procedure for estimating snow depth and to compare the two experiments.

Monitoring of the Liquid Water Content During Snowmelt Using C-Band SAR Data and the Snow Model CROCUS

NASA Astrophysics Data System (ADS)

Rondeau-Genesse, G.; Trudel, M.; Leconte, R.

2014-12-01

Coupling C-Band synthetic aperture radar (SAR) data to a multilayer snow model is a step in better understanding the temporal evolution of the radar backscattering coefficient during snowmelt. The watershed used for this study is the Nechako River Basin, located in the Rocky Mountains of British-Columbia (Canada). This basin has a snowpack of several meters in depth and part of its water is diverted to the Kemano hydropower system, managed by Rio-Tinto Alcan. Eighteen RADARSAT-2 ScanSAR Wide archive images were acquired in VV/VH polarization for the winter of 2011-2012, under different snow conditions. They are interpreted along with CROCUS, a multilayer physically-based snow model developed by Météo-France. This model discretizes the snowpack into 50 layers, which makes it possible to monitor various characteristics, such as liquid water content (LWC), throughout the season. CROCUS is used to model three specific locations of the Nechako River Basin. Results vary from one site to another, but in general there is a good agreement between the modeled LWC of the first layer of the snowpack and the backscattering coefficient of the RADARSAT-2 images, with a coefficient of determination (R²) of 0.80 and more. The radar images themselves were processed using an updated version of Nagler's methodology, which consists of subtracting an image in wet snow conditions to one in dry snow conditions, as wet snow can then be identified using a soft threshold centered around -3 dB. A second filter was used in order to differentiate dry snow and bare soil. That filter combines a VH/VV ratio threshold and an altitude criterion. The ensuing maps show a good agreement with the MODIS snow-covered area, which is already obtained daily over the Nechako River Basin, but with additional information on the location of wet snow and without sensibility to cloud cover. As a next step, the outputs of CROCUS will be used in Mätzler's Microwave Emission Model of Layered Snowpacks (MEMLS) to simulate the backscattering coefficient at different locations in the basin.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ruan, Leilei; Robertson, G. Philip

Throughout most of the northern hemisphere, snow cover decreased in almost every winter month from 1967 to 2012. Because snow is an effective insulator, snow cover loss has likely enhanced soil freezing and the frequency of soil freeze–thaw cycles, which can disrupt soil nitrogen dynamics including the production of nitrous oxide (N 2O). Here, we used replicated automated gas flux chambers deployed in an annual cropping system in the upper Midwest US for three winters (December–March, 2011–2013) to examine the effects of snow removal and additions on N 2O fluxes. Diminished snow cover resulted in increased N2O emissions each year;more » over the entire experiment, cumulative emissions in plots with snow removed were 69% higher than in ambient snow control plots and 95% higher than in plots that received additional snow (P < 0.001). Higher emissions coincided with a greater number of freeze–thaw cycles that broke up soil macroaggregates (250–8000 µm) and significantly increased soil inorganic nitrogen pools. We conclude that winters with less snow cover can be expected to accelerate N 2O fluxes from agricultural soils subject to wintertime freezing.« less

Spectral reflectance characteristics of different snow and snow-covered land surface objects and mixed spectrum fitting

USGS Publications Warehouse

Zhang, J.-H.; Zhou, Z.-M.; Wang, P.-J.; Yao, F.-M.; Yang, L.

2011-01-01

The field spectroradiometer was used to measure spectra of different snow and snow-covered land surface objects in Beijing area. The result showed that for a pure snow spectrum, the snow reflectance peaks appeared from visible to 800 nm band locations; there was an obvious absorption valley of snow spectrum near 1030 nm wavelength. Compared with fresh snow, the reflection peaks of the old snow and melting snow showed different degrees of decline in the ranges of 300~1300, 1700~1800 and 2200~2300 nm, the lowest was from the compacted snow and frozen ice. For the vegetation and snow mixed spectral characteristics, it was indicated that the spectral reflectance increased for the snow-covered land types(including pine leaf with snow and pine leaf on snow background), due to the influence of snow background in the range of 350~1300 nm. However, the spectrum reflectance of mixed pixel remained a vegetation spectral characteristic. In the end, based on the spectrum analysis of snow, vegetation, and mixed snow/vegetation pixels, the mixed spectral fitting equations were established, and the results showed that there was good correlation between spectral curves by simulation fitting and observed ones(correlation coefficient R2=0.9509).

Spectral signature of alpine snow cover from the Landsat Thematic Mapper

NASA Technical Reports Server (NTRS)

Dozier, Jeff

1989-01-01

In rugged terrain, snow in the shadows can appear darker than soil or vegetation in the sunlight, making it difficult to interpret satellite data images of rugged terrains. This paper discusses methods for using Thematic Mapper (TM) and SPOT data for automatic analyses of alpine snow cover. Typical spectral signatures of the Landsat TM are analyzed for a range of snow types, atmospheric profiles, and topographic illumination conditions. A number of TM images of Sierra Nevada are analyzed to distinguish several classes of snow from other surface covers.

An improved snow scheme for the ECMWF land surface model: Description and offline validation

Treesearch

Emanuel Dutra; Gianpaolo Balsamo; Pedro Viterbo; Pedro M. A. Miranda; Anton Beljaars; Christoph Schar; Kelly Elder

2010-01-01

A new snow scheme for the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model has been tested and validated. The scheme includes a new parameterization of snow density, incorporating a liquid water reservoir, and revised formulations for the subgrid snow cover fraction and snow albedo. Offline validation (covering a wide range of spatial and...

The Studies on Aerosol Transport, Its Deposition, and Its Impact on Climate - the Study on the Surface Material Circulation can Connect from the Past to the Future

NASA Technical Reports Server (NTRS)

Yasunari, Teppei

2012-01-01

Recently the issue on glacier retreats comes up and many factors should be relevant to the issue. The absorbing aerosols such as dust and black carbon (BC) are considered to be one of the factors. After they deposited onto the snow surface, it will reduce snow albedo (called snow darkening effect) and probably contribute to further melting of glacier. The Goddard Earth Observing System version 5 (GEOS-5) has developed at NASAlGSFC. However, the original snowpack model used in the land surface model in the GEOS-5 did not consider the snow darkening effect. Here we developed the new snow albedo scheme which can consider the snow darkening effect. In addition, another scheme on calculating mass concentrations on the absorbing aerosols in snowpack was also developed, in which the direct aerosol depositions from the chemical transport model in the GEOS-5 were used. The scheme has been validated with the observed data obtained at backyard of the Institute of Low Temperature Science, Hokkaido University, by Dr. Teruo Aoki (Meteorological Research Institute) et al. including me. The observed data was obtained when I was Ph.D. candidate. The original GEOS-5 during 2007-2009 over the Himalayas and Tibetan Plateau region showed more reductions of snow than that of the new GEOS-5 because the original one used lower albedo settings. On snow cover fraction, the new GEOS-5 simulated more realistic snow-covered area comparing to the MODIS snow cover fraction. The reductions on snow albedo, snow cover fraction, and snow water equivalent were seen with statistically significance if we consider the snow darkening effect comparing to the results without the snow darkening effect. In the real world, debris-cover, inside refreezing process, surface flow of lacier, etc. affect glacier mass balance and the simu.latedresults immediately do not affect whole glacier retreating. However, our results indicate that some surface melting over non debris-covered parts of the glacier would be explained by the snow darkening effect. Further discussion and observations are necessary to assess the glacier issue.

The Various Influences due to Aerosol Depositions

NASA Technical Reports Server (NTRS)

Yasunari, Teppei

2011-01-01

Recently the issue on glacier retreats comes up and many factors should be relevant to the issue. The absorbing aerosols such as dust and black carbon (BC) are considered to be one of the factors. After they deposited onto the snow surface, it will reduce snow albedo (called snow darkening effect) and probably contribute to further melting of glacier. The Goddard Earth Observing System version 5 (GEOS-5) has developed at NASA/GSFC. However, the original snowpack model used in the land surface model in the GEOS-5 did not consider the snow darkening effect. Here we developed the new snow albedo scheme which can consider the snow darkening effect. In addition, another scheme on calculating mass concentrations on the absorbing aerosols in snowpack was also developed, in which the direct aerosol depositions from the chemical transport model in the GEOS-5 were used. The scheme has been validated with the observed data obtained at backyard of the Institute of Low Temperature Science, Hokkaido University, by Dr. Teruo Aoki (Meteorological Research Institute) et al. including me. The observed data was obtained when I was Ph.D.caftdidate. The original GEOS-5 during 2007-2009 over the Himalayas and Tibetan Plateau region showed more reductions of snow than that of the new GEOS-5 because the original one used lower albedo settings. On snow cover fraction, the new GEOS-5 simulated more realistic snow-covered area comparing to the MODIS snow cover fraction. The reductions on snow albedo, snow cover fraction, and snow water equivalent were seen with statistically significance if we consider the snow darkening effect comparing to the results without the snow darkening effect. In the real world, debris cover, inside refreezing process, surface flow of glacier, etc. affect glacier mass balance and the simulated results immediately do not affect whole glacier retreating. However, our results indicate that some surface melting over non debris covered parts of the glacier would be explained by the snow darkening effect. Further discussion and observations are necessary to assess the glacier issue.

Oxygen isotope and deuterium composition of snow cover on the profile of Western Siberia from Tomsk to the Gulf of Ob

NASA Astrophysics Data System (ADS)

Vasil'chuk, Yu. K.; Shevchenko, V. P.; Lisitzin, A. P.; Budantseva, N. A.; Vorobiov, S. N.; Kirpotin, S. N.; Krizkov, I. V.; Manasypov, R. M.; Pokrovsky, O. S.; Chizhova, Ju. N.

2016-12-01

The purpose of this work is to study the variability of the isotope composition (δ18O, δD, d exc) of the snow cover on a long transect of Western Siberia from the southern taiga to the tundra. The study of the snow cover is of paleogeographic, paleogeocryological, and paleohydrological value. The snow cover of western Siberia was sampled on a broadly NS transzonal profile from the environs of Tomsk (southern taiga zone) to the eastern coast of the Gulf of Ob (tundra zone) from February 19 to March 4, 2014. Snow samples were collected at 31 sites. Most of the samples represented by fresh snow, i.e., snow that had fallen a day before the moment of sampling were collected in two areas. In the area of Yamburg, the snow specimens collected from the surface are most probably settled snow of different ages. The values of δ18O in the snow from Tomsk to Yamburg varied from-21.89 to-32.82‰, and the values of δD, from-163.3 to-261.2‰. The value of deuterium excess was in the range of 4.06-19.53‰.

Simulating polarized light scattering in terrestrial snow based on bicontinuous random medium and Monte Carlo ray tracing

NASA Astrophysics Data System (ADS)

Xiong, Chuan; Shi, Jiancheng

2014-01-01

To date, the light scattering models of snow consider very little about the real snow microstructures. The ideal spherical or other single shaped particle assumptions in previous snow light scattering models can cause error in light scattering modeling of snow and further cause errors in remote sensing inversion algorithms. This paper tries to build up a snow polarized reflectance model based on bicontinuous medium, with which the real snow microstructure is considered. The accurate specific surface area of bicontinuous medium can be analytically derived. The polarized Monte Carlo ray tracing technique is applied to the computer generated bicontinuous medium. With proper algorithms, the snow surface albedo, bidirectional reflectance distribution function (BRDF) and polarized BRDF can be simulated. The validation of model predicted spectral albedo and bidirectional reflectance factor (BRF) using experiment data shows good results. The relationship between snow surface albedo and snow specific surface area (SSA) were predicted, and this relationship can be used for future improvement of snow specific surface area (SSA) inversion algorithms. The model predicted polarized reflectance is validated and proved accurate, which can be further applied in polarized remote sensing.

Evaluation of Existing Image Matching Methods for Deriving Glacier Surface Displacements Globally from Optical Satellite Imagery

NASA Astrophysics Data System (ADS)

Heid, T.; Kääb, A.

2011-12-01

Automatic matching of images from two different times is a method that is often used to derive glacier surface velocity. Nearly global repeat coverage of the Earth's surface by optical satellite sensors now opens the possibility for global-scale mapping and monitoring of glacier flow with a number of applications in, for example, glacier physics, glacier-related climate change and impact assessment, and glacier hazard management. The purpose of this study is to compare and evaluate different existing image matching methods for glacier flow determination over large scales. The study compares six different matching methods: normalized cross-correlation (NCC), the phase correlation algorithm used in the COSI-Corr software, and four other Fourier methods with different normalizations. We compare the methods over five regions of the world with different representative glacier characteristics: Karakoram, the European Alps, Alaska, Pine Island (Antarctica) and southwest Greenland. Landsat images are chosen for matching because they expand back to 1972, they cover large areas, and at the same time their spatial resolution is as good as 15 m for images after 1999 (ETM+ pan). Cross-correlation on orientation images (CCF-O) outperforms the three similar Fourier methods, both in areas with high and low visual contrast. NCC experiences problems in areas with low visual contrast, areas with thin clouds or changing snow conditions between the images. CCF-O has problems on narrow outlet glaciers where small window sizes (about 16 pixels by 16 pixels or smaller) are needed, and it also obtains fewer correct matches than COSI-Corr in areas with low visual contrast. COSI-Corr has problems on narrow outlet glaciers and it obtains fewer correct matches compared to CCF-O when thin clouds cover the surface, or if one of the images contains snow dunes. In total, we consider CCF-O and COSI-Corr to be the two most robust matching methods for global-scale mapping and monitoring of glacier velocities. If combining CCF-O with locally adaptive template sizes and by filtering the matching results automatically by comparing the displacement matrix to its low pass filtered version, the matching process can be automated to a large degree. This allows the derivation of glacier velocities with minimal (but not without!) user interaction and hence also opens up the possibility of global-scale mapping and monitoring of glacier flow.

Alaska: Glaciers of Kenai Fjords National Park and Katmai National Park and Preserve

NASA Technical Reports Server (NTRS)

Giffens, Bruce A.; Hall, Dorothy K.; Chien, Janet Y. L.

2014-01-01

There are hundreds of glaciers in Kenai Fjords National Park (KEFJ) and Katmai National Park and Preserve (KATM) covering over 2,276 sq km of park land (ca. 2000). There are two primary glacierized areas in KEFJ (the Harding Icefield and the Grewingk-Yalik Glacier Complex) and three primary glacierized areas in KATM (the Mt. Douglas area, the Kukak Volcano to Mt. Katmai area, and the Mt. Martin area). Most glaciers in these parks terminate on land, though a few terminate in lakes. Only KEFJ has tidewater glaciers, which terminate in the ocean. Glacier mapping and analysis of the change in glacier extent has been accomplished on a decadal scale using satellite imagery, primarily Landsat data from the 1970s, 1980s, and from2000. Landsat Multispectral Scanner (MSS),Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM) imagery was used to map glacier extent on a park-wide basis. Classification of glacier ice using image-processing software, along with extensive manual editing, was employed to create Geographic Information System (GIS)outlines of the glacier extent for each park. Many glaciers that originate in KEFJ but terminate outside the park boundaries were also mapped. Results of the analysis show that there has been a reduction in the amount of glacier ice cover in the two parks over the study period. Our measurements show a reduction of approximately 21 sq km, or 1.5(from 1986 to 2000), and 76 sq km, or 7.7 (from19861987 to 2000), in KEFJ and KATM, respectively. This work represents the first comprehensive study of glaciers of KATM. Issues that complicate the mapping of glacier extent include debris cover(moraine and volcanic ash), shadows, clouds, fresh snow, lingering snow from the previous season, and differences in spatial resolution between the MSS,TM, or ETM sensors. Similar glacier mapping efforts in western Canada estimate mapping errors of 34. Measurements were also collected from a suite of glaciers in KEFJ and KATM detailing terminus positions and rates of recession using datasets including 15 min USGS quadrangle maps(19501951), Landsat imagery (19861987, 2000,2006), and 2005 IKONOS imagery (KEFJ only).

Alaska: Glaciers of Kenai Fjords National Park and Katmai and Lake Clark National Parks and Preserve

NASA Technical Reports Server (NTRS)

Giffen, bruce A.; Hall, Dorothy K.; Chien, Janet Y. L.

2011-01-01

There are hundreds of glaciers in Kenai Fjords National Park (KEFJ) and Katmai National Park and Preserve (KATM) covering over 2276 sq km of park land (circa 2000). There are two primary glacierized areas in KEFJ -- the Harding Icefield and the Grewingk-Yalik Glacier Complex, and three primary glacierized areas in KATM - the Mt. Douglas area, the Kukak Volcano to Mt. Katmai area and the Mt. Martin area. Most glaciers in these parks terminate on land, though a few terminate in lakes. Only KEFJ has tidewater glaciers, which terminate in the ocean. Glacier mapping and analysis of the change in glacier extent has been accomplished on a decadal scale using satellite imagery, primarily Landsat data from the 1970s, 1980s, and from 2000. Landsat Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery was used to map glacier extent on a park-wide basis. Classification of glacier ice using image processing software, along with extensive manual editing, was employed to create Geographic Information System (GIS) outlines of the glacier extent for each park. Many glaciers that originate in KEFJ but terminate outside the park boundaries were also mapped. Results of the analysis show that there has been a reduction in the amount of glacier ice cover in the two parks over the study period. Our measurements show a reduction of approximately 21 sq km, or -1.5% (from 1986 to 2000), and 76 sq km, or -7.7% (from 1986/87 to 2000), in KEFJ and KATM, respectively. This work represents the first comprehensive study of glaciers of KATM. Issues that complicate the mapping of glacier extent include: debris-cover (moraine and volcanic ash), shadows, clouds, fresh snow, lingering snow from the previous season, and differences in spatial resolution between the MSS and TM or ETM+ sensors. Similar glacier mapping efforts in western Canada estimate mapping errors of 3-4%. Measurements were also collected from a suite of glaciers in KEFJ and KATM detailing terminus positions and rates of recession using datasets including the 15-minute USGS quadrangle maps (1950/1951), Landsat imagery (1986/1987, 2000, 2006) and 2005 Ikonos imagery (KEFJ only).

Modeling of surface dust concentration in snow cover at industrial area using neural networks and kriging

NASA Astrophysics Data System (ADS)

Sergeev, A. P.; Tarasov, D. A.; Buevich, A. G.; Shichkin, A. V.; Tyagunov, A. G.; Medvedev, A. N.

2017-06-01

Modeling of spatial distribution of pollutants in the urbanized territories is difficult, especially if there are multiple emission sources. When monitoring such territories, it is often impossible to arrange the necessary detailed sampling. Because of this, the usual methods of analysis and forecasting based on geostatistics are often less effective. Approaches based on artificial neural networks (ANNs) demonstrate the best results under these circumstances. This study compares two models based on ANNs, which are multilayer perceptron (MLP) and generalized regression neural networks (GRNNs) with the base geostatistical method - kriging. Models of the spatial dust distribution in the snow cover around the existing copper quarry and in the area of emissions of a nickel factory were created. To assess the effectiveness of the models three indices were used: the mean absolute error (MAE), the root-mean-square error (RMSE), and the relative root-mean-square error (RRMSE). Taking into account all indices the model of GRNN proved to be the most accurate which included coordinates of the sampling points and the distance to the likely emission source as input parameters for the modeling. Maps of spatial dust distribution in the snow cover were created in the study area. It has been shown that the models based on ANNs were more accurate than the kriging, particularly in the context of a limited data set.

BOREAS HYD-2 Estimated Snow Water Equivalent (SWE) from Microwave Measurements

NASA Technical Reports Server (NTRS)

Powell, Hugh; Chang, Alfred T. C.; Hall, Forrest G. (Editor); Knapp, David E. (Editor); Smith, David E. (Technical Monitor)

2000-01-01

The surface meteorological data collected at the Boreal Ecosystem-Atmosphere Study (BOREAS) tower and ancillary sites are being used as inputs to an energy balance model to monitor the amount of snow storage in the boreal forest region. The BOREAS Hydrology (HYD)-2 team used Snow Water Equivalent (SWE) derived from an energy balance model and in situ observed SWE to compare the SWE inferred from airborne and spaceborne microwave data, and to assess the accuracy of microwave retrieval algorithms. The major external measurements that are needed are snowpack temperature profiles, in situ snow areal extent, and SWE data. The data in this data set were collected during February 1994 and cover portions of the Southern Study Area (SSA), Northern Study Area (NSA), and the transect areas. The data are available from BORIS as comma-delimited tabular ASCII files. The SWE data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Harder, Phillip; Schirmer, Michael; Pomeroy, John; Helgason, Warren

2016-11-01

Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land-atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from differencing snow-covered and non-snow-covered DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided useful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to-noise ratio. The application of SfM to UAV photographs returns meaningful information in areas with mean snow depth > 30 cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 10 m s-1, clear skies, high sun angles and surfaces with negligible vegetation cover.

Satellite geological and geophysical remote sensing of Iceland

NASA Technical Reports Server (NTRS)

Williams, R. S., Jr. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Under a binational, multidisciplinary experiment ERTS-1 imagery is being used to measure and map dynamic natural phenomena in Iceland. A few of the initial results from the project are: (1) a large variety of geological and volcanic features can be studied, particularly on imagery acquired at low sun angle ( 10 deg), which have not been previously recognized; (2) under optimum snow cover conditions, geothermal areas can be discerned by their snowmelt pattern or by warm spring discharge into frozen lakes; (3) a variety of map types at scale of 1:1,000,000 and 1:500,000, can be compiled, made more accurate, or updated (changes in coastline, glaciers, lakes, etc.); (4) the persistence of snow in the highland areas, during the summer months, has important ramifications to rangeland management; (5) false color composites (MSS) permitted the mapping of four types of vegetation; forested, reclaimed, cultivated areas and grasslands, and the mapping of the seasonal change of the vegetation, all of high value to rangeland management when complete, repetitive coverage of Iceland becomes a reality with an operational satellite; and (6) the volcanic eruption on Heimaey was recorded.

Cropland Area Extraction in China with Multi-Temporal MODIS Data

NASA Astrophysics Data System (ADS)

Bagan, H.; Baruah, P. J.; Wang, Q.; Yasuoka, Y.

2007-12-01

: extracting the area of cropland in China is very important for agricultural management, land degradation and ecosystem assessment. In this study we investigate the potential and the methodology for the cropland area extraction using multi-temporal MODIS EVI data and some ancillary data. A 16-day composite EVI time-series data for 2003 (6 March 2003 - 2 December 2003) with a spatial resolution of 500 m, and the ancillary data included Land-use GIS data, Landsat TM/ETM, ASTER data, and county-level cultivated land statistical data of year 2000. The Self-Organizing Map (SOM) neural network classification algorithm was applied to the EVI data set. To focus on agricultural and desertification, we designed 9 land-cover types: 1) water, 2) woodland, 3) grassland, 4) dry cropland, 5) sandy, 6) paddy, 7) wetland, 8) urban/bare, and 9) snow/ice. The overall classification accuracy was 85% with a kappa coefficient of 0.84. The EVI data sets were sensitive and performed well in distinguishing the majority of land cover types. We also used county-level cultivated land statistical data from the year 2000 to evaluate the accuracy of the agricultural area from classification results, and found that the correlation coefficient was high in most counties. The result of this study shows that the methodology used in this study is, in general, feasible for cropland extraction in China. Keywords: MODIS, EVI, SOM, Cropland, land cover.

Estimating COCOM Natural Background Dormancy

DTIC Science & Technology

2015-04-01

Goode and Espenshade 1953). Maps from Goode’s World Atlas included natural vegetation, agricultural regions, and soils . Clark University provided...and shallow, high mountain soils (Chambers 1967). The vertical stripes indi- cate a probability of more than two weeks of snow cover during the month...needle-leaf evergreen and deciduous trees during dormant or dry season, and desert alluvial deposits, sand, and thin mountain soils 6 Needle-leaf

Satellite geological and geophysical remote sensing of Iceland

NASA Technical Reports Server (NTRS)

Williams, R. S., Jr. (Principal Investigator)

1978-01-01

The author has identified the following significant results. The ERTS imagery has sufficient resolution to map, from MSS color composites, areas of altered ground caused by high temperature geothermal activity at the Namafjall, Torfajokull, and Reykjanes geothermal areas. The major axes of the fallout pattern of tephra from the May - July 1970 volcanic eruption from Hekla Volcano can be mapped where sufficient depth of deposition was present to seriously affect the normal vegetation. Lava flows from the 1961 volcanic eruption at Askja; some of the lava flows from the 1947-48 eruption, and and all of the lava flows from the 1970 eruption at Hekla; and the areas covered by tephra and lava from the 1973 eruption on Heimaey could be delineated. Low sun angle imagery of less than 10 deg of snow covered terrain was particularly valuable in mapping structural and volcanic features concealed beneath glacial ice in the active volcanic zones of Iceland.

Snow depth on Arctic and Antarctic sea ice derived from autonomous (Snow Buoy) measurements

NASA Astrophysics Data System (ADS)

Nicolaus, Marcel; Arndt, Stefanie; Hendricks, Stefan; Heygster, Georg; Huntemann, Marcus; Katlein, Christian; Langevin, Danielle; Rossmann, Leonard; Schwegmann, Sandra

2016-04-01

The snow cover on sea ice received more and more attention in recent sea ice studies and model simulations, because its physical properties dominate many sea ice and upper ocean processes. In particular; the temporal and spatial distribution of snow depth is of crucial importance for the energy and mass budgets of sea ice, as well as for the interaction with the atmosphere and the oceanic freshwater budget. Snow depth is also a crucial parameter for sea ice thickness retrieval algorithms from satellite altimetry data. Recent time series of Arctic sea ice volume only use monthly snow depth climatology, which cannot take into account annual changes of the snow depth and its properties. For Antarctic sea ice, no such climatology is available. With a few exceptions, snow depth on sea ice is determined from manual in-situ measurements with very limited coverage of space and time. Hence the need for more consistent observational data sets of snow depth on sea ice is frequently highlighted. Here, we present time series measurements of snow depths on Antarctic and Arctic sea ice, recorded by an innovative and affordable platform. This Snow Buoy is optimized to autonomously monitor the evolution of snow depth on sea ice and will allow new insights into its seasonality. In addition, the instruments report air temperature and atmospheric pressure directly into different international networks, e.g. the Global Telecommunication System (GTS) and the International Arctic Buoy Programme (IABP). We introduce the Snow Buoy concept together with technical specifications and results on data quality, reliability, and performance of the units. We highlight the findings from four buoys, which simultaneously drifted through the Weddell Sea for more than 1.5 years, revealing unique information on characteristic regional and seasonal differences. Finally, results from seven snow buoys co-deployed on Arctic sea ice throughout the winter season 2015/16 suggest the great importance of local effects, weather events, and potential influences of dynamic sea ice processes on snow accumulation.

Snow cover monitoring model and change over both time and space in pastoral area of northern China

NASA Astrophysics Data System (ADS)

Cui, Yan; Li, Suju; Wang, Ping; Zhang, Wei; Nie, Juan; Wen, Qi

2014-11-01

Snow disaster is a natural phenomenon owning to widespread snowfall for a long time and usually affect people's life, property and economic. During the whole disaster management circle, snow disaster in pastoral area of northern china which including Xinjiang, Inner Mongolia, Qinghai, Tibet has been paid more attention. Thus do a good job in snow cover monitoring then found snow disaster in time can help the people in disaster area to take effective rescue measures, which always been the central and local government great important work. Remote sensing has been used widely in snow cover monitoring for its wide range, high efficiency, less conditions, more methods and large information. NOAA/AVHRR data has been used for wide range, plenty bands information and timely acquired and act as an import data of Snow Cover Monitoring Model (SCMM). SCMM including functions list below: First after NOAA/AVHRR data has been acquired, geometric calibration, radiometric calibration and other pre-processing work has been operated. Second after band operation, four threshold conditions are used to extract snow spectrum information among water, cloud and other features in NOAA/AVHRR image. Third snow cover information has been analyzed one by one and the maximum snow cover from about twenty images in a week has been selected. Then selected image has been mosaic which covered the pastoral area of China. At last both time and space analysis has been carried out through this operational model ,such as analysis on the difference between this week and the same period of last year , this week and last week in three level regional. SCMM have been run successfully for three years, and the results have been take into account as one of the three factors which led to risk warning of snow disaster and analysis results from it always play an important role in disaster reduction and relief.

Operational monitoring of land-cover change using multitemporal remote sensing data

NASA Astrophysics Data System (ADS)

Rogan, John

2005-11-01

Land-cover change, manifested as either land-cover modification and/or conversion, can occur at all spatial scales, and changes at local scales can have profound, cumulative impacts at broader scales. The implication of operational land-cover monitoring is that researchers have access to a continuous stream of remote sensing data, with the long term goal of providing for consistent and repetitive mapping. Effective large area monitoring of land-cover (i.e., >1000 km2) can only be accomplished by using remotely sensed images as an indirect data source in land-cover change mapping and as a source for land-cover change model projections. Large area monitoring programs face several challenges: (1) choice of appropriate classification scheme/map legend over large, topographically and phenologically diverse areas; (2) issues concerning data consistency and map accuracy (i.e., calibration and validation); (3) very large data volumes; (4) time consuming data processing and interpretation. Therefore, this dissertation research broadly addresses these challenges in the context of examining state-of-the-art image pre-processing, spectral enhancement, classification, and accuracy assessment techniques to assist the California Land-cover Mapping and Monitoring Program (LCMMP). The results of this dissertation revealed that spatially varying haze can be effectively corrected from Landsat data for the purposes of change detection. The Multitemporal Spectral Mixture Analysis (MSMA) spectral enhancement technique produced more accurate land-cover maps than those derived from the Multitemporal Kauth Thomas (MKT) transformation in northern and southern California study areas. A comparison of machine learning classifiers showed that Fuzzy ARTMAP outperformed two classification tree algorithms, based on map accuracy and algorithm robustness. Variation in spatial data error (positional and thematic) was explored in relation to environmental variables using geostatistical interpolation techniques. Finally, the land-cover modification maps generated for three time intervals (1985--1990--1996--2000), with nine change-classes revealed important variations in land-cover gain and loss between northern and southern California study areas.

Development and evaluation of ice phenology algorithm from space-borne active and passive microwave measurements

NASA Astrophysics Data System (ADS)

Kang, K.; Duguay, C. R.

2013-12-01

The presence (or absence) of ice cover plays an important role in lake-atmosphere interactions at high latitudes during the winter months. Knowledge of ice phenology (i.e. freeze-onset/melt-onset, ice-on/ice-off dates, and ice cover duration) is crucial for understanding both the role of lake ice cover in and its response to regional weather and climate. Shortening of the ice cover season in many regions of the Northern Hemisphere over recent decades has been shown to significantly influence the thermal regime as well as the water quality and quantity of lakes. In this respect, satellite remote sensing instruments are providing invaluable measurements for monitoring changes in timing of ice phenological events and the length of the ice cover (or open water) season on large northern lakes, and also for providing more spatially representative limnological information than available from in situ measurements. In this study, we present a new ice phenology retrieval algorithm developed from the synergistic use of Quick Scatterometer (QuikSCAT), Oceansat-2 Scatterometer (OSCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E). Retrieved ice dates are then evaluated against those derived from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) 4 km resolution product (2004-2011) during the freeze-up and break-up periods (2002-2012) for 11 lakes (Amadjuak, Nettilling, Great Bear, Great Slave, Manitoba, and Winnipeg in North America as well as Inarijrvi, Ladoga, Onega, Qinghai (Koko Nor), and Baikal in Eurasia). In addition, daily wind speed derived from QuikSCAT/OSCAT is analyzed along with WindSAT surface wind vector products (2002-2012) during the open water season for the large lakes. A detailed evaluation of the new algorithm conducted over Great Slave Lake (GSL) and Great Bear Lake (GBL) reveals that estimated ice-on/ice-off dates are within 4-7 days of those derived from the IMS product. Preliminary analysis of ice dates show that ice-on occurs three to five months earlier and ice-off two months later on GSL and GBL (Canada) compared to Lake Ladoga and Lake Onega (Russia) mostly due to regional climate differences. Overall, the synergistic use of microwave satellite data from various sensors provides an invaluable opportunity for operational monitoring of ice cover on large northern lakes.

Evaluation of the satellite derived snow cover area - Runoff forecasting models for the inaccessible basins of western Himalayas

NASA Technical Reports Server (NTRS)

Dey, B.

1985-01-01

In this study, the existing seasonal snow cover area runoff forecasting models of the Indus, Kabul, Sutlej and Chenab basins were evaluated with the concurrent flow correlation model for the period 1975-79. In all the basins under study, correlation of concurrent flow model explained the variability in flow better than by the snow cover area runoff models. Actually, the concurrent flow correlation model explained more than 90 percent of the variability in the flow of these rivers. Compared to this model, the snow cover area runoff models explained less of the variability in flow. In the Himalayan river basins under study and at least for the period under observation, the concurrent flow correlation model provided a set of results with which to compare the estimates from the snow cover area runoff models.

The Cycles of Snow Cover in Pyrenees Mountain and Mont Lebanon Analyzed Using the Global Modeling Technique.

NASA Astrophysics Data System (ADS)

Drapeau, L.; Mangiarotti, S.; Le Jean, F.; Gascoin, S.; Jarlan, L.

2014-12-01

The global modeling technique provides a way to obtain ordinary differential equations from single time series1. This technique, initiated in the 1990s, could be applied successfully to numerous theoretic and experimental systems. More recently it could be applied to environmental systems2,3. Here this technique is applied to seasonal snow cover area in the Pyrenees mountain (Europe) and Mont Lebanon (Mediterranean region). The snowpack evolution is complex because it results from combination of processes driven by physiography (elevation, slope, land cover...) and meteorological variables (precipitation, temperature, wind speed...), which are highly heterogeneous in such regions. Satellite observations in visible bands offer a powerful tool to monitor snow cover areas at global scale, with large resolutions range. Although this observable does not directly inform about snow water equivalent, its dynamical behavior strongly relies on it. Therefore, snow cover area is likely to be a good proxy of the global dynamics and global modeling technique a well adapted approach. The MOD10A2 product (500m) generated from MODIS by the NASA is used after a pretreatment is applied to minimize clouds effect. The global modeling technique is then applied using two packages4,5. The analysis is performed with two time series for the whole period (2000-2012) and year by year. Low-dimensional chaotic models are obtained in many cases. Such models provide a strong argument for chaos since involving the two necessary conditions in a synthetic way: determinism and strong sensitivity to initial conditions. The models comparison suggests important non-stationnarities at interannual scale which prevent from detecting long term changes. 1: Letellier et al 2009. Frequently asked questions about global modeling, Chaos, 19, 023103. 2: Maquet et al 2007. Global models from the Canadian lynx cycles as a direct evidence for chaos in real ecosystems. J. of Mathematical Biology, 55 (1), 21-39 3: Mangiarotti et al 2014. Two chaotic global models for cereal crops cycles observed from satellite in Northern Morocco. Chaos, 24, 023130. 4 : Mangiarotti et al 2012. Polynomial search and Global modelling: two algorithms for modeling chaos. Physical Review E, 86(4), 046205. 5: http://cran.r-project.org/web/packages/PoMoS/index.html.

Land Surface Model Biases and their Impacts on the Assimilation of Snow-related Observations

NASA Astrophysics Data System (ADS)

Arsenault, K. R.; Kumar, S.; Hunter, S. M.; Aman, R.; Houser, P. R.; Toll, D.; Engman, T.; Nigro, J.

2007-12-01

Some recent snow modeling studies have employed a wide range of assimilation methods to incorporate snow cover or other snow-related observations into different hydrological or land surface models. These methods often include taking both model and observation biases into account throughout the model integration. This study focuses more on diagnosing the model biases and presenting their subsequent impacts on assimilating snow observations and modeled snowmelt processes. In this study, the land surface model, the Community Land Model (CLM), is used within the Land Information System (LIS) modeling framework to show how such biases impact the assimilation of MODIS snow cover observations. Alternative in-situ and satellite-based observations are used to help guide the CLM LSM in better predicting snowpack conditions and more realistic timing of snowmelt for a western US mountainous region. Also, MODIS snow cover observation biases will be discussed, and validation results will be provided. The issues faced with inserting or assimilating MODIS snow cover at moderate spatial resolutions (like 1km or less) will be addressed, and the impacts on CLM will be presented.

Emerging climate-driven disturbance processes: widespread mortality associated with snow-to-rain transitions across 10° of latitude and half the range of a climate-threatened conifer.

PubMed

Buma, Brian; Hennon, Paul E; Harrington, Constance A; Popkin, Jamie R; Krapek, John; Lamb, Melinda S; Oakes, Lauren E; Saunders, Sari; Zeglen, Stefan

2017-07-01

Climate change is causing rapid changes to forest disturbance regimes worldwide. While the consequences of climate change for existing disturbance processes, like fires, are relatively well studied, emerging drivers of disturbance such as snow loss and subsequent mortality are much less documented. As the climate warms, a transition from winter snow to rain in high latitudes will cause significant changes in environmental conditions such as soil temperatures, historically buffered by snow cover. The Pacific coast of North America is an excellent test case, as mean winter temperatures are currently at the snow-rain threshold and have been warming for approximately 100 years post-Little Ice Age. Increased mortality in a widespread tree species in the region has been linked to warmer winters and snow loss. Here, we present the first high-resolution range map of this climate-sensitive species, Callitropsis nootkatensis (yellow-cedar), and document the magnitude and location of observed mortality across Canada and the United States. Snow cover loss related mortality spans approximately 10° latitude (half the native range of the species) and 7% of the overall species range and appears linked to this snow-rain transition across its range. Mortality is commonly >70% of basal area in affected areas, and more common where mean winter temperatures is at or above the snow-rain threshold (>0 °C mean winter temperature). Approximately 50% of areas with a currently suitable climate for the species (<-2 °C) are expected to warm beyond that threshold by the late 21st century. Regardless of climate change scenario, little of the range which is expected to remain suitable in the future (e.g., a climatic refugia) is in currently protected landscapes (<1-9%). These results are the first documentation of this type of emerging climate disturbance and highlight the difficulties of anticipating novel disturbance processes when planning for conservation and management. © 2016 John Wiley & Sons Ltd.

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

USGS Publications Warehouse

Hansen, M.C.; Reed, B.

2000-01-01

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.

A simple algorithm for large-scale mapping of evergreen forests in tropical America, Africa and Asia

Treesearch

Xiangming Xiao; Chandrashekhar M. Biradar; Christina Czarnecki; Tunrayo Alabi; Michael Keller

2009-01-01

The areal extent and spatial distribution of evergreen forests in the tropical zones are important for the study of climate, carbon cycle and biodiversity. However, frequent cloud cover in the tropical regions makes mapping evergreen forests a challenging task. In this study we developed a simple and novel mapping algorithm that is based on the temporal profile...

Airborne Spectral Measurements of Surface-Atmosphere Anisotropy for Arctic Sea Ice and Tundra

NASA Technical Reports Server (NTRS)

Arnold, G. Thomas; Tsay, Si-Chee; King, Michael D.; Li, Jason Y.; Soulen, Peter F.

1999-01-01

Angular distributions of spectral reflectance for four common arctic surfaces: snow-covered sea ice, melt-season sea ice, snow-covered tundra, and tundra shortly after snowmelt were measured using an aircraft based, high angular resolution (1-degree) multispectral radiometer. Results indicate bidirectional reflectance is higher for snow-covered sea ice than melt-season sea ice at all wavelengths between 0.47 and 2.3 pm, with the difference increasing with wavelength. Bidirectional reflectance of snow-covered tundra is higher than for snow-free tundra for measurements less than 1.64 pm, with the difference decreasing with wavelength. Bidirectional reflectance patterns of all measured surfaces show maximum reflectance in the forward scattering direction of the principal plane, with identifiable specular reflection for the melt-season sea ice and snow-free tundra cases. The snow-free tundra had the most significant backscatter, and the melt-season sea ice the least. For sea ice, bidirectional reflectance changes due to snowmelt were more significant than differences among the different types of melt-season sea ice. Also the spectral-hemispherical (plane) albedo of each measured arctic surface was computed. Comparing measured nadir reflectance to albedo for sea ice and snow-covered tundra shows albedo underestimated 5-40%, with the largest bias at wavelengths beyond 1 pm. For snow-free tundra, nadir reflectance underestimates plane albedo by about 30-50%.

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

NASA Technical Reports Server (NTRS)

Basu, Saikat; Ganguly, Sangram; Michaelis, Andrew; Votava, Petr; Roy, Anshuman; Mukhopadhyay, Supratik; Nemani, Ramakrishna

2015-01-01

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery using a Probabilistic Learning Framework

NASA Astrophysics Data System (ADS)

Basu, S.; Ganguly, S.; Michaelis, A.; Votava, P.; Roy, A.; Mukhopadhyay, S.; Nemani, R. R.

2015-12-01

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

Estimation of daily Snow Cover Area combining MODIS and LANDSAT information by using cellular automata

NASA Astrophysics Data System (ADS)

Pardo-Iguzquiza, Eulogio; Juan Collados Lara, Antonio; Pulido-Velazquez, David

2016-04-01

The snow availability in Alpine catchments is essential for the economy of these areas. It plays an important role in tourist development but also in the management of the Water Resources Snow is an important water resource in many river basins with mountains in the catchment area. The determination of the snow water equivalent requires the estimation of the evolution of the snow pack (cover area, thickness and snow density) along the time. Although there are complex physical models of the dynamics of the snow pack, sometimes the data available are scarce and a stochastic model like the cellular automata (CA) can be of great practical interest. CA can be used to model the dynamics of growth and wane of the snow pack. The CA is calibrated with historical data. This requires the determination of transition rules that are capable of modeling the evolution of the spatial pattern of snow cover area. Furthermore, CA requires the definition of states and neighborhoods. We have included topographical variables and climatological variables in order to define the state of each pixel. The evolution of snow cover in a pixel depends on its state, the state of the neighboring pixels and the transition rules. The calibration of the CA is done using daily MODIS data, available for the period 24/02/2002 to present with a spatial resolution of 500 m, and the LANDSAT information available with a sixteen-day periodicity from 1984 to the present and with spatial resolution of 30 m. The methodology has been applied to estimation of the snow cover area of Sierra Nevada mountain range in the Southern of Spain to obtain snow cover area daily information with 500 m spatial resolution for the period 1980-2014. Acknowledgments: This research has been partially supported by the GESINHIMPADAPT project (CGL2013-48424-C2-2-R) with Spanish MINECO funds. We would also like to thank NASA DAAC and LANDSAT project for the data provided for this study.

Global land-atmosphere coupling associated with cold climate processes

NASA Astrophysics Data System (ADS)

Dutra, Emanuel

This dissertation constitutes an assessment of the role of cold processes, associated with snow cover, in controlling the land-atmosphere coupling. The work was based on model simulations, including offline simulations with the land surface model HTESSEL, and coupled atmosphere simulations with the EC-EARTH climate model. A revised snow scheme was developed and tested in HTESSEL and EC-EARTH. The snow scheme is currently operational at the European Centre for Medium-Range Weather Forecasts integrated forecast system, and in the default configuration of EC-EARTH. The improved representation of the snowpack dynamics in HTESSEL resulted in improvements in the near surface temperature simulations of EC-EARTH. The new snow scheme development was complemented with the option of multi-layer version that showed its potential in modeling thick snowpacks. A key process was the snow thermal insulation that led to significant improvements of the surface water and energy balance components. Similar findings were observed when coupling the snow scheme to lake ice, where lake ice duration was significantly improved. An assessment on the snow cover sensitivity to horizontal resolution, parameterizations and atmospheric forcing within HTESSEL highlighted the role of the atmospheric forcing accuracy and snowpack parameterizations in detriment of horizontal resolution over flat regions. A set of experiments with and without free snow evolution was carried out with EC-EARTH to assess the impact of the interannual variability of snow cover on near surface and soil temperatures. It was found that snow cover interannual variability explained up to 60% of the total interannual variability of near surface temperature over snow covered regions. Although these findings are model dependent, the results showed consistency with previously published work. Furthermore, the detailed validation of the snow dynamics simulations in HTESSEL and EC-EARTH guarantees consistency of the results.

What do We Know the Snow Darkening Effect Over Himalayan Glaciers?

NASA Technical Reports Server (NTRS)

Yasunari, T. J.; Lau, K.-U.; Koster, R. D.; Suarez, M.; Mahanama, S. P.; Gautam, R.; Kim, K. M.; Dasilva, A. M.; Colarco, P. R.

2011-01-01

The atmospheric absorbing aerosols such as dust, black carbon (BC), organic carbon (OC) are now well known warming factors in the atmosphere. However, when these aerosols deposit onto the snow surface, it causes darkening of snow and thereby absorbing more energy at the snow surface leading to the accelerated melting of snow. If this happens over Himalayan glacier surface, the glacier meltings are expected and may contribute the mass balance changes though the mass balance itself is more complicated issue. Glacier has mainly two parts: ablation and accumulation zones. Those are separated by the Equilibrium Line Altitude (ELA). Above and below ELA, snow accumulation and melting are dominant, respectively. The change of ELA will influence the glacier disappearance in future. In the Himalayan region, many glacier are debris covered glacier at the terminus (i.e., in the ablation zone). Debris is pieces of rock from local land and the debris covered parts are probably not affected by any deposition of the absorbing aerosols because the snow surface is already covered by debris (the debris covered parts have different mechanism of melting). Hence, the contribution of the snow darkening effect is considered to be most important "over non debris covered part" of the Himalayan glacier (i.e., over the snow or ice surface area). To discuss the whole glacier retreat, mass balance of each glacier is most important including the discussion on glacier flow, vertical compaction of glacier, melting amount, etc. The contribution of the snow darkening is mostly associated with "the snow/ice surface melting". Note that the surface melting itself is not always directly related to glacier retreats because sometimes melt water refreezes inside of the glacier. We should discuss glacier retreats in terms of not only the snow darkening but also other contributions to the mass balance.

The AMSR2 Satellite-based Microwave Snow Algorithm (SMSA) to estimate regional to global snow depth and snow water equivalent

NASA Astrophysics Data System (ADS)

Kelly, R. E. J.; Saberi, N.; Li, Q.

2017-12-01

With moderate to high spatial resolution (<1 km) regional to global snow water equivalent (SWE) observation approaches yet to be fully scoped and developed, the long-term satellite passive microwave record remains an important tool for cryosphere-climate diagnostics. A new satellite microwave remote sensing approach is described for estimating snow depth (SD) and snow water equivalent (SWE). The algorithm, called the Satellite-based Microwave Snow Algorithm (SMSA), uses Advanced Microwave Scanning Radiometer - 2 (AMSR2) observations aboard the Global Change Observation Mission - Water mission launched by the Japan Aerospace Exploration Agency in 2012. The approach is unique since it leverages observed brightness temperatures (Tb) with static ancillary data to parameterize a physically-based retrieval without requiring parameter constraints from in situ snow depth observations or historical snow depth climatology. After screening snow from non-snow surface targets (water bodies [including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]), moderate and shallow snow depths are estimated by minimizing the difference between Dense Media Radiative Transfer model estimates (Tsang et al., 2000; Picard et al., 2011) and AMSR2 Tb observations to retrieve SWE and SD. Parameterization of the model combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Evaluation of the SMSA performance is achieved using in situ snow depth data from a variety of standard and experiment data sources. Results presented from winter seasons 2012-13 to 2016-17 illustrate the improved performance of the new approach in comparison with the baseline AMSR2 algorithm estimates and approach the performance of the model assimilation-based approach of GlobSnow. Given the variation in estimation power of SWE by different land surface/climate models and selected satellite-derived passive microwave approaches, SMSA provides SWE estimates that are independent of real or near real-time in situ and model data.

Global land cover mapping using Earth observation satellite data: Recent progresses and challenges

NASA Astrophysics Data System (ADS)

Ban, Yifang; Gong, Peng; Giri, Chandra

2015-05-01

Land cover is an important variable for many studies involving the Earth surface, such as climate, food security, hydrology, soil erosion, atmospheric quality, conservation biology, and plant functioning. Land cover not only changes with human caused land use changes, but also changes with nature. Therefore, the state of land cover is highly dynamic. In winter snow shields underneath various other land cover types in higher latitudes. Floods may persist for a long period in a year over low land areas in the tropical and subtropical regions. Forest maybe burnt or clear cut in a few days and changes to bare land. Within several months, the coverage of crops may vary from bare land to nearly 100% crops and then back to bare land following harvest. The highly dynamic nature of land cover creates a challenge in mapping and monitoring which remains to be adequately addressed. As economic globalization continues to intensify, there is an increasing trend of land cover/land use change, environmental pollution, land degradation, biodiversity loss at the global scale, timely and reliable information on global land cover and its changes is urgently needed to mitigate the negative impact of global environment change.

MODIS Snowcover in North America: A Comparison of Winter 2013/14 and 2014/15 to Median Condition

NASA Astrophysics Data System (ADS)

Trubilowicz, J. W.; Floyd, B. C.; D'Amore, D. V.; Bidlack, A.

2015-12-01

The winters from 2013-2015 had exceptionally low snow-packs in much of western North America. In particular, the winter of 2014/2015 had the lowest peak snow-water-equivalent (SWE) depths ever recorded in many areas of the Pacific Northwest. These low snow-packs have contributed to drought conditions from British Columbia to California. Along with the low SWE values, the snow covered area (SCA) of the previous two winters has been a significant departure from normal conditions. SCA is related to SWE, rain-on-snow events and the seasonal water supply, provides insulation for plant root systems from late season frost, and is an important factor in forest fire hazard, delaying the start of soil and fuel drying. Remote sensing can be a useful tool to monitor SCA over large regions, with the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments providing a suitable temporal (twice daily), and spatial resolution (500m) to create detailed maps, even with high frequencies of cloud covered days. While comparison of SWE at snow monitoring sites to historical values is a standard analysis, doing the same for SCA has been difficult due to the technical and logistical problems related to processing the large amounts of spatial data required to determine a 'normal' annual SCA cycle. Through the use of new cloud-based computation methods from Google Earth Engine, we have calculated the monthly median (from 2002-2015) MODIS SCA, at a 500 m resolution, for all of the major Pacific draining watersheds of North America. Determining the 'normal' SCA cycle of the past 13 years allowed us to compare the past two winters to the median SCA levels, showing which basins have seen the most significant departures from normal SCA levels. Results indicate more significant departures from normal in basins with significant maritime-influenced snow-packs.

Soil Moisture and Snow Cover: Active or Passive Elements of Climate?

NASA Technical Reports Server (NTRS)

Oglesby, Robert J.; Marshall, Susan; Erickson, David J., III; Robertson, Franklin R.; Roads, John O.; Arnold, James E. (Technical Monitor)

2002-01-01

A key question in the study of the hydrologic cycle is the extent to which surface effects such as soil moisture and snow cover are simply passive elements or whether they can affect the evolution of climate on seasonal and longer time scales. We have constructed ensembles of predictability studies using the NCAR CCM3 in which we compared the relative roles of initial surface and atmospheric conditions over the central and western U.S. in determining the subsequent evolution of soil moisture and of snow cover. We have also made sensitivity studies with exaggerated soil moisture and snow cover anomalies in order to determine the physical processes that may be important. Results from simulations with realistic soil moisture anomalies indicate that internal climate variability may be the strongest factor, with some indication that the initial atmospheric state is also important. The initial state of soil moisture does not appear important, a result that held whether simulations were started in late winter or late spring. Model runs with exaggerated soil moisture reductions (near-desert conditions) showed a much larger effect, with warmer surface temperatures, reduced precipitation, and lower surface pressures; the latter indicating a response of the atmospheric circulation. These results suggest the possibility of a threshold effect in soil moisture, whereby an anomaly must be of a sufficient size before it can have a significant impact on the atmospheric circulation and hence climate. Results from simulations with realistic snow cover anomalies indicate that the time of year can be crucial. When introduced in late winter, these anomalies strongly affected the subsequent evolution of snow cover. When introduced in early winter, however, little or no effect is seen on the subsequent snow cover. Runs with greatly exaggerated initial snow cover indicate that the high reflectively of snow is the most important process by which snow cover cart impact climate, through lower surface temperatures and increased surface pressures. In early winter, the amount of solar radiation is very small and so this albedo effect is inconsequential while in late winter, with the sun higher in the sky and period of daylight longer, the effect is much stronger.

Evaluating UAV and LiDAR Retrieval of Snow Depth in a Coniferous Forest in Arizona

NASA Astrophysics Data System (ADS)

Van Leeuwen, W. J. D.; Broxton, P.; Biederman, J. A.

2017-12-01

Remote sensing of snow depth and cover in forested environments is challenging. Trees interfere with the remote sensing of snowpack below the canopy and cause large variations in the spatial distribution of the snowpack itself (e.g. between below canopy environments to shaded gaps to open clearings). The distribution of trees and topographic variation make it challenging to monitor the snowpack with in-situ observations. Airborne LiDAR has improved our ability to monitor snowpack over large areas in montane and forested environments because of its high sampling rate and ability to penetrate the canopy. However, these LiDAR flights can be too expensive and time-consuming to process, making it hard to use them for real-time snow monitoring. In this research, we evaluate Structure from Motion (SfM) as an alternative to Airborne LiDAR to generate high-resolution snow depth data in forested environments. This past winter, we conducted a snow field campaign over Arizona's Mogollon Rim where we acquired aerial LiDAR, multi-angle aerial photography from a UAV, and extensive field observations of snow depth at two sites. LiDAR and SFM derived snow depth maps were generated by comparing "snow-on" and "snow-off" LiDAR and SfM data. The SfM- and LiDAR-generated snow depth maps were similar at a site with fewer trees, though there were more discrepancies at a site with more trees. Both compared reasonably well with the field observations at the sparser forested site, with poorer agreement at the denser forested site. Finally, although the SfM produced point clouds with much higher point densities than the aerial LiDAR, the SfM was not able to produce meaningful snow depth estimates directly underneath trees and had trouble in areas with deep shadows. Based on these findings, we are optimizing our UAV data acquisition strategies for this upcoming field season. We are using these data, along with high-resolution hydrological modeling, to gain a better understanding of how different forest structural, climatic and topographic conditions affect the snowpack and consequently the water resources available to the Salt River Project, a water utility providing power and water to millions of customers in the Phoenix area

A simple algorithm for identifying periods of snow accumulation on a radiometer

NASA Astrophysics Data System (ADS)

Lapo, Karl E.; Hinkelman, Laura M.; Landry, Christopher C.; Massmann, Adam K.; Lundquist, Jessica D.

2015-09-01

Downwelling solar, Qsi, and longwave, Qli, irradiances at the earth's surface are the primary energy inputs for many hydrologic processes, and uncertainties in measurements of these two terms confound evaluations of estimated irradiances and negatively impact hydrologic modeling. Observations of Qsi and Qli in cold environments are subject to conditions that create additional uncertainties not encountered in other climates, specifically the accumulation of snow on uplooking radiometers. To address this issue, we present an automated method for estimating these periods of snow accumulation. Our method is based on forest interception of snow and uses common meteorological observations. In this algorithm, snow accumulation must exceed a threshold to obscure the sensor and is only removed through scouring by wind or melting. The algorithm is evaluated at two sites representing different mountain climates: (1) Snoqualmie Pass, Washington (maritime) and (2) the Senator Beck Basin Study Area, Colorado (continental). The algorithm agrees well with time-lapse camera observations at the Washington site and with multiple measurements at the Colorado site, with 70-80% of observed snow accumulation events correctly identified. We suggest using the method for quality controlling irradiance observations in snow-dominated climates where regular, daily maintenance is not possible.

Dynamics of actual aggregation of petroleum products in snow cover

NASA Astrophysics Data System (ADS)

Begunova, L. A.; Kuznetsova, O. V.; Begunov, D. A.; Kuznetsova, A. N.

2017-11-01

The paper presents issues of snow cover pollution by petroleum products. Petroleum products content was determined using the fluorimetric method of analysis. The samples of snow were selected on the territory of Angarsk and Irkutsk cities. According to the obtained data, the content of petroleum products in the analyzed samples exceeds the background value up to 6 times. Analysis of the reference data for similar research confirms need for creation of an environmental monitoring centralized system to monitor atmospheric precipitation and, particularly, snow cover.

Space-time analysis of snow cover change in the Romanian Carpathians (2001-2016)

NASA Astrophysics Data System (ADS)

Micu, Dana; Cosmin Sandric, Ionut

2017-04-01

Snow cover is recognized as an essential climate variable, highly sensitive to the ongoing climate warming, which plays an important role in regulating mountain ecosystems. Evidence from the existing weather stations located above 800 m over the last 50 years points out that the climate of the Romanian Carpathians is visibly changing, showing an ongoing and consistent warming process. Quantifying and attributing the changes in snow cover on various spatial and temporal scales have a great environmental and socio-economic importance for this mountain region. The study is revealing the inter-seasonal changes in the timing and distribution of snow cover across the Romanian Carpathians, by combining gridded snow data (CARPATCLIM dataset, 1961-2010) and remote sensing data (2001-2016) in specific space-time assessment at regional scale. The geostatistical approach applied in this study, based on a GIS hotspot analysis, takes advantage of all the dimensions in the datasets, in order to understand the space-time trends in this climate variable at monthly time-scale. The MODIS AQUA and TERRA images available from 2001 to 2016 have been processed using ArcGIS for Desktop and Python programming language. All the images were masked out with the Carpathians boundary. Only the pixels with snow have been retained for analysis. The regional trends in snow cover distribution and timing have been analysed using Space-Time cube with ArcGIS for Desktop, according with Esri documentation using the Mann-Kendall trend test on every location with data as an independent bin time-series test. The study aimed also to assess the location of emerging hotspots of snow cover change in Carpathians. These hotspots have been calculated using Getis-Ord Gi* statistic for each bin using Hot Spot Analysis implemented in ArcGIS for Desktop. On regional scale, snow cover appear highly sensitive to the decreasing trends in air temperatures and land surface temperatures, combined with the decrease in seasonal precipitation, especially at lower elevations in all the three divisions of the Romanian Carpathians (generally below 1,700-1,800 m). The space-time patterns of snow cover change are dominated by a significant decreasing trend of snow days and earlier spring snow melt. The key findings of this study provides robust indication of a decreasing snow trends across the Carpathian Mountain region and could provide valuable spatial and temporal snow information for other related research fields as well as for an effective environmental monitoring in the mountain ecosystems of the Carpathian region

Routine Mapping of the Snow Depth Distribution on Sea Ice

NASA Astrophysics Data System (ADS)

Farrell, S. L.; Newman, T.; Richter-Menge, J.; Dattler, M.; Paden, J. D.; Yan, S.; Li, J.; Leuschen, C.

2016-12-01

The annual growth and retreat of the polar sea ice cover is influenced by the seasonal accumulation, redistribution and melt of snow on sea ice. Due to its high albedo and low thermal conductivity, snow is also a controlling parameter in the mass and energy budgets of the polar climate system. Under a changing climate scenario it is critical to obtain reliable and routine measurements of snow depth, across basin scales, and long time periods, so as to understand regional, seasonal and inter-annual variability, and the subsequent impacts on the sea ice cover itself. Moreover the snow depth distribution remains a significant source of uncertainty in the derivation of sea ice thickness from remote sensing measurements, as well as in numerical model predictions of future climate state. Radar altimeter systems flown onboard NASA's Operation IceBridge (OIB) mission now provide annual measurements of snow across both the Arctic and Southern Ocean ice packs. We describe recent advances in the processing techniques used to interpret airborne radar waveforms and produce accurate and robust snow depth results. As a consequence of instrument effects and data quality issues associated with the initial release of the OIB airborne radar data, the entire data set was reprocessed to remove coherent noise and sidelobes in the radar echograms. These reprocessed data were released to the community in early 2016, and are available for improved derivation of snow depth. Here, using the reprocessed data, we present the results of seven years of radar measurements collected over Arctic sea ice at the end of winter, just prior to melt. Our analysis provides the snow depth distribution on both seasonal and multi-year sea ice. We present the inter-annual variability in snow depth for both the Central Arctic and the Beaufort/Chukchi Seas. We validate our results via comparison with temporally and spatially coincident in situ measurements gathered during many of the OIB surveys. The results will influence future sensor suite development for sea ice studies, and they provide a new metric for comparison with other sea ice observations. Integrating these novel snow depth observations with modeling studies will help inform model development, and advance our predictive capabilities to help better understand how sea ice is responding to a changing climate.

Climate change predicted to shift wolverine distributions, connectivity, and dispersal corridors

Treesearch

Kevin S. McKelvey; Jeffrey P. Copeland; Michael K. Schwartz; Jeremy S. Littell; Keith B. Aubry; John R. Squires; Sean A. Parks; Marketa M. Elsner; Guillaume S. Mauger

2011-01-01

Boreal species sensitive to the timing and duration of snow cover are particularly vulnerable to global climate change. Recent work has shown a link between wolverine (Gulo gulo) habitat and persistent spring snow cover through 15 May, the approximate end of the wolverine's reproductive denning period. We modeled the distribution of snow cover within the Columbia...

Space Radar Image of Mammoth, California

NASA Technical Reports Server (NTRS)

1999-01-01

These two images were created using data from the Spaceborne Imaging Radar C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). The image on the left is a false-color composite of the Mammoth Mountain area in California's Sierra Nevada Mountains centered at 37.6 degrees north, 119.0 degrees west. It was acquired on-board the space shuttle Endeavour on its 67th orbit on April 13, 1994. In the image on the left, red is C-band HV-polarization, green is C-band HH-polarization and blue is the ratio of C-band VV-polarization to C-band HV-polarization. On the right is a classification map of the surface features which was developed by SIR-C/X-SAR science team members at the University of California, Santa Barbara. The area is about 23 by 46 kilometers (14 by 29 miles). In the classification image, the colors represent the following surfaces: White snow Red frozen lake, covered by snow Brown bare ground Blue lake (open water) Yellow short vegetation (mainly brush) Green sparse forest Dark green dense forest Maps like this one are helpful to scientists studying snow wetness and snow water equivalent in the snow pack. Across the globe, over major portions of the middle and high latitudes, and at high elevations in the tropical latitudes, snow and alpine glaciers are the largest contributors to run-off in rivers and to ground-water recharge. Snow hydrologists are using radar in an attempt to estimate both the quantity of water held by seasonal snow packs and the timing of snow melt. Snow and ice also play important roles in regional climates; understanding the processes in seasonal snow cover is also important for studies of the chemical balance of alpine drainage basins. SIR-C/X-SAR is a powerful tool because it is sensitive to most snow pack conditions and is less influenced by weather conditions than other remote sensing instruments, such as the Landsat satellite. Spaceborne Imaging Radar-C and X-Synthetic Aperture Radar (SIR-C/X-SAR) is part of NASA's Mission to Planet Earth. The radars illuminate Earth with microwaves allowing detailed observations at any time, regardless of weather or sunlight conditions. SIR-C/X-SAR uses three microwave wavelengths: L-band (24 cm), C-band (6 cm) and X-band (3 cm). The multi-frequency data will be used by the international scientific community to better understand the global environment and how it is changing. The SIR-C/X-SAR data, complemented by aircraft and ground studies, will give scientists clearer insights into those environmental changes which are caused by nature and those changes which are induced by human activity. SIR-C was developed by NASA's Jet Propulsion Laboratory. X-SAR was developed by the Dornier and Alenia Spazio companies for the German space agency, Deutsche Agentur fuer Raumfahrtangelegenheiten (DARA), and the Italian space agency, Agenzia Spaziale Italiana (ASI), with the Deutsche Forschungsanstalt fuer Luft und Raumfahrt e.v. (DLR), the major partner in science, operations and data processing of X-SAR.

The Impact of Detailed Snow Physics on the Simulation of Snow Cover and Subsurface Thermodynamics at Continental Scales

NASA Technical Reports Server (NTRS)

Stieglitz, Marc; Ducharne, Agnes; Koster, Randy; Suarez, Max; Busalacchi, Antonio J. (Technical Monitor)

2000-01-01

The three-layer snow model is coupled to the global catchment-based Land Surface Model (LSM) of the NASA Seasonal to Interannual Prediction Project (NSIPP) project, and the combined models are used to simulate the growth and ablation of snow cover over the North American continent for the period 1987-1988. The various snow processes included in the three-layer model, such as snow melting and re-freezing, dynamic changes in snow density, and snow insulating properties, are shown (through a comparison with the corresponding simulation using a much simpler snow model) to lead to an improved simulation of ground thermodynamics on the continental scale.

Comparison of methods for quantifying surface sublimation over seasonally snow-covered terrain

USGS Publications Warehouse

Sexstone, Graham A.; Clow, David W.; Stannard, David I.; Fassnacht, Steven R.

2016-01-01

Snow sublimation can be an important component of the snow-cover mass balance, and there is considerable interest in quantifying the role of this process within the water and energy balance of snow-covered regions. In recent years, robust eddy covariance (EC) instrumentation has been used to quantify snow sublimation over snow-covered surfaces in complex mountainous terrain. However, EC can be challenging for monitoring turbulent fluxes in snow-covered environments because of intensive data, power, and fetch requirements, and alternative methods of estimating snow sublimation are often relied upon. To evaluate the relative merits of methods for quantifying surface sublimation, fluxes calculated by the EC, Bowen ratio–energy balance (BR), bulk aerodynamic flux (BF), and aerodynamic profile (AP) methods and their associated uncertainty were compared at two forested openings in the Colorado Rocky Mountains. Biases between methods are evaluated over a range of environmental conditions, and limitations of each method are discussed. Mean surface sublimation rates from both sites ranged from 0.33 to 0.36 mm day−1, 0.14 to 0.37 mm day−1, 0.10 to 0.17 mm day−1, and 0.03 to 0.10 mm day−1 for the EC, BR, BF and AP methods, respectively. The EC and/or BF methods are concluded to be superior for estimating surface sublimation in snow-covered forested openings. The surface sublimation rates quantified in this study are generally smaller in magnitude compared with previously published studies in this region and help to refine sublimation estimates for forested openings in the Colorado Rocky Mountains.

Estimating Snow Water Storage in North America Using CLM4, DART, and Snow Radiance Data Assimilation

NASA Technical Reports Server (NTRS)

Kwon, Yonghwan; Yang, Zong-Liang; Zhao, Long; Hoar, Timothy J.; Toure, Ally M.; Rodell, Matthew

2016-01-01

This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature T(sub B) at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting T(sub B) based on their correlations with the prior T(sub B) (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171m RMSE), the overall snow depth estimates are improved by 1.6% (0.168m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177mRMSE). Significant improvement of the snow depth estimates in the rule-based RA as observed for tundra snow class (11.5%, p < 0.05) and bare soil land-cover type (13.5%, p < 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.

[Characteristics of chemical pollution of snow cover in Aktobe areas].

PubMed

Iskakov, A Zh

2010-01-01

The paper gives data on the nature of snow cover pollution in the urbanized areas in relation to the remoteness from the basic sources of ambient air pollution. The total snow content of carcinogens has been estimated.

Snow-cover condition in Japan and damage of the Sugi (Cryptomeria Japonica D. Don)

Treesearch

Taira Hideaki

1991-01-01

Japan is one of the most snowiest regions in the world. Particularly the mountainous area of Honshu (the main island), along the Japan Sea has heavy snow in winter. In some places, snow piles up more than four meters and the ground is covered with snow about one hundred and forty days a year. The sugi tree is widely planted in snowy regions, and snow-pressure damages,...

Earth observations taken during the STS-71 mission

NASA Image and Video Library

1995-07-06

STS071-705-055 (27 June-7 July 1995) --- This vertical view over the central Andes Mountains was photographed from the Earth-orbiting space shuttle Atlantis during the ten-day STS-71 mission. It is one of many still visuals shown by the returning crew during its post-flight press briefing on July 18, 1995. Views of Earth from orbit often make landscapes seem flat, but this view taken with the Sun near the horizon and with a 250mm lens able to pick up detail reveals the conical peaks of numerous volcanoes. The dusting of snow makes the view more vivid as the peaks cast black shadows. The snow is a few days old in this view since several roads can be seen crossing dry lake beds (smooth white areas between mountains, top right), indicating that vehicles have crushed and melted the thin snow. According to NASA scientists observing the STS-71 photography, some volcanoes are not yet mapped. They believe that most of the snow-covered peaks reach more than 16,000 feet in altitude.

Seasonal albedo of an urban/rural landscape from satellite observations

NASA Technical Reports Server (NTRS)

Brest, Christopher L.

1987-01-01

Using data from 27 calibrated Landsat observations of the Hartford, Connecticut area, the spatial distribution and seasonal variation of surface reflectance and albedo were examined. Mean values of visible reflectance, near-IR reflectance, and albedo are presented (for both snow-free and snow-cover observations) according to 14 land use/land cover categories. A diversity of albedo values was found to exist in this type of environment, associated with land cover. Many land-cover categories display a seasonal dependence, with intracategory seasonal differences being of comparable magnitude to intercategory differences. Key factors in determining albedo (and its seasonal dynamics) are the presence or absence of vegetation and the canopy structure. Snow-cover/snow-free differences range from a few percent (for urban land covers) to over 40 percent (for low-canopy vegetation).

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

USGS Publications Warehouse

Selkowitz, D.J.

2010-01-01

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km2 study area in Arctic Alaska. The study area is centered at 69 ??N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10??, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000-2009.

Applications systems verification and transfer project. Volume 8: Satellite snow mapping and runoff prediction handbook

NASA Technical Reports Server (NTRS)

Bowley, C. J.; Barnes, J. C.; Rango, A.

1981-01-01

The purpose of the handbook is to update the various snowcover interpretation techniques, document the snow mapping techniques used in the various ASVT study areas, and describe the ways snowcover data have been applied to runoff prediction. Through documentation in handbook form, the methodology developed in the Snow Mapping ASVT can be applied to other areas.

Soil Moisture and Snow Cover: Active or Passive Elements of Climate

NASA Technical Reports Server (NTRS)

Oglesby, Robert J.; Marshall, Susan; Erickson, David J., III; Robertson, Franklin R.; Roads, John O.; Arnold, James E. (Technical Monitor)

2002-01-01

A key question is the extent to which surface effects such as soil moisture and snow cover are simply passive elements or whether they can affect the evolution of climate on seasonal and longer time scales. We have constructed ensembles of predictability studies using the NCAR CCM3 in which we compared the relative roles of initial surface and atmospheric conditions over the central and western U.S. in determining the subsequent evolution of soil moisture and of snow cover. Results from simulations with realistic soil moisture anomalies indicate that internal climate variability may be the strongest factor, with some indication that the initial atmospheric state is also important. Model runs with exaggerated soil moisture reductions (near-desert conditions) showed a much larger effect, with warmer surface temperatures, reduced precipitation, and lower surface pressures; the latter indicating a response of the atmospheric circulation. These results suggest the possibility of a threshold effect in soil moisture, whereby an anomaly must be of a sufficient size before it can have a significant impact on the atmospheric circulation and climate. Results from simulations with realistic snow cover anomalies indicate that the time of year can be crucial. When introduced in late winter, these anomalies strongly affected the subsequent evolution of snow cover. When introduced in early winter, however, little or no effect is seen on the subsequent snow cover. Runs with greatly exaggerated initial snow cover indicate that the high reflectivity of snow is the most important process by which snow cover can impact climate, through lower surface temperatures and increased surface pressures. The results to date were obtained for model runs with present-day conditions. We are currently analyzing runs made with projected forcings for the 21st century to see if these results are modified in any way under likely scenarios of future climate change. An intriguing new statistical technique involving 'clustering' is developed to assist in this analysis.

Sea Ice Mass Balance Buoys (IMBs): First Results from a Data Processing Intercomparison Study

NASA Astrophysics Data System (ADS)

Hoppmann, Mario; Tiemann, Louisa; Itkin, Polona

2017-04-01

IMBs are autonomous instruments able to continuously monitor the growth and melt of sea ice and its snow cover at a single point on an ice floe. Complementing field expeditions, remote sensing observations and modelling studies, these in-situ data are crucial to assess the mass balance and seasonal evolution of sea ice and snow in the polar oceans. Established subtypes of IMBs combine coarse-resolution temperature profiles through air, snow, ice and ocean with ultrasonic pingers to detect snow accumulation and ice thermodynamic growth. Recent technological advancements enable the use of high-resolution temperature chains, which are also able to identify the surrounding medium through a „heating cycle". The temperature change during this heating cycle provides additional information on the internal properties and processes of the ice. However, a unified data processing technique to reliably and accurately determine sea ice thickness and snow depth from this kind of data is still missing, and an unambiguous interpretation remains a challenge. Following the need to improve techniques for remotely measuring sea ice mass balance, an international IMB working group has recently been established. The main goals are 1) to coordinate IMB deployments, 2) to enhance current IMB data processing and -interpretation techniques, and 3) to provide standardized IMB data products to a broader community. Here we present first results from two different data processing algorithms, applied to selected IMB datasets from the Arctic and Antarctic. Their performance with regard to sea ice thickness and snow depth retrieval is evaluated, and an uncertainty is determined. Although several challenges and caveats in IMB data processing and -interpretation are found, such datasets bear great potential and yield plenty of useful information about sea ice properties and processes. It is planned to include many more algorithms from contributors within the working group, and we explicitly invite other interested scientists to join this promising effort.

Automatic crown cover mapping to improve forest inventory

Treesearch

Claude Vidal; Jean-Guy Boureau; Nicolas Robert; Nicolas Py; Josiane Zerubia; Xavier Descombes; Guillaume Perrin

2009-01-01

To automatically analyze near infrared aerial photographs, the French National Institute for Research in Computer Science and Control developed together with the French National Forest Inventory (NFI) a method for automatic crown cover mapping. This method uses a Reverse Jump Monte Carlo Markov Chain algorithm to locate the crowns and describe those using ellipses or...

Changes in Andes snow cover from MODIS data, 2000-2016

NASA Astrophysics Data System (ADS)

Saavedra, Freddy A.; Kampf, Stephanie K.; Fassnacht, Steven R.; Sibold, Jason S.

2018-03-01

The Andes span a length of 7000 km and are important for sustaining regional water supplies. Snow variability across this region has not been studied in detail due to sparse and unevenly distributed instrumental climate data. We calculated snow persistence (SP) as the fraction of time with snow cover for each year between 2000 and 2016 from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors (500 m, 8-day maximum snow cover extent). This analysis is conducted between 8 and 36° S due to high frequency of cloud (> 30 % of the time) south and north of this range. We ran Mann-Kendall and Theil-Sens analyses to identify areas with significant changes in SP and snowline (the line at lower elevation where SP = 20 %). We evaluated how these trends relate to temperature and precipitation from Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA2) and University of Delaware datasets and climate indices as El Niño-Southern Oscillation (ENSO), Southern Annular Mode (SAM), and Pacific Decadal Oscillation (PDO). Areas north of 29° S have limited snow cover, and few trends in snow persistence were detected. A large area (34 370 km2) with persistent snow cover between 29 and 36° S experienced a significant loss of snow cover (2-5 fewer days of snow year-1). Snow loss was more pronounced (62 % of the area with significant trends) on the east side of the Andes. We also found a significant increase in the elevation of the snowline at 10-30 m year-1 south of 29-30° S. Decreasing SP correlates with decreasing precipitation and increasing temperature, and the magnitudes of these correlations vary with latitude and elevation. ENSO climate indices better predicted SP conditions north of 31° S, whereas the SAM better predicted SP south of 31° S.

Average snowcover density values in Eastern Alps mountain

NASA Astrophysics Data System (ADS)

Valt, M.; Moro, D.

2009-04-01

The Italian Avalanche Warning Services monitor the snow cover characteristics through networks evenly distributed all over the alpine chain. Measurements of snow stratigraphy and density are very frequently performed with sampling rates of 1 -2 times per week. Snow cover density values are used to compute the dimensions of the building roofs as well as to design avalanche barriers. Based on the measured snow densities the Electricity Board can predict the amount of water resources deriving from snow melt in high relieves drainage basins. In this work it was possible to compute characteristic density values of the snow cover in the Eastern Alps using the information contained in the database from the ARPA (Agenzia Regionale Protezione Ambiente)-Centro Valanghe di Arabba, and Ufficio Valanghe- Udine. Among the other things, this database includes 15 years of stratigraphic measurements. More than 6,000 snow stratigraphic logs were analysed, in order to derive typical values as for geographical area, altitude, exposure, snow cover thickness and season. Computed values were compared to those established by the current Italian laws. Eventually, experts identified and evaluated the correlations between the seasonal variations of the average snow density and the variations related to the snowfall rate in the period 1994-2008 in the Eastern Alps mountain range

A new glacier inventory for the Karakoram-Pamir region

NASA Astrophysics Data System (ADS)

Rastner, P.; Paul, F.; Bolch, T.; Moelg, N.

2015-12-01

High-quality glacier inventories are required as a reference dataset to determine glacier changes and model their reaction to climate change, among others. In particular in High Mountain Asia such an inventory was missing for several heavily glacierized regions with reportedly strongly changing glaciers. As a contribution to GLIMS and the Randolph Glacier Inventory (RGI) we have mapped all glaciers in the Karakoram and Pamir region within the framework of ESAs Glaciers_cci project. Glacier mapping was performed using the band ratio method (TM3/TM5) and manual editing of Landsat TM/ETM+ imagery acquired around the year 2000. The mapping was challenged by frequent seasonal snow at high elevations, debris-covered glacier tongues, and several surging glaciers. We addressed the snow issue by utilizing multi-temporal imagery and improved manual mapping of debris-covered glacier tongues with ALOS PALSAR coherence images. Slow disintegration of glacier tongues after a surge (leaving still-connected dead ice) results in a difficult identification of the terminus and assignment of entities. Drainage divides were derived from the ASTER GDEM II and manually corrected to calculate topographic parameters. All glaciers larger 0.02 km2 cover an area of about 21,700 km2 in the Karakoram and about 11,800 km² in the Pamir region. Most glaciers are in the 0.1-0.5 km2 size class for Pamir, whereas for the Karakoram they are in the class <0.1 km2. Glaciers between 1 and 5 km2 contribute more than 30% to the total area in Pamir, whereas for the Karakoram region it is only 17%. The mean glacier elevation in the Karakoram (Pamir) region is 5426 (4874) m. A comparison with other recently published inventories reveals differences in the interpretation of glacier extents (mainly in the accumulation region) that would lead to large area changes if unconsidered for change assessment across different inventories.

Towards a well-founded and reproducible snow load map for Austria

NASA Astrophysics Data System (ADS)

Winkler, Michael; Schellander, Harald

2017-04-01

"EN 1991-1-3 Eurocode 1: Part 1-3: Snow Loads" provides standard for the determination of the snow load to be used for the structural design of buildings etc. Since 2006 national specifications for Austria define a snow load map with four "load zones", allowing the calculation of the characteristic ground snow load sk for locations below 1500 m asl. A quadratic regression between altitude and sk is used, as suggested by EN 1991-1-3. The actual snow load map is based on best meteorological practice, but still it is somewhat subjective and non-reproducible. Underlying snow data series often end in the 1980s; in the best case data until about 2005 is used. Moreover, extreme value statistics only rely on the Gumbel distribution and the way in which snow depths are converted to snow loads is generally unknown. This might be enough reasons to rethink the snow load standard for Austria, all the more since today's situation is different to what it was some 15 years ago: Firstly, Austria is rich of multi-decadal, high quality snow depth measurements. These data are not well represented in the actual standard. Secondly, semi-empirical snow models allow sufficiently precise calculations of snow water equivalents and snow loads from snow depth measurements without the need of other parameters like temperature etc. which often are not available at the snow measurement sites. With the help of these tools, modelling of daily snow load series from daily snow depth measurements is possible. Finally, extreme value statistics nowadays offers convincing methods to calculate snow depths and loads with a return period of 50 years, which is the base of sk, and allows reproducible spatial extrapolation. The project introduced here will investigate these issues in order to update the Austrian snow load standard by providing a well-founded and reproducible snow load map for Austria. Not least, we seek for contact with standards bodies of neighboring countries to find intersections as well as to avoid inconsistencies and duplications of effort.