Relevant bibliographies by topics / MODIS snow cover

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'MODIS snow cover'

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Published: 6 September 2023

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Journal articles on the topic "MODIS snow cover"

1

Hall, Dorothy K., George A. Riggs, Vincent V. Salomonson, Nicolo E. DiGirolamo, and Klaus J. Bayr. "MODIS snow-cover products." Remote Sensing of Environment 83, no. 1-2 (November 2002): 181–94. http://dx.doi.org/10.1016/s0034-4257(02)00095-0.

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Riggs, George, Dorothy Hall, Carrie Vuyovich, and Nicolo DiGirolamo. "Development of Snow Cover Frequency Maps from MODIS Snow Cover Products." Remote Sensing 14, no. 22 (November 9, 2022): 5661. http://dx.doi.org/10.3390/rs14225661.

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With a decade scale record of global snow cover extent (SCE) at up to 500 m from the Moderate-resolution Imaging Spectroradiometer (MODIS), the dynamics of snow cover can be mapped at local to global scales. We developed daily snow cover frequency maps from 2001–2020 using a ~5 km resolution MODIS snow cover map. For each day of the year the maps show the frequency of snow cover for the 20-year period on a per-grid cell basis. Following on from other work to develop snow frequency maps using MODIS snow cover products, we include spatial filtering to reduce errors caused by ‘false snow’ that occurs primarily due to cloud-snow confusion. On our snow frequency maps, there were no regions or time periods with a noticeable absence of snow where snow was expected. In one example, the MODIS derived frequency of snow cover on 25 December compares well with NOAA’s historical probability of snow on the same day. Though the MODIS derived snow frequency and NOAA probabilities are computed from very different data sources, they compare well. Though this preliminary research is promising, much future evaluation is needed.
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Thapa, Shubhechchha, Parveen K. Chhetri, and Andrew G. Klein. "Cross-Comparison between MODIS and VIIRS Snow Cover Products for the 2016 Hydrological Year." Climate 7, no. 4 (April 16, 2019): 57. http://dx.doi.org/10.3390/cli7040057.

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The VIIRS (Visible Infrared Imaging Radiometer Suite) instrument on board the Suomi-NPP (National Polar-Orbiting Partnership) satellite aims to provide long-term continuity of several environmental data series including snow cover initiated with MODIS (Moderate Resolution Imaging Spectroradiometer). Although 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 (Environmental Data Records) (VSCMO/binary) were intercompared using confusion matrices, comparison maps and false color imagery. Thresholding the MODIS NDSI (Normalized Difference Snow Index) Snow Cover product at a snow cover fraction of 30% generated binary snow maps are 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. The agreement was highest during the winter but lower during late fall and spring. MODIS and VIIRS often mapped snow/no-snow transition zones as a cloud. The assessment of total snow and cloud pixels and comparison snow maps of MODIS and VIIRS indicate 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. While VIIRS and MODIS have a similar capacity to map snow cover, VIIRS has the potential to map snow cover area more accurately, for the successful development of climate data records.
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Hall, Dorothy K., James L. Foster, Alfred T. C. Chang, Carl S. Benson, and Janet Y. L. Chien. "Determination of snow-covered area in different land covers in central Alaska, U.S.A., from aircraft data — April 1995." Annals of Glaciology 26 (1998): 149–55. http://dx.doi.org/10.3189/1998aog26-1-149-155.

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During April 1995, a field and aircraft experiment was conducted in central Alaska in support of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-mapping project. The MODIS Airborne Simulator (MAS), a 50 channel spectroradiometer, was flown on board the NASA ER-2 aircraft. An objective of the mission was to determine the accuracy of mapping snow in different surface covers using an algorithm designed to map global snow cover after the launch of MODIS in 1998. The surface cover in this area of central Alaska is typically spruce, birch, aspen, mixed forest and muskeg. Integrated reflectance, Ri was calculated from the visible/near-infrared channels of the MAS sensor. The Ri was used to estimate different vegetation-cover densities because there is an inverse relationship between vegetation-cover density and albedo in snow-covered terrain. A vegetation-cover density map was constructed using MAS data acquired on 13 April 1995 over central Alaska. In the part of the scene that was mapped as having a vegetation-cover density of < 50%, the snow-mapping algorithm mapped 96.41% snow cover. These areas are generally composed of muskeg and mixed forests and include frozen lake. In the part of the scene that was estimated to have a vegetation-cover density of ≥50%, the snow-mapping algorithm mapped 71.23% snow cover. These areas are generally composed of dense coniferous or deciduous forests. Overall, the accuracy of the snow-mapping algorithm is > 87.41% for a 13 April MAS scene with a variety of surface covers (coniferous and deciduous and mixed forests, muskeg, tundra and frozen lake).
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Hall, Dorothy K., James L. Foster, Alfred T. C. Chang, Carl S. Benson, and Janet Y. L. Chien. "Determination of snow-covered area in different land covers in central Alaska, U.S.A., from aircraft data — April 1995." Annals of Glaciology 26 (1998): 149–55. http://dx.doi.org/10.1017/s0260305500014725.

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During April 1995, a field and aircraft experiment was conducted in central Alaska in support of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-mapping project. The MODIS Airborne Simulator (MAS), a 50 channel spectroradiometer, was flown on board the NASA ER-2 aircraft. An objective of the mission was to determine the accuracy of mapping snow in different surface covers using an algorithm designed to map global snow cover after the launch of MODIS in 1998. The surface cover in this area of central Alaska is typically spruce, birch, aspen, mixed forest and muskeg. Integrated reflectance,Riwas calculated from the visible/near-infrared channels of the MAS sensor. TheRiwas used to estimate different vegetation-cover densities because there is an inverse relationship between vegetation-cover density and albedo in snow-covered terrain. A vegetation-cover density map was constructed using MAS data acquired on 13 April 1995 over central Alaska. In the part of the scene that was mapped as having a vegetation-cover density of &lt; 50%, the snow-mapping algorithm mapped 96.41% snow cover. These areas are generally composed of muskeg and mixed forests and include frozen lake. In the part of the scene that was estimated to have a vegetation-cover density of ≥50%, the snow-mapping algorithm mapped 71.23% snow cover. These areas are generally composed of dense coniferous or deciduous forests. Overall, the accuracy of the snow-mapping algorithm is &gt; 87.41% for a 13 April MAS scene with a variety of surface covers (coniferous and deciduous and mixed forests, muskeg, tundra and frozen lake).
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Parajka, J., and G. Blöschl. "Validation of MODIS snow cover images over Austria." Hydrology and Earth System Sciences 10, no. 5 (September 27, 2006): 679–89. http://dx.doi.org/10.5194/hess-10-679-2006.

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Abstract. This study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product over the territory of Austria. The aims are (a) to analyse the spatial and temporal variability of the MODIS snow product classes, (b) to examine the accuracy of the MODIS snow product against in situ snow depth data, and (c) to identify the main factors that may influence the MODIS classification accuracy. We use daily MODIS grid maps (version 4) and daily snow depth measurements at 754 climate stations in the period from February 2000 to December 2005. The results indicate that, on average, clouds obscured 63% of Austria, which may significantly restrict the applicability of the MODIS snow cover images to hydrological modelling. On cloud-free days, however, the classification accuracy is very good with an average of 95%. There is no consistent relationship between the classification errors and dominant land cover type and local topographical variability but there are clear seasonal patterns to the errors. In December and January the errors are around 15% while in summer they are less than 1%. This seasonal pattern is related to the overall percentage of snow cover in Austria, although in spring, when there is a well developed snow pack, errors tend to be smaller than they are in early winter for the same overall percent snow cover. Overestimation and underestimation errors balance during most of the year which indicates little bias. In November and December, however, there appears to exist a tendency for overestimation. Part of the errors may be related to the temporal shift between the in situ snow depth measurements (07:00 a.m.) and the MODIS acquisition time (early afternoon). The comparison of daily air temperature maps with MODIS snow cover images indicates that almost all MODIS overestimation errors are caused by the misclassification of cirrus clouds as snow.
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Gafurov, A., D. Kriegel, S. Vorogushyn, and B. Merz. "Evaluation of remotely sensed snow cover product in Central Asia." Hydrology Research 44, no. 3 (December 19, 2012): 506–22. http://dx.doi.org/10.2166/nh.2012.094.

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Central Asian countries depend highly on water resources from snow and glacier melt, which has to be studied thoroughly to estimate water availability. However, the observation network in Central Asia is poor to carry out such studies in detail. Observations from space using remote sensing techniques might fill this observation gap, which needs to be validated. Therefore, this study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover product in Central Asia. For the evaluation, in situ snow depth data from 30 meteorological stations and higher resolution Landsat data are used. The results show an overall snow agreement between MODIS and ground observations of 93.1 and 92.7% for MODIS Terra and MODIS Aqua snow products, respectively. The agreement between MODIS and Landsat is 91.9% when considering snow and land agreements. The snow fraction product from MODIS is also validated using Landsat data, and varying accuracies are obtained. The main disadvantage of the MODIS snow cover product are the cloud induced gaps. Therefore, cloud covered pixels are eliminated using the ModSnow algorithm. Using the in situ data, the snow agreement of cloud removed snow cover data is checked, and an accuracy of 84.4% is achieved.
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Li, Xinghua, Yinghong Jing, Huanfeng Shen, and Liangpei Zhang. "The recent developments in cloud removal approaches of MODIS snow cover product." Hydrology and Earth System Sciences 23, no. 5 (May 17, 2019): 2401–16. http://dx.doi.org/10.5194/hess-23-2401-2019.

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Abstract. The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.
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Chen, Baoying, Xianfeng Zhang, Miao Ren, Xiao Chen, and Junyi Cheng. "Snow Cover Mapping Based on SNPP-VIIRS Day/Night Band: A Case Study in Xinjiang, China." Remote Sensing 15, no. 12 (June 8, 2023): 3004. http://dx.doi.org/10.3390/rs15123004.

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Detailed snow cover maps are essential for estimating the earth’s energy balance and hydrological cycle. Mapping the snow cover across spatially extensive and topographically complex areas with less or no cloud obscuration is challenging, but the SNPP-VIIRS Day/Night Band (DNB) nighttime light data offers a potential solution. This paper aims to map snow cover distribution at 750 m resolution across the diverse 1,664,900 km2 of Xinjiang, China, based on SNPP-VIIRS DNB radiance. We implemented a swarm intelligent optimization technique Krill Herd algorithm, which finds the optimal threshold value by taking Otsu’s method as the objective function. We derived SNPP-VIIRS DNB snow maps of 14 consecutive scenes in December 2021, compared our snow-covered area estimations with those from MODIS and AMSR2 standard snow cover products, and generated composite snow maps by merging MODIS and SNPP-VIIRS DNB data. Results show that SNPP-VIIRS DNB snow maps are capable of providing reliable snow cover maps superior to MODIS and AMSR2, with an overall accuracy level of 84.66%. The composite snow maps at 500 m spatial resolution provided 55.85% more information on snow cover distribution than standard MODIS products and achieved an overall accuracy of 84.69%. Our study demonstrated the feasibility of snow cover detection in Xinjiang based on SNPP-VIIRS DNB, which can serve as a supplementary dataset for MODIS estimations where clouded pixels are present.
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Muhammad, Sher, and Amrit Thapa. "An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018." Earth System Science Data 12, no. 1 (February 13, 2020): 345–56. http://dx.doi.org/10.5194/essd-12-345-2020.

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Abstract. Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).
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Dissertations / Theses on the topic "MODIS snow cover"

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Lopez-Burgos, Viviana. "Reducing cloud obscuration on MODIS Snow Cover Area products by applying spatio-temporal techniques combined with topographic effects." Thesis, The University of Arizona, 2010. http://hdl.handle.net/10150/193442.

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Rapid population growth in Arizona is leading to increasing demand and decreasing availability of water, requiring a detailed quantification of hydrological processes. The integration of detailed spatial water fluxes information from remote sensing platforms, and hydrological models is one of the steps towards this goal. One example step is the use of MODIS Snow Cover Area (SCA) information to update the snow component of a land surface model (LSM). Because cloud cover obscures the images, this project explores a rule-based method to remove the clouds. The rules include: combination of SCA maps from two satellites; time interpolation method; spatial interpolation method; and the probability of snow occurrence in a pixel based on topographic variables. The application in sequence of these rules over the Upper Salt River Basin for WY 2005 resulted in a reduction of cloud obscuration by 93.7878% and the resulting images' accuracy is similar to the accuracy of the original SCA maps. The results of this research will be used on a LSM to improve the management of reservoirs on the Salt River. This research seeks to improve SCA data for further use in a LSM to increase the knowledge base used to manage water resources. It will be relevant for regions were snow is the primary source of water supply.
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Broxton, Patrick. "Improving Distributed Hydrologic Modeling and Global Land Cover Data." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/307009.

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Distributed models of the land surface are essential for global climate models because of the importance of land-atmosphere exchanges of water, energy, momentum. They are also used for high resolution hydrologic simulation because of the need to capture non-linear responses to spatially variable inputs. Continued improvements to these models, and the data which they use, is especially important given ongoing changes in climate and land cover. In hydrologic models, important aspects are sometimes neglected due to the need to simplify the models for operational simulation. For example, operational flash flood models do not consider the role of snow and are often lumped (i.e. do not discretize a watershed into multiple units, and so do not fully consider the effect of intense, localized rainstorms). To address this deficiency, an overland flow model is coupled with a subsurface flow model to create a distributed flash flood forecasting system that can simulate flash floods that involve rain on snow. The model is intended for operational use, and there are extensive algorithms to incorporate high-resolution hydrometeorologic data, to assist in the calibration of the models, and to run the model in real time. A second study, which is designed to improve snow simulation in forested environments, demonstrates the importance of explicitly representing a near canopy environment in snow models, instead of only representing open and canopy covered areas (i.e. with % canopy fraction), as is often done. Our modeling, which uses canopy structure information from Aerial Laser Survey Mapping at 1 meter resolution, suggests that areas near trees have more net snow water input than surrounding areas because of the lack of snow interception, shading by the trees, and the effects of wind. In addition, the greatest discrepancy between our model simulations that explicitly represent forest structure and those that do not occur in areas with more canopy edges. In addition, two value-added Land Cover products (land cover type and maximum green vegetation fraction; MGVF) are developed and evaluated. The new products are good successors to current generation land cover products that are used in global models (many of which rely on 20 year old AVHRR land cover data from a single year) because they are based on 10 years of recent MODIS data. There is substantial spurious interannual variability in the MODIS land cover type data, and the MGVF product can vary substantially from year to year depending on climate conditions, suggesting the importance of using climatologies for land cover data. The new land cover type climatology also agrees better with validation sites, and the MGVF climatology is more consistent with other measures of vegetation (e.g. Leaf Area Index) than the older land cover data.
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Kadlec, Jiri. "Design, Development and Testing of Web Services for Multi-Sensor Snow Cover Mapping." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/5727.

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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.
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Marim, Gokhan. "Temporal Evaluation Of Snow Depletion Curves Derived For Upper Euphrates Basin And Applications Of Snowmelt Runoff Model (srm)." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609938/index.pdf.

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TEMPORAL EVALUATION OF SNOW DEPLETION CURVES DERIVED FOR UPPER EUPHRATES BASIN AND APPLICATIONS OF SNOWMELT RUNOFF MODEL Marim, Gö
khan M.S., Department of Geodetic and Geographic Information Technologies Supervisor: Prof.Dr.A.Ü
nal Sorman September 2008, 112 pages Water is becoming very important issue day by day with descending usable water and energy resources. In the aspect of water resources management, especially for the optimum reservoir management, predicting runoff for large reservoirs by applying hydrologic model is a recent and crucial topic. The most important model input and predictor parameters to estimate runoff for the mountainous regions are to be distribution of rainfall
temperature and snow cover area, (SCA). It is seen that many predictor variables should be integrated with Geographic Information Systems (GIS) and Remote Sensing Techniques especially for hydrologic model variable preparation. Satellite products have the potential for obtaining those kinds of data in near real time. In this study, the changes of SDC are generated by the analysis of optical satellite and by using SDC as an input to hydrological models runoff is simulated for Upper Euphrates Basin (10215.7 km2) which is a sub basin of Euphrates Basin. Largest dams of Turkey
Keban, Karakaya and Atatü
rk are located on Euphrates River. Optimum operations of these dams depend on forecasting incoming water in early summer season. Euphrates River is fed mainly from snowmelts in spring or early summer time.65-70 % of the annual flow is contributed from snowmelt in that region. Main objective of this study is to obtain the spatially and temporally distributed SCA percentages from optical satellite, which are required as one of the main input variables of the hydrological model used in the application. SCA percentages and SDC are obtained for snowmelt years 2004-2007 by using high temporal resolution optical remote sensing data: Terra Moderate Resolution Imaging Spectroradiometer (MODIS). In this study, Terra MODIS snow cover map product, MOD10A1 which has a spatial resolution of 500 m is used. As a hydrological model Snowmelt Runoff Model (SRM) was applied. SRM was built up on the well-known degree day approach. In this study SRM is simulated for two years 2006 and 2007.The simulation results are compared and resultant model parameters are obtained for future runoff forecast studies. In this study, beside recommendations, discussions on the variables and SRM parameters are also provided.
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FUGAZZA, DAVIDE. "ALL EYES ON GLACIERS: REMOTE SENSING OF THE CRYOSPHERE." Doctoral thesis, Università degli Studi di Milano, 2019. http://hdl.handle.net/2434/623598.

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Remote sensing has become an invaluable tool for glaciologists. The keys to its success are the ability to repeatedly monitor small to large, often inaccessible areas and quickly process data through the integration in geographical information system (GIS) environments. From aerial photography in the early days, remote sensing has evolved owing to the ever-increasing input of large amounts of data from satellite platforms, while traditional photogrammetric techniques are now experiencing a renaissance thanks to the growing importance of UAVs and terrestrial photography. This Ph.D. thesis explores remote sensing applications for cryospheric monitoring and is aimed at improving our understanding of processes of glacier retreat, energy budget, mass balance and dynamics and of snow cover variability, using a variety of sensors (optical and thermal cameras, laser scanners) and platforms, including satellites, planes and unmanned aerial vehicles (UAVs). Each chapter of the thesis presents a case study: • The creation of an inventory of glaciers and potentially dangerous glacial lakes of the Central Karakorum National Park, Pakistan (Chapter 3) using Landsat satellite data. Both glaciers and lakes are very important to monitor as they are both a source of water and a potential hazard in the area. • The analysis of thickness changes and hazards related to the downwasting and collapse of Forni Glacier, a large glacier in the Italian Alps, through a combination of approaches, i.e. Laser scanner, UAV and terrestrial photogrammetry (Chapter 4). • The spatial distribution of glacier albedo (Chapter 5), important to estimate ice and snow melt for a large valley glacier in the Italian Alps (Forni Glacier) validating a pre-existing methodology using Landsat satellite data • The evolution of glacier albedo through time (chapter 6) for a selection of glaciers in the Italian Alps, Ortles-Cevedale group, extending the approach described in chapter 5, to understand the extent of glacier darkening and the potential causes. • The evolution of supraglacial debris cover for a selection of glaciers in the Italian Alps, Ortles-Cevedale group (chapter 7) using aerial orthoimagery. This analysis is fundamental to model the melt of debris covered glaciers and understand their evolution. • The structural evolution of the glacier tongue of a large valley glacier (Forni Glacier) using aerial and UAV imagery, to investigate the changes in glacier dynamics over time (Chapter 8). • The analysis of multiannual variability of snow cover in a large river basin of Central Asia (Upper Irtysh, Kazakhstan) and the relationship between snow cover depletion and atmospheric circulation indices using MODIS satellite data (Chapter 9), to provide useful tools for flood forecasting. The key findings from Chapters 4 to 8 permit to assess the evolution of a wide glacier area of the Italian Alps, highlighting the increase in supraglacial debris cover and related decrease in glacier albedo, increased rates of glacier downwasting and structural collapse, increasing the potential for glacier hazards. The analysis of glacier hazards was also an important part of the CKNP study, including an assessment of the current state of glaciers and glacial lakes. The focus on snow cover variability in Central Asia shows instead the importance of atmospheric circulation and the possibility of long-term forecasts using widely available teleconnection indices. Most relevant processes in the cryosphere were touched upon in this thesis, from accumulation processes (i.e. snow cover), to melt (including albedo and debris cover which influence it) and structural characteristics. Furthermore, while the case studies can be read as independent units, all the methodologies presented here transcend the single example and are widely applicable to different glaciers or areas. In particular, glacier albedo or debris cover maps are an essential input to improve glacier melt models, and could be used in the CKNP to model melt more accurately. Glacier mass balance and dynamics could also be monitored more accurately and at unprecedented spatial resolution using UAVs.
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Dobreva, Iliyana D. "Fractional Snow-Cover Mapping Through Artificial Neural Network Analysis of MODIS Surface Reflectance." 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7476.

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Accurate areal measurements of snow-cover extent are important for hydrological and climate modeling. The traditional method of mapping snow cover is binary where a pixel is approximated to either snow-covered or snow-free. Fractional snow cover (FSC) mapping achieves a more precise estimate of areal snow-cover extent by determining the fraction of a pixel that is snow-covered. The two most common FSC methods using Moderate Resolution Imaging Spectroradiometer (MODIS) images are linear spectral unmixing and the empirical Normalized Difference Snow Index (NDSI) method. Machine learning is an alternative to these approaches for estimating FSC, as Artificial Neural Networks (ANNs) have been used for estimating the subpixel abundances of other surfaces. The advantages of ANNs over the other approaches are that they can easily incorporate auxiliary information such as land-cover type and are capable of learning nonlinear relationships between surface reflectance and snow fraction. ANNs are especially applicable to mapping snow-cover extent in forested areas where spatial mixing of surface components is nonlinear. This study developed an ANN approach to snow-fraction mapping. A feed-forward ANN was trained with backpropagation to estimate FSC from MODIS surface reflectance, NDSI, Normalized Difference Vegetation Index (NDVI) and land cover as inputs. The ANN was trained and validated with high spatial-resolution FSC derived from Landsat Enhanced Thematic Mapper Plus (ETM+) binary snow-cover maps. ANN achieved best result in terms of extent of snow-covered area over evergreen forests, where the extent of snow cover was slightly overestimated. Scatter plot graphs of the ANN and reference FSC showed that the neural network tended to underestimate snow fraction in high FSC and overestimate it in low FSC. The developed ANN compared favorably to the standard MODIS FSC product with the two methods estimating the same amount of total snow-covered area in the test scenes.
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Svacina, Nicolas Andreas. "Evaluation of the albedo parameterization of the Canadian Lake Ice Model and MODIS albedo products during the ice cover season." Thesis, 2013. http://hdl.handle.net/10012/7643.

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Snow and lake ice have very high albedos compared to other surfaces found in nature. Surface albedo is an important component of the surface energy budget especially when albedos are high since albedo governs how much shortwave radiation is absorbed or reflected at a surface. In particular, snow and lake ice albedos have been shown to affect the timing of lake ice break-up. Lakes are found throughout the Northern Hemisphere and lake ice has been shown to be sensitive to climatic variability. Therefore, the modelling of lake ice phenology, using lake ice models such as the Canadian Lake Ice Model (CLIMo), is important to the study of climatic variability in the Arctic and sub-Arctic regions and accurate snow and lake ice albedo measurements are required to ensure the accuracy of the simulations. However, snow and lake ice albedo can vary from day-to-day depending on factors such as air temperature, presence of impurities, age, and composition. Some factors are more difficult than others to model (e.g. presence of impurities). It would be more straight forward to just gather field measurements, but such measurements would be costly and lakes can be in remote locations and difficult to access. Instead, CLIMo contains an albedo parameterization scheme that models the evolution of snow and lake ice albedo in its simulations. However, parts of the albedo parameterization are based on sea-ice observations (which inherently have higher albedos due to brine inclusions) and the albedo parameterization does not take ice type (e.g. clear ice or snow ice) into account. Satellite remote sensing via the Moderate Resolution Imaging Spectroradiometer (MODIS) provides methods for retrieving albedo that may help enhance CLIMo’s albedo parameterization. CLIMo’s albedo parameterization as well the MODIS daily albedo products (MOD10A1 and MYD10A1) and 16-day product (MCD43A3) were evaluated against in situ albedo observations made over Malcolm Ramsay Lake near Churchill, Manitoba, during the winter of 2012. It was found that the snow albedo parameterization of CLIMo performs well when compared to average in situ observations, but the bare ice parameterization overestimated bare ice albedo observations. The MODIS albedo products compared well when evaluated against the in situ albedo observations and were able to capture changes in albedo throughout the study period. The MODIS albedo products were also compared against CLIMo’s melting ice parameterization, because the equipment had to be removed from the lake to prevent it from falling into the water during the melt season. Cloud cover interfered with the MODIS observations, but the comparison suggests that MODIS albedo products retrieved higher albedo values than the melting ice parameterization of CLIMo. The MODIS albedo products were then integrated directly into CLIMo in substitution of the albedo parameterization to see if they could enhance break-up date (ice off) simulations. MODIS albedo retrievals (MOD10A1, MYD10A1, and MCD43A3) were collected over Back Bay, Great Slave Lake (GSL) near Yellowknife, Northwest Territories, from 2000-2011. CLIMo was then run with and without the MODIS albedos integrated and compared against MODIS observed break-up dates. Simulations were also run under three difference snow cover scenarios (0%, 68%, and 100% snow cover). It was found that CLIMo without MODIS albedos performed better with the 0% snow cover scenario than with the MODIS albedos integrated in. Both simulations (with and without MODIS albedos) performed well with the snow cover scenarios. The MODIS albedo products slightly improved CLIMo break-up simulations when integrated up to a month in advance of actual lake ice break-up for Back Bay. With the MODIS albedo products integrated into CLIMo, break-up dates were simulated within 3-4 days of MODIS observed break-up. CLIMo without the MODIS albedos still performed very well simulating break-up within 4-5 days of MODIS observed break-up. It is uncertain whether this was a significant improvement or not with such a small study period and with the investigation being conducted at a single site (Back Bay). However, it has been found that CLIMo performs well with the original albedo parameterization and that MODIS albedos could potentially complement lake-wide break-up simulations in future studies.
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Book chapters on the topic "MODIS snow cover"

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Madhavi Supriya, V., B. Simhadri Rao, Ch Sai Krishna, P. Venkat Raju, and V. Venkateshwar Rao. "Spatio-Temporal Variability Analysis of Snow Cover in Sutlej Basin Using MODIS Snow Cover Data." In Lecture Notes in Civil Engineering, 293–304. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9147-9_23.

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Bhardwaj, Nishu, Bhaskar R. Nikam, S. K. S. Yadav, Sudhakar Shukla, Manaruchi Mohapatra, S. P. Aggarwal, and Joyeeta Poddar. "Snow Cover Mapping Over the Region of Hindu Kush Himalaya (HKH) for 2008–2018 Using Cloud Mitigated Moderate Resolution Spectroradiometer (MODIS) Snow Cover Data." In Lecture Notes in Civil Engineering, 105–15. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6647-6_10.

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Rawat, Manish, Sateesh Karwariya, Ritik Raushan, Shruti Kanga, Ajay Kumar Taloor, and Asha Thapliyal. "Snow Cover and Land Surface Temperature Assessment of Mana Basin Uttarakhand India Using MODIS Satellite Data." In Water, Cryosphere, and Climate Change in the Himalayas, 159–74. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67932-3_10.

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Parajka, Juraj, and Günter Blöschl. "MODIS-Based Snow Cover Products, Validation, and Hydrologic Applications." In Multiscale Hydrologic Remote Sensing, 185–212. CRC Press, 2012. http://dx.doi.org/10.1201/b11279-9.

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Qualls, Russell J., and Ayodeji Arogundade. "Modeling Snowmelt Runoff under Climate Change Scenarios Using MODIS-Based Snow Cover Products." In Multiscale Hydrologic Remote Sensing, 213–49. CRC Press, 2012. http://dx.doi.org/10.1201/b11279-10.

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Sinha, Pushpalata Kumari, Pratibha Warwade, A. B. Pachore, and Renji Remesan. "SRM-based quantification of snowmelt runoff in the Beas River Basin of the Himalayan region with the aid of MODIS/TERRA snow cover data products." In Modeling and Mitigation Measures for Managing Extreme Hydrometeorological Events Under a Warming Climate, 277–97. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-443-18640-0.00009-2.

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Conference papers on the topic "MODIS snow cover"

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Wang, Gongxue, Lingmei Jiang, Shirui Hao, Xiaojing Liu, and Huizhen Cui. "Improving snow and cloud discrimination in MODIS snow cover products." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127042.

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Hou, Huishu, Hongye Yang, and Xiumei Wang. "Modis-based Inner Mongolia grassland snow-cover mapping." In The Pacific Rim Conference on Lasers and Electro-Optics (CLEO/PACIFIC RIM). IEEE, 2009. http://dx.doi.org/10.1109/cleopr.2009.5292720.

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Huang, Chunlin. "Assimilation of MODIS snow cover fraction for improving snow variables estimation in west China." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2012. http://dx.doi.org/10.1117/12.974512.

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Boni, G., F. Castelli, S. Gabellani, G. Machiavello, and R. Rudari. "Assimilation of MODIS snow cover and real time snow depth point data in a snow dynamic model." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5648989.

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Hall, Dorothy, and Horace Mitchell. "MODIS daily global snow cover and sea ice surface temperature." In the ACM SIGGRAPH 05 electronic art and animation catalog. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1086057.1086160.

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Xu, Lina, Ruiqing Niu, Yannan Zhao, Jiong Li, and Ting Wu. "Snow cover mapping over the Tibetan Plateau with MODIS and ASTER data." In Second International Conference on Earth Observation for Global Changes, edited by Xianfeng Zhang, Jonathan Li, Guoxiang Liu, and Xiaojun Yang. SPIE, 2009. http://dx.doi.org/10.1117/12.836457.

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Tang, Bo-Hui, Basanta Shrestha, Zhao-Liang Li, Gaohuan Liu, Hua Ouyang, Deo Raj Gurung, Giriraj Amarnath, and Aung Khun San. "Improvement of MODIS snow cover algorithm for the Hindu Kush-Himalayan region." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5651098.

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Athick, A. S. Mohammed Abdul, and Hasan Raja Naqvi. "A method for compositing MODIS images to remove cloud cover over Himalayas for snow cover mapping." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7730279.

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Kangbin Li and Bing Shen. "Mutation analysis of snow cover based on the MODIS remote sensing image data." In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). IEEE, 2011. http://dx.doi.org/10.1109/rsete.2011.5964535.

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Cea, C., J. Cristobal, and X. Pons. "An improved methodology to map Snow Cover by means of Landsat and MODIS imagery." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423781.

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Reports on the topic "MODIS snow cover"

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Simic, A., R. Fernandes, R. Brown, P. Romanov, W M Park, and D. Hall. Validation of MODIS, VEGETATION, and GOES+SSM/I Snow Cover Products over Canada Based on Surface Snow Depth Observations. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/220008.

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Simic, A., R. Fernandes, R. Brown, P. Romanov, and W M Park. Validation of VEGETATION, MODIS, and GOES+SSM/I Snow Cover Products over Canada Based on Surface Snow Depth Observations. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/220033.

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