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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 < 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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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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Dong, Jiarui, Mike Ek, Dorothy Hall, Christa Peters-Lidard, Brian Cosgrove, Jeff Miller, George Riggs, and Youlong Xia. "Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental United States." Journal of Hydrometeorology 15, no. 2 (April 1, 2014): 551–62. http://dx.doi.org/10.1175/jhm-d-13-060.1.
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Abstract Understanding and quantifying satellite-based, remotely sensed snow cover uncertainty are critical for its successful utilization. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover errors have been previously recognized to be associated with factors such as cloud contamination, snowpack grain sizes, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of the MODIS fractional snow cover (FSC) from Collection 6 (C6) and in situ air temperature and snow water equivalent measurements provides a unique look at the error structure of the MODIS C6 FSC products. Analysis of the MODIS FSC dataset over the period from 2000 to 2005 was undertaken over the continental United States (CONUS) with an extensive observational network. When compared to MODIS Collection 5 (C5) snow cover area, the MODIS C6 FSC product demonstrates a substantial improvement in detecting the presence of snow cover in Nevada [30% increase in probability of detection (POD)], especially in the early and late snow seasons; some improvement over California (10% POD increase); and a relatively small improvement over Colorado (2% POD increase). However, significant spatial and temporal variations in accuracy still exist, and a proxy is required to adequately predict the expected errors in MODIS C6 FSC retrievals. A relationship is demonstrated between the MODIS FSC retrieval errors and temperature over the CONUS domain, captured by a cumulative double exponential distribution function. This relationship is shown to hold for both in situ and modeled daily mean air temperature. Both of them are useful indices in filtering out the misclassification of MODIS snow cover pixels and in quantifying the errors in the MODIS C6 product for various hydrological applications.
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Liu, Anwei, Tao Che, Xiaodong Huang, Liyun Dai, Jing Wang, and Jie Deng. "Effect of Cloud Mask on the Consistency of Snow Cover Products from MODIS and VIIRS." Remote Sensing 14, no. 23 (December 3, 2022): 6134. http://dx.doi.org/10.3390/rs14236134.
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Snow cover has significant impacts on the global water cycle, ecosystem, and climate change. At present, satellite remote sensing is regarded as the most efficient approach to detect long-term and multiscale observations of snow cover extent. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard Joint Polar Satellite System (JPSS) satellites will replace the Moderate-Resolution Imaging Spectroradiometer (MODIS) to prolong data recording in the future. Therefore, it is a fundamental task to analyze and evaluate the consistency of the snow cover products retrieved from these two sensors. In this study, we performed comparisons and a consistency evaluation between the MODIS and VIIRS snow cover products in three major snow distribution regions in China: Northeast China (NE), Northwest China (NW) and the Qinghai–Tibet Plateau (QT). The results demonstrated that (1) the normalized difference snow index (NDSI)-derived snow cover products showed suitable consistency between VIIRS and MODIS under clear sky conditions, with a mean difference value of less than 5%; (2) the VIIRS snow cover product presented much more snow and fewer clouds than that of MODIS in the snow season due to the differences in cloud-masking algorithms; (3) cloud mask strongly affects the potential of snow cover observation, and presents seasonal pattern in the test regions; and (4) VIIRS is able to distinguish clouds from snow with greater accuracy. The comparisons indicated that the greater the difference in cloud cover, the poorer the agreement in snow cover. This evaluation implies that perfecting the cloud-masking algorithm of VIIRS to update the MODIS would be the best solution to achieve better consistency for long-term and high-quality snow cover products.
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Parajka, J., and G. Blöschl. "Validation of MODIS snow cover images over Austria." Hydrology and Earth System Sciences Discussions 3, no. 4 (July 13, 2006): 1569–601. http://dx.doi.org/10.5194/hessd-3-1569-2006.
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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).
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He, Z. H., J. Parajka, F. Q. Tian, and G. Blöschl. "Estimating degree-day factors from MODIS for snowmelt runoff modeling." Hydrology and Earth System Sciences 18, no. 12 (December 3, 2014): 4773–89. http://dx.doi.org/10.5194/hess-18-4773-2014.
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Abstract. Degree-day factors are widely used to estimate snowmelt runoff in operational hydrological models. Usually, they are calibrated on observed runoff, and sometimes on satellite snow cover data. In this paper, we propose a new method for estimating the snowmelt degree-day factor (DDFS) directly from MODIS snow covered area (SCA) and ground-based snow depth data without calibration. Subcatchment snow volume is estimated by combining SCA and snow depths. Snow density is estimated to be the ratio between observed precipitation and changes in the snow volume for days with snow accumulation. Finally, DDFS values are estimated to be the ratio between changes in the snow water equivalent and difference between the daily temperature and the melt threshold value for days with snow melt. We compare simulations of basin runoff and snow cover patterns using spatially variable DDFS estimated from snow data with those using spatially uniform DDFS calibrated on runoff. The runoff performances using estimated DDFS are slightly improved, and the simulated snow cover patterns are significantly more plausible. The new method may help reduce some of the runoff model parameter uncertainty by reducing the total number of calibration parameters. This method is applied to the Lienz catchment in East Tyrol, Austria, which covers an area of 1198 km2. Approximately 70% of the basin is covered by snow in the early spring season.
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Liu, Changyu, Xiaodong Huang, Xubing Li, and Tiangang Liang. "MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area." Remote Sensing 12, no. 6 (March 17, 2020): 962. http://dx.doi.org/10.3390/rs12060962.
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To improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based on MODIS over this terrain. Three machine learning models, including random forest, support vector machine, and back-propagation artificial neural network models, are trained and compared in this study. The results indicate that compared with the MODIS daily fractional snow cover product, the introduction of a highly accurate snow map acquired by unmanned aerial vehicles as a reference into machine learning models can significantly improve the MODIS fractional snow cover mapping accuracy. The random forest model shows the best accuracy among the three machine learning models, with an RMSE (root-mean-square error) of 0.23, especially over forestland and shrubland, with RMSEs of 0.13 and 0.18, respectively. Although the accuracy of the support vector machine and back-propagation artificial neural network models are worse over forestland and shrubland, their average errors are still better than that of MOD10A1. Different fractional snow cover gradients also affect the accuracy of the machine learning algorithms. Nevertheless, the random forest model remains stable in different fractional snow cover gradients and is, therefore, the best machine learning algorithm for MODIS fractional snow cover mapping in Tibetan Plateau areas with complex terrain and severely fragmented snow cover.
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Riggs, George A., Dorothy K. Hall, and Miguel O. Román. "Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records." Earth System Science Data 9, no. 2 (October 10, 2017): 765–77. http://dx.doi.org/10.5194/essd-9-765-2017.
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Abstract. Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.
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Şorman, A. Ü., Z. Akyürek, A. Şensoy, A. A. Şorman, and A. E. Tekeli. "Commentary on comparison of MODIS snow cover and albedo products with ground observations over the mountainous terrain of Turkey." Hydrology and Earth System Sciences Discussions 3, no. 6 (December 11, 2006): 3655–73. http://dx.doi.org/10.5194/hessd-3-3655-2006.
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Abstract. The MODerate-resolution Imaging Spectroradiometer (MODIS) snow cover product was evaluated by Parajka and Blösch (2006) over the territory of Austria. The spatial and temporal variability of the MODIS snow product classes are analyzed, the accuracy of the MODIS snow product against numerous in situ snow depth data are examined and the main factors that may influence the MODIS classification accuracy are identified in their studies. The authors of this paper would like to provide more discussion to the scientific community on the "Validation of MODIS snow cover images" when similar methodology is applied to mountainous regions covered with abundant snow but with limited number of ground survey and automated stations. Daily snow cover maps obtained from MODIS images are compared with ground observations in mountainous terrain of Turkey for the winter season of 2002–2003 and 2003–2004 during the accumulation and ablation periods of snow. Snow depth and density values are recorded to determine snow water equivalent values at 19 points in and around the study area in Turkey. Comparison of snow maps with in situ data show good agreement with overall accuracies in between 62 to 82 percent considering a 2-day shift during cloudy days. Studies show that the snow cover extent can be used for forecasting of runoff hydrographs resulting mostly from snowmelt for a mountainous basin in Turkey. MODIS-Terra snow albedo products are also compared with ground based measurements over the ablation stage of 2004 using the automated weather operating stations (AWOS) records at fixed locations as well as from the temporally assessed measuring sites during the passage of the satellite. Temporarily assessed 20 ground measurement sites are randomly distributed around one of the AWOS stations and both MODIS and ground data were aggregated in GIS for analysis. Reduction in albedo is noticed as snow depth decreased and SWE values increased.
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Şorman, A. Ü., Z. Akyürek, A. Şensoy, A. A. Şorman, and A. E. Tekeli. "Commentary on comparison of MODIS snow cover and albedo products with ground observations over the mountainous terrain of Turkey." Hydrology and Earth System Sciences 11, no. 4 (May 22, 2007): 1353–60. http://dx.doi.org/10.5194/hess-11-1353-2007.
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Abstract. The MODerate-resolution Imaging Spectroradiometer (MODIS) snow cover product was evaluated by Parajka and Blösch (2006) over the territory of Austria. The spatial and temporal variability of the MODIS snow product classes are analyzed, the accuracy of the MODIS snow product against numerous in situ snow depth data are examined and the main factors that may influence the MODIS classification accuracy are identified in their studies. The authors of this paper would like to provide more discussion to the scientific community on the "Validation of MODIS snow cover images" when similar methodology is applied to mountainous regions covered with abundant snow but with limited number of ground survey and automated stations. Daily snow cover maps obtained from MODIS images are compared with ground observations in mountainous terrain of Turkey for the winter season of 2002–2003 and 2003–2004 during the accumulation and ablation periods of snow. Snow depth and density values are recorded to determine snow water equivalent (SWE) values at 19 points in and around the study area in Turkey. Comparison of snow maps with in situ data show good agreement with overall accuracies in between 62 to 82 percent considering a 2-day shift during cloudy days. Studies show that the snow cover extent can be used for forecasting of runoff hydrographs resulting mostly from snowmelt for a mountainous basin in Turkey. MODIS-Terra snow albedo products are also compared with ground based measurements over the ablation stage of 2004 using the automated weather operating stations (AWOS) records at fixed locations as well as from the temporally assessed measuring sites during the passage of the satellite. Temporarily assessed 20 ground measurement sites are randomly distributed around one of the AWOS stations and both MODIS and ground data were aggregated in GIS for analysis. Reduction in albedo is noticed as snow depth decreased and SWE values increased.
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Jiang, Yingsha, Fei Chen, Yanhong Gao, Michael Barlage, and Jianduo Li. "Using Multisource Satellite Data to Assess Recent Snow-Cover Variability and Uncertainty in the Qinghai–Tibet Plateau." Journal of Hydrometeorology 20, no. 7 (June 25, 2019): 1293–306. http://dx.doi.org/10.1175/jhm-d-18-0220.1.
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Abstract Snow cover in the Qinghai–Tibet Plateau (QTP) is a critical component in the water cycle and regional climate of East Asia. Fractional snow cover (FSC) derived from five satellite sources [the three satellites comprising the multisensor synergy of FengYun-3 (FY-3A/B/C), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Interactive Multisensor Snow and Ice Mapping System (IMS)] were intercompared over the QTP to examine uncertainties in mean snow cover and interannual variability over the last decade. A four-step cloud removal procedure was developed for MODIS and FY-3 data, which effectively reduced the cloud percentage from about 40% to 2%–3% with an error of about 2% estimated by a random sampling method. Compared to in situ snow-depth observations, the cloud-removed FY-3B data have an annual classification accuracy of about 94% for both 0.04° and 0.01° resolutions, which is higher than other datasets and is recommended for use in QTP studies. Among the five datasets analyzed, IMS has the largest snow extent (22% higher than MODIS) and the highest FSC (4.7% higher than MODIS), while the morning-overpass MODIS and FY-3A/C FSC are similar and are around 5% higher than the afternoon-overpass FY-3B FSC. Contrary to MODIS, IMS shows increasing variability in snow cover and snow duration over the last decade (2006–17). Differences in variabilities of FSC and snow duration between products are greater at 5–6 km than lower elevations, with seasonal snow-cover change showing the largest uncertainty in snowmelt date.
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Fayaz, N., M. Vazifedoust, and S. Araghinejad. "MONITORING OF SNOW COVER VARIATION USING MODIS SNOW PRODUCT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W3 (September 24, 2013): 165–68. http://dx.doi.org/10.5194/isprsarchives-xl-1-w3-165-2013.
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Hall, Dorothy K., Richard E. J. Kelly, George A. Riggs, Alfred T. C. Chang, and James L. Foster. "Assessment of the relative accuracy of hemispheric-scale snow-cover maps." Annals of Glaciology 34 (2002): 24–30. http://dx.doi.org/10.3189/172756402781817770.
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AbstractThere are several hemispheric-scale satellite-derived snow-cover maps available, but none has been fully validated. For the period 23 October–25 December 2000, we compare snow maps of North America derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and operational snow maps from the U.S. National Oceanic and Atmospheric Administration (NOAA) National Operational Hydrologic Remote Sensing Center (NOHRSC), both of which rely on satellite data from the visible and near-infrared parts of the spectrum; we also compare MODIS maps with Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) passive-microwave snow maps for the same period. The maps derived from visible and near-infrared data are more accurate for mapping snow cover than are the passive-microwave-derived maps, but discrepancies exist as to the location and extent of the snow cover even between operational snow maps. The MODIS snow-cover maps show more snow in each of the 8 day periods than do the NOHRSC maps, in part because MODIS maps the effects of fleeting snowstorms due to its frequent coverage. The large (~30 km) footprint of the SSM/I pixel, and the difficulty in distinguishing wet and shallow snow from wet or snow-free ground, reveal differences up to 5.33 x 106 km2 in the amount of snow mapped using MODIS vs SSM/I data. Algorithms that utilize both visible and passive-microwave data, which would take advantage of the all-weather mapping capability of the passive-microwave data, will be refined following the launch of the Advanced Microwave Scanning Radiometer (AMSR) in the fall of 2001.
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Abudurexiti, Xieraili, and Meiliguli Maimaiti. "Analysis of Snow Cover in River Drainage basin Based on MODIS Data." E3S Web of Conferences 136 (2019): 04081. http://dx.doi.org/10.1051/e3sconf/201913604081.
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In this paper, MODIS snow cover remote sensing data and Geographic Information System (GIS) technology were used to analyze extraction methods of the snow cover area information. And taking a river drainage basin as an example, this paper studied the variation characteristics of snow cover area in mountainous watershed from 2005 to 2007 in spring and summer to further provide a basis and reference for understanding the process of snowmelt in this drainage basin. The results show that the snow cover area of the river drainage basin has gradually decreased from March, and the smallest snow cover area in the drainage basin appeared in July. The snow cover area has gradually increased since August, and the annual snow melting period is from April to July.
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Fletcher, Steven J., Glen E. Liston, Christopher A. Hiemstra, and Steven D. Miller. "Assimilating MODIS and AMSR-E Snow Observations in a Snow Evolution Model." Journal of Hydrometeorology 13, no. 5 (October 1, 2012): 1475–92. http://dx.doi.org/10.1175/jhm-d-11-082.1.
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Abstract In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in snow cover; 2) iteratively change the model SWE values to match the AMSR-E equivalent value; 3) AMSR-E scheme with MODIS observations constraining which cells can be changed, when both sets of observations are available; and 4) MODIS-only scheme when the AMSR-E observations are not available, otherwise scheme 3. These schemes are used in the winter of 2006/07 over the southeast corner of Colorado and the tri-state area: Wyoming, Colorado, and Nebraska. It is shown that the inclusion of MODIS data enables the model in the north domain to have a 15% improvement in number of days with a less than 10% disagreement with the MODIS observation 24 h later and approximately 5% for the south domain. It is shown that the AMSR-E scheme has more of an impact in the south domain than the north domain. The assimilation results are also compared to station snow-depth data in both domains, where there is up-to-a-factor-of-5 underestimation of snow depth by the assimilation schemes compared with the station data but the snow evolution is fairly consistent.
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Ali, Sikandar, Muhammad Jehanzeb Masud Cheema, Muhammad Mohsin Waqas, Muhammad Waseem, Usman Khalid Awan, and Tasneem Khaliq. "Changes in Snow Cover Dynamics over the Indus Basin: Evidences from 2008 to 2018 MODIS NDSI Trends Analysis." Remote Sensing 12, no. 17 (August 27, 2020): 2782. http://dx.doi.org/10.3390/rs12172782.
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The frozen water reserves on the Earth are not only very dynamic in their nature, but also have significant effects on hydrological response of complex and dynamic river basins. The Indus basin is one of the most complex river basins in the world and receives most of its share from the Asian Water Tower (Himalayas). In such a huge river basin with high-altitude mountains, the regular quantification of snow cover is a great challenge to researchers for the management of downstream ecosystems. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily (MOD09GA) and 8-day (MOD09A1) products were used for the spatiotemporal quantification of snow cover over the Indus basin and the western rivers’ catchments from 2008 to 2018. The high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) was used as a standard product with a minimum Normalized Difference Snow Index (NDSI) threshold (0.4) to delineate the snow cover for 120 scenes over the Indus basin on different days. All types of errors of commission/omission were masked out using water, sand, cloud, and forest masks at different spatiotemporal resolutions. The snow cover comparison of MODIS products with Landsat ETM+, in situ snow data and Google Earth imagery indicated that the minimum NDSI threshold of 0.34 fits well compared to the globally accepted threshold of 0.4 due to the coarser resolution of MODIS products. The intercomparison of the time series snow cover area of MODIS products indicated R2 values of 0.96, 0.95, 0.97, 0.96 and 0.98, for the Chenab, Jhelum, Indus and eastern rivers’ catchments and Indus basin, respectively. A linear least squares regression analysis of the snow cover area of the Indus basin indicated a declining trend of about 3358 and 2459 km2 per year for MOD09A1 and MOD09GA products, respectively. The results also revealed a decrease in snow cover area over all the parts of the Indus basin and its sub-catchments. Our results suggest that MODIS time series NDSI analysis is a useful technique to estimate snow cover over the mountainous areas of complex river basins.
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He, Z. H., J. Parajka, F. Q. Tian, and G. Blöschl. "Estimating degree day factors from MODIS for snowmelt runoff modeling." Hydrology and Earth System Sciences Discussions 11, no. 7 (July 25, 2014): 8697–735. http://dx.doi.org/10.5194/hessd-11-8697-2014.
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Abstract. Degree-day factors are widely used to estimate snowmelt runoff in operational hydrological models. Usually, they are calibrated on observed runoff, and sometimes on satellite snow cover data. In this paper, we propose a new method for estimating the snowmelt degree-day factor (DDFS) directly from MODIS snow covered area (SCA) and ground based snow depth data without calibration. Subcatchment snow volume is estimated by combining SCA and snow depths. Snow density is estimated as the ratio of observed precipitation and changes in the snow volume for days with snow accumulation. Finally, DDFS values are estimated as the ratio of changes in the snow water equivalent and degree-day temperatures for days with snow melt. We compare simulations of basin runoff and snow cover patterns using spatially variable DDFS estimated from snow data with those using spatially uniform DDFS calibrated on runoff. The runoff performances using estimated DDFS are slightly improved, and the simulated snow cover patterns are significantly more plausible. The new method may help reduce some of the runoff model parameter uncertainty by reducing the total number of calibration parameters.
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Revuelto, Jesús, Esteban Alonso-González, Simon Gascoin, Guillermo Rodríguez-López, and Juan Ignacio López-Moreno. "Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products." Remote Sensing 13, no. 22 (November 10, 2021): 4513. http://dx.doi.org/10.3390/rs13224513.
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Understanding those processes in which snow dynamics has a significant influence requires long-term and high spatio-temporal resolution observations. While new optical space-borne sensors overcome many previous snow cover monitoring limitations, their short temporal length limits their application in climatological studies. This work describes and evaluates a probabilistic spatial downscaling of MODIS snow cover observations in mountain areas. The approach takes advantage of the already available high spatial resolution Sentinel-2 snow observations to obtain a snow probability occurrence, which is then used to determine the snow-covered areas inside partially snow-covered MODIS pixels. The methodology is supported by one main hypothesis: the snow distribution is strongly controlled by the topographic characteristics and this control has a high interannual persistence. Two approaches are proposed to increase the 500 m resolution MODIS snow cover observations to the 20 m grid resolution of Sentinel-2. The first of these computes the probability inside partially snow-covered MODIS pixels by determining the snow occurrence frequency for the 20 m Sentinel-2 pixels when clear-sky conditions occurred for both platforms. The second approach determines the snow probability occurrence for each Sentinel-2 pixel by computing the number of days in which snow was observed on each grid cell and then dividing it by the total number of clear-sky days per grid cell. The methodology was evaluated in three mountain areas in the Iberian Peninsula from 2015 to 2021. The 20 m resolution snow cover maps derived from the two probabilistic methods provide better results than those obtained with MODIS images downscaled to 20 m with a nearest-neighbor method in the three test sites, but the first provides superior performance. The evaluation showed that mean kappa values were at least 10% better for the two probabilistic methods, improving the scores in one of these sites by 25%. In addition, as the Sentinel-2 dataset becomes longer in time, the probabilistic approaches will become more robust, especially in areas where frequent cloud cover resulted in lower accuracy estimates.
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Parajka, J., L. Holko, Z. Kostka, and G. Blöschl. "MODIS snow cover mapping accuracy in a small mountain catchment – comparison between open and forest sites." Hydrology and Earth System Sciences 16, no. 7 (July 30, 2012): 2365–77. http://dx.doi.org/10.5194/hess-16-2365-2012.
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Abstract. Numerous global and regional validation studies have examined MODIS snow mapping accuracy by using measurements at climate stations, which are mainly at open sites. MODIS accuracy in alpine and forested regions is, however, still not well understood. The main objective of this study is to evaluate MODIS (MOD10A1 and MYD10A1) snow cover products in a small experimental catchment by using extensive snow course measurements at open and forest sites. The MODIS accuracy is tested in the Jalovecky creek catchment (northern Slovakia) in the period 2000–2011. The results show that the combined Terra and Aqua images enable snow mapping at an overall accuracy of 91.5%. The accuracies at forested, open and mixed land uses at the Červenec sites are 92.7%, 98.3% and 81.8%, respectively. The use of a 2-day temporal filter enables a significant reduction in the number of days with cloud coverage and an increase in overall snow mapping accuracy. In total, the 2-day temporal filter decreases the number of cloudy days from 61% to 26% and increases the snow mapping accuracy to 94%. The results indicate three possible factors leading to misclassification of snow as land: patchy snow cover, limited MODIS geolocation accuracy and mapping algorithm errors. Out of a total of 27 misclassification cases, patchy snow cover, geolocation issues and mapping errors occur in 12, 12 and 3 cases, respectively.
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Parajka, J., L. Holko, Z. Kostka, and G. Blöschl. "MODIS snow cover mapping accuracy in small mountain catchment – comparison between open and forest sites." Hydrology and Earth System Sciences Discussions 9, no. 3 (March 28, 2012): 4073–100. http://dx.doi.org/10.5194/hessd-9-4073-2012.
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Abstract. Numerous global and regional validation studies examined MODIS snow mapping accuracy by using measurements at climate stations, which are mainly at grassy sites. MODIS accuracy in alpine and forested regions is, however, still not well understood. The main objective of this study is to evaluate MODIS (MOD10A1 and MYD10A1) snow cover products in a small experimental catchment by using extensive snow course measurements at open and forest sites. The MODIS accuracy is tested in the Jalovecky creek catchment (Northern Slovakia) in the period 2000–2011. The results show that the combined Terra and Aqua images enables snow mapping to an overall accuracy of 91.5%. The accuracy at forested, open and mixed land uses at the Červenec sites is 92.7%, 98.3% and 81.8%, respectively. The use of a 2-day temporal filter enables a significant reduction in the number of days with cloud coverage and an increase in overall snow mapping accuracy. In total, the 2-day temporal filter decreases the number of cloudy days from 61% to 26% and increases the snow mapping accuracy to 94%. The results indicate three possible factors leading to misclassification of snow as land: patchy snow cover, limited MODIS geolocation accuracy and mapping algorithm errors. Out of a total of 27 misclassification cases, patchy snow cover, geolocation issues and mapping errors occur in 12, 12 and 3 cases, respectively.
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Gunnarsson, Andri, Sigurður M. Garðarsson, and Óli G. B. Sveinsson. "Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product." Hydrology and Earth System Sciences 23, no. 7 (July 17, 2019): 3021–36. http://dx.doi.org/10.5194/hess-23-3021-2019.
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Abstract. This study presents a spatio-temporal continuous data set for snow cover in Iceland based on the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2018. Cloud cover and polar darkness are the main limiting factors for data availability of remotely sensed optical data at higher latitudes. In Iceland the average cloud cover is 75 % with some spatial variations, and polar darkness reduces data availability from the MODIS sensor from late November until mid January. In this study MODIS snow cover data were validated over Iceland with comparison to manned in situ observations and Landsat 7/8 and Sentinel 2 data. Overall a good agreement was found between in situ observed snow cover, with an average agreement of 0.925. Agreement of Landsat 7/8 and Sentinel 2 was found to be acceptable, with R2 values 0.96, 0.92 and 0.95, respectively, and in agreement with other studies. By applying daily data merging from Terra and Aqua and a temporal aggregation of 7 d, unclassified pixels were reduced from 75 % to 14 %. The remaining unclassified pixels after daily merging and temporal aggregation were removed with classification learners trained with classified data, pixel location, aspect and elevation. Various snow cover characteristic metrics were derived for each pixel such as snow cover duration, first and last snow-free dates, deviation and dynamics of snow cover and trends during the study period. On average the first snow-free date in Iceland is 27 June, with a standard deviation of 19.9 d. For the study period a trend of increasing snow cover duration was observed for all months except October and November. However, statistical testing of the trends indicated that there was only a significant trend in June.
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Gascoin, S., O. Hagolle, M. Huc, L. Jarlan, J. F. Dejoux, C. Szczypta, R. Marti, and R. Sánchez. "A snow cover climatology for the Pyrenees from MODIS snow products." Hydrology and Earth System Sciences Discussions 11, no. 11 (November 12, 2014): 12531–71. http://dx.doi.org/10.5194/hessd-11-12531-2014.
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Abstract. 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.
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Gascoin, S., O. Hagolle, M. Huc, L. Jarlan, J. F. Dejoux, C. Szczypta, R. Marti, and R. Sánchez. "A snow cover climatology for the Pyrenees from MODIS snow products." Hydrology and Earth System Sciences 19, no. 5 (May 19, 2015): 2337–51. http://dx.doi.org/10.5194/hess-19-2337-2015.
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Abstract. 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.
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Gafurov, A., and A. Bárdossy. "Snow cover data derived from MODIS for water balance applications." Hydrology and Earth System Sciences Discussions 6, no. 1 (February 11, 2009): 791–841. http://dx.doi.org/10.5194/hessd-6-791-2009.
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Abstract. Snow cover information is of central importance for the estimation of water storage in cold mountainous regions. It is difficult to assess distributed snow cover information in a catchment in order to estimate possible water resources. It is especially a challenge to obtain snow cover information for high mountainous areas. Usually, snow depth is measured at meteorological stations, and it is relatively difficult to extrapolate this spatially or temporally since it highly depends on available energy and topography. The snow coverage of a catchment gives detailed information about the catchment's potential source for water. Many regions lack meteorological stations that measure snow, and usually no stations are available at high elevations. Satellite information is a very valuable source for obtaining several environmental parameters. One of the advantages is that the data is mostly provided in a spatially distributed format. This study uses satellite data to estimate snow coverage on high mountainous areas. Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover data is used in the Kokcha Catchment located in the north-eastern part of Afghanistan. The main disadvantage of MODIS data that restricts its direct use in environmental applications is cloud coverage. This is why this study is focused on eliminating cloud covered cells and estimating cell information under cloud covered cells using six logical, spatial and temporal approaches. The results give total cloud removal and mapping of snow cover for the study areas.
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Mattar, Cristian, Rodrigo Fuster, and Tomás Perez. "Application of a Cloud Removal Algorithm for Snow-Covered Areas from Daily MODIS Imagery over Andes Mountains." Atmosphere 13, no. 3 (February 26, 2022): 392. http://dx.doi.org/10.3390/atmos13030392.
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Snow cover area is dramatically decreasing across the Los Andes Mountains and the most relevant water reservoir under drought conditions. In this sense, monitoring of snow cover is key to analyzing the hydrologic balance in snowmelt-driven basins. MODIS Snow Cover daily products (MOD10A1 and MYD10A1) allow snow cover to be monitored at regular time intervals and in large areas, although the images often are affected by cloud cover. The main objective of this technical note is to evaluate the application of an algorithm to remove cloud cover in MODIS snow cover imagery in the Chilean Andes mountains. To this end, the northern region of Chile (Pulido river basin) during the period between December 2015 and December 2016 was selected. Results were validated against meteorological data from a ground station. The cloud removal algorithm allowed the overall cloud cover to be reduced from 26.56% to 7.69% in the study area and a snow cover mapping overall accuracy of 86.66% to be obtained. Finally, this work allows new cloud-free snow cover imagery to be produced for long term analysis and hydrologic models, reducing the lack of data and improving the daily regional snow mapping.
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Taia, Soufiane, Lamia Erraioui, Youssef Arjdal, Jamal Chao, Bouabid El Mansouri, and Andrea Scozzari. "The Application of SWAT Model and Remotely Sensed Products to Characterize the Dynamic of Streamflow and Snow in a Mountainous Watershed in the High Atlas." Sensors 23, no. 3 (January 21, 2023): 1246. http://dx.doi.org/10.3390/s23031246.
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Snowfall, snowpack, and snowmelt are among the processes with the greatest influence on the water cycle in mountainous watersheds. Hydrological models may be significantly biased if snow estimations are inaccurate. However, the unavailability of in situ snow data with enough spatiotemporal resolution limits the application of spatially distributed models in snow-fed watersheds. This obliges numerous modellers to reduce their attention to the snowpack and its effect on water distribution, particularly when a portion of the watershed is predominately covered by snow. This research demonstrates the added value of remotely sensed snow cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) in evaluating the performance of hydrological models to estimate seasonal snow dynamics and discharge. The Soil and Water Assessment Tool (SWAT) model was used in this work to simulate discharge and snow processes in the Oued El Abid snow-dominated watershed. The model was calibrated and validated on a daily basis, for a long period (1981–2015), using four discharge-gauging stations. A spatially varied approach (snow parameters are varied spatially) and a lumped approach (snow parameters are unique across the whole watershed) have been compared. Remote sensing data provided by MODIS enabled the evaluation of the snow processes simulated by the SWAT model. Results illustrate that SWAT model discharge simulations were satisfactory to good according to the statistical criteria. In addition, the model was able to reasonably estimate the snow-covered area when comparing it to the MODIS daily snow cover product. When allowing snow parameters to vary spatially, SWAT model results were more consistent with the observed streamflow and the MODIS snow-covered area (MODIS-SCA). This paper provides an example of how hydrological modelling using SWAT and snow coverage products by remote sensing may be used together to examine seasonal snow cover and snow dynamics in the High Atlas watershed.
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Yuan, Yecheng, Baolin Li, Xizhang Gao, Wei Liu, Ying Li, and Rui Li. "Validation of Cloud-Gap-Filled Snow Cover of MODIS Daily Cloud-Free Snow Cover Products on the Qinghai–Tibetan Plateau." Remote Sensing 14, no. 22 (November 8, 2022): 5642. http://dx.doi.org/10.3390/rs14225642.
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Accurate daily snow cover extent is a significant input for hydrological applications in the Qinghai–Tibetan Plateau (QTP). Although several Moderate Resolution Imaging Spectroradiometer (MODIS) daily cloud-free snow cover products over the QTP are openly accessible, the cloud-gap-filled snow cover from these products has not yet been validated. This study assessed the accuracy of cloud-gap-filled snow cover from three open accessible MODIS daily products based on snow maps retrieved from Landsat TM images. The F1-score (FS) from daily cloud-free MODIS snow cover for the combined MOD10A1F and MYD10A1F (SC1) was 64.4%, which was 7.4% points and 5.3% points higher than the other two commonly used products (SC2 and SC3), respectively. The superior accuracies from SC1 were more evident in regions with altitudes lower than 5000 m, with a weighted average FS by the area percentage of the altitude regions of 58.3%, which was 6.9% points and 9.1% points higher than SC2 and SC3. The improved SC1 accuracies also indicated regional clustering characteristics with higher FS values compared to SC2 and SC3. The lower accuracies of cloud-gap-filled snow cover from SC2 and SC3 were mainly due to the limitation in determining snow cover based on the method of the inferred snow line and the overestimation of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) snow water equivalent (SWE). These results indicate that the temporal filter approach used in SC1 is a good solution to produce daily cloud-gap-filled snow cover data for the QTP because of its higher accuracy and simple computation. The findings can be helpful for the selection of cloud-removal algorithms for determining snow cover dynamics and phenological parameters on the QTP.
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Rasputina, Е. А., and A. S. Korepova. "Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”." Geodesy and Cartography 973, no. 7 (August 20, 2021): 21–31. http://dx.doi.org/10.22389/0016-7126-2021-973-7-21-31.
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The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.
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Takibayev, Zhassulan, Mariya Tatkova, and Nina Pimankina. "Application of the MODIS radiometer data to the snow cover investigations." Geography and water resources, no. 4 (December 26, 2022): 3–10. http://dx.doi.org/10.55764/2957-9856/2022-4-3-10.18.
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Based on the data of the MODIS spectroradiometer, the area occupied by snow cover in the upper reaches of the Syrdarya river basin was estimated. According to Kyrgyzhydromet data for 1960-2021, the variability of air temperature and precipitation in the basin was assessed. At the Tien Shan meteorological station (Ak-Shyiryak mountains), the average temperature increased by 1.9°С, at the Uzgen meteorological station – by 1.1°С over 70 years of observations. The amount of precipitation, in general, has changed insignificantly, an anomalous reduction in the amount of precipitation during the cold period has not been noted. Comparison of the snow cover area in April and May according to MODIS data for 2000-2009 and 2010-2018 showed opposite trends in the development of the situation.
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Saavedra, Freddy A., Stephanie K. Kampf, Steven R. Fassnacht, and Jason S. Sibold. "Changes in Andes snow cover from MODIS data, 2000–2016." Cryosphere 12, no. 3 (March 23, 2018): 1027–46. http://dx.doi.org/10.5194/tc-12-1027-2018.
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Abstract. 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.
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Lee, Kyeong-Sang, Donghyun Jin, Jong-Min Yeom, Minji Seo, Sungwon Choi, Jae-Jin Kim, and Kyung-Soo Han. "New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data." Journal of Sensors 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/4820905.
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Snow cover plays an important role in climate and hydrology, at both global and regional scales. Most previous studies have used static threshold techniques to detect snow cover, which can lead to errors such as misclassification of snow and clouds, because the reflectance of snow cover exhibits variability and is affected by several factors. Therefore, we present a simple new algorithm for mapping snow cover from Moderate Resolution Imaging Spectroradiometer (MODIS) data using dynamic wavelength warping (DWW), which is based on dynamic time warping (DTW). DTW is a pattern recognition technique that is widely used in various fields such as human action recognition, anomaly detection, and clustering. Before performing DWW, we constructed 49 snow reflectance spectral libraries as reference data for various solar zenith angle and digital elevation model conditions using approximately 1.6 million sampled data. To verify the algorithm, we compared our results with the MODIS swath snow cover product (MOD10_L2). Producer’s accuracy, user’s accuracy, and overall accuracy values were 92.92%, 78.41%, and 92.24%, respectively, indicating good overall classification accuracy. The proposed algorithm is more useful for discriminating between snow cover and clouds than threshold techniques in some areas, such as those with a high viewing zenith angle.
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Brubaker, K. L., R. T. Pinker, and E. Deviatova. "Evaluation and Comparison of MODIS and IMS Snow-Cover Estimates for the Continental United States Using Station Data." Journal of Hydrometeorology 6, no. 6 (December 1, 2005): 1002–17. http://dx.doi.org/10.1175/jhm447.1.
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Abstract Satellite-derived information on fractional snow cover is essential to resource monitoring, hydrologic modeling, and climate change assessment. Evaluating the accuracy of remotely sensed snow-cover products is important but difficult, largely because point-scale surface observations are spatially sparse and generally nonrepresentative of the remote sensor footprint. In this study, two remotely sensed snow-cover products [the Interactive Multisensor Snow and Ice Mapping System (IMS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG), v.3] are evaluated against ground observations from the Cooperative Observing Network and SNOTEL on a daily basis over the continental United States for calendar year 2000. Ground observations are treated as points in space and time; no physical modeling or statistical interpolation is applied. Hypothesis tests based on discrete and continuous distributions are developed to assess agreement between ground observations and the remotely sensed snow-cover products at 0.25° resolution. (The MODIS CMG product was degraded from 0.05° for this study, thus its potential is not fully evaluated.) As overall snow extent increases in the course of the season, both MODIS and IMS improve in identifying snow-covered areas (fewer errors of omission), but deteriorate in identifying snow-free areas (more errors of commission). The detection of scattered areas of snow is generally better during ablation than during accumulation. Weaknesses of the statistical methods and assumptions are discussed. This work will help to identify areas for improvement in snow-cover detection algorithms and provides a framework to assess the accuracy of remotely sensed snow cover used as model input and/or confirmation.
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Lee, Hwa-Seon, and Kyu-Sung Lee. "A MULTI-TEMPORAL APPROACH FOR DETECTING SNOW COVER AREA USING GEOSTATIONARY IMAGERY DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 511–12. http://dx.doi.org/10.5194/isprs-archives-xli-b8-511-2016.
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In this study, we attempt to detect snow cover area using multi-temporal geostationary satellite imagery based on the difference of spectral and temporal characteristics between snow and clouds. The snow detection method is based on sequential processing of simple thresholds on multi-temporal GOCI data. We initially applied a simple threshold of blue reflectance and then root mean square deviation (RMSD) threshold of near infrared (NIR) reflectance that were calculated from time-series GOCI data. Snow cover detected by the proposed method was compared with the MODIS snow products. The proposed snow detection method provided very similar results with the MODIS cloud products. Although the GOCI data do not have shortwave infrared (SWIR) band, which can spectrally separate snow cover from clouds, the high temporal resolution of the GOCI was effective for analysing the temporal variations between snow and clouds.
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Lee, Hwa-Seon, and Kyu-Sung Lee. "A MULTI-TEMPORAL APPROACH FOR DETECTING SNOW COVER AREA USING GEOSTATIONARY IMAGERY DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 511–12. http://dx.doi.org/10.5194/isprsarchives-xli-b8-511-2016.
Full textAbstract:
In this study, we attempt to detect snow cover area using multi-temporal geostationary satellite imagery based on the difference of spectral and temporal characteristics between snow and clouds. The snow detection method is based on sequential processing of simple thresholds on multi-temporal GOCI data. We initially applied a simple threshold of blue reflectance and then root mean square deviation (RMSD) threshold of near infrared (NIR) reflectance that were calculated from time-series GOCI data. Snow cover detected by the proposed method was compared with the MODIS snow products. The proposed snow detection method provided very similar results with the MODIS cloud products. Although the GOCI data do not have shortwave infrared (SWIR) band, which can spectrally separate snow cover from clouds, the high temporal resolution of the GOCI was effective for analysing the temporal variations between snow and clouds.
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Rodell, M., and P. R. Houser. "Updating a Land Surface Model with MODIS-Derived Snow Cover." Journal of Hydrometeorology 5, no. 6 (December 1, 2004): 1064–75. http://dx.doi.org/10.1175/jhm-395.1.
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Abstract A simple scheme for updating snow-water storage in a land surface model using snow cover observations is presented. The scheme makes use of snow cover observations retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra and Aqua satellites. Simulated snow-water equivalent is adjusted when and where the model and MODIS observation differ, following an internal accounting of the observation quality, by either removing the simulated snow or adding a thin layer. The scheme is tested in a 101-day global simulation of the Mosaic land surface model driven by the NASA/NOAA Global Land Data Assimilation System. Output from this simulation is compared to that from a control (not updated) simulation, and both are assessed using a conventional snow cover product and data from ground-based observation networks over the continental United States. In general, output from the updated simulation displays more accurate snow coverage and compares more favorably with in situ snow time series. Both the control and updated simulations have serious deficiencies on occasion and in certain areas when and where the precipitation and/or surface air temperature forcing inputs are unrealistic, particularly in mountainous regions. Suggestions for developing a more sophisticated updating scheme are presented.
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Da Ronco, P., and C. De Michele. "Cloudiness and snow cover in Alpine areas from MODIS products." Hydrology and Earth System Sciences Discussions 11, no. 4 (April 10, 2014): 3967–4015. http://dx.doi.org/10.5194/hessd-11-3967-2014.
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Abstract. Snow cover maps provide an information of great practical interest for hydrologic purposes: when combined with point values of snow water equivalent (SWE), they allow to estimate the regional snow resource. Earth observation satellites are an interesting tool for evaluating large scale snow distribution and extension. In this context, MODIS (MODerate resolution Imaging Spectroradiometeron on board Terra and Aqua satellites) daily Snow Covered Area product has been widely tested and proved to be appropriate for hydrologic applications. However, within a daily map the presence of cloudiness can hide the ground, thus preventing snow detection. Here, we considered MODIS binary products for daily snow mapping over Po river basin. Modeling the variability of snow cover duration, distribution and snow water equivalent is a first important step in investigating climate change impacts on the regime of the major Italian river. Ten years (2003–2012) of MOD10A1 and MYD10A1 snow maps have been analyzed and processed with the support of 500 m-resolution Digital Elevation Model (DEM). We firstly investigated the issue of cloudiness, highlighting its dependence on altitude and season. Snow maps seem to suffer the influence of overcast conditions mainly in mountain and during the melting season. Such a result is certainly related to satellite crossing times, since cloud coverage over mountains usually increases in the afternoon: however, in Aqua and Terra snow products it highly influences those areas where snow detection is regarded with more interest. In spring, the average percentages of area lying beneath clouds are in the order of 70%, for altitudes over 1000 m a.s.l. Then, on the basis of previous studies, we proposed a cloud removal procedure and its application to a wide area, characterized by high topographic and geomorphological heterogeneities such as northern Italy. While conceiving the new method, our first target was to preserve the daily temporal resolution of the product. Regional snow and land lines were estimated for detecting snow cover dependence on elevation. In cases when there were not enough information on the same day within the cloud-free areas, we improved a temporal filter with the aim of reproducing the micro-cycles which characterize the transition altitudes, where snow does not stand continually over the entire winter. In the validation stage, the proposed procedure has been compared against others, showing improvements in the performance for our case study. At the same time it results quite handy both in terms of input data required and computational effort.
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Shrestha, Maheswor, Lei Wang, Toshio Koike, Yongkang Xue, and Yukiko Hirabayashi. "Modeling the Spatial Distribution of Snow Cover in the Dudhkoshi Region of the Nepal Himalayas." Journal of Hydrometeorology 13, no. 1 (February 1, 2012): 204–22. http://dx.doi.org/10.1175/jhm-d-10-05027.1.
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Abstract In this study, a distributed biosphere hydrological model with three-layer energy-balance snow physics [an improved version of the Water and Energy Budget–based Distributed Hydrological Model (WEB-DHM-S)] is applied to the Dudhkoshi region of the eastern Nepal Himalayas to estimate the spatial distribution of snow cover. Simulations are performed at hourly time steps and 1-km spatial resolution for the 2002/03 snow season during the Coordinated Enhanced Observing Period (CEOP) third Enhanced Observing Period (EOP-3). Point evaluations (snow depth and upward short- and longwave radiation) at Pyramid (a station of the CEOP Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region. The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating the model’s capability to accurately capture the spatiotemporal variations in snow cover across the study area. The qualitative pixel-to-pixel comparisons for the snow-free and snow-covered grids reveal that the simulations agree well with the MODIS data to an accuracy of 90%. Simulated nighttime land surface temperatures (LST) are comparable to the MODIS LST (MOD11A2) with mean absolute error of 2.42°C and mean relative error of 0.77°C during the study period. The effects of uncertainty in air temperature lapse rate, initial snow depth, and snow albedo on the snow-cover area (SCA) and LST simulations are determined through sensitivity runs. In addition, it is found that ignoring the spatial variability of remotely sensed cloud coverage greatly increases bias in the LST and SCA simulations. To the authors’ knowledge, this work is the first to adopt a distributed hydrological model with a physically based multilayer snow module to estimate the spatial distribution of snow cover in the Himalayan region.
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Gafurov, A., and A. Bárdossy. "Cloud removal methodology from MODIS snow cover product." Hydrology and Earth System Sciences 13, no. 7 (July 30, 2009): 1361–73. http://dx.doi.org/10.5194/hess-13-1361-2009.
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Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) employed by Terra and Aqua satellites provides spatially snow covered data with 500 m and daily temporal resolution. It delivers public domain data in raster format. The main disadvantage of the MODIS sensor is that it is unable to record observations under cloud covered regions. This is why this study focuses on estimating the pixel cover for cloud covered areas where no information is available. Our step to this product involves employing methodology based on six successive steps that estimate the pixel cover using different temporal and spatial information. The study was carried out for the Kokcha River basin located in northeastern part of Afghanistan. Snow coverage in catchments, like Kokcha, is very important where the melt-water from snow dominates the river discharge in vegetation period for irrigation purposes. Since no snow related observations were available from the region, the performance of the proposed methodology was tested using the cloud generated MODIS snow cover data as possible "ground truth" information. The results show successful performances arising from the methods applied, which resulted in all cloud coverage being removed. A validation was carried out for all subsequent steps, to be outlined below, where each step removes progressively more cloud coverage. Steps 2 to 5 (step 1 was not validated) performed very well with an average accuracy of between 90–96%, when applied one after another for the selected valid days in this study. The sixth step was the least accurate at 78%, but it led to the removal of all remaining cloud cover.
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Aghelpour, Pouya, Yiqing Guan, Hadigheh Bahrami-Pichaghchi, Babak Mohammadi, Ozgur Kisi, and Danrong Zhang. "Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area." Remote Sensing 12, no. 20 (October 19, 2020): 3437. http://dx.doi.org/10.3390/rs12203437.
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Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) points and meteorological stations. The data scale was monthly and belonged to the 2000–2014 period. In the first part of the study, snow cover estimation was conducted by Multiple Linear Regression (MLR), Least Square Support Vector Machine (LSSVM), Group Method of Data Handling (GMDH), Multilayer Perceptron (MLP), and MLP with Grey Wolf Optimization (MLP-GWO) models. The most accurate estimations were produced by the MLP-GWO and GMDH models. The models produced better snow cover estimations for the northern slope compared to the southern slope. The GWO improved the MLP’s accuracy by 10.7%. In the second part, seven drought indices, including the Palmer Drought Severity Index (PDSI), Bahlme–Mooley Drought Index (BMDI), Standardized Precipitation Index (SPI), Multivariate Standardized Precipitation Index (MSPI), Modified Standardized Precipitation Index (SPImod), Joint Deficit Index (JDI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were calculated for both slopes. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region. The highest impact of drought appears after two months in the snow cover area, and the drought index most related to snow cover variations is the 2–month time window of SPI (SPI2). The results of both subjects were promising and the methods can be examined in other snowy areas of the world.
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Young, Kathy L., Laura Brown, and Claude Labine. "Snow cover variability at Polar Bear Pass, Nunavut." Arctic Science 4, no. 4 (December 1, 2018): 669–90. http://dx.doi.org/10.1139/as-2017-0016.
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Information on arctic snow covers is relevant for climate and hydrology studies and investigations into the sustainability of both arctic fauna and flora. This study aims to (1) highlight the variability of snow cover at Polar Bear Pass (PBP) at a range of scales: point, local, and regional using both in situ snow cover measurements and remote sensing imagery products; and (2) consider how snow cover at PBP might change in the future. Terrain-based snow surveys documented the end-of-winter snowpacks over several seasons (2008–2010, 2012–2013), and snowmelt was measured daily at typical terrain types. MODIS products (snow cover) were used to document spatial snow cover variability across PBP and Bathurst and Cornwallis Islands. Due to limited data, no significant difference in snow cover duration can be identified at PBP over the period of record. Locally, end-of-winter snow cover does vary across a range of terrain types with snow depths and densities reflecting polar oasis sites. Aspect remains a defining factor in terms of snow cover variability at PBP. Northern areas of the Pass melt earlier. Regionally, PBP tends to melt out earlier than most of Bathurst Island. In the future, we surmise that snowpacks at PBP will be thinner and disappear earlier.
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Shreve, Cheney M., Gregory S. Okin, and Thomas H. Painter. "Indices for estimating fractional snow cover in the western Tibetan Plateau." Journal of Glaciology 55, no. 192 (2009): 737–45. http://dx.doi.org/10.3189/002214309789470996.
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AbstractSnow 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.
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Seyedielmabad, M., and H. R. Moradi. "Snow cover mapping using IRS-P6 AWiFS data and the relationships between some climatic factors with snowpack in the northwest of Iran." Journal of Water and Climate Change 7, no. 2 (December 16, 2015): 415–29. http://dx.doi.org/10.2166/wcc.2015.009.
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In this study, we explored the potential of the multispectral and multi-temporal IRS Advanced Wide Field Sensor (AWiFS) data for mapping of the snow cover in the northwest regions of Iran. The AWiFS snow cover maps, based on the unsupervised classification method, were compared with the estimates of snow cover area derived from the moderate resolution imaging spectroradiometer (MODIS) images based on the normalized difference snow index. Good concurrence was observed with respect to the snow area between the AWiFS features and the MODIS features; however, the snow spatial distribution of the AWiFS features differed from those of the MODIS based on the nonentity of the temporal accordance between two types of features. Also, we explored the relationships between some climatic and topographic factors with the snowpack in the northwest part of Iran. Relationships between some climatic factors with snowpack specifications were obtained, which showed significant correlation only between the components of daily temperature and snow density. The other results showed that the amounts of snowpack depth have significant correlations with the height of the stations and the height classes in 1% surface and snowpack depths showed significant differences together within the different height classes.