Articles | Volume 23, issue 5
https://doi.org/10.5194/hess-23-2401-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-2401-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
17 May 2019
Review article |
| 17 May 2019
| 17 May 2019
The recent developments in cloud removal approaches of MODIS snow cover product
Xinghua Li
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Yinghong Jing
School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
Huanfeng Shen
CORRESPONDING AUTHOR
School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
Liangpei Zhang
State Key Laboratory of Information Engineering in Surveying, Mapping and
Remote Sensing, Wuhan University, Wuhan, China
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Snow variation is a vital factor in global climate change. Satellite-based approaches are effective for large-scale environmental monitoring. Nevertheless, the high cloud fraction seriously impedes the remote-sensed investigation. Therefore, a recent 20-year cloud-free snow cover collection in China is generated for the first time. This collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues.
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The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
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Earth Syst. Sci. Data, 14, 3137–3156, https://doi.org/10.5194/essd-14-3137-2022, https://doi.org/10.5194/essd-14-3137-2022, 2022
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Zhiwei Li, Huanfeng Shen, Yancong Wei, Qing Cheng, and Qiangqiang Yuan
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Hongyan Zhang, Han Zhai, Wenzhi Liao, Liqin Cao, Liangpei Zhang, and Aleksandra Pižurica
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Related subject area
Subject: Snow and Ice | Techniques and Approaches: Remote Sensing and GIS
Detecting snowfall events over the Arctic using optical and microwave satellite measurements
Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation
Assimilation of airborne gamma observations provides utility for snow estimation in forested environments
Characterizing 4 decades of accelerated glacial mass loss in the west Nyainqentanglha Range of the Tibetan Plateau
Estimating spatiotemporally continuous snow water equivalent from intermittent satellite observations: an evaluation using synthetic data
Development and validation of a new MODIS snow-cover-extent product over China
Processes governing snow ablation in alpine terrain – detailed measurements from the Canadian Rockies
Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record
Characterising spatio-temporal variability in seasonal snow cover at a regional scale from MODIS data: the Clutha Catchment, New Zealand
Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product
Now you see it, now you don't: a case study of ephemeral snowpacks and soil moisture response in the Great Basin, USA
Assessment of a multiresolution snow reanalysis framework: a multidecadal reanalysis case over the upper Yampa River basin, Colorado
Snow cover dynamics in Andean watersheds of Chile (32.0–39.5° S) during the years 2000–2016
A new remote hazard and risk assessment framework for glacial lakes in the Nepal Himalaya
A snow cover climatology for the Pyrenees from MODIS snow products
Cloud obstruction and snow cover in Alpine areas from MODIS products
Application of MODIS snow cover products: wildfire impacts on snow and melt in the Sierra Nevada
LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California
Early 21st century snow cover state over the western river basins of the Indus River system
Validation of the operational MSG-SEVIRI snow cover product over Austria
Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach
CREST-Snow Field Experiment: analysis of snowpack properties using multi-frequency microwave remote sensing data
Snow cover dynamics and hydrological regime of the Hunza River basin, Karakoram Range, Northern Pakistan
Responses of snowmelt runoff to climatic change in an inland river basin, Northwestern China, over the past 50 years
Assessing the application of a laser rangefinder for determining snow depth in inaccessible alpine terrain
Emmihenna Jääskeläinen, Kerttu Kouki, and Aku Riihelä
Hydrol. Earth Syst. Sci., 28, 3855–3870, https://doi.org/10.5194/hess-28-3855-2024, https://doi.org/10.5194/hess-28-3855-2024, 2024
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Snow cover is an important variable when studying the effect of climate change in the Arctic. Therefore, the correct detection of snowfall is important. In this study, we present methods to detect snowfall accurately using satellite observations. The snowfall event detection results of our limited area are encouraging. We find that further development could enable application over the whole Arctic, providing necessary information on precipitation occurrence over remote areas.
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 28, 631–648, https://doi.org/10.5194/hess-28-631-2024, https://doi.org/10.5194/hess-28-631-2024, 2024
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Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by assimilating observations representative of a snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both spring snow and runoff in many regions.
Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 27, 4039–4056, https://doi.org/10.5194/hess-27-4039-2023, https://doi.org/10.5194/hess-27-4039-2023, 2023
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An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.
Shuhong Wang, Jintao Liu, Hamish D. Pritchard, Linghong Ke, Xiao Qiao, Jie Zhang, Weihua Xiao, and Yuyan Zhou
Hydrol. Earth Syst. Sci., 27, 933–952, https://doi.org/10.5194/hess-27-933-2023, https://doi.org/10.5194/hess-27-933-2023, 2023
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We assessed and compared the glacier areal retreat rate and surface thinning rate and the effects of topography, debris cover and proglacial lakes in the west Nyainqentanglha Range (WNT) during 1976–2000 and 2000–2020. Our study will help us to better understand the glacier change characteristics in the WNT on a long timescale and will serve as a reference for glacier changes in other regions on the Tibetan Plateau.
Xiaoyu Ma, Dongyue Li, Yiwen Fang, Steven A. Margulis, and Dennis P. Lettenmaier
Hydrol. Earth Syst. Sci., 27, 21–38, https://doi.org/10.5194/hess-27-21-2023, https://doi.org/10.5194/hess-27-21-2023, 2023
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We explore satellite retrievals of snow water equivalent (SWE) along hypothetical ground tracks that would allow estimation of SWE over an entire watershed. The retrieval of SWE from satellites has proved elusive, but there are now technological options that do so along essentially one-dimensional tracks. We use machine learning (ML) algorithms as the basis for a track-to-area (TTA) transformation and show that at least one is robust enough to estimate domain-wide SWE with high accuracy.
Xiaohua Hao, Guanghui Huang, Zhaojun Zheng, Xingliang Sun, Wenzheng Ji, Hongyu Zhao, Jian Wang, Hongyi Li, and Xiaoyan Wang
Hydrol. Earth Syst. Sci., 26, 1937–1952, https://doi.org/10.5194/hess-26-1937-2022, https://doi.org/10.5194/hess-26-1937-2022, 2022
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We develop and validate a new 20-year MODIS snow-cover-extent product over China, which is dedicated to addressing known problems of the standard snow products. As expected, the new product significantly outperforms the state-of-the-art MODIS C6.1 products; improvements are particularly clear in forests and for the daily cloud-free product. Our product has provided more reliable snow knowledge over China and can be accessible freely https://dx.doi.org/10.11888/Snow.tpdc.271387.
Michael Schirmer and John W. Pomeroy
Hydrol. Earth Syst. Sci., 24, 143–157, https://doi.org/10.5194/hess-24-143-2020, https://doi.org/10.5194/hess-24-143-2020, 2020
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The spatial distribution of snow water equivalent (SWE) and melt are important for hydrological applications in alpine terrain. We measured the spatial distribution of melt using a drone in very high resolution and could relate melt to topographic characteristics. Interestingly, melt and SWE were not related spatially, which influences the speed of areal melt out. We could explain this by melt varying over larger distances than SWE.
Dorothy K. Hall, George A. Riggs, Nicolo E. DiGirolamo, and Miguel O. Román
Hydrol. Earth Syst. Sci., 23, 5227–5241, https://doi.org/10.5194/hess-23-5227-2019, https://doi.org/10.5194/hess-23-5227-2019, 2019
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Global snow cover maps have been available since 2000 from the MODerate resolution Imaging Spectroradiometer (MODIS), and since 2000 and 2011 from the Suomi National Polar-orbiting Partnership (S-NPP) and the Visible Infrared Imaging Radiometer Suite (VIIRS), respectively. These products are used extensively in hydrological modeling and climate studies. New, daily cloud-gap-filled snow products are available from both MODIS and VIIRS, and are being used to develop an Earth science data record.
Todd A. N. Redpath, Pascal Sirguey, and Nicolas J. Cullen
Hydrol. Earth Syst. Sci., 23, 3189–3217, https://doi.org/10.5194/hess-23-3189-2019, https://doi.org/10.5194/hess-23-3189-2019, 2019
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Spatio-temporal variability of seasonal snow cover is characterised from 16 years of MODIS data for the Clutha Catchment, New Zealand. No trend was detected in snow-covered area. Spatial modes of variability reveal the role of anomalous winter airflow. The sensitivity of snow cover duration to temperature and precipitation variability is found to vary spatially across the catchment. These findings provide new insight into seasonal snow processes in New Zealand and guidance for modelling efforts.
Andri Gunnarsson, Sigurður M. Garðarsson, and Óli G. B. Sveinsson
Hydrol. Earth Syst. Sci., 23, 3021–3036, https://doi.org/10.5194/hess-23-3021-2019, https://doi.org/10.5194/hess-23-3021-2019, 2019
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In this study a gap-filled snow cover product for Iceland is developed using MODIS satellite data and validated with both in situ observations and alternative remote sensing data sources with good agreement. Information about snow cover extent, duration and changes over time is presented, indicating that snow cover extent has been increasing slightly for the past few years.
Rose Petersky and Adrian Harpold
Hydrol. Earth Syst. Sci., 22, 4891–4906, https://doi.org/10.5194/hess-22-4891-2018, https://doi.org/10.5194/hess-22-4891-2018, 2018
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Ephemeral snowpacks are snowpacks that persist for less than 2 months. We show that ephemeral snowpacks melt earlier and provide less soil water input in the spring. Elevation is strongly correlated with whether snowpacks are ephemeral or seasonal. Snowpacks were also more likely to be ephemeral on south-facing slopes than north-facing slopes at high elevations. In warm years, the Great Basin shifts to ephemerally dominant as rain becomes more prevalent at increasing elevations.
Elisabeth Baldo and Steven A. Margulis
Hydrol. Earth Syst. Sci., 22, 3575–3587, https://doi.org/10.5194/hess-22-3575-2018, https://doi.org/10.5194/hess-22-3575-2018, 2018
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Montane snowpacks are extremely complex to represent and usually require assimilating remote sensing images at very fine spatial resolutions, which is computationally expensive. Adapting the grid size of the terrain to its complexity was shown to cut runtime and storage needs by half while preserving the accuracy of ~ 100 m snow estimates. This novel approach will facilitate the large-scale implementation of high-resolution remote sensing data assimilation over snow-dominated montane ranges.
Alejandra Stehr and Mauricio Aguayo
Hydrol. Earth Syst. Sci., 21, 5111–5126, https://doi.org/10.5194/hess-21-5111-2017, https://doi.org/10.5194/hess-21-5111-2017, 2017
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In Chile there is a lack of hydrological data, which complicates the analysis of important hydrological processes. In this study we validate a remote sensing product, i.e. the MODIS snow product, in Chile using ground observations, obtaining good results. Then MODIS was use to evaluated snow cover dynamic during 2000–2016 at five watersheds in Chile. The analysis shows that there is a significant reduction in snow cover area in two watersheds located in the northern part of the study area.
David R. Rounce, Daene C. McKinney, Jonathan M. Lala, Alton C. Byers, and C. Scott Watson
Hydrol. Earth Syst. Sci., 20, 3455–3475, https://doi.org/10.5194/hess-20-3455-2016, https://doi.org/10.5194/hess-20-3455-2016, 2016
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Glacial lake outburst floods pose a significant threat to downstream communities and infrastructure as they rapidly unleash stored lake water. Nepal is home to many potentially dangerous glacial lakes, yet a holistic understanding of the hazards faced by these lakes is lacking. This study develops a framework using remotely sensed data to investigate the hazards and risks associated with each glacial lake and discusses how this assessment may help inform future management actions.
S. Gascoin, O. Hagolle, M. Huc, L. Jarlan, J.-F. Dejoux, C. Szczypta, R. Marti, and R. Sánchez
Hydrol. Earth Syst. Sci., 19, 2337–2351, https://doi.org/10.5194/hess-19-2337-2015, https://doi.org/10.5194/hess-19-2337-2015, 2015
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There is a good agreement between the MODIS snow products and observations from automatic stations and Landsat snow maps in the Pyrenees. The optimal thresholds for which a MODIS pixel is marked as snow-covered are 40mm in water equivalent and 150mm in snow depth.
We generate a gap-filled snow cover climatology for the Pyrenees. We compute the mean snow cover duration by elevation and aspect classes. We show anomalous snow patterns in 2012 and consequences on hydropower production.
P. Da Ronco and C. De Michele
Hydrol. Earth Syst. Sci., 18, 4579–4600, https://doi.org/10.5194/hess-18-4579-2014, https://doi.org/10.5194/hess-18-4579-2014, 2014
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The negative impacts of cloud obstruction in snow mapping from MODIS and a new reliable cloud removal procedure for the Italian Alps.
P. D. Micheletty, A. M. Kinoshita, and T. S. Hogue
Hydrol. Earth Syst. Sci., 18, 4601–4615, https://doi.org/10.5194/hess-18-4601-2014, https://doi.org/10.5194/hess-18-4601-2014, 2014
P. B. Kirchner, R. C. Bales, N. P. Molotch, J. Flanagan, and Q. Guo
Hydrol. Earth Syst. Sci., 18, 4261–4275, https://doi.org/10.5194/hess-18-4261-2014, https://doi.org/10.5194/hess-18-4261-2014, 2014
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In this study we present results from LiDAR snow depth measurements made over 53 sq km and a 1600 m elevation gradient. We found a lapse rate of 15 cm accumulated snow depth and 6 cm SWE per 100 m in elevation until 3300 m, where depth sharply decreased. Residuals from this trend revealed the role of aspect and highlighted the importance of solar radiation and wind for snow distribution. Lastly, we compared LiDAR SWE estimations with four model estimates of SWE and total precipitation.
S. Hasson, V. Lucarini, M. R. Khan, M. Petitta, T. Bolch, and G. Gioli
Hydrol. Earth Syst. Sci., 18, 4077–4100, https://doi.org/10.5194/hess-18-4077-2014, https://doi.org/10.5194/hess-18-4077-2014, 2014
S. Surer, J. Parajka, and Z. Akyurek
Hydrol. Earth Syst. Sci., 18, 763–774, https://doi.org/10.5194/hess-18-763-2014, https://doi.org/10.5194/hess-18-763-2014, 2014
V. López-Burgos, H. V. Gupta, and M. Clark
Hydrol. Earth Syst. Sci., 17, 1809–1823, https://doi.org/10.5194/hess-17-1809-2013, https://doi.org/10.5194/hess-17-1809-2013, 2013
T. Y. Lakhankar, J. Muñoz, P. Romanov, A. M. Powell, N. Y. Krakauer, W. B. Rossow, and R. M. Khanbilvardi
Hydrol. Earth Syst. Sci., 17, 783–793, https://doi.org/10.5194/hess-17-783-2013, https://doi.org/10.5194/hess-17-783-2013, 2013
A. A. Tahir, P. Chevallier, Y. Arnaud, and B. Ahmad
Hydrol. Earth Syst. Sci., 15, 2275–2290, https://doi.org/10.5194/hess-15-2275-2011, https://doi.org/10.5194/hess-15-2275-2011, 2011
J. Wang, H. Li, and X. Hao
Hydrol. Earth Syst. Sci., 14, 1979–1987, https://doi.org/10.5194/hess-14-1979-2010, https://doi.org/10.5194/hess-14-1979-2010, 2010
J. L. Hood and M. Hayashi
Hydrol. Earth Syst. Sci., 14, 901–910, https://doi.org/10.5194/hess-14-901-2010, https://doi.org/10.5194/hess-14-901-2010, 2010
Cited articles
Akyurek, Z., Hall, D. K., Riggs, G. A., and Sensoy, A.: Evaluating the utility
of the ANSA blended snow cover product in the mountains of eastern Turkey,
Int. J. Remote Sens., 31, 3727–3744, https://doi.org/10.1080/01431161.2010.483484, 2010.
Anthony, P. W., Markus, T., Adam, D. S., Victoria, I. L., and Robert, A. M.:
Evaluation of AMSR-E snow depth product over East Antarctic sea ice using in
situ measurements and aerial photography, J. Geophys. Res.-Oceans, 113, C05S94,
https://doi.org/10.1029/2007JC004181, 2008.
Ault, T. W., Czajkowski, K. P., Benko, T., Coss, J., Struble, J., Spongberg, A.,
Templin, M., and Gross, C.: Validation of the MODIS snow product and cloud mask
using student and NWS cooperative station observations in the Lower Great Lakes
Region, Remote Sens. Environ., 105, 341–353, https://doi.org/10.1016/j.rse.2006.07.004, 2006.
Baghdadi, N., Gauthier, Y., and Bernier, M.: Capability of multitemporal ERS-1
SAR data for wet-snow mapping, Remote Sens. Environ., 60, 174–186,
https://doi.org/10.1016/S0034-4257(96)00180-0, 1997.
Bergeron, J., Royer, A., Turcotte, R., and Roy, A.: Snow cover estimation using
blended MODIS and AMSR-E data for improved watershed-scale spring streamflow
simulation in Quebec, Canada, Hydrol. Process., 28, 4626–4639, https://doi.org/10.1002/hyp.10123, 2014.
Bitner, D., Carroll, T., Cline, D., and Romanov, P.: An assessment of the
differences between three satellite snow cover mapping techniques, Hydrol.
Process., 16, 3723–3733, https://doi.org/10.1002/hyp.1231, 2002.
Brown, L. C., Howell, S. E. L., Mortin, J., and Derksen, C.: Evaluation of the
Interactive Multisensor Snow and Ice Mapping System (IMS) for monitoring sea
ice phenology, Remote Sens. Environ., 147, 65–78, https://doi.org/10.1016/j.rse.2014.02.012, 2014.
Brown, R., Derksen, C., and Wang, L.: A multi-data set analysis of
variability and change in Arctic spring snow cover extent, 1967–2008, J.
Geophys. Res.-Atmos., 115, D16111, https://doi.org/10.1029/2010JD013975, 2010.
Brown, R. D.: Northern Hemisphere Snow Cover Variability and Change, 1915–97,
J. Climate, 13, 2339–2355, https://doi.org/10.1175/1520-0442(2000)013<2339:NHSCVA>2.0.CO;2, 2000.
Brown, R. D. and Braaten, R. O.: Spatial and temporal variability of Canadian
monthly snow depths, 1946–1995, Atmosphere-Ocean, 36, 37–54, https://doi.org/10.1080/07055900.1998.9649605, 1998.
Chang, A. T. C., Foster, J. L., and Hall, D. K.: Nimbus-7 SMMR Derived Global
Snow Cover Parameters, Ann. Glaciol., 9, 39–44, https://doi.org/10.3189/S0260305500200736, 1987.
Che, T., Dai, L., Zheng, X., Li, X., and Zhao, K.: Estimation of snow depth
from passive microwave brightness temperature data in forest regions of
northeast China, Remote Sens. Environ., 183, 334–349, https://doi.org/10.1016/j.rse.2016.06.005, 2016.
Chelamallu, H. P., Venkataraman, G., and Murti, M. V. R.: Accuracy assessment
of MODIS/Terra snow cover product for parts of Indian Himalayas, Geocarto Int.,
29, 592–608, https://doi.org/10.1080/10106049.2013.819041, 2014.
Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., and Eklundh, L.:
A simple method for reconstructing a high-quality NDVI time-series data set
based on the Savitzky–Golay filter, Remote Sens. Environ., 91, 332–344,
https://doi.org/10.1016/j.rse.2004.03.014, 2004.
Chen, S., Yang, Q., Xie, H., Zhang, H., Lu, P., and Zhou, C.: Spatiotemporal
variations of snow cover in northeast China based on flexible multiday
combinations of moderate resolution imaging spectroradiometer snow cover
products, J. Appl. Remote Sens., 8, 084685, https://doi.org/10.1117/1.JRS.8.084685, 2014.
Cohen, J. and Entekhabi, D.: Eurasian snow cover variability and northern
hemisphere climate predictability, Geophys. Res. Lett., 26, 345–348, https://doi.org/10.1029/1998GL900321, 1999.
Coll, J. and Li, X.: Comprehensive accuracy assessment of MODIS daily snow cover
products and gap filling methods, ISPRS J. Photogram. Remote Sens., 144, 435–452,
https://doi.org/10.1016/j.isprsjprs.2018.08.004, 2018.
Crawford, C. J.: MODIS Terra Collection 6 fractional snow cover validation in
mountainous terrain during spring snowmelt using Landsat TM and ETM+, Hydrol.
Process., 29, 128–138, https://doi.org/10.1002/hyp.10134, 2015.
Crawford, C. J., Manson, S. M., Bauer, M. E., and Hall, D. K.: Multitemporal
snow cover mapping in mountainous terrain for Landsat climate data record
development, Remote Sens. Environ., 135, 224–233, https://doi.org/10.1016/j.rse.2013.04.004, 2013.
Dariane, A. B., Khoramian, A., and Santi, E.: Investigating spatiotemporal snow
cover variability via cloud-free MODIS snow cover product in Central Alborz
Region, Remote Sens. Environ., 202, 152–165, https://doi.org/10.1016/j.rse.2017.05.042, 2017.
Da Ronco, P. and De Michele, C.: Cloud obstruction and snow cover in Alpine
areas from MODIS products, Hydrol. Earth Syst. Sci., 18, 4579–4600,
https://doi.org/10.5194/hess-18-4579-2014, 2014a.
Da Ronco, P. and De Michele, C.: Cloudiness and snow cover in Alpine areas from
MODIS products, Hydrol. Earth Syst. Sci. Discuss., 11, 3967–4015,
https://doi.org/10.5194/hessd-11-3967-2014, 2014b.
Deng, J., Huang, X., Feng, Q., Ma, X., and Liang, T.: Toward Improved Daily
Cloud-Free Fractional Snow Cover Mapping with Multi-Source Remote Sensing Data
in China, Remote Sensing, 7, 6986–7006, https://doi.org/10.3390/rs70606986, 2015.
Déry, S. J., Salomonson, V. V., Stieglitz, M., Hall, D. K., and Appel, I.:
An approach to using snow areal depletion curves inferred from MODIS and its
application to land surface modelling in Alaska, Hydrol. Process., 19,
2755–2774, https://doi.org/10.1002/hyp.5784, 2005.
Dietz, A. J., Kuenzer, C., Gessner, U., and Dech, S.: Remote sensing of snow – a
review of available methods, Int. J. Remote Sens., 33, 4094–4134,
https://doi.org/10.1080/01431161.2011.640964, 2012a.
Dietz, A. J., Wohner, C., and Kuenzer, C.: European Snow Cover Characteristics
between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products,
Remote Sensing, 4, 2432–2454, https://doi.org/10.3390/rs4082432, 2012b.
Dietz, A. J., Kuenzer, C., and Conrad, C.: Snow-cover variability in central
Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products,
Int. J. Remote Sens., 34, 3879–3902, https://doi.org/10.1080/01431161.2013.767480, 2013.
Dong, C. and Menzel, L.: Producing cloud-free MODIS snow cover products with
conditional probability interpolation and meteorological data, Remote Sens.
Environ., 186, 439–451, https://doi.org/10.1016/j.rse.2016.09.019, 2016.
Drusch, M., Vasiljevic, D., and Viterbo, P.: ECMWF's Global Snow Analysis:
Assessment and Revision Based on Satellite Observations, J. Appl. Meteorol.,
43, 1282–1294, https://doi.org/10.1175/1520-0450(2004)043<1282:EGSAAA>2.0.CO;2, 2004.
Foppa, N. and Seiz, G.: Inter-annual variations of snow days over Switzerland
from 2000–2010 derived from MODIS satellite data, The Cryosphere, 6, 331–342,
https://doi.org/10.5194/tc-6-331-2012, 2012.
Foster, J. L., Hall, D. K., Chang, A. T. C., and Rango, A.: An overview of
passive microwave snow research and results, Rev. Geophys., 22, 195–208,
https://doi.org/10.1029/RG022i002p00195, 1984.
Foster, J. L., Hall, D. K., Eylander, J. B., Riggs, G. A., Nghiem, S. V.,
Tedesco, M., Kim, E., Montesano, P. M., Kelly, R. E. J., Casey, K. A., and
Choudhury, B.: A blended global snow product using visible, passive microwave
and scatterometer satellite data, Int. J. Remote Sens., 32, 1371–1395,
https://doi.org/10.1080/01431160903548013, 2011.
Frei, A., Tedesco, M., Lee, S., Foster, J., Hall, D. K., Kelly, R., and Robinson,
D. A.: A review of global satellite-derived snow products, Adv. Space Res., 50,
1007–1029, https://doi.org/10.1016/j.asr.2011.12.021, 2012.
Gafurov, A. and Bárdossy, A.: Cloud removal methodology from MODIS snow
cover product, Hydrol. Earth Syst. Sci., 13, 1361–1373, https://doi.org/10.5194/hess-13-1361-2009, 2009.
Gafurov, A., Kriegel, D., Vorogushyn, S., and Merz, B.: Evaluation of remotely
sensed snow cover product in Central Asia, Hydrol. Res., 44, 506–522,
https://doi.org/10.2166/nh.2012.094, 2013.
Gafurov, A., Vorogushyn, S., Farinotti, D., Duethmann, D., Merkushkin, A.,
and Merz, B.: Snow-cover reconstruction methodology for mountainous regions
based on historic in situ observations and recent remote sensing data, The
Cryosphere, 9, 451–463, https://doi.org/10.5194/tc-9-451-2015, 2015.
Gafurov, A., Lüdtke, S., Unger-Shayesteh, K., Vorogushyn, S., Schöne,
T., Schmidt, S., Kalashnikova, O., and Merz, B.: MODSNOW-Tool: an operational
tool for daily snow cover monitoring using MODIS data, Environ. Earth Sci., 75,
1078, https://doi.org/10.1007/s12665-016-5869-x, 2016.
Gao, J., Williams, M. W., Fu, X., Wang, G., and Gong, T.: Spatiotemporal
distribution of snow in eastern Tibet and the response to climate change,
Remote Sens. Environ., 121, 1–9, https://doi.org/10.1016/j.rse.2012.01.006, 2012.
Gao, Y., Xie, H., Lu, N., Yao, T., and Liang, T.: Toward advanced daily
cloud-free snow cover and snow water equivalent products from Terra–Aqua MODIS
and Aqua AMSR-E measurements, J. Hydrol., 385, 23–35, https://doi.org/10.1016/j.jhydrol.2010.01.022, 2010a.
Gao, Y., Xie, H., Yao, T., and Xue, C.: Integrated assessment on multi-temporal
and multi-sensor combinations for reducing cloud obscuration of MODIS snow cover
products of the Pacific Northwest USA, Remote Sens. Environ., 114, 1662–1675,
https://doi.org/10.1016/j.rse.2010.02.017, 2010b.
Gao, Y., Lu, N., and Yao, T.: Evaluation of a cloud-gap-filled MODIS daily snow
cover product over the Pacific Northwest USA, J. Hydrol., 404, 157–165,
https://doi.org/10.1016/j.jhydrol.2011.04.026, 2011a.
Gao, Y., Xie, H., and Yao, T.: Developing Snow Cover Parameters Maps from MODIS,
AMSR-E, and Blended Snow Products, Photogram. Eng. Remote Sens., 77, 351–361,
https://doi.org/10.14358/PERS.77.4.351, 2011b.
Gladkova, I., Grossberg, M., Bonev, G., Romanov, P., and Shahriar, F.:
Increasing the Accuracy of MODIS/Aqua Snow Product Using Quantitative Image
Restoration Technique, IEEE Geosci. Remote Sens. Lett., 9, 740–743,
https://doi.org/10.1109/LGRS.2011.2180505, 2012.
Grody, N. C. and Basist, A. N.: Global identification of snowcover using SSM/I
measurements, IEEE T. Geosci. Remote, 34, 237–249, https://doi.org/10.1109/36.481908, 1996.
Gurung, D. R., Kulkarni, A. V., Giriraj, A., Aung, K. S., Shrestha, B., and
Srinivasan, J.: Changes in seasonal snow cover in Hindu Kush-Himalayan region,
The Cryosphere Discuss., 5, 755–777, https://doi.org/10.5194/tcd-5-755-2011, 2011.
Hall, D. K. and Riggs, G. A.: Accuracy assessment of the MODIS snow products,
Hydrol. Process., 21, 1534–1547, https://doi.org/10.1002/hyp.6715, 2007.
Hall, D. K. and Riggs, G. A.: MODIS/Aqua Snow Cover Daily L3 Global 500 m
Grid, Version 6, available at: http://nsidc.org/data/MYD10A1/versions/6
(last access: 13 May 2019), 2016a.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500 m
Grid, Version 6, available at: http://nsidc.org/data/MYD10A1/versions/6
(last access: 13 May 2019), 2016b.
Hall, D. K., Riggs, G. A., and Salomonson, V. V.: Development of methods for
mapping global snow cover using moderate resolution imaging spectroradiometer
data, Remote Sens. Environ., 54, 127–140, https://doi.org/10.1016/0034-4257(95)00137-P, 1995.
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr, K.
J.: MODIS snow-cover products, Remote Sens. Environ., 83, 181–194,
https://doi.org/10.1016/S0034-4257(02)00095-0, 2002.
Hall, D. K., Riggs, G. A., Foster, J. L., and Kumar, S. V.: Development and
evaluation of a cloud-gap-filled MODIS daily snow-cover product, Remote Sens.
Environ., 114, 496–503, https://doi.org/10.1016/j.rse.2009.10.007, 2010.
Hori, M., Sugiura, K., Kobayashi, K., Aoki, T., Tanikawa, T., Kuchiki, K.,
Niwano, M., and Enomoto, H.: A 38-year (1978–2015) Northern Hemisphere daily
snow cover extent product derived using consistent objective criteria from
satellite-borne optical sensors, Remote Sens. Environ., 191, 402–418,
https://doi.org/10.1016/j.rse.2017.01.023, 2017.
Huang, X., Liang, T., Zhang, X., and Guo, Z.: Validation of MODIS snow cover
products using Landsat and ground measurements during the 2001–2005 snow
seasons over northern Xinjiang, China, Int. J. Remote Sens., 32, 133–152,
https://doi.org/10.1080/01431160903439924, 2011.
Huang, X., Hao, X., Feng, Q., Wang, W., and Liang, T.: A new MODIS daily cloud
free snow cover mapping algorithm on the Tibetan Plateau, Sci. Cold Arid Reg.,
6, 116–123, https://doi.org/10.3724/SP.J.1226.2014.00116, 2014.
Huang, X., Deng, J., Ma, X., Wang, Y., Feng, Q., Hao, X., and Liang, T.:
Spatiotemporal dynamics of snow cover based on multi-source remote sensing
data in China, The Cryosphere, 10, 2453–2463, https://doi.org/10.5194/tc-10-2453-2016, 2016.
Huang, Y., Liu, H., Yu, B., Wu, J., Kang, E. L., Xu, M., Wang, S., Klein, A.,
and Chen, Y.: Improving MODIS snow products with a HMRF-based spatio-temporal
modeling technique in the Upper Rio Grande Basin, Remote Sens. Environ., 204,
568–582, https://doi.org/10.1016/j.rse.2017.10.001, 2018.
Hüsler, F., Jonas, T., Riffler, M., Musial, J. P., and Wunderle, S.: A
satellite-based snow cover climatology (1985–2011) for the European Alps
derived from AVHRR data, The Cryosphere, 8, 73–90, https://doi.org/10.5194/tc-8-73-2014, 2014.
Klein, A. G. and Barnett, A. C.: Validation of daily MODIS snow cover maps of
the Upper Rio Grande River Basin for the 2000–2001 snow year, Remote Sens.
Environ., 86, 162–176, https://doi.org/10.1016/S0034-4257(03)00097-X, 2003.
Kostadinov, T. S. and Lookingbill, T. R.: Snow cover variability in a forest
ecotone of the Oregon Cascades via MODIS Terra products, Remote Sens. Environ.,
164, 155–169, https://doi.org/10.1016/j.rse.2015.04.002, 2015.
Krajčí, P., Holko, L., Perdigão, R. A. P., and Parajka, J.:
Estimation of regional snowline elevation (RSLE) from MODIS images for
seasonally snow covered mountain basins, J. Hydrol., 519, 1769–1778,
https://doi.org/10.1016/j.jhydrol.2014.08.064, 2014.
Krajčí, P., Holko, L., and Parajka, J.: Variability of snow line
elevation, snow cover area and depletion in the main Slovak basins in
winters 2001–2014, J. Hydrol. Hydromech., 64, 12–22, https://doi.org/10.1515/johh-2016-0011, 2016.
Kuter, S., Akyurek, Z., and Weber, G.-W.: Retrieval of fractional snow covered
area from MODIS data by multivariate adaptive regression splines, Remote Sens.
Environ., 205, 236–252, https://doi.org/10.1016/j.rse.2017.11.021, 2018.
Lei, L., Zeng, Z., and Zhang, B.: Method for Detecting Snow Lines From MODIS
Data and Assessment of Changes in the Nianqingtanglha Mountains of the Tibet
Plateau, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 5, 769–776,
https://doi.org/10.1109/JSTARS.2012.2200654, 2012.
Li, B., Zhu, A. X., Zhou, C., Zhang, Y., Pei, T., and Qin, C.: Automatic mapping
of snow cover depletion curves using optical remote sensing data under conditions
of frequent cloud cover and temporary snow, Hydrol. Process., 22, 2930–2942,
https://doi.org/10.1002/hyp.6891, 2008.
Li, C., Su, F., Yang, D., Tong, K., Meng, F., and Kan, B.: Spatiotemporal
variation of snow cover over the Tibetan Plateau based on MODIS snow product,
2001–2014, Int. J. Climatol., 38, 708–728, https://doi.org/10.1002/joc.5204 2018.
Li, H., Tang, Z., Wang, J., Che, T., Pan, X., Huang, C., Wang, X., Hao, X., and
Sun, S.: Synthesis method for simulating snow distribution utilizing remotely
sensed data for the Tibetan Plateau, J. Appl. Remote Sens., 8, 084696,
https://doi.org/10.1117/1.JRS.8.084696, 2014.
Li, X., Shen, H., Zhang, L., Zhang, H., Yuan, Q., and Yang, G.: Recovering
Quantitative Remote Sensing Products Contaminated by Thick Clouds and Shadows
Using Multitemporal Dictionary Learning, IEEE T. Geosci. Remote, 52, 7086–7098,
https://doi.org/10.1109/TGRS.2014.2307354, 2014.
Li, X., Shen, H., Li, H., and Zhang, L.: Patch Matching-Based Multitemporal
Group Sparse Representation for the Missing Information Reconstruction of
Remote-Sensing Images, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 9,
3629–3641, https://doi.org/10.1109/JSTARS.2016.2533547, 2016.
Li, X., Fu, W., Shen, H., Huang, C., and Zhang, L.: Monitoring snow cover
variability (2000–2014) in the Hengduan Mountains based on cloud-removed MODIS
products with an adaptive spatio-temporal weighted method, J. Hydrol., 551,
314–327, https://doi.org/10.1016/j.jhydrol.2017.05.049, 2017.
Liang, H., Huang, X., Sun, Y., Wang, Y., and Liang, T.: Fractional Snow-Cover
Mapping Based on MODIS and UAV Data over the Tibetan Plateau, Remote Sensing,
9, 1332, https://doi.org/10.3390/rs9121332, 2017.
Liang, T., Zhang, X., Xie, H., Wu, C., Feng, Q., Huang, X., and Chen, Q.:
Toward improved daily snow cover mapping with advanced combination of MODIS
and AMSR-E measurements, Remote Sens. Environ., 112, 3750–3761, https://doi.org/10.1016/j.rse.2008.05.010, 2008a.
Liang, T. G., Huang, X. D., Wu, C. X., Liu, X. Y., Li, W. L., Guo, Z. G., and
Ren, J. Z.: An application of MODIS data to snow cover monitoring in a pastoral
area: A case study in Northern Xinjiang, China, Remote Sens. Environ., 112,
1514–1526, https://doi.org/10.1016/j.rse.2007.06.001, 2008b.
Lindsay, C., Zhu, J., Miller, E. A., Kirchner, P., and Wilson, L. T.: Deriving
Snow Cover Metrics for Alaska from MODIS, Remote Sensing, 7, 12961–12985,
https://doi.org/10.3390/rs71012961, 2015.
Lopez, P., Sirguey, P., Arnaud, Y., Pouyaud, B., and Chevallier, P.: Snow cover
monitoring in the Northern Patagonia Icefield using MODIS satellite
images (2000–2006), Global Planet. Change, 61, 103–116, https://doi.org/10.1016/j.gloplacha.2007.07.005, 2008.
López-Burgos, V., Gupta, H. V., and Clark, M.: Reducing cloud obscuration
of MODIS snow cover area products by combining spatio-temporal techniques with
a probability of snow approach, Hydrol. Earth Syst. Sci., 17, 1809–1823,
https://doi.org/10.5194/hess-17-1809-2013, 2013.
Lovell, J. and Graetz, R.: Filtering pathfinder AVHRR land NDVI data for
Australia, Int. J. Remote Sens., 22, 2649–2654, https://doi.org/10.1080/01431160116874, 2001.
Malmros, J. K., Mernild, S. H., Wilson, R., Tagesson, T., and Fensholt, R.:
Snow cover and snow albedo changes in the central Andes of Chile and Argentina
from daily MODIS observations (2000–2016), Remote Sens. Environ., 209, 240–252,
https://doi.org/10.1016/j.rse.2018.02.072, 2018.
Marchane, A., Jarlan, L., Hanich, L., Boudhar, A., Gascoin, S., Tavernier, A.,
Filali, N., Le Page, M., Hagolle, O., and Berjamy, B.: Assessment of daily
MODIS snow cover products to monitor snow cover dynamics over the Moroccan
tlas mountain range, Remote Sens. Environ., 160, 72–86, https://doi.org/10.1016/j.rse.2015.01.002, 2015.
Maskey, S., Uhlenbrook, S., and Ojha, S.: An analysis of snow cover changes in
the Himalayan region using MODIS snow products and in-situ temperature data,
Climatic Change, 108, 391–400, https://doi.org/10.1007/s10584-011-0181-y, 2011.
Mazari, N., Tekeli, A. E., Xie, H., Sharif, H. I., and Hassan, A. A. E.:
Assessment of ice mapping system and moderate resolution imaging spectroradiometer
snow cover maps over Colorado Plateau, J. Appl. Remote Sens., 7, 073540,
https://doi.org/10.1117/1.JRS.7.073540, 2013.
McFadden, E. M., Ramage, J., and Rodbell, D. T.: Landsat TM and ETM+ derived
snowline altitudes in the Cordillera Huayhuash and Cordillera Raura, Peru,
1986–2005, The Cryosphere, 5, 419–430, https://doi.org/10.5194/tc-5-419-2011, 2011.
Metsämäki, S., Pulliainen, J., Salminen, M., Luojus, K., Wiesmann, A.,
Solberg, R., Böttcher, K., Hiltunen, M., and Ripper, E.: Introduction to
GlobSnow Snow Extent products with considerations for accuracy assessment,
Remote Sens. Environ., 156, 96–108, https://doi.org/10.1016/j.rse.2014.09.018, 2015.
Molotch, N. P. and Margulis, S. A.: Estimating the distribution of snow water
equivalent using remotely sensed snow cover data and a spatially distributed
snowmelt model: A multi-resolution, multi-sensor comparison, Adv. Water Resour.,
31, 1503–1514, https://doi.org/10.1016/j.advwatres.2008.07.017, 2008.
Nagler, T., Rott, H., Ripper, E., Bippus, G., and Hetzenecker, M.: Advancements
for Snowmelt Monitoring by Means of Sentinel-1 SAR, Remote Sensing, 8, 348,
https://doi.org/10.3390/rs8040348, 2016.
Nagler, T., Rott, H., Ossowska, J., Schwaizer, G., Small, D., Malnes, E.,
Luojus, K., Metsämäki, S., and Pinnock, S.: Snow Cover Monitoring by
Synergistic Use of Sentinel-3 Slstr and Sentinel-L Sar Data, in: IEEE
International Geoscience and Remote Sensing Symposium, 22–27 July 2018, Valencia, Spain, 8727–8730, 2018.
Parajka, J. and Blöschl, G.: Validation of MODIS snow cover images over
Austria, Hydrol. Earth Syst. Sci., 10, 679–689, https://doi.org/10.5194/hess-10-679-2006, 2006.
Parajka, J. and Blöschl, G.: Spatio-temporal combination of MODIS images – potential
for snow cover mapping, Water Resour. Res., 44, W03406, https://doi.org/10.1029/2007WR006204, 2008.
Parajka, J., Pepe, M., Rampini, A., Rossi, S., and Blöschl, G.: A regional
snow-line method for estimating snow cover from MODIS during cloud cover, J.
Hydrol., 381, 203–212, https://doi.org/10.1016/j.jhydrol.2009.11.042, 2010.
Parajka, J., Holko, L., Kostka, Z., and Blöschl, G.: MODIS snow cover
mapping accuracy in a small mountain catchment-comparison between open and
forest sites, Hydrol. Earth Syst. Sci., 16, 2365–2377, https://doi.org/10.5194/hess-16-2365-2012, 2012.
Paudel, K. P. and Andersen, P.: Monitoring snow cover variability in an
agropastoral area in the Trans Himalayan region of Nepal using MODIS data with
improved cloud removal methodology, Remote Sens. Environ., 115, 1234–1246,
https://doi.org/10.1016/j.rse.2011.01.006, 2011.
Poggio, L. and Gimona, A.: Sequence-based mapping approach to spatio-temporal
snow patterns from MODIS time-series applied to Scotland, Int. J. Appl. Earth
Obs. Geoinf., 34, 122–135, https://doi.org/10.1016/j.jag.2014.08.005, 2015.
Poggio, L., Gimona, A., and Brown, I.: Spatio-temporal MODIS EVI gap filling
under cloud cover: An example in Scotland, ISPRS J. Photogram. Remote Sens.,
72, 56–72, https://doi.org/10.1016/j.isprsjprs.2012.06.003, 2012.
Ramsay, B. H.: The interactive multisensor snow and ice mapping system, Hydrol.
Process., 12, 1537–1546, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1537::AID-HYP679>3.0.CO;2-A, 1998.
Riggs, G. A., Hall, D. K., and Román, M. O.: Overview of NASA's MODIS and
Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data
Records, Earth Syst. Sci. Data, 9, 765–777, https://doi.org/10.5194/essd-9-765-2017, 2017.
Rittger, K., Painter, T. H., and Dozier, J.: Assessment of methods for mapping
snow cover from MODIS, Adv. Water Resour., 51, 367–380, https://doi.org/10.1016/j.advwatres.2012.03.002, 2013.
Robinson, D. A., Dewey, K. F., and Heim, R. R.: Global Snow Cover Monitoring:
An Update, B. Am. Meteorol. Soc., 74, 1689–1696, https://doi.org/10.1175/1520-0477(1993)074<1689:GSCMAU>2.0.CO;2, 1993.
Roerink, G. J., Menenti, M., and Verhoef, W.: Reconstructing cloudfree NDVI
composites using Fourier analysis of time series, Int. J. Remote Sens., 21,
1911–1917, https://doi.org/10.1080/014311600209814, 2000.
Romanov, P., Gutman, G., and Csiszar, I.: Automated Monitoring of Snow Cover
over North America with Multispectral Satellite Data, J. Appl. Meteorol., 39,
1866–1880, https://doi.org/10.1175/1520-0450(2000)039<1866:AMOSCO>2.0.CO;2, 2000.
Rosenthal, W. and Dozier, J.: Automated Mapping of Montane Snow Cover at
Subpixel Resolution from the Landsat Thematic Mapper, Water Resour. Res., 32,
115–130, https://doi.org/10.1029/95WR02718, 1996.
Salomonson, V. V. and Appel, I.: Estimating fractional snow cover from MODIS
using the normalized difference snow index, Remote Sens. Environ., 89, 351–360,
https://doi.org/10.1016/j.rse.2003.10.016, 2004.
Sharma, V., Mishra, V. D., and Joshi, P. K.: Topographic controls on
spatio-temporal snow cover distribution in Northwest Himalaya, Int. J. Remote
Sens., 35, 3036–3056, https://doi.org/10.1080/01431161.2014.894665, 2014.
Shea, J. M., Menounos, B., Moore, R. D., and Tennant, C.: An approach to derive
regional snow lines and glacier mass change from MODIS imagery, western North
America, The Cryosphere, 7, 667–680, https://doi.org/10.5194/tc-7-667-2013, 2013.
Shen, H. and Zhang, L.: A MAP-Based Algorithm for Destriping and Inpainting of
Remotely Sensed Images, IEEE T. Geosci. Remote, 47, 1492–1502, https://doi.org/10.1109/TGRS.2008.2005780, 2009.
Shen, H., Li, X., Zhang, L., Tao, D., and Zeng, C.: Compressed Sensing-Based
Inpainting of Aqua Moderate Resolution Imaging Spectroradiometer Band 6 Using
Adaptive Spectrum-Weighted Sparse Bayesian Dictionary Learning, IEEE T. Geosci.
Remote, 52, 894–906, https://doi.org/10.1109/TGRS.2013.2245509, 2014.
Shen, H., Li, X., Cheng, Q., Zeng, C., Yang, G., Li, H., and Zhang, L.: Missing
Information Reconstruction of Remote Sensing Data: A Technical Review, IEEE
Geosci. Remote Sens. Mag., 3, 61–85, https://doi.org/10.1109/MGRS.2015.2441912, 2015.
Shen, H., Meng, X., and Zhang, L.: An Integrated Framework for the Spatio-Temporal – Spectral
Fusion of Remote Sensing Images, IEEE T. Geosci. Remote., 54, 7135–7148,
https://doi.org/10.1109/TGRS.2016.2596290, 2016.
Simic, A., Fernandes, R., Brown, R., Romanov, P., and Park, W.: Validation of
VEGETATION, MODIS, and GOES+SSM/I snow-cover products over Canada based on
surface snow depth observations, Hydrol. Process., 18, 1089–1104, https://doi.org/10.1002/hyp.5509, 2004.
Simpson, J. J., Stitt, J. R., and Sienko, M.: Improved estimates of the areal
extent of snow cover from AVHRR data, J. Hydrol., 204, 1–23, https://doi.org/10.1016/S0022-1694(97)00087-5, 1998.
Singer, F. S. and Popham, R. W.: Non-meteorological observations from weather
satellites, Astronaut. Aerospace Eng., 1, 89–92, 1963.
Singh, S. K., Rathore, B. P., Bahuguna Ajai, I. M.: Snow cover
variability in the Himalayan–Tibetan region, Int. J. Climatol., 34, 446–452,
https://doi.org/10.1002/joc.3697, 2014.
Snapir, B., Momblanch, A., Jain, S. K., Waine, T. W., and Holman, I. P.: A
method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS:
Application to a Himalayan river basin, Int. J. Appl. Earth Obs. Geoinf., 74,
222–230, https://doi.org/10.1016/j.jag.2018.09.011, 2019.
Şorman, A. A., Uysal, G., and Şensoy, A.: Probabilistic snow cover and
ensemble streamflow estimations in the Upper Euphrates Basin, J. Hydrol.
Hydromech., 67, 82–92, https://doi.org/10.2478/johh-2018-0025, 2019.
Şorman, A. Ü., Akyürek, Z., Şensoy, A., Şorman, A. A., and
Tekeli, A. E.: Commentary on comparison of MODIS snow cover and albedo products
with ground observations over the mountainous terrain of Turkey, Hydrol. Earth
Syst. Sci., 11, 1353–1360, https://doi.org/10.5194/hess-11-1353-2007, 2007.
Tahsin, S., Medeiros, C. S., Hooshyar, M., and Singh, A.: Optical Cloud Pixel
Recovery via Machine Learning, Remote Sensing, 9, 527, https://doi.org/10.3390/rs9060527, 2017.
Tait, A. B., Hall, D. K., Foster, J. L., and Armstrong, R. L.: Utilizing
Multiple Datasets for Snow-Cover Mapping, Remote Sens. Environ., 72, 111–126,
https://doi.org/10.1016/S0034-4257(99)00099-1, 2000.
Tang, Z., Wang, J., Li, H., and Yan, L.: Spatiotemporal changes of snow cover
over the Tibetan plateau based on cloud-removed moderate resolution imaging
spectroradiometer fractional snow cover product from 2001 to 2011, J. Appl.
Remote Sens., 7, 073582, https://doi.org/10.1117/1.JRS.7.073582, 2013.
Tang, Z., Wang, X., Wang, J., Wang, X., Li, H., and Jiang, Z.: Spatiotemporal
Variation of Snow Cover in Tianshan Mountains, Central Asia, Based on Cloud-Free
MODIS Fractional Snow Cover Product, 2001–2015, Remote Sensing, 9, 1045,
https://doi.org/10.3390/rs9101045, 2017.
Tekeli, Y. and Tekeli, A. E.: A technique for improving MODIS standard snow
products for snow cover monitoring over Eastern Turkey, Arab. J. Geosci., 5,
353–363, https://doi.org/10.1007/s12517-010-0274-3, 2012.
Tong, J., Déry, S. J., and Jackson, P. L.: Interrelationships between
MODIS/Terra remotely sensed snow cover and the hydrometeorology of the Quesnel
River Basin, British Columbia, Canada, Hydrol. Earth Syst. Sci., 13, 1439–1452,
https://doi.org/10.5194/hess-13-1439-2009, 2009a.
Tong, J., Déry, S. J., and Jackson, P. L.: Topographic control of snow
distribution in an alpine watershed of western Canada inferred from
spatially-filtered MODIS snow products, Hydrol. Earth Syst. Sci., 13, 319–326,
https://doi.org/10.5194/hess-13-319-2009, 2009b.
Wang, W., Huang, X., Deng, J., Xie, H., and Liang, T.: Spatio-Temporal Change
of Snow Cover and Its Response to Climate over the Tibetan Plateau Based on an
Improved Daily Cloud-Free Snow Cover Product, Remote Sensing, 7, 169–194,
https://doi.org/10.3390/rs70100169, 2015.
Wang, X. and Xie, H.: New methods for studying the spatiotemporal variation of
snow cover based on combination products of MODIS Terra and Aqua, J. Hydrol.,
371, 192–200, https://doi.org/10.1016/j.jhydrol.2009.03.028, 2009.
Wang, X., Xie, H., and Liang, T.: Evaluation of MODIS snow cover and cloud mask
and its application in Northern Xinjiang, China, Remote Sens. Environ., 112,
1497–1513, https://doi.org/10.1016/j.rse.2007.05.016, 2008.
Wang, X., Xie, H., Liang, T., and Huang, X.: Comparison and validation of MODIS
standard and new combination of Terra and Aqua snow cover products in northern
Xinjiang, China, Hydrol. Process., 23, 419–429, https://doi.org/10.1002/hyp.7151, 2009.
Wang, X., Zheng, H., Chen, Y., Liu, H., Liu, L., Huang, H., and Liu, K.:
Mapping snow cover variations using a MODIS daily cloud-free snow cover product
in northeast China, J. Appl. Remote Sens., 8, 084681, https://doi.org/10.1117/1.JRS.8.084681, 2014.
Wang, Y., Huang, X., Liang, H., Sun, Y., Feng, Q., and Liang, T.: Tracking Snow
Variations in the Northern Hemisphere Using Multi-Source Remote Sensing
Data (2000–2015), Remote Sensing, 10, 136, https://doi.org/10.3390/rs10010136, 2018.
Wulder, M. A., Nelson, T. A., Derksen, C., and Seemann, D.: Snow cover
variability across central Canada (1978–2002) derived from satellite passive
microwave data, Climatic Change, 82, 113–130, https://doi.org/10.1007/s10584-006-9148-9, 2007.
Xia, Q., Gao, X., Chu, W., and Sorooshian, S.: Estimation of daily cloud-free,
snow-covered areas from MODIS based on variational interpolation, Water Resour.
Res., 48, W09523, https://doi.org/10.1029/2011WR011072, 2012.
Xiao, X., Zhang, Q., Boles, S., Rawlins, M., and Moore, B.: Mapping snow cover
in the pan-Arctic zone, using multi-year (1998–2001) images from optical
VEGETATION sensor, Int. J. Remote Sens., 25, 5731–5744, https://doi.org/10.1080/01431160410001719867, 2004.
Xie, H., Wang, X., and Liang, T.: Development and assessment of combined Terra
and Aqua snow cover products in Colorado Plateau, USA and northern Xinjiang,
China, J. Appl. Remote Sens., 3, 033559, https://doi.org/10.1117/1.3265996, 2009.
Xu, W., Ma, H., Wu, D., and Yuan, W.: Assessment of the Daily Cloud-Free MODIS
Snow-Cover Product for Monitoring the Snow-Cover Phenology over the
Qinghai-Tibetan Plateau, Remote Sensing, 9, 585, https://doi.org/10.3390/rs9060585, 2017.
Yang, G., Shen, H., Zhang, L., He, Z., and Li, X.: A Moving Weighted Harmonic
Analysis Method for Reconstructing High-Quality SPOT VEGETATION NDVI Time-Series
Data, IEEE T. Geosci. Remote, 53, 6008–6021, https://doi.org/10.1109/TGRS.2015.2431315, 2015.
Yang, J., Jiang, L., Shi, J., Wu, S., Sun, R., and Yang, H.: Monitoring snow
cover using Chinese meteorological satellite data over China, Remote Sens.
Environ., 143, 192–203, https://doi.org/10.1016/j.rse.2013.12.022, 2014.
Yu, H., Zhang, X., Liang, T., Xie, H., Wang, X., Feng, Q., and Chen, Q.: A new
approach of dynamic monitoring of 5-day snow cover extent and snow depth based
on MODIS and AMSR-E data from Northern Xinjiang region, Hydrol. Process., 26,
3052–3061, https://doi.org/10.1002/hyp.8253, 2012.
Yu, J., Zhang, G., Yao, T., Xie, H., Zhang, H., Ke, C., and Yao, R.: Developing
Daily Cloud-Free Snow Composite Products From MODIS Terra-Aqua and IMS for the
Tibetan Plateau, IEEE T. Geosci. Remote, 54, 2171–2180, https://doi.org/10.1109/TGRS.2015.2496950, 2016.
Zeng, C., Shen, H., and Zhang, L.: Recovering missing pixels for Landsat ETM+
SLC-off imagery using multi-temporal regression analysis and a regularization
method, Remote Sens. Environ., 131, 182–194, https://doi.org/10.1016/j.rse.2012.12.012, 2013.
Zhang, G., Xie, H., Yao, T., Liang, T., and Kang, S.: Snow cover dynamics of
four lake basins over Tibetan Plateau using time series MODIS data (2001–2010),
Water Resour. Res., 48, W10529, https://doi.org/10.1029/2012WR011971, 2012.
Zhang, Y., Kan, X., Ren, W., Cao, T., Tian, W., and Wang, J.: Snow Cover
Monitoring in Qinghai-Tibetan Plateau Based on Chinese Fengyun-3/VIRR Data, J.
Indian Soc. Remote Sens., 45, 271–283, https://doi.org/10.1007/s12524-015-0527-4, 2017.
Zhao, H. and Fernandes, R.: Daily snow cover estimation from Advanced Very High
Resolution Radiometer Polar Pathfinder data over Northern Hemisphere land
surfaces during 1982–2004, J. Geophys. Res.-Atmos., 114, D05113, https://doi.org/10.1029/2008JD011272, 2009.
Zhou, H., Aizen, E., and Aizen, V.: Deriving long term snow cover extent dataset
from AVHRR and MODIS data: Central Asia case study, Remote Sens. Environ., 136,
146–162, https://doi.org/10.1016/j.rse.2013.04.015, 2013.
Zhou, X., Xie, H., and Hendrickx, J. M. H.: Statistical evaluation of remotely
ensed snow-cover products with constraints from streamflow and SNOTEL measurements,
Remote Sens. Environ., 94, 214–231, https://doi.org/10.1016/j.rse.2004.10.007, 2005.
Zhu, Z., Wang, S., and Woodcock, C. E.: Improvement and expansion of the Fmask
algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and
Sentinel 2 images, Remote Sens. Environ., 159, 269–277, https://doi.org/10.1016/j.rse.2014.12.014, 2015.
Short summary
This paper is a review article on the cloud removal methods of MODIS snow cover products.
This paper is a review article on the cloud removal methods of MODIS snow cover products.
