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Snow Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (15 December 2017) | Viewed by 109518

Special Issue Editor

Dr. Claudia Notarnicola

E-Mail Website
Guest Editor
EURAC Research – Institute for Earth Observation, Viale Druso 1, 39100 Bolzano, Italy
Interests: retrieval of bio-physical parameters from optical and radar data; multi-sensor data fusion; integrated approach for environmental monitoring in mountain areas
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Snow is one of the most relevant natural water resources present in nature. It stores water in winter and releases it in spring during the melting season. Monitoring snow cover and its variability is thus of great importance for a proactive management of water-resources. Of particular interest is the identification of snowmelt processes, which could significantly support water administration, flood prediction and prevention.

In the past years, remote sensing has demonstrated to be an essential tool for providing accurate inputs to hydrological models concerning the spatial and temporal variability of snow. In particular, the SAR images have demonstrated to be effective and robust measures to identify wet snow, whereas optical data have proven to be an effective source of information to identify the snow cover extension when cloud cover is not present.

Moreover, remote sensing from space and aircraft, combined with complementary terrestrial observations and with physical models, have been used to monitor snow evolution and changes in relation to different climate conditions. Of course, an important aspect of space-based (and airborne) remote sensing is that we can investigate areas in which ground observations are not possible due to physical or political constraints. Actually, this scenario has changed with the introduction of the Sentinel family.

This Special Issue invites innovative remote sensing methods and applications on monitoring and modeling snow. Submissions are encouraged to cover a broad range of topics, which may include, but are not limited to, the following activities:

  • Remote sensing algorithm development, automation, implementation, and validation
  • Synergy of optical and SAR images to monitor snow status
  • Integration of remote sensing and hydrological modeling
  • Investigation on spatial and temporal variability of snow cover
  • Detection and monitoring of snow parameters (snow depth, snow density, snow water equivalent, etc.)
  • Analysis of time series of satellite snow cover extent

Dr. Claudia Notarnicola
Guest Editor

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Published Papers (16 papers)

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Research

16 pages, 3841 KiB  
Article
Snow Wetness Retrieved from L-Band Radiometry
by Reza Naderpour and Mike Schwank
Remote Sens. 2018, 10(3), 359; https://doi.org/10.3390/rs10030359 - 26 Feb 2018
Cited by 25 | Viewed by 4918
Abstract
The present study demonstrates the successful use of the high sensitivity of L-band brightness temperatures to snow liquid water in the retrieval of snow liquid water from multi-angular L-band brightness temperatures. The emission model employed was developed from parts of the “microwave emission [...] Read more.
The present study demonstrates the successful use of the high sensitivity of L-band brightness temperatures to snow liquid water in the retrieval of snow liquid water from multi-angular L-band brightness temperatures. The emission model employed was developed from parts of the “microwave emission model of layered snowpacks” (MEMLS), coupled with components adopted from the “L-band microwave emission of the biosphere” (L-MEB) model. Two types of snow liquid water retrievals were performed based on L-band brightness temperatures measured over (i) areas with a metal reflector placed on the ground (“reflector area”— T B , R ), and (ii) natural snow-covered ground (“natural area”— T B , N ). The reliable representation of temporal variations of snow liquid water is demonstrated for both types of the aforementioned quasi-simultaneous retrievals. This is verified by the fact that both types of snow liquid water retrievals indicate a dry snowpack throughout the “cold winter period” with frozen ground and air temperatures well below freezing, and synchronously respond to snowpack moisture variations during the “early spring period”. The robust and reliable performance of snow liquid water retrieved from T B , R , together with their level of detail, suggest the use of these retrievals as “references” to assess the meaningfulness of the snow liquid water retrievals based on T B , N . It is noteworthy that the latter retrievals are achieved in a two-step retrieval procedure using exclusively L-band brightness temperatures, without the need for in-situ measurements, such as ground permittivity ε G and snow mass-density ρ S . The latter two are estimated in the first retrieval-step employing the well-established two-parameter ( ρ S , ε G ) retrieval scheme designed for dry snow conditions and explored in the companion paper that is included in this special issue in terms of its sensitivity with respect to disturbative melting effects. The two-step retrieval approach proposed and investigated here, opens up the possibility of using airborne or spaceborne L-band radiometry to estimate ( ρ S , ε G ) and additionally snow liquid water as a new passive L-band data product. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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26 pages, 5527 KiB  
Article
Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: Melting Effects
by Mike Schwank and Reza Naderpour
Remote Sens. 2018, 10(2), 354; https://doi.org/10.3390/rs10020354 - 24 Feb 2018
Cited by 25 | Viewed by 5163
Abstract
Ground permittivity and snow density retrievals for the “snow-free period”, “cold winter period”, and “early spring period” are performed using the experimental L-band radiometry data from the winter 2016/2017 campaign at the Davos-Laret Remote Sensing Field Laboratory. The performance of the single-angle and [...] Read more.
Ground permittivity and snow density retrievals for the “snow-free period”, “cold winter period”, and “early spring period” are performed using the experimental L-band radiometry data from the winter 2016/2017 campaign at the Davos-Laret Remote Sensing Field Laboratory. The performance of the single-angle and multi-angle two-parameter retrieval algorithms employed during each of the aforementioned three periods is assessed using in-situ measured ground permittivity and snow density. Additionally, a synthetic sensitivity analysis is conducted that studies melting effects on the retrievals in the form of two types of “geophysical noise” (snow liquid water and footprint-dependent ground permittivity). Experimental and synthetic analyses show that both types of investigated “geophysical noise” noticeably disturb the retrievals and result in an increased correlation between them. The strength of this correlation is successfully used as a quality-indicator flag for the purpose of filtering out highly correlated ground permittivity and snow density retrievals. It is demonstrated that this filtering significantly improves the accuracy of both ground permittivity and snow density retrievals compared to corresponding reference in-situ data. Experimental and synthetic retrievals are performed in retrieval modes RM = “H”, “V”, and “HV”, where brightness temperatures from polarizations p = H, p = V, or both p = H and V are used, respectively, in the retrieval procedure. Our analysis shows that retrievals for RM = “V” are predominantly least prone to the investigated “geophysical noise”. The presented experimental results indicate that retrievals match in-situ observations best for the “snow-free period” and the “cold winter period” when “geophysical noise” is at minimum. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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22 pages, 7397 KiB  
Article
Tracking Snow Variations in the Northern Hemisphere Using Multi-Source Remote Sensing Data (2000–2015)
by Yunlong Wang, Xiaodong Huang, Hui Liang, Yanhua Sun, Qisheng Feng and Tiangang Liang
Remote Sens. 2018, 10(1), 136; https://doi.org/10.3390/rs10010136 - 18 Jan 2018
Cited by 47 | Viewed by 6744
Abstract
Multi-source remote sensing data were used to generate 500-m resolution cloud-free daily snow cover images for the Northern Hemisphere. Simultaneously, the spatial and temporal dynamic variations of snow in the Northern Hemisphere were evaluated from 2000 to 2015. The results indicated that (1) [...] Read more.
Multi-source remote sensing data were used to generate 500-m resolution cloud-free daily snow cover images for the Northern Hemisphere. Simultaneously, the spatial and temporal dynamic variations of snow in the Northern Hemisphere were evaluated from 2000 to 2015. The results indicated that (1) the maximum, minimum, and annual average snow-covered area (SCA) in the Northern Hemisphere exhibited a fluctuating downward trend; the variation of snow cover in the Northern Hemisphere had well-defined inter-annual and regional differences; (2) the average SCA in the Northern Hemisphere was the largest in January and the smallest in August; the SCA exhibited a downward trend for the monthly variations from February to April; and the seasonal variation in the SCA exhibited a downward trend in the spring, summer, and fall in the Northern Hemisphere (no pronounced variation trend in the winter was observed) during the 2000–2015 period; (3) the spatial distribution of the annual average snow-covered day (SCD) was related to the latitudinal zonality, and the areas exhibiting an upward trend were mainly at the mid to low latitudes with unstable SCA variations; and (4) the snow reduction was significant in the perennial SCA in the Northern Hemisphere, including high-latitude and high-elevation mountainous regions (between 35° and 50°N), such as the Tibetan Plateau, the Tianshan Mountains, the Pamir Plateau in Asia, the Alps in Europe, the Caucasus Mountains, and the Cordillera Mountains in North America. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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3422 KiB  
Article
Inter-Calibration of Passive Microwave Satellite Brightness Temperatures Observed by F13 SSM/I and F17 SSMIS for the Retrieval of Snow Depth on Arctic First-Year Sea Ice
by Qingquan Liu, Qing Ji, Xiaoping Pang, Xin Gao, Xi Zhao and Ruibo Lei
Remote Sens. 2018, 10(1), 36; https://doi.org/10.3390/rs10010036 - 26 Dec 2017
Cited by 8 | Viewed by 4625
Abstract
Passive microwave satellite brightness temperatures (TB) that were observed by the F13 Special Sensor Microwave/Imager (SSM/I) and the subsequent F17 Special Sensor Microwave Imager/Sounder (SSMIS) were inter-calibrated using empirical relationship models during their overlap period. Snow depth (SD) on the Arctic first-year sea [...] Read more.
Passive microwave satellite brightness temperatures (TB) that were observed by the F13 Special Sensor Microwave/Imager (SSM/I) and the subsequent F17 Special Sensor Microwave Imager/Sounder (SSMIS) were inter-calibrated using empirical relationship models during their overlap period. Snow depth (SD) on the Arctic first-year sea ice was further retrieved. The SDs derived from F17 TB and F13C TB which were calibrated F17 TB using F13 TB as the baseline were then compared and evaluated against in situ SD measurements based on the Operational IceBridge (OIB) airborne observations from 2009 to 2013. Results show that Cavalieri inter-calibration models (CA models) perform smaller root mean square error (RMSE) than Dai inter-calibration models (DA models), and the standard deviation of OIB SDs in the 25 km pixels is around 6 cm on first-year sea ice. Moreover, the SDs derived from the calibrated F17 TB using F13 TB as the baseline were in better agreement than the F17 SDs as compared with OIB SDs, with the biases of −2 cm (RMSE of 5 cm) and −9 cm (RMSE of 10 cm), respectively. We conclude that TB observations from F17 SSMIS calibrated to F13 SSM/I as the baseline should be recommended when performing the sensors’ biases correction for SD purpose based on the existing algorithm. These findings could serve as a reference for generating more consistent and reliable TB, which could help to improve the retrieval and analysis of long-term snow depth on the Arctic first-year sea ice. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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5342 KiB  
Article
Estimating Snow Depth Using Multi-Source Data Fusion Based on the D-InSAR Method and 3DVAR Fusion Algorithm
by Yang Liu, Lanhai Li, Jinming Yang, Xi Chen and Jiansheng Hao
Remote Sens. 2017, 9(11), 1195; https://doi.org/10.3390/rs9111195 - 21 Nov 2017
Cited by 24 | Viewed by 6290
Abstract
Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the [...] Read more.
Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the accuracy of snow depth inversion to a certain extent. This study proposes a scheme to estimate spatial snow depth that combines remote sensing with site observation. On the one hand, this scheme adopts the Sentinel-1 C-band of the European Space Agency (ESA), making use of the two-pass method of differential interferometry for inversion of spatial snow depth. On the other hand, the 3DVAR (three dimensional variational) fusion algorithm is used to integrate actual snow depth data of virtual stations and real-world observation stations into the snow depth inversion results. Thus, the accuracy of snow inversion will be improved. This scheme is applied in the study area of Bayanbulak Basin, which is located in the central hinterland of Tianshan Mountains in Xinjiang, China. Observation data from stations in different altitudes are selected to test the fusion method. According to the results, most of the obtained snow depth values using interferometry are lower than the observed ones. However, after the fusion using the 3DVAR algorithm, the snow depth accuracy is slightly higher than it was in the inversion results (R2 = 0.31 vs. R2 = 0.50, RMSE = 2.51 cm vs. RMSE = 1.96 cm; R2 = 0.27 vs. R2 = 0.46, RMSE = 4.04 cm vs. RMSE = 3.65 cm). When compared with the inversion results, the relative error (RE) improved by 6.97% and 3.59%, respectively. This study shows that the scheme can effectively improve the accuracy of regional snow depth estimation. Therefore, its future application is of great potential. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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10174 KiB  
Article
Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis
by Reza Naderpour, Mike Schwank and Christian Mätzler
Remote Sens. 2017, 9(11), 1185; https://doi.org/10.3390/rs9111185 - 21 Nov 2017
Cited by 27 | Viewed by 5562
Abstract
The L-band radiometry data and in-situ ground and snow measurements performed during the 2016/2017 winter campaign at the Davos-Laret remote sensing field laboratory are presented and discussed. An improved version of the procedure for the computation of L-band brightness temperatures from ELBARA radiometer [...] Read more.
The L-band radiometry data and in-situ ground and snow measurements performed during the 2016/2017 winter campaign at the Davos-Laret remote sensing field laboratory are presented and discussed. An improved version of the procedure for the computation of L-band brightness temperatures from ELBARA radiometer raw data is introduced. This procedure includes a thorough explanation of the calibration and filtering including a refined radio frequency interference (RFI) mitigation approach. This new mitigation approach not only performs better than conventional “normality” tests (kurtosis and skewness) but also allows for the quantification of measurement uncertainty introduced by non-thermal noise contributions. The brightness temperatures of natural snow covered areas and areas with a reflector beneath the snow are simulated for varying amounts of snow liquid water content distributed across the snow profile. Both measured and simulated brightness temperatures emanating from natural snow covered areas and areas with a reflector beneath the snow reveal noticeable sensitivity with respect to snow liquid water. This indicates the possibility of estimating snow liquid water using L-band radiometry. It is also shown that distinct daily increases in brightness temperatures measured over the areas with the reflector placed on the ground indicate the onset of the snow melting season, also known as “early-spring snow”. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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27596 KiB  
Article
Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images
by Chunxia Zhou and Lei Zheng
Remote Sens. 2017, 9(11), 1171; https://doi.org/10.3390/rs9111171 - 15 Nov 2017
Cited by 44 | Viewed by 8712
Abstract
Surface snowmelt causes changes in mass and energy balance, and endangers the stabilities of the ice shelves in the Antarctic Peninsula (AP). The dynamic changes of the snow and ice conditions in the AP were observed by Sentinel-1 images with a spatial resolution [...] Read more.
Surface snowmelt causes changes in mass and energy balance, and endangers the stabilities of the ice shelves in the Antarctic Peninsula (AP). The dynamic changes of the snow and ice conditions in the AP were observed by Sentinel-1 images with a spatial resolution of 40 m in this study. Snowmelt detected by the special sensor microwave/imager (SSM/I) is used to study the relationship between summer snowmelt and winter synthetic aperture radar (SAR) backscatter. Radar glacier zones (RGZs) classifications were conducted based on their differences in liquid snow content, snow grain size, and the relative elevations. We developed a practical method based on the simulations of a microwave scattering model to classify RGZs by using Sentinel-1 images in the AP. The summer snowmelt detected by SSM/I and Sentinel-1 data are compared between 2014 and 2015. The SSM/I-derived melting days is used to validate the winter dry snow line (DSL). RGZs derived from Sentinel-1 images suggest that snowmelt expanded from inland of the Larsen C Ice Shelf to the coastal area, whereas an opposite direction was found in the George VI Ice Shelf. The long melting season in the grounding zone of the Larsen C Ice Shelf may result from the adiabatically-dried föhn winds on the east side of the AP. As the uppermost limit of summer snowmelt, DSL was mapped based on the winter Sentinel-1 mosaic of the AP. Compared with the SSM/I-derived melting days, the winter DSL mainly distributed in the areas melted for one to three days in summer. DSL elevations on the Palmer Land increased from south to north. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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5569 KiB  
Article
Application of Low-Cost UASs and Digital Photogrammetry for High-Resolution Snow Depth Mapping in the Arctic
by Emiliano Cimoli, Marco Marcer, Baptiste Vandecrux, Carl E. Bøggild, Guy Williams and Sebastian B. Simonsen
Remote Sens. 2017, 9(11), 1144; https://doi.org/10.3390/rs9111144 - 7 Nov 2017
Cited by 39 | Viewed by 8355
Abstract
The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternative methodology [...] Read more.
The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternative methodology based on Unmanned Aerial Systems (UASs) and digital photogrammetry was tested over varying surveying conditions in the Arctic employing two diverse and low-cost UAS-camera combinations (500 and 1700 USD, respectively). Six areas, two in Svalbard and four in Greenland, were mapped covering from 1386 to 38,410 m2. The sites presented diverse snow surface types, underlying topography and light conditions in order to test the method under potentially limiting conditions. The resulting snow depth maps achieved spatial resolutions between 0.06 and 0.09 m. The average difference between UAS-estimated and measured snow depth, checked with conventional snow probing, ranged from 0.015 to 0.16 m. The impact of image pre-processing was explored, improving point cloud density and accuracy for different image qualities and snow/light conditions. Our UAS photogrammetry results are expected to be scalable to larger areal extents. While further validation is needed, with the inclusion of extra validation points, the study showcases the potential of this cost-effective methodology for high-resolution monitoring of snow dynamics in the Arctic and beyond. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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10284 KiB  
Article
Spatiotemporal Variation of Snow Cover in Tianshan Mountains, Central Asia, Based on Cloud-Free MODIS Fractional Snow Cover Product, 2001–2015
by Zhiguang Tang, Xiaoru Wang, Jian Wang, Xin Wang, Hongyi Li and Zongli Jiang
Remote Sens. 2017, 9(10), 1045; https://doi.org/10.3390/rs9101045 - 13 Oct 2017
Cited by 85 | Viewed by 7737
Abstract
The change in snow cover under climate change is poorly understood in Tianshan Mountains. Here, we investigate the spatiotemporal characteristics and trends of snow-covered area (SCA) and snow-covered days (SCD) in the Tianshan Mountains by using the cloud-removed MODIS fractional snow cover datasets [...] Read more.
The change in snow cover under climate change is poorly understood in Tianshan Mountains. Here, we investigate the spatiotemporal characteristics and trends of snow-covered area (SCA) and snow-covered days (SCD) in the Tianshan Mountains by using the cloud-removed MODIS fractional snow cover datasets from 2001–2015. The possible linkage between the snow cover and temperature and precipitation changes over the Tianshan Mountains is also investigated. The results are as follows: (1) The distribution of snow cover over the Tianshan Mountains exhibits a large spatiotemporal heterogeneity. The areas with SCD greater than 120 days are distributed in the principal mountains with elevations of above 3000 m. (2) In total, 26.39% (5.09% with a significant decline) and 34.26% (2.81% with a significant increase) of the study area show declining and increasing trend in SCD, respectively. The SCD mainly decreases in Central and Eastern Tianshan (decreased by 11.88% and 8.03%, respectively), while it increases in Northern and Western Tianshan (increased by 9.36% and 7.47%). (3) The snow cover variations are linked to the temperature and precipitation changes. Temperature tends to be the major factor effecting the snow cover changes in the Tianshan Mountains during 2001–2015. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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5699 KiB  
Article
Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial Imagery
by Rafael Pimentel, Javier Herrero and María José Polo
Remote Sens. 2017, 9(10), 995; https://doi.org/10.3390/rs9100995 - 26 Sep 2017
Cited by 23 | Viewed by 6408
Abstract
Mediterranean mountainous regions constitute a climate change hotspot where snow plays a crucial role in water resources. The characteristic snow-patched distribution over these areas makes spatial resolution the limiting factor for its correct representation. This work assesses the estimation of snow cover area [...] Read more.
Mediterranean mountainous regions constitute a climate change hotspot where snow plays a crucial role in water resources. The characteristic snow-patched distribution over these areas makes spatial resolution the limiting factor for its correct representation. This work assesses the estimation of snow cover area and the contribution of the patchy areas to the seasonal and annual regime of the snow in a semiarid mountainous range, the Sierra Nevada Mountains in southern Spain, by means of Landsat imagery combined with terrestrial photography (TP). Two methodologies were tested: (1) difference indexes to produce binary maps; and (2) spectral mixture analysis (SMA) to obtain fractional maps; their results were validated from “ground-truth” data by means of TP in a small monitored control area. Both methods provided satisfactory results when the snow cover was above 85% of the study area; below this threshold, the use of spectral mixture analysis is clearly recommended. Mixed pixels can reach up to 40% of the area during wet and cold years, their importance being larger as altitude increases, proving the usefulness of TP for assessing the accuracy of remote data sources. Mixed pixels identification allows for determining the more vulnerable areas facing potential changes of the snow regime due to global warming and climate variability. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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8904 KiB  
Article
Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China
by Gongxue Wang, Lingmei Jiang, Shengli Wu, Jiancheng Shi, Shirui Hao and Xiaojing Liu
Remote Sens. 2017, 9(10), 983; https://doi.org/10.3390/rs9100983 - 22 Sep 2017
Cited by 17 | Viewed by 7403
Abstract
Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary [...] Read more.
Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary orbit (GEO) satellites (namely, the FY-2 series) by taking advantage of their high temporal resolution. The method proposed in this study combines a newly developed binary snow cover detection algorithm designed for the Visible and Infrared Spin Scan Radiometer (VISSR) onboard FY-2F with a simple linear spectral mixture technique applied to the visible (VIS) band. This method relies upon full snow cover and snow-free end-members to estimate the daily FSC. The FY-2E/F VISSR FSC maps of China were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) FSC data based on the multiple end-member spectral mixture analysis (MESMA), and with Landsat-8 Operational Land Imager (OLI) FSC maps based on the SNOWMAP approach. The FY-2E/F VISSR FSC maps, which demonstrate a lower cloud coverage, exhibit the root mean squared errors (RMSEs) of 0.20/0.19 compared with the MODIS FSC data. When validated against the Landsat-8 OLI FSC data, the FY-2E/F VISSR FSC maps, which display overall accuracies that can reach 0.92, have an RMSE of 0.18~0.29 with R2 values ranging from 0.46 to 0.80. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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2837 KiB  
Article
Impact Analysis of Climate Change on Snow over a Complex Mountainous Region Using Weather Research and Forecast Model (WRF) Simulation and Moderate Resolution Imaging Spectroradiometer Data (MODIS)-Terra Fractional Snow Cover Products
by Xiaoduo Pan, Xin Li, Guodong Cheng, Rensheng Chen and Kuolin Hsu
Remote Sens. 2017, 9(8), 774; https://doi.org/10.3390/rs9080774 - 29 Jul 2017
Cited by 10 | Viewed by 6427
Abstract
Climate change has a complex effect on snow at the regional scale. The change in snow patterns under climate change remains unknown for certain regions. Here, we used high spatiotemporal resolution snow-related variables simulated by a weather research and forecast model (WRF) including [...] Read more.
Climate change has a complex effect on snow at the regional scale. The change in snow patterns under climate change remains unknown for certain regions. Here, we used high spatiotemporal resolution snow-related variables simulated by a weather research and forecast model (WRF) including snowfall, snow water equivalent and snow depth along with fractional snow cover (FSC) data extracted from Moderate Resolution Imaging Spectroradiometer Data (MODIS)-Terra to evaluate the effects of climate change on snow over the Heihe River Basin (HRB), a typical inland river basin in arid northwestern China from 2000 to 2013. We utilized Empirical Orthogonal Function (EOF) analysis and Mann-Kendall/Theil-Sen trend analysis to evaluate the results. The results are as follows: (1) FSC, snow water equivalent, and snow depth across the entire HRB region decreased, especially at elevations over 4500 m; however, snowfall increased at mid-altitude ranges in the upstream area of the HRB. (2) Total snowfall also increased in the upstream area of the HRB; however, the number of snowfall days decreased. Therefore, the number of extreme snow events in the upstream area of the HRB may have increased. (3) Snowfall over the downstream area of the HRB decreased. Thus, ground stations, WRF simulations and remote sensing products can be used to effectively explore the effect of climate change on snow at the watershed scale. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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2976 KiB  
Article
Terrestrial Remote Sensing of Snowmelt in a Diverse High-Arctic Tundra Environment Using Time-Lapse Imagery
by Daniel Kępski, Bartłomiej Luks, Krzysztof Migała, Tomasz Wawrzyniak, Sebastian Westermann and Bronisław Wojtuń
Remote Sens. 2017, 9(7), 733; https://doi.org/10.3390/rs9070733 - 15 Jul 2017
Cited by 26 | Viewed by 9108
Abstract
Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation [...] Read more.
Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation should be analyzed spatially. In this study, we correlate spatial data sets on tundra vegetation types with snow cover information obtained from orthorectification and classification of images collected from a time-lapse camera installed on a mountain summit. The spatial analysis was performed over an area of 0.72 km2, representing a coastal tundra environment in southern Svalbard. The three-year monitoring is supplemented by manual measurements of snow depth, which show a statistically significant relationship between snow abundance and the occurrence of some of the analyzed land cover types. The longest snow cover duration was found on “rock debris” type and the shortest on “lichen-herb-heath tundra”, resulting in melt-out time-lag of almost two weeks between this two land cover types. The snow distribution proved to be consistent over the different years with a similar melt-out pattern occurring in every analyzed season, despite changing melt-out dates related to different weather conditions. The data set of 203 high resolution processed images used in this work is available for download in the supplementary materials. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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8025 KiB  
Article
Possibility of Estimating Seasonal Snow Depth Based Solely on Passive Microwave Remote Sensing on the Greenland Ice Sheet in Spring
by Hiroyuki Tsutsui and Takashi Maeda
Remote Sens. 2017, 9(6), 523; https://doi.org/10.3390/rs9060523 - 25 May 2017
Cited by 7 | Viewed by 6122
Abstract
Sea level rise related to the melting and thinning of the Greenland Ice Sheet (GrIS), a subject of growing concern in recent years, will eventually affect the global climate. Although the melting of snow on the GrIS is actively monitored by passive microwave [...] Read more.
Sea level rise related to the melting and thinning of the Greenland Ice Sheet (GrIS), a subject of growing concern in recent years, will eventually affect the global climate. Although the melting of snow on the GrIS is actively monitored by passive microwave remote sensing, very few studies have estimated the seasonal GrIS snow depth using this technique. In this study, to estimate seasonal snowpack on GrIS, we investigated the microwave property and optimum physical parameters. We used our microwave radiative transfer model to create a lookup table and a simple satellite retrieval algorithm to estimate seasonal snow depth on GrIS in spring, based on the microwave satellite brightness temperature from AMSR-E and AMSR2. Our research suggests there is potential for estimating snow depth based solely on GrIS passive microwave remote sensing data. We validated these estimates against in situ snow depths at several sites and compared them with the snow spatial distributions over the entire GrIS of several major products (ERA-interim, MAR ver. 5.3.1 and GLDAS-CLM) that evaluate snow depth. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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7031 KiB  
Article
Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China
by Jinlong Gao, Xiaodong Huang, Xiaofang Ma, Qisheng Feng, Tiangang Liang and Hongjie Xie
Remote Sens. 2017, 9(5), 475; https://doi.org/10.3390/rs9050475 - 12 May 2017
Cited by 20 | Viewed by 5899
Abstract
It is important to predict snow disasters to prevent and reduce hazards in pastoral areas. In this study, we build a potential risk assessment model based on a logistic regression of 33 snow disaster events that occurred in Qinghai Province. A simulation model [...] Read more.
It is important to predict snow disasters to prevent and reduce hazards in pastoral areas. In this study, we build a potential risk assessment model based on a logistic regression of 33 snow disaster events that occurred in Qinghai Province. A simulation model of the snow disaster early warning is established using a back propagation artificial neural network (BP-ANN) method and is then validated. The results show: (1) the potential risk of a snow disaster in the Qinghai Province is mainly determined by five factors. Three factors are positively associated, the maximum snow depth, snow-covered days (SCDs), and slope, and two are negative factors, annual mean temperature and per capita gross domestic product (GDP); (2) the key factors that contribute to the prediction of a snow disaster are (from the largest to smallest contribution): the mean temperature, probability of a spring snow disaster, potential risk of a snow disaster, continual days of a mean daily temperature below −5 °C, and fractional snow-covered area; and (3) the BP-ANN model for an early warning of snow disaster is a practicable predictive method with an overall accuracy of 80%. This model has quite a few advantages over previously published models, such as it is raster-based, has a high resolution, and has an ideal capacity of generalization and prediction. The model output not only tells which county has a disaster (published models can) but also tells where and the degree of damage at a 500 m pixel scale resolution (published models cannot). Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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5574 KiB  
Article
Evaluating Consistency of Snow Water Equivalent Retrievals from Passive Microwave Sensors over the North Central U. S.: SSM/I vs. SSMIS and AMSR-E vs. AMSR2
by Eunsang Cho, Samuel E. Tuttle and Jennifer M. Jacobs
Remote Sens. 2017, 9(5), 465; https://doi.org/10.3390/rs9050465 - 10 May 2017
Cited by 20 | Viewed by 6657
Abstract
For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals [...] Read more.
For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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