Global Snow Cover Extent Mapping Using Sentinel-1

2020 ◽  
Author(s):  
Ya-Lun Tsai ◽  
Soner Uereyen ◽  
Andreas Dietz ◽  
Claudia Kuenzer ◽  
Natascha Oppelt
Keyword(s):  
Snow Cover ◽  
Low Cost ◽  
Global Radiation ◽  
Radiation Balance ◽  
Human Society ◽  
Vegetation Growth ◽  
Winter Tourism ◽  
Snow Cover Extent ◽  
Near Future

<p>Seasonal snow cover extent (SCE) is a critical component not only for the global radiation balance and climatic behavior but also for water availability of mountainous and arid regions, vegetation growth, permafrost, and winter tourism. However, due to the effects of the global warming, SCE has been observed to behave in much more irregular and extreme patterns in both temporal and spatial aspects. Therefore, a continuous SCE monitoring strategy is necessary to understand the effect of climate change on the cryosphere and to assess the corresponding impacts on human society and the environment. Nevertheless, although conventional optical sensor-based sensing approaches are mature, they suffer from cloud coverage and illumination dependency. Consequently, spaceborne Synthetic Aperture Radar (SAR) provides a pragmatic solution for achieving all-weather and day-and-night monitoring at low cost, especially after the launch of the Sentinel-1 constellation. </p><p>In the present study, we propose a new global SCE mapping approach, which utilizes dual-polarization intensity-composed bands, polarimetric H/A/α decomposition information, topographical factors, and a land cover layer to detect the SCE. By including not only amplitude but also phase information, we overcome the limitations of previous studies, which can only map wet SCE. Additionally, a layer containing the misclassification probability is provided as well for measuring the uncertainty. Based on the validation with in-situ stations and optical imagery, around 85% accuracy of the classification is ensured. Consequently, by implementing the proposed method globally, we can provide a novel way to map high resolution (20 m) and cloud-free SCE even under cloud covered/night conditions. Preparations to combine this product with the optical-based DLR Global SnowPack are already ongoing, offering the opportunity to provide a daily snow mapping service in the near future which is totally independent from clouds or polar darkness.</p>

scholarly journals Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco

2018 ◽  
Author(s):  
Mohamed Wassim Baba ◽  
Simon Gascoin ◽  
Lahoucine Hanich
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Snow Cover Area ◽  
High Atlas ◽  
Data Assimilation Scheme ◽  
Snow Cover Extent ◽  
Assimilation Scheme ◽  
Sentinel 2

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.

scholarly journals Interrelationships between MODIS/Terra remotely sensed snow cover and the hydrometeorology of the Quesnel River Basin, British Columbia, Canada

2009 ◽  
Vol 13 (8) ◽  
pp. 1439-1452 ◽  
Author(s):  
J. Tong ◽  
S. J. Déry ◽  
P. L. Jackson
Keyword(s):  
British Columbia ◽  
Snow Cover ◽  
Correlation Coefficient ◽  
River Basin ◽  
Time Lag ◽  
Spatial Filter ◽  
In Situ Observation ◽  
Cloud Coverage ◽  
Snow Cover Extent

Abstract. A spatial filter (SF) method is adopted to reduce the cloud coverage from the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day snow products (MOD10A2) between 2000–2007 in the Quesnel River Basin (QRB) of British Columbia, Canada. A threshold of k = 2 cm of snow depth measurements at four in-situ observation stations in the QRB are used to evaluate the accuracy of MODIS snow products MOD10A1, MOD10A2, and SF. Using the MOD10A2 and the SF, the relationships between snow ablation, snow cover extent (SCE), snow cover fraction (SCF), streamflow and climate variability are assessed. Based on our results we are able to draw several interesting conclusions. Firstly, the SF method reduces the average cloud coverage in the QRB from 15% for MOD10A2 to 9%. Secondly, the SF increases the overall accuracy (OA) based on the threshold k = 2 cm by about 2% compared to MOD10A2 and by about 10% compared to MOD10A1 at higher elevations. The OA for the four in-situ stations decreases with elevation with 93.1%, 87.9%, 84.0%, and 76.5% at 777 m, 1265 m, 1460 m, and 1670 m, respectively. Thirdly, an aggregated 1°C rise in average air temperature during spring leads to a 10-day advance in reaching 50% SCF (SCF50%) in the QRB. The correlation coefficient between normalized SCE of the SF and normalized streamflow is −0.84 (p<0.001) for snow ablation seasons. There is a 32-day time lag for snow ablation to impact the streamflow the strongest at the basin outlet. The linear correlation coefficient between SCF50% and 50% normalized accumulated runoff (R50%) attains 0.82 (p<0.01). This clearly demonstrates the strong links that exist between the SCF depletion and the hydrology of this sub-boreal, mountainous watershed.

scholarly journals Interrelationships between MODIS/Terra remotely sensed snow cover and the hydrometeorology of the Quesnel River Basin, British Columbia, Canada

2009 ◽  
Vol 6 (3) ◽  
pp. 3687-3723 ◽  
Author(s):  
J. Tong ◽  
S. J. Déry ◽  
P. L. Jackson
Keyword(s):  
British Columbia ◽  
Snow Cover ◽  
Correlation Coefficient ◽  
River Basin ◽  
Time Lag ◽  
Spatial Filter ◽  
In Situ Observation ◽  
Cloud Coverage ◽  
Snow Cover Extent

Abstract. A spatial filter (SF) method is adopted to reduce the cloud coverage from the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day snow products (MOD10A2) between 2000–2007 in the Quesnel River Basin (QRB) of British Columbia, Canada. A threshold of k=2 cm of snow depth measurements at four in-situ observation stations in the QRB are used to evaluate the accuracy of MODIS snow products MOD10A1, MOD10A2, and SF. Based on the MOD10A2 and the SF, the relationships between snow ablation, snow cover extent (SCE), snow cover fraction (SCF), streamflow and climate variability are assessed. Based on our results we are able to draw several interesting conclusions. Firstly, the SF method reduces the average cloud coverage in the QRB from 15% for MOD10A2 to 9%. Secondly, the SF increases the overall accuracy (OA) based on the threshold k=2 cm by about 2% compared to MOD10A2 and by about 10% compared to MOD10A1 at higher elevations. The OA for the four in-situ stations decreases with elevation with 93.1%, 87.9%, 84.0%, and 76.5% at 777 m, 1265 m, 1460 m, and 1670 m, respectively. Thirdly, an aggregated 1°C rise in average air temperature during spring leads to a 10-day advance in reaching 50% SCF (SCF50%) in the QRB. The correlation coefficient between normalized SCE of the SF and normalized streamflow is −0.84 (p<0.001) for snow ablation seasons. There is a 32-day time lag for snow ablation to impact the streamflow the strongest at the basin outlet. The linear correlation coefficient between SCF50% and 50% normalized accumulated runoff (R50%) attains 0.82 (p<0.01). This clearly demonstrates the strong links that exist between the SCF depletion and the hydrology of this sub-boreal, mountainous watershed.

scholarly journals Commentary on comparison of MODIS snow cover and albedo products with ground observations over the mountainous terrain of Turkey

2006 ◽  
Vol 3 (6) ◽  
pp. 3655-3673 ◽  
Author(s):  
A. Ü. Şorman ◽  
Z. Akyürek ◽  
A. Şensoy ◽  
A. A. Şorman ◽  
A. E. Tekeli
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Spatial And Temporal Variability ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
In Situ Data ◽  
Snow Cover Extent

Abstract. The MODerate-resolution Imaging Spectroradiometer (MODIS) snow cover product was evaluated by Parajka and Blösch (2006) over the territory of Austria. The spatial and temporal variability of the MODIS snow product classes are analyzed, the accuracy of the MODIS snow product against numerous in situ snow depth data are examined and the main factors that may influence the MODIS classification accuracy are identified in their studies. The authors of this paper would like to provide more discussion to the scientific community on the "Validation of MODIS snow cover images" when similar methodology is applied to mountainous regions covered with abundant snow but with limited number of ground survey and automated stations. Daily snow cover maps obtained from MODIS images are compared with ground observations in mountainous terrain of Turkey for the winter season of 2002–2003 and 2003–2004 during the accumulation and ablation periods of snow. Snow depth and density values are recorded to determine snow water equivalent values at 19 points in and around the study area in Turkey. Comparison of snow maps with in situ data show good agreement with overall accuracies in between 62 to 82 percent considering a 2-day shift during cloudy days. Studies show that the snow cover extent can be used for forecasting of runoff hydrographs resulting mostly from snowmelt for a mountainous basin in Turkey. MODIS-Terra snow albedo products are also compared with ground based measurements over the ablation stage of 2004 using the automated weather operating stations (AWOS) records at fixed locations as well as from the temporally assessed measuring sites during the passage of the satellite. Temporarily assessed 20 ground measurement sites are randomly distributed around one of the AWOS stations and both MODIS and ground data were aggregated in GIS for analysis. Reduction in albedo is noticed as snow depth decreased and SWE values increased.

scholarly journals Validation of snow extent time series derived from AVHRR GAC data at Himalaya-Hindukush

2021 ◽  
Author(s):  
Xiaodan Wu ◽  
Kathrin Naegeli ◽  
Carlo Marin ◽  
Stefan Wunderle
Keyword(s):  
Land Cover ◽  
Snow Cover ◽  
Environmental Changes ◽  
Slope Aspect ◽  
Adaptation Studies ◽  
Snow Cover Extent ◽  
Coarse Spatial Resolution ◽  
Increasing Demand

Abstract. Long-term monitoring of snow cover is crucial for climate and hydrology studies. To meet the increasing demand for a long-term, consistent snow product, an exceptional snow cover climatology was generated dating back to the 1980s using AVHRR GAC data. However, the retrieval of snow extent is not straightforward due to artifacts introduced during data processing, which are partly caused by the coarse spatial resolution of AVHRR GAC data, but also heterogeneous land cover/topography. Therefore, the accuracy and consistency of this long-term AVHRR GAC snow cover climatology needs to be carefully evaluated prior to its application. Here, we extensively validate the AVHRR GAC snow cover extent dataset for the Hindu Kush Himalaya (HKH) region. The mountainous HKH region is of high importance for climate change, impact and adaptation studies. Additionally, the influences of snow depth, land cover type, elevation, slope, aspect, and topographical variability, as well as the sensor-to-sensor consistency have been explored using a snow dataset based on long-term in situ stations and high-resolution Landsat TM data. Moreover, the performance of the AVHRR GAC snow cover dataset was also compared to that of MODIS (MOD10A1 V006). Our analysis shows an overall accuracy of 94 % in comparison with in situ station data. Using a ±3 days temporal filter caused a slight decrease in accuracy (from 94 to 92 %), which is still comparable to MOD10A1 V006 (93.6 %). Validation against Landsat5 TM data over region of P140-R40/41 indicated overall RMSEs of about 13 % and 16 % and overall Biases of about −1 % and −2 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH due to its good quality, unique temporal coverage (1982–2018), and inter-sensor/satellite consistency.

scholarly journals Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco

2018 ◽  
Vol 10 (12) ◽  
pp. 1982 ◽  
Author(s):  
Mohamed Baba ◽  
Simon Gascoin ◽  
Lahoucine Hanich
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Snow Cover Area ◽  
High Atlas ◽  
Data Assimilation Scheme ◽  
Snow Cover Extent ◽  
Assimilation Scheme ◽  
Sentinel 2

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements of MERRA-2 reanalysis and the full revisit capacity of Sentinel-2.

scholarly journals Commentary on comparison of MODIS snow cover and albedo products with ground observations over the mountainous terrain of Turkey

2007 ◽  
Vol 11 (4) ◽  
pp. 1353-1360 ◽  
Author(s):  
A. Ü. Şorman ◽  
Z. Akyürek ◽  
A. Şensoy ◽  
A. A. Şorman ◽  
A. E. Tekeli
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Spatial And Temporal Variability ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
In Situ Data ◽  
Snow Cover Extent

Abstract. The MODerate-resolution Imaging Spectroradiometer (MODIS) snow cover product was evaluated by Parajka and Blösch (2006) over the territory of Austria. The spatial and temporal variability of the MODIS snow product classes are analyzed, the accuracy of the MODIS snow product against numerous in situ snow depth data are examined and the main factors that may influence the MODIS classification accuracy are identified in their studies. The authors of this paper would like to provide more discussion to the scientific community on the "Validation of MODIS snow cover images" when similar methodology is applied to mountainous regions covered with abundant snow but with limited number of ground survey and automated stations. Daily snow cover maps obtained from MODIS images are compared with ground observations in mountainous terrain of Turkey for the winter season of 2002–2003 and 2003–2004 during the accumulation and ablation periods of snow. Snow depth and density values are recorded to determine snow water equivalent (SWE) values at 19 points in and around the study area in Turkey. Comparison of snow maps with in situ data show good agreement with overall accuracies in between 62 to 82 percent considering a 2-day shift during cloudy days. Studies show that the snow cover extent can be used for forecasting of runoff hydrographs resulting mostly from snowmelt for a mountainous basin in Turkey. MODIS-Terra snow albedo products are also compared with ground based measurements over the ablation stage of 2004 using the automated weather operating stations (AWOS) records at fixed locations as well as from the temporally assessed measuring sites during the passage of the satellite. Temporarily assessed 20 ground measurement sites are randomly distributed around one of the AWOS stations and both MODIS and ground data were aggregated in GIS for analysis. Reduction in albedo is noticed as snow depth decreased and SWE values increased.

scholarly journals A New Approach for Estimating Soil Salinity Using A Low-Cost Soil Sensor In Situ: A Case Study in Saline Regions of China’s East Coast

2020 ◽  
Vol 12 (2) ◽  
pp. 239 ◽  
Author(s):  
Jianjun Wang ◽  
Quan Sun ◽  
Jiali Shang ◽  
Jiahua Zhang ◽  
Fei Wu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Salinity ◽  
Sandy Loam ◽  
Low Cost ◽  
Eastern China ◽  
Cost Effective ◽  
Curvilinear Relationship ◽  
Coastal Regions ◽  
Vegetation Growth

Accurate and timely information on soil salinity is crucial for vegetation growth and agricultural productivity in coastal regions. This study investigates the potential of using Wifi POGO, an in situ electromagnetic sensor, for soil salinity assessment over saline coastal regions in eastern China. The sensor readings, soil moisture, and temperature-corrected apparent electrical conductivity (ECa) were used to generate models for EC1:5 (a surrogate for soil salinity) estimation. Two salty areas with distinct soil textures, sandy loam (Shuntai) and clay (Dongxin), were selected. This study revealed that the difference between soil salinity and the in situ measured soil ECa (i.e., EC1:5-ECa) had a strong curvilinear relationship with soil moisture. Such a relationship allows for the direct estimation of soil salinity from soil ECa with the aid of soil moisture information. Both ECa and soil moisture can be measured in situ using a Wifi POGO, a low-cost ground-based soil sensor. By using the leave-one-out cross-validation (LOOCV), the achieved root mean square error (RMSE) and relative RMSE (RRMSE) in EC1:5 estimation were 0.0109 S/m and 19.24% respectively in Shuntai, and 0.0157 S/m and 16.05%, in Dongxin. This new method offers a simple, cost-effective and reliable tool for assessing soil salinity in dynamic coastal regions.

To Eliminate Curtain Effect of FIB TEM Samples by a Combination of Sample Dicing and Backside Milling

2014 ◽  
Author(s):  
Jian-Shing Luo ◽  
Hsiu Ting Lee
Keyword(s):  
Sample Preparation ◽  
Focused Ion Beam ◽  
Preparation Method ◽  
Low Cost ◽  
Ion Beam ◽  
Sample Preparation Method ◽  
Site Specific ◽  
Dual Beam ◽  
Transmission Electron

Abstract Several methods are used to invert samples 180 deg in a dual beam focused ion beam (FIB) system for backside milling by a specific in-situ lift out system or stages. However, most of those methods occupied too much time on FIB systems or requires a specific in-situ lift out system. This paper provides a novel transmission electron microscopy (TEM) sample preparation method to eliminate the curtain effect completely by a combination of backside milling and sample dicing with low cost and less FIB time. The procedures of the TEM pre-thinned sample preparation method using a combination of sample dicing and backside milling are described step by step. From the analysis results, the method has applied successfully to eliminate the curtain effect of dual beam FIB TEM samples for both random and site specific addresses.

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