scholarly journals A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling

2020 ◽  
Vol 12 (21) ◽  
pp. 3577
Author(s):  
Siyong Chen ◽  
Xiaoyan Wang ◽  
Hui Guo ◽  
Peiyao Xie ◽  
Jian Wang ◽  
...  
Keyword(s):  
Snow Cover ◽  
Conditional Probability ◽  
Interpolation Method ◽  
Space Time ◽  
Landsat 8 ◽  
Snow Cover Area ◽  
Northern Xinjiang ◽  
Central Pixel ◽  
Snow Cover Extent ◽  
Snow Condition

Seasonal snow cover is closely related to regional climate and hydrological processes. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products from 2001 to 2018 were applied to analyze the snow cover variation in northern Xinjiang, China. As cloud obscuration causes significant spatiotemporal discontinuities in the binary snow cover extent (SCE), we propose a conditional probability interpolation method based on a space-time cube (STCPI) to remove clouds completely after combining Terra and Aqua data. First, the conditional probability that the central pixel and every neighboring pixel in a space-time cube of 5 × 5 × 5 with the same snow condition is counted. Then the snow probability of the cloud pixels reclassified as snow is calculated based on the space-time cube. Finally, the snow condition of the cloud pixels can be recovered by snow probability. The validation experiments with the cloud assumption indicate that STCPI can remove clouds completely and achieve an overall accuracy of 97.44% under different cloud fractions. The generated daily cloud-free MODIS SCE products have a high agreement with the Landsat–8 OLI image, for which the overall accuracy is 90.34%. The snow cover variation in northern Xinjiang, China, from 2001 to 2018 was investigated based on the snow cover area (SCA) and snow cover days (SCD). The results show that the interannual change of SCA gradually decreases as the elevation increases, and the SCD and elevation have a positive correlation. Furthermore, the interannual SCD variation shows that the area of increase is higher than that of decrease during the 18 years.

scholarly journals Simulation of snow distribution and melt under cloudy conditions in an alpine watershed

2010 ◽  
Vol 7 (3) ◽  
pp. 3189-3211 ◽  
Author(s):  
H.-Y. Li ◽  
J. Wang
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Interpolation Method ◽  
Snow Accumulation ◽  
Energy Budgets ◽  
Bias Error ◽  
Snow Cover Area ◽  
Snow Cover Extent ◽  
Snow Distribution ◽  
Alpine Watershed

Abstract. An energy balance method and remote sensing data were used to simulate snow distribution and melt in an alpine watershed in Northwestern China within a complete snow accumulation-melt period. Spatial energy budgets were simulated using the meteorological observations and digital elevation model of the watershed. A linear interpolation method was used to discriminate daily snow cover area under cloudy conditions, using Moderate Resolution Imaging Spectroradiometer data. Hourly snow distribution and melt, snow cover extent, and daily discharge were included in the simulated results. The bias error between field snow water equivalent samplings and simulated results is −2.1 cm, and Root Mean Square Error is 33.9 cm. The Nash and Sutcliffe efficiency statistic (R2) between measured and simulated discharges is 0.673, and the volume difference (Dv) is 3.9%. Using the method introduced in this article, modeling spatial snow distribution and melt runoff will become relatively convenient.

scholarly journals Simulation of snow distribution and melt under cloudy conditions in an Alpine watershed

2011 ◽  
Vol 15 (7) ◽  
pp. 2195-2203 ◽  
Author(s):  
H.-Y. Li ◽  
J. Wang
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Interpolation Method ◽  
Remote Sensing Data ◽  
Snow Accumulation ◽  
Energy Budgets ◽  
Snow Cover Area ◽  
Snow Cover Extent ◽  
Snow Distribution ◽  
Alpine Watershed

Abstract. An energy balance method and remote-sensing data were used to simulate snow distribution and melt in an alpine watershed in northwestern China within a complete snow accumulation-melt period. The spatial energy budgets were simulated using meteorological observations and a digital elevation model of the watershed. A linear interpolation method was used to estimate the daily snow cover area under cloudy conditions, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Hourly snow distribution and melt, snow cover extent and daily discharge were included in the simulated results. The root mean square error between the measured snow-water equivalent samplings and the simulated results is 3.2 cm. The Nash and Sutcliffe efficiency statistic (NSE) between the measured and simulated discharges is 0.673, and the volume difference (Dv) is 3.9 %. Using the method introduced in this article, modelling spatial snow distribution and melt runoff will become relatively convenient.

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 Modeling the Spatial Distribution of Snow Cover in the Dudhkoshi Region of the Nepal Himalayas

2012 ◽  
Vol 13 (1) ◽  
pp. 204-222 ◽  
Author(s):  
Maheswor Shrestha ◽  
Lei Wang ◽  
Toshio Koike ◽  
Yongkang Xue ◽  
Yukiko Hirabayashi
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Hydrological Model ◽  
Lapse Rate ◽  
Distributed Hydrological Model ◽  
Snow Cover Area ◽  
Temperature Lapse Rate ◽  
Snow Cover Extent

Abstract In this study, a distributed biosphere hydrological model with three-layer energy-balance snow physics [an improved version of the Water and Energy Budget–based Distributed Hydrological Model (WEB-DHM-S)] is applied to the Dudhkoshi region of the eastern Nepal Himalayas to estimate the spatial distribution of snow cover. Simulations are performed at hourly time steps and 1-km spatial resolution for the 2002/03 snow season during the Coordinated Enhanced Observing Period (CEOP) third Enhanced Observing Period (EOP-3). Point evaluations (snow depth and upward short- and longwave radiation) at Pyramid (a station of the CEOP Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region. The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating the model’s capability to accurately capture the spatiotemporal variations in snow cover across the study area. The qualitative pixel-to-pixel comparisons for the snow-free and snow-covered grids reveal that the simulations agree well with the MODIS data to an accuracy of 90%. Simulated nighttime land surface temperatures (LST) are comparable to the MODIS LST (MOD11A2) with mean absolute error of 2.42°C and mean relative error of 0.77°C during the study period. The effects of uncertainty in air temperature lapse rate, initial snow depth, and snow albedo on the snow-cover area (SCA) and LST simulations are determined through sensitivity runs. In addition, it is found that ignoring the spatial variability of remotely sensed cloud coverage greatly increases bias in the LST and SCA simulations. To the authors’ knowledge, this work is the first to adopt a distributed hydrological model with a physically based multilayer snow module to estimate the spatial distribution of snow cover in the Himalayan region.

scholarly journals Glacier Area and Snow Cover Changes in the Range System Surrounding Tarim from 2000 to 2020 Using Google Earth Engine

2021 ◽  
Vol 13 (24) ◽  
pp. 5117
Author(s):  
Jing Zhang ◽  
Li Jia ◽  
Massimo Menenti ◽  
Jie Zhou ◽  
Shaoting Ren
Keyword(s):  
Snow Cover ◽  
Tarim Basin ◽  
Reduction Rate ◽  
Google Earth ◽  
Tien Shan ◽  
Relative Reduction ◽  
Landsat 8 ◽  
Glacier Area ◽  
The West ◽  
Snow Cover Area

Glacier and snow are sensitive indicators of regional climate variability. In the early 21st century, glaciers in the West Kunlun and Pamir regions showed stable or even slightly positive mass budgets, and this is anomalous in a worldwide context of glacier recession. We studied the evolution of snow cover to understand whether it could explain the evolution of glacier area. In this study, we used the thresholding of the NDSI (Normalized Difference Snow Index) retrieved with MODIS data to extract annual glacier area and snow cover. We evaluated how the glacier trends related to snow cover area in five subregions in the Tarim Basin. The uncertainty in our retrievals was assessed by comparing MODIS results with the Landsat-5 TM in 2000 and Landsat-8 OLI in 2020 glacier delineation in five subregions. The glacier area in the Tarim Basin decreased by 1.32%/a during 2000–2020. The fastest reductions were in the East Tien Shan region, while the slowest relative reduction rate was observed in the West Tien Shan and Pamir, i.e., 0.69%/a and 1.08%/a, respectively, during 2000–2020. The relative glacier stability in Pamir may be related to the westerlies weather system, which dominates climate in this region. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. During the summer months, the sequence of multiple snowfall and snowmelt events leads to intermittent snow cover, which was the key feature applied to discriminate snow and glacier.

scholarly journals Theia Snow collection: high resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

2018 ◽  
Author(s):  
Simon Gascoin ◽  
Manuel Grizonnet ◽  
Marine Bouchet ◽  
Germain Salgues ◽  
Olivier Hagolle
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Snow Depth ◽  
Western Europe ◽  
Landsat 8 ◽  
Mountain Regions ◽  
Snow Cover Area ◽  
Level 2 ◽  
Sentinel 2

Abstract. The Theia Snow collection routinely provides high resolution maps of the snow cover area from Sentinel-2 and Landsat-8 observations. The collection covers selected areas worldwide including the main mountain regions in Western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of the Snow collection contains four classes: snow, no-snow, cloud and no-data. We present the algorithm to generate the snow products and provide an evaluation of their accuracy using in situ snow depth measurements, higher resolution snow maps, and visual control. The results suggest that the snow is accurately detected in the Theia snow collection, and that the snow detection is more accurate than the sen2cor outputs (ESA level 2 product). An issue that should be addressed in a future release is the occurrence of false snow detection in some large clouds. The snow maps are currently produced and freely distributed in average 5 days after the image acquisition as raster and vector files via the Theia portal (http://doi.org/10.24400/329360/F7Q52MNK).

scholarly journals Using MODIS estimates of fractional snow cover extent to improve streamflow forecasts in Interior Alaska

2018 ◽  
Author(s):  
Katrina E. Bennett ◽  
Jessica E. Cherry ◽  
Ben Balk ◽  
Scott Lindsey
Keyword(s):  
Snow Cover ◽  
Remotely Sensed ◽  
Poor Quality ◽  
Water Model ◽  
Streamflow Forecasting ◽  
Snow Cover Area ◽  
Snow Cover Extent ◽  
The Us ◽  
Physically Based ◽  
Streamflow Forecasts

Abstract. Remotely sensed snow cover observations provide an opportunity to improve operational snowmelt and streamflow forecasting in remote regions. This is particularly true in Alaska, where remote basins and a spatially and temporally sparse gaging network plague efforts to understand and forecast the hydrology of subarctic boreal watersheds and where climate change is leading to rapid shifts in watershed function. In this study, the operational framework employed by the US National Weather Service, including the Alaska Pacific River Forecast Center, is adapted to integrate Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed observations of snow cover extent (SCE) to determine if these data improve streamflow forecasts in Interior Alaskan river basins. Two versions of MODIS fractional SCE are tested in this study: the MODIS 10A1 (MOD10A1), and the MODIS Snow Cover Area and Grain size (MODSCAG) product. Observed runoff is compared to simulated runoff to calibrate both iterations of the model. MODIS-forced runs have improved snow depletion timing compared with snow telemetry sites in the basins, with discernable increases in skill for the streamflow simulations. The MODSCAG SCE version provides moderate increases in skill, but is similar to the MOD10A1 results in these watersheds. The basins with the greatest improvement in streamflow simulations have the sparsest streamflow observations. Considering the numerous low-quality gages (discontinuous, short, or unreliable) and ungaged systems throughout the high latitude regions of the globe, this result is of great value and indicates the utility of the MODIS SCE data in these regions. Additionally, while improvements in predicted discharge values are subtle, the snow model better represents the physical conditions of the snow pack and therefore provides more robust simulations, which are consistent with the US National Weather Service's move toward a physically-based National Water Model. Physically-based models may be more capable of adapting to changing climates than statistical models tuned to past regimes. This work provides direction for both the Alaska Pacific River Forecast Center and other forecast centers across the US to implement remote sensing observations within their operational framework, to refine the representation of snow, and to improve streamflow forecasting skill in basins with few or poor-quality observations.

scholarly journals A satellite-based snow cover climatology (1985–2011) for the European Alps derived from AVHRR data

2013 ◽  
Vol 7 (3) ◽  
pp. 3001-3042 ◽  
Author(s):  
F. Hüsler ◽  
T. Jonas ◽  
M. Riffler ◽  
J. P. Musial ◽  
S. Wunderle
Keyword(s):  
Snow Cover ◽  
Climate Models ◽  
Regional Scale ◽  
Alpine Region ◽  
European Alps ◽  
Monitoring Networks ◽  
Snow Cover Area ◽  
Snow Cover Extent ◽  
Avhrr Data

Abstract. Seasonal snow cover is of great environmental and socio-economic importance for the European Alps. Therefore a high priority has been assigned to quantifying its temporal and spatial variability. Complementary to land-based monitoring networks, optical satellite observations can be used to derive spatially comprehensive information on snow cover extent. For understanding long-term changes in alpine snow cover extent, the data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensors mounted onboard the National Oceanic and Atmospheric Association (NOAA) and Meteorological Operational satellite (MetOp) platforms offer a~unique source of information. In this paper, we present the first space-borne 1 km snow extent climatology for the Alpine region derived from AVHRR data over the period 1985–2011. The objective of this study is twofold: first, to generate a new set of cloud-free satellite snow products using a specific cloud gap-filling technique and second, to examine the spatiotemporal distribution of snow cover in the European Alps over the last 27 yr from the satellite perspective. For this purpose, snow parameters such as snow onset day, snow cover duration (SCD), melt-out date and the snow cover area percentage (SCA) were employed to analyze spatio-temporal variability of snow cover over the course of 3 decades. On the regional scale, significant trends were found toward a shorter SCD at lower elevations in the south-east and south-west. However, our results do not show any significant trends in the monthly mean SCA over the last 27 yr. This is in agreement with other research findings and may indicate a~deceleration of the decreasing snow trend in the Alpine region. Given the importance of mountain regions for climate change assessment, this study recommends the complementary use of remote sensing data for long-term snow applications. It bears the potential to provide spatially and temporally comprehensive snow information for use in related research fields or to serve as a reference for climate models.

scholarly journals Snow-cover reconstruction methodology for mountainous regions based on historic in situ observations and recent remote sensing data

2015 ◽  
Vol 9 (2) ◽  
pp. 451-463 ◽  
Author(s):  
A. Gafurov ◽  
S. Vorogushyn ◽  
D. Farinotti ◽  
D. Duethmann ◽  
A. Merkushkin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Remote Sensing Data ◽  
Snow Cover Area ◽  
Sensing Data ◽  
Mountainous Regions ◽  
Snow Cover Extent ◽  
Spatially Distributed ◽  
Data Gap ◽  
Availability Assessment

Abstract. Spatially distributed snow-cover extent can be derived from remote sensing data with good accuracy. However, such data are available for recent decades only, after satellite missions with proper snow detection capabilities were launched. Yet, longer time series of snow-cover area are usually required, e.g., for hydrological model calibration or water availability assessment in the past. We present a methodology to reconstruct historical snow coverage using recently available remote sensing data and long-term point observations of snow depth from existing meteorological stations. The methodology is mainly based on correlations between station records and spatial snow-cover patterns. Additionally, topography and temporal persistence of snow patterns are taken into account. The methodology was applied to the Zerafshan River basin in Central Asia – a very data-sparse region. Reconstructed snow cover was cross validated against independent remote sensing data and shows an accuracy of about 85%. The methodology can be used in mountainous regions to overcome the data gap for earlier decades when the availability of remote sensing snow-cover data was strongly limited.

Arctic Radiative Fluxes: Present-Day Biases and Future Projections in CMIP5 Models

2015 ◽  
Vol 28 (15) ◽  
pp. 6019-6038 ◽  
Author(s):  
Jason M. English ◽  
Andrew Gettelman ◽  
Gina R. Henderson
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Radiant Energy ◽  
Cloud Amount ◽  
The Arctic ◽  
Cmip5 Models ◽  
Snow Cover Area ◽  
Radiative Fluxes ◽  
Future Projections ◽  
Snow Cover Extent

Abstract Radiative fluxes are critical for understanding the energy budget of the Arctic region, where the climate has been changing rapidly and is projected to continue to change. This work investigates causes of present-day biases and future projections of top-of-atmosphere (TOA) Arctic radiative fluxes in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Compared to Clouds and the Earth’s Radiant Energy System Energy Balanced and Filled (CERES-EBAF), CMIP5 net TOA downward shortwave (SW) flux biases are larger than outgoing longwave radiation (OLR) biases. The primary contributions to modeled TOA SW flux biases are biases in cloud amount and snow cover extent compared to the GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) and the newly developed Making Earth System Data Records for Use in Research Environments (MEaSUREs) dataset, respectively (with most models predicting insufficient cloud amount and snow cover in the Arctic), and biases with sea ice albedo. Future projections (2081–90) with representative concentration pathway 8.5 (RCP8.5) simulations suggest increasing net TOA downward SW fluxes (+8 W m−2) over the Arctic basin due to a decrease of surface albedo from melting snow and ice, and increasing OLR (+6 W m−2) due to an increase in surface temperatures. The largest contribution to future Arctic net TOA downward SW flux increases is declining sea ice area, followed by declining snow cover area on land, reductions to sea ice albedo, and reductions to snow albedo on land. Cloud amount is not projected to change significantly. These results suggest the importance of accurately representing both the surface area and albedos of sea ice and snow cover as well as cloud amount in order to accurately represent TOA radiative fluxes for the present-day climate and future projections.

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