Model-driven retrieval of fractional snow cover area

Abstract Understanding and quantifying satellite-based, remotely sensed snow cover uncertainty are critical for its successful utilization. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover errors have been previously recognized to be associated with factors such as cloud contamination, snowpack grain sizes, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of the MODIS fractional snow cover (FSC) from Collection 6 (C6) and in situ air temperature and snow water equivalent measurements provides a unique look at the error structure of the MODIS C6 FSC products. Analysis of the MODIS FSC dataset over the period from 2000 to 2005 was undertaken over the continental United States (CONUS) with an extensive observational network. When compared to MODIS Collection 5 (C5) snow cover area, the MODIS C6 FSC product demonstrates a substantial improvement in detecting the presence of snow cover in Nevada [30% increase in probability of detection (POD)], especially in the early and late snow seasons; some improvement over California (10% POD increase); and a relatively small improvement over Colorado (2% POD increase). However, significant spatial and temporal variations in accuracy still exist, and a proxy is required to adequately predict the expected errors in MODIS C6 FSC retrievals. A relationship is demonstrated between the MODIS FSC retrieval errors and temperature over the CONUS domain, captured by a cumulative double exponential distribution function. This relationship is shown to hold for both in situ and modeled daily mean air temperature. Both of them are useful indices in filtering out the misclassification of MODIS snow cover pixels and in quantifying the errors in the MODIS C6 product for various hydrological applications.

Download Full-text

Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska

Hydrology and Earth System Sciences ◽

10.5194/hess-23-2439-2019 ◽

2019 ◽

Vol 23 (5) ◽

pp. 2439-2459

Author(s):

Katrina E. Bennett ◽

Jessica E. Cherry ◽

Ben Balk ◽

Scott Lindsey

Keyword(s):

Snow Cover ◽

The United States ◽

Remotely Sensed ◽

Streamflow Forecasting ◽

Snow Cover Area ◽

Interior Alaska ◽

Fractional Snow Cover ◽

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 basins and where climate change is leading to rapid shifts in basin function. In this study, the operational framework employed by the United States (US) National Weather Service, including the Alaska Pacific River Forecast Center, is adapted to integrate Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed observations of fractional snow cover area (fSCA) to determine if these data improve streamflow forecasts in interior Alaska river basins. Two versions of MODIS fSCA are tested against a base case extent of snow cover derived by aerial depletion curves: the MODIS 10A1 (MOD10A1) and the MODIS Snow Cover Area and Grain size (MODSCAG) product over the period 2000–2010. Observed runoff is compared to simulated runoff to calibrate both iterations of the model. MODIS-forced simulations have improved snow depletion timing compared with snow telemetry sites in the basins, with discernable increases in skill for the streamflow simulations. The MODSCAG fSCA version provides moderate increases in skill but is similar to the MOD10A1 results. The basins with the largest improvement in streamflow simulations have the sparsest streamflow observations. Considering the numerous low-quality gages (discontinuous, short, or unreliable) and ungauged systems throughout the high-latitude regions of the globe, this result is valuable and indicates the utility of the MODIS fSCA data in these regions. Additionally, while improvements in predicted discharge values are subtle, the snow model better represents the physical conditions of the snowpack 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 also be more capable of adapting to changing climates than statistical models corrected 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.

Download Full-text

Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America

10.5194/tc-2019-280 ◽

2020 ◽

Author(s):

Xiongxin Xiao ◽

Shunlin Liang ◽

Tao He ◽

Daiqiang Wu ◽

Congyuan Pei ◽

...

Keyword(s):

North America ◽

Land Cover ◽

Brightness Temperature ◽

Snow Cover ◽

Temperature Data ◽

Passive Microwave ◽

Retrieval Models ◽

Snow Cover Area ◽

Microwave Brightness Temperature ◽

Fractional Snow Cover

Abstract. The dynamic characteristics of seasonal snow cover are critical for hydrology management, climate system, and ecosystem function. Although optical satellite remote sensing has proved to be an effective tool for monitoring global and regional variations of snow cover, it is still problematic to accurately capture the snow dynamics characteristics at a finer spatiotemporal resolution, because the observations from optical satellite sensors are seriously affected by clouds and solar illumination. Besides, traditional methods of mapping snow cover from passive microwave data only provide binary information with a 25-km spatial resolution. In this study, we first present an approach to predict fractional snow cover over North America under all-weather conditions, derived from the enhanced resolution passive microwave brightness temperature data (6.25 km). This estimation algorithm used Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products between 2010 and 2017 to create the reference fractional snow cover data as the "true" observations. Further, the influence of many factors, including land cover, topography, and location, were incorporated into the retrieval models. The results show that the proposed retrieval models based on random forest regression technique perform much better using independent test data for all land cover classes, with higher accuracy and no out-of-range estimated values, when compared to the other three approaches (linear regression, artificial neural networks (ANN), and multivariate adaptive regression splines (MARS)). The results of the output evaluated by using independent data indicate that the root-mean-square error (RMSE) of the estimated fractional snow cover ranges from 16.7 % to 19.8 %. In addition, the estimated fractional snow cover is verified in the snow mapping aspect by using snow cover observation data from meteorological stations (more than 0.31 million records). The result shows that the binary snow cover obtained by the proposed retrieval algorithm is in a good agreement with the ground measurements (kappa: 0.67). The accuracy of our algorithm estimation in the snow cover identification shows significant improvement when benchmarked against the Grody’s snow cover mapping algorithm: overall accuracy is increased by 18 % (from 0.71 to 0.84), and omission error is reduced by 71 % (from 0.48 to 0.14). Daily time-series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. According to our experiment results, we can conclude that it is feasible for estimating fractional snow cover from passive microwave brightness temperature data, and this strategy also has a great advantage in detecting snow cover area.

Download Full-text

Improving the Informational Value of MODIS Fractional Snow Cover Area Using Fuzzy Logic Based Ensemble Smoother Data Assimilation Frameworks

Remote Sensing ◽

10.3390/rs11010028 ◽

2018 ◽

Vol 11 (1) ◽

pp. 28 ◽

Cited By ~ 1

Author(s):

Aynom Teweldebrhan ◽

John Burkhart ◽

Thomas Schuler ◽

Chong-Yu Xu

Keyword(s):

Data Assimilation ◽

Snow Cover ◽

Critical Points ◽

Snow Water Equivalent ◽

Model Parameters ◽

Snow Cover Area ◽

Relative Significance ◽

Main Challenge ◽

Fractional Snow Cover ◽

Informational Value

Remote sensing fractional snow cover area (fSCA) has been increasingly used to get an improved estimate of the spatiotemporal distribution of snow water equivalent (SWE) through reanalysis using different data assimilation (DA) schemes. Although the effective assimilation period of fSCA is well recognized in previous studies, little attention has been given to explicitly account for the relative significance of measurements in constraining model parameters and states. Timing of the more informative period varies both spatially and temporally in response to various climatic and physiographic factors. Here we use an automatic detection approach to locate the critical points in the time axis where the mean snow cover changes and where the melt-out period starts. The assimilation period was partitioned into three timing windows based on these critical points. A fuzzy coefficient was introduced in two ensemble-based DA schemes to take into account for the variability in informational value of fSCA observations with time. One of the DA schemes used in this study was the particle batch smoother (Pbs). The main challenge in Pbs and other Bayesian-based DA schemes is, that most of the weights are carried by few ensemble members. Thus, we also considered an alternative DA scheme based on the limits of acceptability concept (LoA) and certain hydrologic signatures and it has yielded an encouraging result. An improved estimate of SWE was also obtained in most of the analysis years as a result of introducing the fuzzy coefficients in both DA schemes. The most significant improvement was obtained in the correlation coefficient between the predicted and observed SWE values (site-averaged); with an increase by 8% and 16% after introducing the fuzzy coefficient in Pbs and LoA, respectively.

Download Full-text

Benefits of Combining Satellite-Derived Snow Cover Data and Discharge Data to Calibrate a Glaciated Catchment in Sub-Arctic Iceland

Water ◽

10.3390/w12040975 ◽

2020 ◽

Vol 12 (4) ◽

pp. 975 ◽

Cited By ~ 2

Author(s):

Julia de Niet ◽

David Christian Finger ◽

Arvid Bring ◽

David Egilson ◽

David Gustafsson ◽

...

Keyword(s):

Snow Cover ◽

Model Calibration ◽

Limited Data ◽

Snow Cover Area ◽

Discharge Data ◽

Sources Of Uncertainty ◽

Low Degree ◽

Fractional Snow Cover ◽

Daily Discharge ◽

Overall Performance

The benefits of fractional snow cover area, as an additional dataset for calibration, were evaluated for an Icelandic catchment with a low degree of glaciation and limited data. For this purpose, a Hydrological Projections for the Environment (HYPE) model was calibrated for the Geithellnaá catchment in south-east Iceland using daily discharge (Q) data and satellite-retrieved MODIS snow cover (SC) images, in a multi-dataset calibration (MDC) approach. By comparing model results using only daily discharge data with results obtained using both datasets, the value of SC data for model calibration was identified. Including SC data improved the performance of daily discharge simulations by 7% and fractional snow cover area simulations by 11%, compared with using only the daily discharge dataset (SDC). These results indicate that MDC improves the overall performance of the HYPE model, confirming previous findings. Therefore, MDC could improve discharge simulations in areas with extra sources of uncertainty, such as glaciers and snow cover. Since the change in fractional snow cover area was more accurate when MDC was applied, it can be concluded that MDC would also provide more realistic projections when calibrated parameter sets are extrapolated to different situations.

Download Full-text

Impacts of Fractional Snow Cover on Surface Air Temperature in the NCAR Community Atmosphere Model (NCAR-CAM2)

Engineering, Construction, and Operations in Challenging Environments ◽

10.1061/40722(153)38 ◽

2004 ◽

Author(s):

Zong-Liang Yang ◽

Guo-Yue Niu ◽

Qianru Zeng

Keyword(s):

Snow Cover ◽

Air Temperature ◽

Surface Air Temperature ◽

Community Atmosphere Model ◽

Fractional Snow Cover ◽

Atmosphere Model

Download Full-text

Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland

Remote Sensing ◽

10.3390/rs13112045 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2045

Author(s):

Anaí Caparó Bellido ◽

Bradley C. Rundquist

Keyword(s):

Snow Cover ◽

Near Infrared ◽

Accuracy Assessment ◽

Important Variable ◽

Field Station ◽

Near Surface ◽

Fractional Snow Cover ◽

Rgb Images ◽

Phenological Metrics

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability.

Download Full-text

A Response of Snow Cover to the Climate in the Northwest Himalaya (NWH) Using Satellite Products

Remote Sensing ◽

10.3390/rs13040655 ◽

2021 ◽

Vol 13 (4) ◽

pp. 655

Author(s):

Animesh Choudhury ◽

Avinash Chand Yadav ◽

Stefania Bonafoni

Keyword(s):

Snow Cover ◽

Land Surface ◽

Significant Negative Correlation ◽

Shuttle Radar Topography Mission ◽

Vital Role ◽

Snow Cover Area ◽

Study Region ◽

Northwest Himalaya ◽

The Past ◽

Average Annual Temperature

The Himalayan region is one of the most crucial mountain systems across the globe, which has significant importance in terms of the largest depository of snow and glaciers for fresh water supply, river runoff, hydropower, rich biodiversity, climate, and many more socioeconomic developments. This region directly or indirectly affects millions of lives and their livelihoods but has been considered one of the most climatically sensitive parts of the world. This study investigates the spatiotemporal variation in maximum extent of snow cover area (SCA) and its response to temperature, precipitation, and elevation over the northwest Himalaya (NWH) during 2000–2019. The analysis uses Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra 8-day composite snow Cover product (MOD10A2), MODIS/Terra/V6 daily land surface temperature product (MOD11A1), Climate Hazards Infrared Precipitation with Station data (CHIRPS) precipitation product, and Shuttle Radar Topography Mission (SRTM) DEM product for the investigation. Modified Mann-Kendall (mMK) test and Spearman’s correlation methods were employed to examine the trends and the interrelationships between SCA and climatic parameters. Results indicate a significant increasing trend in annual mean SCA (663.88 km2/year) between 2000 and 2019. The seasonal and monthly analyses were also carried out for the study region. The Zone-wise analysis showed that the lower Himalaya (184.5 km2/year) and the middle Himalaya (232.1 km2/year) revealed significant increasing mean annual SCA trends. In contrast, the upper Himalaya showed no trend during the study period over the NWH region. Statistically significant negative correlation (−0.81) was observed between annual SCA and temperature, whereas a nonsignificant positive correlation (0.47) existed between annual SCA and precipitation in the past 20 years. It was also noticed that the SCA variability over the past 20 years has mainly been driven by temperature, whereas the influence of precipitation has been limited. A decline in average annual temperature (−0.039 °C/year) and a rise in precipitation (24.56 mm/year) was detected over the region. The results indicate that climate plays a vital role in controlling the SCA over the NWH region. The maximum and minimum snow cover frequency (SCF) was observed during the winter (74.42%) and monsoon (46.01%) season, respectively, while the average SCF was recorded to be 59.11% during the study period. Of the SCA, 54.81% had a SCF above 60% and could be considered as the perennial snow. The elevation-based analysis showed that 84% of the upper Himalaya (UH) experienced perennial snow, while the seasonal snow mostly dominated over the lower Himalaya (LH) and the middle Himalaya (MH).

Download Full-text

Assessing the quality of a real-time Snow Cover Area product for hydrological applications

Remote Sensing of Environment ◽

10.1016/j.rse.2012.09.006 ◽

2012 ◽

Vol 127 ◽

pp. 271-287 ◽

Cited By ~ 14

Author(s):

G. Thirel ◽

C. Notarnicola ◽

M. Kalas ◽

M. Zebisch ◽

T. Schellenberger ◽

...

Keyword(s):

Snow Cover ◽

Real Time ◽

Snow Cover Area ◽

Area Product ◽

Hydrological Applications

Download Full-text

The optical behaviour of snow during a melting season at Ny Ålesund (Svalbard, Norway)

10.5194/egusphere-egu21-14667 ◽

2021 ◽

Author(s):

Roberto Salzano ◽

Christian Lanconelli ◽

Giulio Esposito ◽

Marco Giusto ◽

Mauro Montagnoli ◽

...

Keyword(s):

Climate Change ◽

Snow Cover ◽

Full Range ◽

Time Lapse ◽

Test Site ◽

Remotely Sensed ◽

Optical Behaviour ◽

Remotely Sensed Data ◽

Snow Cover Area ◽

Significant Information

Polar areas are the most sensitive targets of the climate change and the continuous monitoring of the cryosphere represents a critical issue. The satellite remote sensing can fill this gap but further integration between remotely-sensed multi-spectral images and field data is crucial to validate retrieval algorithms and climatological models. The optical behaviour of snow, at different wavelengths, provides significant information about the micro-physical characteristics of the surface and this allow to discriminate different snow/ice covers. The aim of this work is to present an approach based on combining unmanned observations on spectral albedo and on the analysis of time-lapse images of sky and ground conditions in an Arctic test-site (Svalbard, Norway). Terrestrial photography can provide, in fact, important information about the cloud cover and support the discrimination between white-sky or clear-sky illuminating conditions. Similarly, time-lapse cameras can provide a detailed description of the snow cover, estimating the fractional snow cover area. The spectral albedo was obtained by a narrow band device that was compared to a full-range commercial system and to remotely sensed data acquired during the 2015 spring/summer period at the Amundsen - Nobile Climate Change Tower (Ny &#197;lesund). The results confirmed the possibility to have continuous observations of the snow surface (microphisical) characteristics and highlighted the opportunity to monitor the spectral variations of snowed surfaces during the melting period. It was possible, therefore, to estimate spectral indexes, such as NDSI and SWIR albedo, and to found interesting links between both features and air/ground temperatures, wind-speed and precipitations. Different melting phases were detected and different processes were associated with the observed spectral variations.

Download Full-text